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"Health system costs for individual and comorbid noncommunicable diseases: An analysis of publicly funded health events from New Zealand. BACKGROUND: There is little systematic assessment of how total health expenditure is distributed across diseases and comorbidities. The objective of this study was to use statistical methods to disaggregate all publicly funded health expenditure by disease and comorbidities in order to answer three research questions: (1) What is health expenditure by disease phase for noncommunicable diseases (NCDs) in New Zealand? (2) Is the cost of having two NCDs more or less than that expected given the independent costs of each NCD? (3) How is total health spending disaggregated by NCDs across age and by sex? METHODS AND FINDINGS: We used linked data for all adult New Zealanders for publicly funded events, including hospitalisation, outpatient, pharmaceutical, laboratory testing, and primary care from 1 July 2007 to 30 June 2014. These data include 18.9 million person-years and $26.4 billion in spending (US$ 2016). We used case definition algorithms to identify if a person had any of six NCDs (cancer, cardiovascular disease [CVD], diabetes, musculoskeletal, neurological, and a chronic lung/liver/kidney [LLK] disease). Indicator variables were used to identify the presence of any of the 15 possible comorbidity pairings of these six NCDs. Regression was used to estimate excess annual health expenditure per person. Cause deletion methods were used to estimate total population expenditure by disease. A majority (59%) of health expenditure was attributable to NCDs. Expenditure due to diseases was generally highest in the year of diagnosis and year of death. A person having two diseases simultaneously generally had greater health expenditure than the expected sum of having the diseases separately, for all 15 comorbidity pairs except the CVD-cancer pair. For example, a 60-64-year-old female with none of the six NCDs had $633 per annum expenditure. If she had both CVD and chronic LLK, additional expenditure for CVD separately was $6,443/$839/$9,225 for the first year of diagnosis/prevalent years/last year of life if dying of CVD; additional expenditure for chronic LLK separately was $6,443/$1,291/$9,051; and the additional comorbidity expenditure of having both CVD and LLK was $2,456 (95% confidence interval [CI] $2,238-$2,674). The pattern was similar for males (e.g., additional comorbidity expenditure for a 60-64-year-old male with CVD and chronic LLK was $2,498 [95% CI $2,264-$2,632]). In addition to this, the excess comorbidity costs for a person with two diseases was greater at younger ages, e.g., excess expenditure for 45-49-year-old males with CVD and chronic LLK was 10 times higher than for 75-79-year-old males and six times higher for females. At the population level, 23.8% of total health expenditure was attributable to higher costs of having one of the 15 comorbidity pairs over and above the six NCDs separately; of the remaining expenditure, CVD accounted for 18.7%, followed by musculoskeletal (16.2%), neurological (14.4%), cancer (14.1%), chronic LLK disease (7.4%), and diabetes (5.5%). Major limitations included incomplete linkage to all costed events (although these were largely non-NCD events) and missing private expenditure. CONCLUSIONS: The costs of having two NCDs simultaneously is typically superadditive, and more so for younger adults. Neurological and musculoskeletal diseases contributed the largest health system costs, in accord with burden of disease studies finding that they contribute large morbidity. Just as burden of disease methodology has advanced the understanding of disease burden, there is a need to create disease-based costing studies that facilitate the disaggregation of health budgets at a national level.",0
"Human Neural Stem Cells Reinforce Hippocampal Synaptic Network and Rescue Cognitive Deficits in a Mouse Model of Alzheimer's Disease. Alzheimer's disease (AD) is characterized by memory impairments in its earliest clinical phase. The synaptic loss and dysfunction leading to failures of synaptic networks in AD brain directly cause cognitive deficits of patient. However, it remains unclear whether the synaptic networks in AD brain could be repaired. In this study, we generated functional human induced neural progenitor/stem cells (iNPCs) that had been transplanted into the hippocampus of immunodeficient wild-type and AD mice. The grafted human iNPCs efficiently differentiated into neurons that displayed long-term survival, progressively acquired mature membrane properties, formed graft-host synaptic connections with mouse neurons and functionally integrated into local synaptic circuits, which eventually reinforced and repaired the neural networks of host hippocampus. Consequently, AD mice with human iNPCs exhibited enhanced synaptic plasticity and improved cognitive abilities. Together, our results suggest that restoring synaptic failures by stem cells might provide new directions for the development of novel treatments for human AD.",0
"A management algorithm for patients with intracranial pressure monitoring: the Seattle International Severe Traumatic Brain Injury Consensus Conference (SIBICC). BACKGROUND: Management algorithms for adult severe traumatic brain injury (sTBI) were omitted in later editions of the Brain Trauma Foundation's sTBI Management Guidelines, as they were not evidence-based. METHODS: We used a Delphi-method-based consensus approach to address management of sTBI patients undergoing intracranial pressure (ICP) monitoring. Forty-two experienced, clinically active sTBI specialists from six continents comprised the panel. Eight surveys iterated queries and comments. An in-person meeting included whole- and small-group discussions and blinded voting. Consensus required 80% agreement. We developed heatmaps based on a traffic-light model where panelists' decision tendencies were the focus of recommendations. RESULTS: We provide comprehensive algorithms for ICP-monitor-based adult sTBI management. Consensus established 18 interventions as fundamental and ten treatments not to be used. We provide a three-tier algorithm for treating elevated ICP. Treatments within a tier are considered empirically equivalent. Higher tiers involve higher risk therapies. Tiers 1, 2, and 3 include 10, 4, and 3 interventions, respectively. We include inter-tier considerations, and recommendations for critical neuroworsening to assist the recognition and treatment of declining patients. Novel elements include guidance for autoregulation-based ICP treatment based on MAP Challenge results, and two heatmaps to guide (1) ICP-monitor removal and (2) consideration of sedation holidays for neurological examination. CONCLUSIONS: Our modern and comprehensive sTBI-management protocol is designed to assist clinicians managing sTBI patients monitored with ICP-monitors alone. Consensus-based (class III evidence), it provides management recommendations based on combined expert opinion. It reflects neither a standard-of-care nor a substitute for thoughtful individualized management.",0
"Chemogenomic Analysis of the Druggable Kinome and Its Application to Repositioning and Lead Identification Studies. Owing to the intrinsic polypharmacological nature of most small-molecule kinase inhibitors, there is a need for computational models that enable systematic exploration of the chemogenomic landscape underlying druggable kinome toward more efficient kinome-profiling strategies. We implemented VirtualKinomeProfiler, an efficient computational platform that captures distinct representations of chemical similarity space of the druggable kinome for various drug discovery endeavors. By using the computational platform, we profiled approximately 37 million compound-kinase pairs and made predictions for 151,708 compounds in terms of their repositioning and lead molecule potential, against 248 kinases simultaneously. Experimental testing with biochemical assays validated 51 of the predicted interactions, identifying 19 small-molecule inhibitors of EGFR, HCK, FLT1, and MSK1 protein kinases. The prediction model led to a 1.5-fold increase in precision and 2.8-fold decrease in false-discovery rate, when compared with traditional single-dose biochemical screening, which demonstrates its potential to drastically expedite the kinome-specific drug discovery process. The virtual kinome profiling (VKP) platform uses compound-kinase interaction information to prioritize potent activities for further pre-clinical evaluation. The platform uses the chemogenomic relationships of kinases to expedite the kinase inhibitor screening process, as demonstrated by several case examples. The platform and the accompanying datasets are implemented as a one-click web tool.",0
"Bayesian Inference of Allelic Inclusion Rates in the Human T Cell Receptor Repertoire. High-throughput single-cell sequencing methods allow for the identification of allelic inclusion T cell receptor (TCR) sequences, though experimental errors preclude direct measurement of dual receptor T cell rates. We develop and experimentally validate a statistical inference model in order to accurately estimate the rate of ααβ and αββ allelic inclusion T cells. Our results show that approximately 15% of T cells express more than one unique TCR and suggest that these allelic inclusion cells represent a functionally important component of the human TCR repertoire.",0
"Accuracy of Computer-Aided Diagnosis of Melanoma: A Meta-analysis. Importance: The recent advances in the field of machine learning have raised expectations that computer-aided diagnosis will become the standard for the diagnosis of melanoma. Objective: To critically review the current literature and compare the diagnostic accuracy of computer-aided diagnosis with that of human experts. Data Sources: The MEDLINE, arXiv, and PubMed Central databases were searched to identify eligible studies published between January 1, 2002, and December 31, 2018. Study Selection: Studies that reported on the accuracy of automated systems for melanoma were selected. Search terms included melanoma, diagnosis, detection, computer aided, and artificial intelligence. Data Extraction and Synthesis: Evaluation of the risk of bias was performed using the QUADAS-2 tool, and quality assessment was based on predefined criteria. Data were analyzed from February 1 to March 10, 2019. Main Outcomes and Measures: Summary estimates of sensitivity and specificity and summary receiver operating characteristic curves were the primary outcomes. Results: The literature search yielded 1694 potentially eligible studies, of which 132 were included and 70 offered sufficient information for a quantitative analysis. Most studies came from the field of computer science. Prospective clinical studies were rare. Combining the results for automated systems gave a melanoma sensitivity of 0.74 (95% CI, 0.66-0.80) and a specificity of 0.84 (95% CI, 0.79-0.88). Sensitivity was lower in studies that used independent test sets than in those that did not (0.51; 95% CI, 0.34-0.69 vs 0.82; 95% CI, 0.77-0.86; P <.001); however, the specificity was similar (0.83; 95% CI, 0.71-0.91 vs 0.85; 95% CI, 0.80-0.88; P =.67). In comparison with dermatologists' diagnosis, computer-aided diagnosis showed similar sensitivities and a 10 percentage points lower specificity, but the difference was not statistically significant. Studies were heterogeneous and substantial risk of bias was found in all but 4 of the 70 studies included in the quantitative analysis. Conclusions and Relevance: Although the accuracy of computer-aided diagnosis for melanoma detection is comparable to that of experts, the real-world applicability of these systems is unknown and potentially limited owing to overfitting and the risk of bias of the studies at hand.",0
"Epigenetic modifications but not genetic polymorphisms regulate KEAP1 expression in colorectal cancer. Kelch-like ECH-associated protein 1 (KEAP1), as a negative regulator of nuclear factor erythroid 2 like 2 (NRF2), plays a pivotal role in NRF2 signaling pathway and involves in tumorigenesis. Polymorphisms and methylation in gene promoter region may influence its expression and be related to cancer susceptibility. In this study, we examined the effect of the KEAP1-NRF2 interaction on the risk of colorectal cancer (CRC). The polymorphisms of NRF2 and KEAP1 were genotyped using the improved multiplex ligase detection reaction assay. KEAP1 promoter methylation and histone modification were analyzed using bisulfite genome sequencing and chromatin immunoprecipitation (ChIP) assay, respectively. The KEAP1 rs1048290 CC genotype and C allele were associated with increased risks of CRC (CC vs GG: odds ratio [OR] = 1.39; 95% confidence interval [CI], 1.08-1.78; CC vs GG/GC: OR = 1.29; 95% CI, 1.05-1.58; C vs G: OR = 1.18; 95% CI, 1.04-1.34). The rs1048290-rs11545829 GT haplotype was associated with a reduced risk of CRC. KEAP1-NRF2 interaction analysis revealed that the rs6721961, rs35652124, rs1048290, and rs11545829 conferred the susceptibility to CRC. The hypermethylation of KEAP1 promoter resulted in lower levels of KEAP1 messenger RNA (mRNA). After treatment with 5-aza-2′-deoxycytidine/trichostatin A, KEAP1 promoter methylation was decreased and KEAP1 mRNA levels were increased. ChIP-quantitative polymerase chain reaction results showed an enhanced enrichment of H3K4Me3 and H3K27Ac to the promoter of KEAP1. In vitro methylation analysis showed that the methylated plasmid decreased the transcriptional activity by 70%-84%. These findings suggest that the KEAP1- NRF2 pathway could potentially impact CRC risk and the downregulation of KEAP1 could be explained in part by epigenetic modifications.",0
"High-Frequency Ultrasound Imaging for Examination of Early Dental Caries. The extent of dental tissue destruction during the treatment of white spot lesions (WSLs) increases with the severity of the lesion. If the depth and shape of WSLs can be predicted with a noninvasive diagnostic method before dental caries treatment, more conservative interventions can be planned. Given the superiority of high-frequency ultrasound (HFUS) imaging in observing the internal structures of the body, the present study aimed to verify the possibility of HFUS imaging to examine the depth and shape of WSLs. We prepared tooth samples and developed a biomicroscopic system with a HFUS transducer to obtain images of normal and WSL regions. HFUS images were compared with conventional ultrasound images and micro-computed tomography images. HFUS distinctly differentiated demineralization within WSL and normal regions. WSL depth calculated in the micro-computed tomography image was similar to that in HFUS. This study revealed that HFUS imaging has the potential to detect early dental caries and offer information on the invasion depth of early dental caries quantitatively.",0
"Placental growth factor testing to assess women with suspected pre-eclampsia: a multicentre, pragmatic, stepped-wedge cluster-randomised controlled trial. BACKGROUND: Previous prospective cohort studies have shown that angiogenic factors have a high diagnostic accuracy in women with suspected pre-eclampsia, but we remain uncertain of the effectiveness of these tests in a real-world setting. We therefore aimed to determine whether knowledge of the circulating concentration of placental growth factor (PlGF), an angiogenic factor, integrated with a clinical management algorithm, decreased the time for clinicians to make a diagnosis in women with suspected pre-eclampsia, and whether this approach reduced subsequent maternal or perinatal adverse outcomes. METHODS: We did a multicentre, pragmatic, stepped-wedge cluster-randomised controlled trial in 11 maternity units in the UK, which were each responsible for 3000-9000 deliveries per year. Women aged 18 years and older who presented with suspected pre-eclampsia between 20 weeks and 0 days of gestation and 36 weeks and 6 days of gestation, with a live, singleton fetus were invited to participate by the clinical research team. Suspected pre-eclampsia was defined as new-onset or worsening of existing hypertension, dipstick proteinuria, epigastric or right upper-quadrant pain, headache with visual disturbances, fetal growth restriction, or abnormal maternal blood tests that were suggestive of disease (such as thrombocytopenia or hepatic or renal dysfunction). Women were approached individually, they consented for study inclusion, and they were asked to give blood samples. We randomly allocated the maternity units, representing the clusters, to blocks. Blocks represented an intervention initiation time, which occurred at equally spaced 6-week intervals throughout the trial. At the start of the trial, all units had usual care (in which PlGF measurements were also taken but were concealed from clinicians and women). At the initiation time of each successive block, a site began to use the intervention (in which the circulating PlGF measurement was revealed and a clinical management algorithm was used). Enrolment of women continued for the duration of the blocks either to concealed PlGF testing, or after implementation, to revealed PlGF testing. The primary outcome was the time from presentation with suspected pre-eclampsia to documented pre-eclampsia in women enrolled in the trial who received a diagnosis of pre-eclampsia by their treating clinicians. This trial is registered with ISRCTN, number 16842031. FINDINGS: Between June 13, 2016, and Oct 27, 2017, we enrolled and assessed 1035 women with suspected pre-eclampsia. 12 (1%) women were found to be ineligible. Of the 1023 eligible women, 576 (56%) women were assigned to the intervention (revealed testing) group, and 447 (44%) women were assigned to receive usual care with additional concealed testing (concealed testing group). Three (1%) women in the revealed testing group were lost to follow-up, so 573 (99%) women in this group were included in the analyses. One (<1%) woman in the concealed testing group withdrew consent to follow-up data collection, so 446 (>99%) women in this group were included in the analyses. The median time to pre-eclampsia diagnosis was 4.1 days with concealed testing versus 1.9 days with revealed testing (time ratio 0.36, 95% CI 0.15-0.87; p=0.027). Maternal severe adverse outcomes were reported in 24 (5%) of 447 women in the concealed testing group versus 22 (4%) of 573 women in the revealed testing group (adjusted odds ratio 0.32, 95% CI 0.11-0.96; p=0.043), but there was no evidence of a difference in perinatal adverse outcomes (15% vs 14%, 1.45, 0.73-2.90) or gestation at delivery (36.6 weeks vs 36.8 weeks; mean difference -0.52, 95% CI -0.63 to 0.73). INTERPRETATION: We found that the availability of PlGF test results substantially reduced the time to clinical confirmation of pre-eclampsia. Where PlGF was implemented, we found a lower incidence of maternal adverse outcomes, consistent with adoption of targeted, enhanced surveillance, as recommended in the clinical management algorithm for clinicians. Adoption of PlGF testing in women with suspected pre-eclampsia is supported by the results of this study. FUNDING: National Institute for Health Research.",0
"Mapping the Global Chromatin Connectivity Network for Sox2 Function in Neural Stem Cell Maintenance. Bertolini et al. report that long-range chromatin interactions in neural stem cells (NSCs) are enriched in Sox2-bound enhancers; in Sox2-deleted NSCs, interactions are reduced. Genes downregulated in Sox2-deleted cells are enriched in interactions with enhancers normally Sox2-bound. Overexpression of Socs3, a gene downregulated in mutant NSCs, rescues long-term NSC self-renewal.",0
"USA aid policy and induced abortion in sub-Saharan Africa: an analysis of the Mexico City Policy. Background: The Mexico City Policy, first announced by US President Ronald Reagan and since lifted and reinstated by presidents along partisan lines, prohibits US foreign assistance to any organisation that performs or provides counselling on abortion. Many organisations affected by this policy are also providers of modern contraception. If the policy reduces these organisations' ability to supply modern contraceptives, it could have the unintended consequence of increasing abortion rates. Methods: We empirically examined patterns of modern contraception use, pregnancies, and abortion among women in 26 countries in sub-Saharan Africa in response to the reinstatement and subsequent repeal of the Mexico City Policy across three presidential administrations (William Clinton, George W Bush, and Barack Obama). We combine individual-level data on pregnancies and abortions from 743 691 women, country-year data on modern contraception use, and annual data on development assistance for family planning and reproductive health in a difference-in-difference framework to examine relative changes in use of modern contraception, pregnancy, and abortion in response to the policy. Findings: We found that when the Mexico City Policy was in effect (2001–08), abortion rates rose among women in countries highly exposed to the policy by 4·8 abortions per 10 000 woman-years (95% CI 1·5 to 8·1, p=0·0041) relative to women in low-exposure countries and relative to periods when the policy was rescinded in 1995–2000 and 2009–14, a rise of approximately 40%. We found a symmetric reduction in use of modern contraception by 3·15 percentage points (relative decrease of 13·5%; 95% CI −4·9 to −1·4; p=0·0006) and increase in pregnancies by 3·2 percentage points (relative increase of 12%; 95% CI 1·6 to 4·8; p<0·0001) while the policy was enacted. Interpretation: Our findings suggest that curbing US assistance to family planning organisations, especially those that consider abortion as a method of family planning, increases abortion prevalence in sub-Saharan African countries most affected by the policy. Funding: The William and Flora Hewlett Foundation, the Doris Duke Charitable Foundation, the David and Lucile Packard Foundation, and the Stanford Earth Dean's Fellowship.",0
"Publisher Correction: Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. In the version of this article originally published, the x axis labels in Fig. 1a were incorrect. The labels originally were 'Specificity,' but should have been '1 - Specificity.' Also, the x axis label in Fig. 2b was incorrect. It was originally 'DNN predicted label,' but should have been 'Average cardiologist label.' The errors have been corrected in the PDF and HTML versions of this article.",0
"Structural and functional features of lysine acetylation of plant and animal tubulins. The study of the genome and the proteome of different species and representatives of distinct kingdoms, especially detection of proteome via wide-scaled analyses has various challenges and pitfalls. Attempts to combine all available information together and isolate some common features for determination of the pathway and their mechanism of action generally have a highly complicated nature. However, microtubule (MT) monomers are highly conserved protein structures, and microtubules are structurally conserved from Homo sapiens to Arabidopsis thaliana. The interaction of MT elements with microtubule-associated proteins and post-translational modifiers is fully dependent on protein interfaces, and almost all MT modifications are well described except acetylation. Crystallography and interactome data using different approaches were combined to identify conserved proteins important in acetylation of microtubules. Application of computational methods and comparative analysis of binding modes generated a robust predictive model of acetylation of the ϵ-amino group of Lys40 in α-tubulins. In turn, the model discarded some probable mechanisms of interaction between elements of interest. Reconstruction of unresolved protein structures was carried out with modeling by homology to the existing crystal structure (PDBID: 1Z2B) from B. taurus using Swiss-model server, followed by a molecular dynamics simulation. Docking of the human tubulin fragment with Lys40 into the active site of α-tubulin acetyltransferase, reproduces the binding mode of peptidomimetic from X-ray structure (PDBID: 4PK3).",0
"Accurate quantification of astrocyte and neurotransmitter fluorescence dynamics for single-cell and population-level physiology. Recent work examining astrocytic physiology centers on fluorescence imaging, due to development of sensitive fluorescent indicators and observation of spatiotemporally complex calcium activity. However, the field remains hindered in characterizing these dynamics, both within single cells and at the population level, because of the insufficiency of current region-of-interest-based approaches to describe activity that is often spatially unfixed, size-varying and propagative. Here we present an analytical framework that releases astrocyte biologists from region-of-interest-based tools. The Astrocyte Quantitative Analysis (AQuA) software takes an event-based perspective to model and accurately quantify complex calcium and neurotransmitter activity in fluorescence imaging datasets. We apply AQuA to a range of ex vivo and in vivo imaging data and use physiologically relevant parameters to comprehensively describe the data. Since AQuA is data-driven and based on machine learning principles, it can be applied across model organisms, fluorescent indicators, experimental modes, and imaging resolutions and speeds, enabling researchers to elucidate fundamental neural physiology.",1
"askMUSIC: Leveraging a Clinical Registry to Develop a New Machine Learning Model to Inform Patients of Prostate Cancer Treatments Chosen by Similar Men. BACKGROUND: Clinical registries provide physicians with a means for making data-driven decisions but few opportunities exist for patients to interact with registry data to help make decisions. OBJECTIVE: We sought to develop a web-based system that uses a prostate cancer (CaP) registry to provide newly diagnosed men with a platform to view predicted treatment decisions based on patients with similar characteristics. DESIGN, SETTING, AND PARTICIPANTS: The Michigan Urological Surgery Improvement Collaborative (MUSIC) is a quality improvement consortium of urology practices that maintains a prospective registry of men with CaP. We used registry data from 45 MUSIC urology practices from 2015 to 2017 to develop and validate a random forest machine learning model. After fitting the random forest model to a derivation cohort consisting of a random two-thirds sample of patients after stratifying by practice location, we evaluated the model performance in a validation cohort consisting of the remaining one-third of patients using a multiclass area under the curve (AUC) measure and calibration plots. RESULTS AND LIMITATIONS: We identified 7543 men diagnosed with CaP, of whom 45% underwent radical prostatectomy, 30% surveillance, 17% radiation therapy, 5.6% androgen deprivation, and 1.8% watchful waiting. The personalized prediction for patients in the validation cohort was highly accurate (AUC 0.81). CONCLUSIONS: Using clinical registry data and machine learning methods, we created a web-based platform for patients that generates accurate predictions for most CaP treatments. PATIENT SUMMARY: We have developed and tested a tool to help men newly diagnosed with prostate cancer to view predicted treatment decisions based on similar patients from our registry. We have made this tool available online for patients to use.",1
"Comparing different supervised machine learning algorithms for disease prediction. BACKGROUND: Supervised machine learning algorithms have been a dominant method in the data mining field. Disease prediction using health data has recently shown a potential application area for these methods. This study ai7ms to identify the key trends among different types of supervised machine learning algorithms, and their performance and usage for disease risk prediction. METHODS: In this study, extensive research efforts were made to identify those studies that applied more than one supervised machine learning algorithm on single disease prediction. Two databases (i.e., Scopus and PubMed) were searched for different types of search items. Thus, we selected 48 articles in total for the comparison among variants supervised machine learning algorithms for disease prediction. RESULTS: We found that the Support Vector Machine (SVM) algorithm is applied most frequently (in 29 studies) followed by the Naïve Bayes algorithm (in 23 studies). However, the Random Forest (RF) algorithm showed superior accuracy comparatively. Of the 17 studies where it was applied, RF showed the highest accuracy in 9 of them, i.e., 53%. This was followed by SVM which topped in 41% of the studies it was considered. CONCLUSION: This study provides a wide overview of the relative performance of different variants of supervised machine learning algorithms for disease prediction. This important information of relative performance can be used to aid researchers in the selection of an appropriate supervised machine learning algorithm for their studies.",0
"Reduced acquisition time PET pharmacokinetic modelling using simultaneous ASL–MRI: proof of concept. Pharmacokinetic modelling on dynamic positron emission tomography (PET) data is a quantitative technique. However, the long acquisition time is prohibitive for routine clinical use. Instead, the semi-quantitative standardised uptake value ratio (SUVR) from a shorter static acquisition is used, despite its sensitivity to blood flow confounding longitudinal analysis. A method has been proposed to reduce the dynamic acquisition time for quantification by incorporating cerebral blood flow (CBF) information from arterial spin labelling (ASL) magnetic resonance imaging (MRI) into the pharmacokinetic modelling. In this work, we optimise and validate this framework for a study of ageing and preclinical Alzheimer's disease. This methodology adapts the simplified reference tissue model (SRTM) for a reduced acquisition time (RT-SRTM) and is applied to [18F]-florbetapir PET data for amyloid-β quantification. Evaluation shows that the optimised RT-SRTM can achieve amyloid burden estimation from a 30-min PET/MR acquisition which is comparable with the gold standard SRTM applied to 60 min of PET data. Conversely, SUVR showed a significantly higher error and bias, and a statistically significant correlation with tracer delivery due to the influence of blood flow. The optimised RT-SRTM produced amyloid burden estimates which were uncorrelated with tracer delivery indicating its suitability for longitudinal studies.",0
"Licochalcone D directly targets JAK2 to induced apoptosis in human oral squamous cell carcinoma. Licochalcone (LC) families have been reported to have a wide range of biological function such as antioxidant, antibacterial, antiviral, and anticancer effects. Although various beneficial effects of LCD were revealed, its anticancer effect in human oral squamous cancer has not been identified. To examine the signaling pathway of LCD’s anticancer effect, we determined whether LCD has physical interaction with Janus kinase (JAK2)/signal transducer and activator of transcription-3 (STAT3) signaling, which is critical in promoting cancer cell survival and proliferation. Our results demonstrated that LCD inhibited the kinase activity of JAK2, soft agar colony formation, and the proliferation of HN22 and HSC4 cells. LCD also induced mitochondrial apoptotic events such as altered mitochondrial membrane potential and reactive oxygen species production. LCD increased the expression of apoptosis-associated proteins in oral squamous cell carcinoma (OSCC) cells. Finally, the xenograft study showed that LCD significantly inhibited HN22 tumor growth. Immunohistochemical data supported that LCD suppressed p-JAK2 and p-STAT3 expression and induced cleaved-caspase-3 expression. These results indicate that the anticancer effect of LCD is due to the direct targeting of JAK2 kinase. Therefore, LCD can be used for therapeutic application against OSCC.",0
"Polygenic risk-tailored screening for prostate cancer: A benefit-harm and cost-effectiveness modelling study. Background The United States Preventive Services Task Force supports individualised decision-making for prostate-specific antigen (PSA)-based screening in men aged 55-69. Knowing how the potential benefits and harms of screening vary by an individual's risk of developing prostate cancer could inform decision-making about screening at both an individual and population level. This modelling study examined the benefit-harm tradeoffs and the cost-effectiveness of a risk-tailored screening programme compared to age-based and no screening. Methods and findings A life-table model, projecting age-specific prostate cancer incidence and mortality, was developed of a hypothetical cohort of 4.48 million men in England aged 55 to 69 years with follow-up to age 90. Risk thresholds were based on age and polygenic profile. We compared no screening, age-based screening (quadrennial PSA testing from 55 to 69), and risk-tailored screening (men aged 55 to 69 years with a 10-year absolute risk greater than a threshold receive quadrennial PSA testing from the age they reach the risk threshold). The analysis was undertaken from the health service perspective, including direct costs borne by the health system for risk assessment, screening, diagnosis, and treatment. We used probabilistic sensitivity analyses to account for parameter uncertainty and discounted future costs and benefits at 3.5% per year. Our analysis should be considered cautiously in light of limitations related to our model's cohort-based structure and the uncertainty of input parameters in mathematical models. Compared to no screening over 35 years follow-up, age-based screening prevented the most deaths from prostate cancer (39,272, 95% uncertainty interval [UI]: 16,792-59,685) at the expense of 94,831 (95% UI: 84,827-105,630) overdiagnosed cancers. Age-based screening was the least cost-effective strategy studied. The greatest number of quality-adjusted life-years (QALYs) was generated by risk-based screening at a 10-year absolute risk threshold of 4%. At this threshold, risk-based screening led to one-third fewer overdiagnosed cancers (64,384, 95% UI: 57,382-72,050) but averted 6.3% fewer (9,695, 95% UI: 2,853-15,851) deaths from prostate cancer by comparison with age-based screening. Relative to no screening, risk-based screening at a 4% 10-year absolute risk threshold was cost-effective in 48.4% and 57.4% of the simulations at willingness-to-pay thresholds of GBP£20,000 (US$26,000) and £30,000 ($39,386) per QALY, respectively. The cost-effectiveness of risk-tailored screening improved as the threshold rose. Conclusions Based on the results of this modelling study, offering screening to men at higher risk could potentially reduce overdiagnosis and improve the benefit-harm tradeoff and the cost-effectiveness of a prostate cancer screening program. The optimal threshold will depend on societal judgements of the appropriate balance of benefits-harms and cost-effectiveness.",0
"Tumor-associated Macrophage-derived Interleukin-23 Interlinks Kidney Cancer Glutamine Addiction with Immune Evasion. BACKGROUND: Glutamine addiction is a hallmark of clear cell renal cell carcinoma (ccRCC); yet whether glutamine metabolism impacts local immune surveillance is unclear. This knowledge may yield novel immunotherapeutic opportunities. OBJECTIVE: To seek a potential therapeutic target in glutamine-addicted ccRCC. DESIGN, SETTING, AND PARTICIPANTS: Tumors from ccRCC patients from a Shanghai cohort and ccRCC tumor data from The Cancer Genome Atlas (TCGA) cohort were analyzed. In vivo and in vitro studies were conducted with fresh human ccRCC tumors and murine tumor cells. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Immune cell numbers and functions were analyzed by flow cytometry. Glutamine and cytokine concentrations were determined. Survival was compared between different subpopulations of patients using Kaplan-Meier and Cox regression analyses. RESULTS AND LIMITATIONS: We found that in ccRCC, high interleukin (IL)-23 expression was significantly associated with poor survival in both TCGA (overall survival [OS] hazard ratio [HR]=2.04, cancer-specific survival [CSS] HR=2.95; all p<0.001) and Shanghai (OS HR=2.07, CSS HR=3.92; all p<0.001) cohorts. IL-23 blockade prolongs the survival of tumor-bearing mice, promotes T-cell cytotoxicity in in vitro cultures of human ccRCC tumors, and augments the therapeutic benefits of anti-PD-1 antibodies. Mechanistically, glutamine consumption by ccRCC tumor cells results in the local deprivation of extracellular glutamine, which induces IL-23 secretion by tumor-infiltrating macrophages via the activation of hypoxia-inducible factor 1alpha (HIF1alpha). IL-23 activates regulatory T-cell proliferation and promotes IL-10 and transforming growth factor beta expression, thereby suppressing tumor cell killing by cytotoxic lymphocytes. The positive correlations between glutamine metabolism, IL-23 levels, and Treg responses are confirmed in both TCGA cohort and tumors from Shanghai ccRCC patients. Study limitations include the unclear impacts of glutamine deprivation and IL-23 on other immune cells. CONCLUSIONS: Macrophage-secreted IL-23 enhanced Treg functions in glutamine-addicted tumors; thus, IL-23 is a promising target for immunotherapy in ccRCC. PATIENT SUMMARY: In this study, we analyzed the immune components in glutamine-addicted clear cell renal cell carcinoma (ccRCC) tumors from two patient cohorts and conducted both in vitro and in vivo studies. We found that ccRCC tumor cell-intrinsic glutamine metabolism orchestrates immune evasion via interleukin (IL)-23, and IL-23-high patients had significantly poorer survival than IL-23-low patients. IL-23 should thus be considered a therapeutic target in ccRCC, either alone or in combination with immune checkpoint inhibitors.",0
"Mitochondrial gene cytochrome c oxidase I (CO1) used for molecular identification of Bactrocera zonata in Pakistan. Bactrocera zonata is fruit pest mostly attacked on peach and cause heavy destruction in production of peach fruits by sucking their juice. For their management, we start to detect them on basis of their molecular characterization. As mitochondrial genome encodes a gene COI used as biomarker for identification of eukaryotes including insects. In present study, we amplified COI gene and cloned into pTZ57R/T vector (Fermentas). Cloned gene was confirmed through restriction analysis and sequenced through its entirety on both strands from Macrogen (South Korea) by Sanger sequencing method. Different computational tools were utilized for comparative analysis of sequence with other related sequences retrieved from databases. Related species were identified through phylogenetic analysis using Mega 7 tool. Pairwise sequence alignment showed the sequence identity about 96% with Bactrocera zonata. By identifying the pests with more authentic molecular biomarker may help the research to control them more effectively in future.",0
"Representation learning for clinical time series prediction tasks in electronic health records. BACKGROUND: Electronic health records (EHRs) provide possibilities to improve patient care and facilitate clinical research. However, there are many challenges faced by the applications of EHRs, such as temporality, high dimensionality, sparseness, noise, random error and systematic bias. In particular, temporal information is difficult to effectively use by traditional machine learning methods while the sequential information of EHRs is very useful. METHOD: In this paper, we propose a general-purpose patient representation learning approach to summarize sequential EHRs. Specifically, a recurrent neural network based denoising autoencoder (RNN-DAE) is employed to encode inhospital records of each patient into a low dimensional dense vector. RESULTS: Based on EHR data collected from Shuguang Hospital affiliated to Shanghai University of Traditional Chinese Medicine, we experimentally evaluate our proposed RNN-DAE method on both mortality prediction task and comorbidity prediction task. Extensive experimental results show that our proposed RNN-DAE method outperforms existing methods. In addition, we apply the ""Deep Feature"" represented by our proposed RNN-DAE method to track similar patients with t-SNE, which also achieves some interesting observations. CONCLUSION: We propose an effective unsupervised RNN-DAE method to summarize patient sequential information in EHR data. Our proposed RNN-DAE method is useful on both mortality prediction task and comorbidity prediction task.",1
"Plasma-based protein biomarkers can predict the risk of acute graft-versus-host disease and non-relapse mortality in patients undergoing allogeneic hematopoietic stem cell transplantation. Predictive biomarkers for acute graft-versus-host disease (aGVHD) is currently lacking. In this study, we employed an unbiased proteome profiling method to prospectively collected plasma samples from allogeneic hematopoietic stem cell transplantation (alloHSCT) recipients to identify protein biomarkers that predict the risk of aGVHD and non-relapse mortality (NRM). In the discovery set, including five aGVHD patients and five controls, we identified seven candidate proteins. Patients with high levels of these proteins tended to exhibit a higher risk of aGVHD and NRM compared to patients with low levels in post-engraftment plasma samples from an independent validation set (n = 89). Tissue inhibitor of metalloproteinase 1, plastin-2, and regenerating islet-derived protein 3-α were selected as the most-predictive biomarkers via an exhaustive variable screening algorithm and were collectively used to develop a biomarker panel score ranging from 0 to 3. The biomarker panel score correlated significantly with aGVHD and NRM risk in univariable and multivariable Cox models. Furthermore, using the biomarker panel score in conjunction with clinical predictors significantly improved the discriminatory performance of the Cox model in predicting aGVHD and NRM risk. Our findings suggest that plasma-derived protein biomarkers can be used to predict aGVHD and NRM before the onset of clinical manifestations.",0
"The MAGIC algorithm probability is a validated response biomarker of treatment of acute graft-versus-host disease. The Mount Sinai Acute GVHD International Consortium (MAGIC) algorithm probability (MAP), derived from 2 serum biomarkers, measures damage to crypts in the gastrointestinal tract during graft-versus-host disease (GVHD). We hypothesized that changes in MAP after treatment could validate it as a response biomarker. We prospectively collected serum samples and clinical stages of acute GVHD from 615 patients receiving hematopoietic cell transplantation in 20 centers at initiation of first-line systemic treatment and 4 weeks later. We computed MAPs and clinical responses and compared their abilities to predict 6-month nonrelapse mortality (NRM) in the validation cohort (n = 367). After 4 weeks of treatment, MAPs predicted NRM better than the change in clinical symptoms in all patients and identified 2 groups with significantly different NRM in both clinical responders (40% vs 12%, P < .0001) and nonresponders (65% vs 25%, P < .0001). MAPs successfully reclassified patients for NRM risk within every clinical grade of acute GVHD after 4 weeks of treatment. At the beginning of treatment, patients with a low MAP that rose above the threshold of 0.290 after 4 weeks of treatment had a significant increase in NRM, whereas patients with a high MAP at onset that fell below that threshold after treatment had a striking decrease in NRM that translated into clear differences in overall survival. We conclude that a MAP measured before and after treatment of acute GVHD is a response biomarker that predicts long-term outcomes more accurately than change in clinical symptoms. MAPs have the potential to guide therapy for acute GVHD and may function as a useful end point in clinical trials.",0
"DeepNEU: Artificially Induced Stem Cell (aiPSC) and Differentiated Skeletal Muscle Cell (aiSkMC) Simulations of Infantile Onset POMPE Disease (IOPD) for Potential Biomarker Identification and Drug Discovery. Infantile onset Pompe disease (IOPD) is a rare and lethal genetic disorder caused by the deletion of the acid alpha-glucosidase (GAA) gene. This gene encodes an essential lysosomal enzyme that converts glycogen to glucose. While enzyme replacement therapy helps some, our understanding of disease pathophysiology is limited. In this project we develop computer simulated stem cells (aiPSC) and differentiated skeletal muscle cells (aiSkMC) to empower IOPD research and drug discovery. Our Artificial Intelligence (AI) platform, DeepNEU v3.6 was used to generate aiPSC and aiSkMC simulations with and without GAA expression. These simulations were validated using peer reviewed results from the recent literature. Once the aiSkMC simulations (IOPD and WT) were validated they were used to evaluate calcium homeostasis and mitochondrial function in IOPD. Lastly, we used aiSkMC IOPD simulations to identify known and novel biomarkers and potential therapeutic targets. The aiSkMC simulations of IOPD correctly predicted genotypic and phenotypic features that were reported in recent literature. The probability that these features were accurately predicted by chance alone using the binomial test is 0.0025. The aiSkMC IOPD simulation correctly identified L-type calcium channels (VDCC) as a biomarker and confirmed the positive effects of calcium channel blockade (CCB) on calcium homeostasis and mitochondrial function. These published data were extended by the aiSkMC simulations to identify calpain(s) as a novel potential biomarker and therapeutic target for IOPD. This is the first time that computer simulations of iPSC and differentiated skeletal muscle cells have been used to study IOPD. The simulations are robust and accurate based on available published literature. We also demonstrated that the IOPD simulations can be used for potential biomarker identification leading to targeted drug discovery. We will continue to explore the potential for calpain inhibitors with and without CCB as effective therapy for IOPD.",0
"Xanthatin inhibits STAT3 and NF-κB signalling by covalently binding to JAK and IKK kinases. Aberrant activation of the signal transducer and activator of transcription 3 (STAT3) and the nuclear factor-κB (NF-κB) signalling pathways is associated with the development of cancer and inflammatory diseases. JAKs and IKKs are the key regulators in the STAT3 and NF-κB signalling respectively. Therefore, the two families of kinases have been the major targets for developing drugs to regulate the two signalling pathways. Here, we report a natural compound xanthatin from the traditional Chinese medicinal herb Xanthium L. as a potent inhibitor of both STAT3 and NF-κB signalling pathways. Our data demonstrated that xanthatin was a covalent inhibitor and its activities depended on its α-methylene-γ-butyrolactone group. It preferentially interacted with the Cys243 of JAK2 and the Cys412 and Cys464 of IKKβ to inactivate their activities. In doing so, xanthatin preferentially inhibited the growth of cancer cell lines that have constitutively activated STAT3 and p65. These data suggest that xanthatin may be a promising anticancer and anti-inflammation drug candidate.",0
"Prediction of acid radical ion binding residues by K-nearest neighbors classifier. Background: Proteins perform their functions by interacting with acid radical ions. Recently, it was a challenging work to precisely predict the binding residues of acid radical ion ligands in the research field of molecular drug design. Results: In this study, we proposed an improved method to predict the acid radical ion binding residues by using K-nearest Neighbors classifier. Meanwhile, we constructed datasets of four acid radical ion ligand (NO2-, CO32-, SO42-, PO43-) binding residues from BioLip database. Then, based on the optimal window length for each acid radical ion ligand, we refined composition information and position conservative information and extracted them as feature parameters for K-nearest Neighbors classifier. In the results of 5-fold cross-validation, the Matthew's correlation coefficient was higher than 0.45, the values of accuracy, sensitivity and specificity were all higher than 69.2%, and the false positive rate was lower than 30.8%. Further, we also performed an independent test to test the practicability of the proposed method. In the obtained results, the sensitivity was higher than 40.9%, the values of accuracy and specificity were higher than 84.2%, the Matthew's correlation coefficient was higher than 0.116, and the false positive rate was lower than 15.4%. Finally, we identified binding residues of the six metal ion ligands. In the predicted results, the values of accuracy, sensitivity and specificity were all higher than 77.6%, the Matthew's correlation coefficient was higher than 0.6, and the false positive rate was lower than 19.6%. Conclusions: Taken together, the good results of our prediction method added new insights in the prediction of the binding residues of acid radical ion ligands.",0
"Three-dimensional US Fractional Moving Blood Volume: Validation of renal perfusion quantification. Background: Three-dimensional (3D) fractional moving blood volume (FMBV) derived from 3D power Doppler US has been proposed for noninvasive approximation of perfusion. However, 3D FMBV has never been applied in animals against a ground truth. Purpose: To determine the correlation between 3D FMBV and the reference standard of fluorescent microspheres (FMS) for measurement of renal perfusion in a porcine model. Materials and Methods: From February 2017 to September 2017, adult pigs were administered FMS before and after measurement of renal 3D FMBV at baseline (100%) and approximately 75%, 50%, and 25% flow levels by using US machines from two different vendors. The 3D power Doppler US volumes were converted and segmented, and correlations between FMS and 3D FMBV were made with simple linear regression (r2). Similarity and reproducibility of manual segmentation were determined with the Dice similarity coefficient and 3D FMBV reproducibility (intraclass correlation coefficient [ICC]). Results: Thirteen pigs were studied with 33 flow measurements. Kidney volume (mean Dice similarity coefficient 6 standard deviation, 0.89 6 0.01) and renal segmentation (coefficient of variation = 12.6%; ICC = 0.86) were consistent. The 3D FMBV calculations had high reproducibility (ICC = 0.97; 95% confidence interval: 0.96, 0.98). The 3D FMBV per-pig correlation showed excellent correlation for US machines from both vendors (mean r2 = 0.96 [range, 0.92–1.0] and 0.93 [range, 0.78–1.0], respectively). The correlation between 3D FMBV and perfusion measured with microspheres was high for both US machines (r2 = 0.80 [P , .001] and 0.70 [P , .001], respectively). Conclusion: The strong correlation between three-dimensional (3D) fractional moving blood volume (FMBV) and fluorescent microspheres indicates that 3D FMBV shows excellent correlation to perfusion and good reproducibility.",0
"Deep-learning based multiclass retinal fluid segmentation and detection in optical coherence tomography images using a fully convolutional neural network. As a non-invasive imaging modality, optical coherence tomography (OCT) can provide micrometer-resolution 3D images of retinal structures. These images can help reveal disease-related alterations below the surface of the retina, such as the presence of edema, or accumulation of fluid which can distort vision, and are an indication of disruptions in the vasculature of the retina. In this paper, a new framework is proposed for multiclass fluid segmentation and detection in the retinal OCT images. Based on the intensity of OCT images and retinal layer segmentations provided by a graph-cut algorithm, a fully convolutional neural network was trained to recognize and label the fluid pixels. Random forest classification was performed on the segmented fluid regions to detect and reject the falsely labeled fluid regions. The proposed framework won the first place in the MICCAI RETOUCH challenge in 2017 on both the segmentation performance (mean Dice: 0.7667) and the detection performance (mean AUC: 1.00) tasks.",1
"A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop. Imaging research laboratories are rapidly creating machine learning systems that achieve expert human performance using open-source methods and tools. These artificial intelligence systems are being developed to improve medical image reconstruction, noise reduction, quality assurance, triage, segmentation, computer-aided detection, computer-aided classification, and radiogenomics. In August 2018, a meeting was held in Bethesda, Maryland, at the National Institutes of Health to discuss the current state of the art and knowledge gaps and to develop a roadmap for future research initiatives. Key research priorities include: 1, new image reconstruction methods that efficiently produce images suitable for human interpretation from source data; 2, automated image labeling and annotation methods, including information extraction from the imaging report, electronic phenotyping, and prospective structured image reporting; 3, new machine learning methods for clinical imaging data, such as tailored, pretrained model architectures, and federated machine learning methods; 4, machine learning methods that can explain the advice they provide to human users (so-called explainable artificial intelligence); and 5, validated methods for image de-identification and data sharing to facilitate wide availability of clinical imaging data sets. This research roadmap is intended to identify and prioritize these needs for academic research laboratories, funding agencies, professional societies, and industry.",0
"The Pancreas as a Site of Metastasis or Second Primary in Patients with Small Bowel Neuroendocrine Tumors. Background: The small bowel and pancreas are the most common primary sites of neuroendocrine tumors (NETs) giving rise to metastatic disease. Some patients with small bowel NETs (SBNETs) present with synchronous or metachronous pancreatic NETs (PNETs), and it is unclear whether these are separate primaries or metastases from one site to the other. Methods: A surgical NET database including patients undergoing operations for SBNETs or PNETs was reviewed. Patients with synchronous or metachronous tumors in both the small bowel and pancreas were identified, and available tissues from primary tumors and metastases were examined using a 4-gene quantitative polymerase chain reaction (qPCR) and immunohistochemistry (IHC) panel developed for evaluating NETs of unknown primary. Results: Of 338 patients undergoing exploration, 11 had NETs in both the small bowel and pancreas. Tissues from 11 small bowel tumors, 9 pancreatic tumors, and 10 metastases were analyzed. qPCR and IHC data revealed that three patients had separate SBNET and PNET primaries, and five patients had SBNETs that metastasized to the pancreas. Pancreatic tissue was unavailable in two patients, and qPCR and IHC gave discrepant results in one patient. Conclusions: NETs in both the small bowel and pancreas were found in 3% of our patients. In nearly two-thirds of evaluable patients, the pancreatic tumor was a metastasis from the SBNET primary, while in the remaining one-third of patients it represented a separate primary. Determining the origin of these tumors can help guide the choice of systemic therapy and surgical management.",0
"Performance of a Multigene Genomic Classifier in Thyroid Nodules with Indeterminate Cytology: A Prospective Blinded Multicenter Study. Importance: Approximately 20% of fine-needle aspirations (FNA) of thyroid nodules have indeterminate cytology, most frequently Bethesda category III or IV. Diagnostic surgeries can be avoided for these patients if the nodules are reliably diagnosed as benign without surgery. Objective: To determine the diagnostic accuracy of a multigene classifier (GC) test (ThyroSeq v3) for cytologically indeterminate thyroid nodules. Design, Setting, and Participants: Prospective, blinded cohort study conducted at 10 medical centers, with 782 patients with 1013 nodules enrolled. Eligibility criteria were met in 256 patients with 286 nodules; central pathology review was performed on 274 nodules. Interventions: A total of 286 FNA samples from thyroid nodules underwent molecular analysis using the multigene GC (ThyroSeq v3). Main Outcomes and Measures: The primary outcome was diagnostic accuracy of the test for thyroid nodules with Bethesda III and IV cytology. The secondary outcome was prediction of cancer by specific genetic alterations in Bethesda III to V nodules. Results: Of the 286 cytologically indeterminate nodules, 206 (72%) were benign, 69 (24%) malignant, and 11 (4%) noninvasive follicular thyroid neoplasms with papillary-like nuclei (NIFTP). A total of 257 (90%) nodules (154 Bethesda III, 93 Bethesda IV, and 10 Bethesda V) had informative GC analysis, with 61% classified as negative and 39% as positive. In Bethesda III and IV nodules combined, the test demonstrated a 94% (95% CI, 86%-98%) sensitivity and 82% (95% CI, 75%-87%) specificity. With a cancer/NIFTP prevalence of 28%, the negative predictive value (NPV) was 97% (95% CI, 93%-99%) and the positive predictive value (PPV) was 66% (95% CI, 56%-75%). The observed 3% false-negative rate was similar to that of benign cytology, and the missed cancers were all low-risk tumors. Among nodules testing positive, specific groups of genetic alterations had cancer probabilities varying from 59% to 100%. Conclusions and Relevance: In this prospective, blinded, multicenter study, the multigene GC test demonstrated a high sensitivity/NPV and reasonably high specificity/PPV, which may obviate diagnostic surgery in up to 61% of patients with Bethesda III to IV indeterminate nodules, and up to 82% of all benign nodules with indeterminate cytology. Information on specific genetic alterations obtained from FNA may help inform individualized treatment of patients with a positive test result..",0
"Hypoxia-induced regulation of mTOR signaling by miR-7 targeting REDD1. Oxygen is an important factor mediating cell growth and survival under physiological and pathological conditions. Therefore, cells have well-regulated response mechanisms in the face of changes in oxygen levels in their environment. A subset of microRNAs (miRNAs) termed the hypoxamir has been suggested to be a critical mediator of the cellular response to hypoxia. Regulated in development and DNA damage response 1 (REDD1) is a negative regulator of mammalian target of rapamycin (mTOR) signaling in the response to cellular stress, and is elevated in many cell types under hypoxia, with consequent inhibition of mTOR signaling. However, the underlying posttranscriptional regulatory mechanism by miRNAs that contribute to this hypoxia-induced reduction in REDD1 expression remain unknown. Therefore, the aim of the current study was to identify the miRNAs participating in the hypoxic cellular response by scanning the 3′-untranslated region (3′-UTR) of REDD1 for potential miRNA-binding sites using a computer algorithm, TargetScan. miR-7 emerged as a novel hypoxamir that regulates REDD1 expression and is involved in mTOR signaling. miR-7 could repress REDD1 expression posttranscriptionally by directly binding with the 3′-UTR. Upon hypoxia, miR-7 expression was downregulated in HeLa cells to consequently derepress REDD1, resulting in inhibition of mTOR signaling. Moreover, overexpression of miR-7 was sufficient to reverse the hypoxia-induced inhibition of mTOR signaling. Therefore, our findings suggest miR-7 as a key regulator of hypoxia-mediated mTOR signaling through modulation of REDD1 expression. These findings contribute new insight into the miRNA-mediated molecular mechanism of the hypoxic response through mTOR signaling, highlighting potential targets for tumor suppression.",0
"Gastroprotective effect of araloside A on ethanol- and aspirin-induced gastric ulcer in mice: involvement of H+/K+-ATPase and mitochondrial-mediated signaling pathway. The aim of this study was to elucidate the gastroprotective activity and possible mechanism of involvement of araloside A (ARA) against ethanol- and aspirin-induced gastric ulcer in mice. The experimental mice were randomly divided into control, model, omeprazole (20 mg/kg, orally) and ARA (10, 20 and 40 mg/kg, orally). Gastric ulcer in mice was induced by intragastric administration of 80% ethanol (10 mL/kg) containing 15 mg/mL aspirin 4 h after drug administration on day 7. The results indicated that ARA could significantly raise gastric juice volume and acidity; ameliorate gastric mucosal blood flow, gastric binding mucus volume, ulcer index and ulcer inhibition rate; suppress H+/K+-ATPase activity, which was confirmed by computer-aided docking simulations; inhibit the release of mitochondrial cytochrome c into the cytoplasm; inhibit caspase-9 and caspase-3 activities and down-regulate mRNA expression levels; down-regulate the mRNA and protein expressions of apoptosis protease-activating factor-1 and protein expression of cleaved poly(ADP ribose) polymerase-1; and up-regulate Bcl-2 mRNA and protein expressions and down-regulate Bax mRNA and protein expressions, thus elevating the Bcl-2/Bax ratio in a dose-dependent manner. Histopathological observations further provided supportive evidence for the aforementioned results. The results demonstrated that ARA exerted beneficial gastroprotective effects on alcohol- and aspirin-induced gastric ulcer in mice, which was related to suppressing H+/K+-ATPase activity as well as pro-apoptotic protein expression, and promoting anti-apoptotic protein expression, thus alleviating gastric mucosal injury and cell death.",0
"Free Pentosidine Assessment Based on Fluorescence Measurements in Spent Dialysate. The aim of this study was to primarily explore the relationship between free pentosidine and the fluorescence properties of spent dialysate, and also to develop a model to assess the levels of free pentosidine in spent dialysate based on the fluorescence measurements. First, 40 patients (20 females and 20 males) were examined during 40 dialysis sessions. High-pressure liquid chromatography (HPLC) was used to measure the free pentosidine concentrations from the spent dialysate. The full fluorescence spectra of the spent dialysates were recorded and single- and multi-wavelength (MW) models were developed. The average free pentosidine concentrations in the spent dialysate measured by HPLC at the start and end of the dialysis session were (mean ± SD) 4.25 ± 3.11 and 0.94 ± 0.69 μg/L respectively. The removal ratios (RRs) between RR-lab and RR-MW were statistically similar (p > 0.2). The concentration of free pentosidine and the RR can therefore be estimated from the spent dialysate when utilising fluorescence measurements.",0
"Physiological Signature of Memory Age in the Prefrontal-Hippocampal Circuit. The long-term storage of episodic memory requires communication between prefrontal cortex and hippocampus. However, how consolidation alters dynamic interactions between these regions during subsequent recall remains unexplored. Here we perform simultaneous electrophysiological recordings from anterior cingulate cortex (ACC) and hippocampal CA1 in mice during recall of recent and remote contextual fear memory. We find that, in contrast to recent memory, remote memory recall is accompanied by increased ACC-CA1 synchronization at multiple frequency bands. The augmented ACC-CA1 interaction is associated with strengthened coupling among distally spaced CA1 neurons, suggesting an ACC-driven organization of a sparse code. This robust shift in physiology permits a support vector machine classifier to accurately determine memory age on the basis of the ACC-CA1 synchronization pattern. Our findings reveal that memory consolidation alters the dynamic coupling of the prefrontal-hippocampal circuit and results in a physiological signature of memory age.",0
"Atomic insight into prion disorder: An intricate detail gained by 0.5 μs molecular dynamics simulation of preventive G127V and deleterious D178V mutation in prion protein. In this study we are looking into two contradicting mutations found in prion protein (PrP) viz G127V and D178V, that are reportedly protective and pathogenic, respectively. Despite significant advances in comprehension of the role of pathogenic mutations, the role of protective mutation in amyloid fold inhibition still lacks a substantial basis. To understand the structural basis of protective mutation, molecular dynamics simulation coupled with protein-protein docking and molecular mechanics/Poisson-Boltzmann surface area analysis was used to understand the instant structural variability brought about by these mutations alone and in combination on PrP and prion-prion complex. Atomic-scale investigations successfully revealed that the binding pattern of prion-prion varies differentially in protective and pathogenic mutations with secondary structure showing distinct contrasting patterns, which could supposedly be a critical factor for differential prion behavior in protective and pathogenic mutations. Considering the reported role of an amyloid fold in prion-prion binding, the contrasting pattern has given us a lead in comprehending the role of these mutations and has been used in this study to look for small molecules that can inhibit amyloid fold for prion-prion interaction in pathogenic mutant carrying PrP.",0
"A new method for excavating feature lncRNA in lung adenocarcinoma based on pathway crosstalk analysis. Recent theoretical and experimental studies indicate that long-chain noncoding RNAs (lncRNAs) are essential for the growth and differentiation of cells and the occurrence and development of tumors in epigenetics, but the regulation of lncRNA on gene expression, transcriptional activation, and transcriptional interference in diseases is still unclear. There is an urgent need for effective methods to discover significant lncRNAs with their functions on gene regulatory mechanisms. For this purpose, a new method of extracting significant lncRNA based on pathway crosstalk and dysfunction caused by the differentially expressed genes in lung adenocarcinoma (LUAD) was proposed. The pathway analysis method based on global influence (PAGI) was first applied to find the feature genes that play an important role in the crosstalks of disease-related pathways. Then to explore the hub lncRNAs, the weighted gene coexpression network analysis (WGCNA) was used to construct coexpression models of the feature genes and lncRNAs. The experiment results showed that 64 out of the 322 hub lncRNAs were closely related to the clinical features of patients with LUAD. Among them, nine lncRNAs (UCA1, LINC00857, PVT1, PCAT6, LINC00460, LINC00319, AP000553.1, AP000439.2, and AP005233.2) were identified to be tightly correlated with non-small–cell lung cancer (NSCLC) pathways. In summary, we offer an effective way to extract significant lncRNA by dysfunctional pathway crosstalk in LUAD which allows the selected lncRNAs with more biologically interpreted and reproducible results. This method can be applied to other diseases and provide useful information for understanding the pathogenesis of human cancer.",0
"GRUU-Net: Integrated convolutional and gated recurrent neural network for cell segmentation. Cell segmentation in microscopy images is a common and challenging task. In recent years, deep neural networks achieved remarkable improvements in the field of computer vision. The dominant paradigm in segmentation is using convolutional neural networks, less common are recurrent neural networks. In this work, we propose a new deep learning method for cell segmentation, which integrates convolutional neural networks and gated recurrent neural networks over multiple image scales to exploit the strength of both types of networks. To increase the robustness of the training and improve segmentation, we introduce a novel focal loss function. We also present a distributed scheme for optimized training of the integrated neural network. We applied our proposed method to challenging data of glioblastoma cell nuclei and performed a quantitative comparison with state-of-the-art methods. Insights on how our extensions affect training and inference are also provided. Moreover, we benchmarked our method using a wide spectrum of all 22 real microscopy datasets of the Cell Tracking Challenge.",0
"Prospective validation of a new airway management algorithm and predictive features of intubation difficulty. BACKGROUND: Some patients have features that indicate possible difficulty with direct laryngoscopy for tracheal intubation. Prediction of the likely outcome and selection of patients for an enhanced management algorithm would reduce the possible harm from failed intubation attempts. METHODS: Adult elective patients were assessed for seven features associated with difficult direct laryngoscopy, ranked in difficulty from 0 to 3. For a patient with at least one Class 3 feature, or two or more features of class 1 or higher, the enhanced management used a channelled videolaryngoscope Airtraq instead of a Macintosh laryngoscope. A long flexible angulated stylet and a flexible fibrescope would be used as the second and third steps. For patients with lesser difficulty scores, a Macintosh laryngoscope was used. Outcomes of enhanced management were analysed. Logistic regression and Random Forest algorithm, using the ranks of the predictive features, were used to predict difficulty during enhanced management. RESULTS: We prospectively studied 16 695 patients. We selected 1501 (9%) for enhanced management, and tracheal intubation was successful in all of them. Of these, 73% were intubated in less than 30 s, and only 4.5% required more than 4 min for intubation. Progression to the second and third steps of enhanced management was predicted by restriction of mouth opening and reduced cervical spine mobility. CONCLUSIONS: An enhanced management algorithm allowed successful tracheal intubation of all patients with anticipated difficult laryngoscopy. The need to combine the use of a stylet and a fibrescope with the Airtraq could be predicted with a high degree of certainty.",0
"Development and validation of serum exosomal microRNAs as diagnostic and prognostic biomarkers for hepatocellular carcinoma. Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related death worldwide. China accounts for over half of the new cases and deaths. Diagnostic imprecision and a lack of complimentary molecular biomarkers are partially responsible for this lack of progress. Herein, serum-derived exosomal microRNA (miRNA) profiling was performed on 80 patients which histologically confirmed HCC and 30 normal controls. A classification of 8 exosomal miRNAs had biologically and statistically significant differences between HCC and normal serum samples, including miR-122, miR-125b, miR-145, miR-192, miR-194, miR-29a, miR-17-5p, and miR-106a. Online algorithm showed strong independent classification accuracy (area under the curve) reached 0.535 to 0.850, separately. The significant correlation between serum exosomal miRNAs and tumor size was observed. In addition, the survival difference of HCC patients with high or low exosomal miR-106a was statistically significant using Kaplan-Meier analysis. Besides, we also measured the proliferation and invasion ability of HCC cells following exosomal miR-106a mimics or inhibitor treatment. After prediction with algorithms, mitogen-activated protein kinase and c-Jun N-terminal kinase pathways were identified associated with miR-106a’s function. In summary, differentially expressed serum exosomal miRNAs can be helpful for diagnostic and prognostic of HCC.",0
"Differential molecular modeling predictions of mid and conventional dialysate flows. Background: High dialysate flow rates (QD) of 500-800 mL/ min are used to maximize urea removal during conventional hemodialysis. There are few data describing hemodialysis with use of mid-rate QD (300 mL/min). Methods: We constructed uremic solute (urea, beta2-microglobulin and phosphate) kinetic models at varying volumes of distribution and blood flow rates to predict solute clearances at QD of 300 and 500 mL/min. Results: Across a range of volumes of distribution a QD of 300 mL/min generally yields a predicted urea spKt/V greater than 1.2 during typical treatment times with a small difference in urea spKt/V between a QD of 300 and 500 mL/min. A larger urea KoA dialyzer and 15 min of additional time narrows the urea spKt/V difference. No substantial differences were observed regarding the kinetics of beta2-microglobulin and phosphate for QD of 300 vs. 500 mL/ min. Conclusion: A QD of 300 mL/min can achieve urea clearance targets. Hemodialysis systems using mid-rate QD can be expected to provide adequate hemodialysis, as currently defined.",0
"NOTCH1 regulates the proliferation and migration of bladder cancer cells by cooperating with long non-coding RNA HCG18 and microRNA-34c-5p. In recent years, the NOTCH signaling pathway has been gradually studied in human malignancies. Inactivation of the NOTCH signaling pathway was uncovered to be correlated with the carcinogenesis of bladder cancer (BCa). Nevertheless, the specific molecular mechanism of NOTCH1 (one of the core factors of the NOTCH signaling pathway) is not well elucidated in BCa. This study focused on the mechanism by which NOTCH1 affects the biological behaviors of BCa cells. According to the experimental results of quantitative real-time polymerase chain reaction, NOTCH1 was dysregulated in BCa tissues and cell lines. The prognostic value of NOTCH1 for the patients with BCa was determined using the Kaplan-Meier method. Mechanism investigations revealed that NOTCH1 is a target of miR-34c-5p in BCa. Furthermore, microarray analysis was used to find the dysregulated long noncoding RNAs (lncRNA), which can bind with miR-34c-5p. Mechanism experiments further demonstrated the rationality of the HCG18-miR-34c-5p-NOTCH1 pathway. Functional assays were then applied to validate the inhibitory influences of NOTCH1 on the proliferation and migration of BCa cells. Furthermore, the inhibitory effects of NOTCH1 could be affected by miR-34c-5p or lncRNA HCG18. All findings in this study revealed that NOTCH1 suppresses the BCa progression by cooperating with lncRNA HCG18 and miR-34c-5p.",0
"Identification of the potential prognostic genes of human melanoma. The melanoma is one of the most dangerous forms of skin diseases. It may spread to other parts of the body and cause serious illness and death. Early detection and diagnosis are crucial. However, the systemic expression analysis for the different staging of melanoma is still lacking to date. In this study, we analyzed the gene expression profiles of the different staging of melanoma by the differential expression analysis and random forest analysis. First, the results of the principal component analysis showed that the clustering of primary tumor samples, normal samples, and pigment nevus samples got closer, while the clustering of tumor metastatic samples and normal samples was far away. Moreover, the gene expression of tumor metastasis stage and the initial stage had obvious differences. Almost 426 genes identified had differential expression. The functional enrichment of differentially expressed genes was associated with the epidermal cell differentiation, epidermis development, and the keratinocyte differentiation. Taken together, our findings identified the differentially expressed signatures between primary melanoma and metastatic melanoma. Our results would provide the potential mechanisms of melanoma.",0
"A dynamic neural network model for predicting risk of Zika in real time. BACKGROUND: In 2015, the Zika virus spread from Brazil throughout the Americas, posing an unprecedented challenge to the public health community. During the epidemic, international public health officials lacked reliable predictions of the outbreak's expected geographic scale and prevalence of cases, and were therefore unable to plan and allocate surveillance resources in a timely and effective manner. METHODS: In this work, we present a dynamic neural network model to predict the geographic spread of outbreaks in real time. The modeling framework is flexible in three main dimensions (i) selection of the chosen risk indicator, i.e., case counts or incidence rate; (ii) risk classification scheme, which defines the high-risk group based on a relative or absolute threshold; and (iii) prediction forecast window (1 up to 12 weeks). The proposed model can be applied dynamically throughout the course of an outbreak to identify the regions expected to be at greatest risk in the future. RESULTS: The model is applied to the recent Zika epidemic in the Americas at a weekly temporal resolution and country spatial resolution, using epidemiological data, passenger air travel volumes, and vector habitat suitability, socioeconomic, and population data for all affected countries and territories in the Americas. The model performance is quantitatively evaluated based on the predictive accuracy of the model. We show that the model can accurately predict the geographic expansion of Zika in the Americas with the overall average accuracy remaining above 85% even for prediction windows of up to 12 weeks. CONCLUSIONS: Sensitivity analysis illustrated the model performance to be robust across a range of features. Critically, the model performed consistently well at various stages throughout the course of the outbreak, indicating its potential value at any time during an epidemic. The predictive capability was superior for shorter forecast windows and geographically isolated locations that are predominantly connected via air travel. The highly flexible nature of the proposed modeling framework enables policy makers to develop and plan vector control programs and case surveillance strategies which can be tailored to a range of objectives and resource constraints.",1
"Novel drug-independent sedation level estimation based on machine learning of quantitative frontal electroencephalogram features in healthy volunteers. BACKGROUND: Sedation indicators based on a single quantitative EEG (QEEG) feature have been criticised for their limited performance. We hypothesised that integration of multiple QEEG features into a single sedation-level estimator using a machine learning algorithm could reliably predict levels of sedation, independent of the sedative drug used. METHODS: In total, 102 subjects receiving propofol (N=36; 16 male/20 female), sevoflurane (N=36; 16 male/20 female), or dexmedetomidine (N=30; 15 male/15 female) were included in this study of healthy volunteers. Sedation level was assessed using the Modified Observer's Assessment of Alertness/Sedation (MOAA/S) score. We used 44 QEEG features estimated from the EEG data in a logistic regression algorithm, and an elastic-net regularisation method was used for feature selection. The area under the receiver operator characteristic curve (AUC) was used to assess the performance of the logistic regression model. RESULTS: The performances obtained when the system was trained and tested as drug-dependent mode to distinguish between awake and sedated states (mean AUC [standard deviation]) were propofol=0.97 (0.03), sevoflurane=0.74 (0.25), and dexmedetomidine=0.77 (0.10). The drug-independent system resulted in mean AUC=0.83 (0.17) to discriminate between the awake and sedated states. CONCLUSIONS: The incorporation of large numbers of QEEG features and machine learning algorithms is feasible for next-generation monitors of sedation level. Different QEEG features were selected for propofol, sevoflurane, and dexmedetomidine groups, but the sedation-level estimator maintained a high performance for predicting MOAA/S independent of the drug used. CLINICAL TRIAL REGISTRATION: NCT02043938; NCT03143972.",1
"Radiomic phenotypes of mammographic parenchymal complexity: Toward augmenting breast density in breast cancer risk assessment. Purpose: To identify phenotypes of mammographic parenchymal complexity by using radiomic features and to evaluate their associations with breast density and other breast cancer risk factors. Materials and Methods: Computerized image analysis was used to quantify breast density and extract parenchymal texture features in a cross-sectional sample of women screened with digital mammography from September 1, 2012, to February 28, 2013 (n = 2029; age range, 35-75 years; mean age, 55.9 years). Unsupervised clustering was applied to identify and reproduce phenotypes of parenchymal complexity in separate training (n = 1339) and test sets (n = 690). Differences across phenotypes by age, body mass index, breast density, and estimated breast cancer risk were assessed by using Fisher exact, x2, and Kruskal-Wallis tests. Conditional logistic regression was used to evaluate preliminary associations between the detected phenotypes and breast cancer in an independent casecontrol sample (76 women diagnosed with breast cancer and 158 control participants) matched on age. Results: Unsupervised clustering in the screening sample identified four phenotypes with increasing parenchymal complexity that were reproducible between training and test sets (P = .001). Breast density was not strongly correlated with phenotype category (R2 = 0.24 for linear trend). The low- to intermediate-complexity phenotype (prevalence, 390 of 2029 [19%]) had the lowest proportion of dense breasts (eight of 390 [2.1%]), whereas similar proportions were observed across other phenotypes (from 140 of 291 [48.1%] in the high-complexity phenotype to 275 of 511 [53.8%] in the low-complexity phenotype). In the independent case-control sample, phenotypes showed a significant association with breast cancer (P = .001), resulting in higher discriminatory capacity when added to a model with breast density and body mass index (area under the curve, 0.84 vs 0.80; P = .03 for comparison). Conclusion: Radiomic phenotypes capture mammographic parenchymal complexity beyond conventional breast density measures and established breast cancer risk factors.",1
"miRNAs responsive to the diabetic microenvironment in the human beta cell line EndoC-βH1 may target genes in the FOXO, HIPPO and Lysine degradation pathways. Altered expression of miRNAs is evident in the islets of diabetic human donors, but the effects of specific aspects of the diabetic microenvironment and identity of gene ontology pathways demonstrating target gene enrichment in response to each is understudied. We assessed changes in the miRNA milieu in response to high/low glucose, hypoxia, dyslipidaemia and inflammatory factors in a humanised EndoC-βH1 beta cell culture system and performed miRPath analysis for each treatment individually. The 10 miRNAs demonstrating the greatest dysregulation across treatments were then independently validated and Gene Set Enrichment Analysis to confirm targeted pathways undertaken. 171 of 392 miRNAs displayed altered expression in response to one or more cellular stressors. miRNA changes were treatment specific, but their target genes were enriched in conserved pathways. 5 miRNAs (miR-136-5p, miR299-5p, miR-454-5p, miR-152 and miR-185) were dysregulated in response to multiple stressors and survived validation in independent samples (p = 0.008, 0.002, 0.012, 0.005 and 0.024 respectively). Target genes of dysregulated miRNAs were clustered into FOXO1, HIPPO and Lysine degradation pathways (p = 0.02, p = 5.84 × 10−5 and p = 3.00 × 10−3 respectively). We provide evidence that the diabetic microenvironment may induce changes to the expression of miRNAs targeting genes enriched in pathways involved in cell stress response and cell survival.",0
"A novel protease-activated receptor 1 inhibitor from the leech Whitmania pigra. Whitmania pigra has been used as a traditional Chinese medicine (TCM) for promoting blood circulation, alleviating blood coagulation, activating meridians and relieving stasis for several hundred years. However, the therapeutic components of this species, especially proteins and peptides were poorly exploited. Until now only a few of them were obtained by using chromatographic isolation and purification. In recent decade, transcriptome techniques were rapidly developed, and have been used to fully reveal the functional components of many animal venoms. In the present study, the cDNA of the salivary gland of Whitmania pigra was sequenced by illumina and the transcriptome was assembled by using Trinity. The proteome were analysed by LC-MS/MS. Based on the data of the transcriptome and the proteome, a potential antiplatelet protein named pigrin was found. Pigrin was cloned and expressed using P. pastoris GS115. The antiplatelet andantithrombotic bioactivities of pigrin were tested by using aggregometer and the rat arterio-venous shunt thrombosis model, respectively. Thebleeding time of pigrin was measured by a mice tail cutting method. The docking of pigrin and protease-activated receptor 1 (PAR1) or collagen were conducted using the ZDOCK Server. Pigrin was able to selectively inhibit platelet aggregation stimulated by PAR1 agonist and collagen. Pigrin attenuated thrombotic formation in vivo in rat, while did not prolong bleeding time at its effective dosage. There are significant differences in the key residues participating in binding of Pigrin-Collagen complex from Pigrin-PAR1 complex. In conclusion,a novel PAR1 inhibitor pigrin was found from the leech Whitmania pigra. This study helped to elucidate the mechanism of the leech for the treatment of cardiovascular disorder.",0
"Predicting skilled delivery service use in Ethiopia: dual application of logistic regression and machine learning algorithms. BACKGROUND: Skilled assistance during childbirth is essential to reduce maternal deaths. However, in Ethiopia, which is among the six countries contributing to more than half of the global maternal deaths, the coverage of births attended by skilled health personnel remains very low. The aim of this study was to identify determinants and develop a predictive model for skilled delivery service use in Ethiopia by applying logistic regression and machine-learning techniques. METHODS: Data from the 2016 Ethiopian Demographic and Health Survey (EDHS) was used for this study. Statistical Package for Social Sciences (SPSS) and Waikato Environment for Knowledge Analysis (WEKA) tools were used for logistic regression and model building respectively. Classification algorithms namely J48, Naïve Bayes, Support Vector Machine (SVM), and Artificial Neural Network (ANN) were used for model development. The validation of the predictive models was assessed using accuracy, sensitivity, specificity, and area under Receiver Operating Characteristics (ROC) curve. RESULTS: Only 27.7% women received skilled delivery assistance in Ethiopia. First antenatal care (ANC) [AOR = 1.83, 95% CI (1.24-2.69)], birth order [AOR = 0.22, 95% CI (0.11-0.46)], television ownership [AOR = 6.83, 95% CI (2.52-18.52)], contraceptive use [AOR = 1.92, 95% CI (1.26-2.97)], cost needed for healthcare [AOR = 2.17, 95% CI (1.47-3.21)], age at first birth [AOR = 1.96, 95% CI (1.31-2.94)], and age at first sex [AOR = 2.72, 95% CI (1.55-4.76)] were determinants for utilizing skilled delivery services during the childbirth. Predictive models were developed and the J48 model had superior predictive accuracy (98%), sensitivity (96%), specificity (99%) and, the area under ROC (98%). CONCLUSIONS: First ANC and contraceptive uses were among the determinants of utilization of skilled delivery services. A predictive model was developed to forecast the likelihood of a pregnant woman seeking skilled delivery assistance; therefore, the predictive model can help to decide targeted interventions for a pregnant woman to ensure skilled assistance at childbirth. The model developed through the J48 algorithm has better predictive accuracy. Web-based application can be build based on results of this study.",1
Cases in Precision Medicine: The Role of Pharmacogenetics in Precision Prescribing. Pharmacogenetics may help physicians deliver individualized treatments based on how a person's genes affect a drug's effects and metabolism. This information can help prevent adverse events or improve drug efficacy by enabling the physician to optimize dosage or to avoid a medication with adverse reactions and to prescribe an alternative therapy. This article discusses the current clinical utility of pharmacogenetic testing in the context of a patient who requires anticoagulation with warfarin.,0
"An automated machine learning-based model predicts postoperative mortality using readily-extractable preoperative electronic health record data. BACKGROUND: Rapid, preoperative identification of patients with the highest risk for medical complications is necessary to ensure that limited infrastructure and human resources are directed towards those most likely to benefit. Existing risk scores either lack specificity at the patient level or utilise the American Society of Anesthesiologists (ASA) physical status classification, which requires a clinician to review the chart. METHODS: We report on the use of machine learning algorithms, specifically random forests, to create a fully automated score that predicts postoperative in-hospital mortality based solely on structured data available at the time of surgery. Electronic health record data from 53 097 surgical patients (2.01% mortality rate) who underwent general anaesthesia between April 1, 2013 and December 10, 2018 in a large US academic medical centre were used to extract 58 preoperative features. RESULTS: Using a random forest classifier we found that automatically obtained preoperative features (area under the curve [AUC] of 0.932, 95% confidence interval [CI] 0.910-0.951) outperforms Preoperative Score to Predict Postoperative Mortality (POSPOM) scores (AUC of 0.660, 95% CI 0.598-0.722), Charlson comorbidity scores (AUC of 0.742, 95% CI 0.658-0.812), and ASA physical status (AUC of 0.866, 95% CI 0.829-0.897). Including the ASA physical status with the preoperative features achieves an AUC of 0.936 (95% CI 0.917-0.955). CONCLUSIONS: This automated score outperforms the ASA physical status score, the Charlson comorbidity score, and the POSPOM score for predicting in-hospital mortality. Additionally, we integrate this score with a previously published postoperative score to demonstrate the extent to which patient risk changes during the perioperative period.",1
"A novel multidimensional signature predicts prognosis in hepatocellular carcinoma patients. The abnormal expression of microRNAs (miRNAs) or protein-coding genes (PCGs) have been found to be associated with the prognosis of hepatocellular carcinoma (HCC) patients. Using bioinformatics analysis methods including Cox’s proportional hazards regression analysis, the random survival forest algorithm, Kaplan–Meier, and receiver operating characteristic (ROC) curve analysis, we mined the gene expression profiles of 469 HCC patients from The Cancer Genome Atlas (n = 379) and Gene Expression Omnibus (GSE14520; n = 90) public database. We selected a signature comprising one protein-coding gene (PCG; DNA polymerase μ) and three miRNAs (hsa-miR-149-5p, hsa-miR-424-5p, hsa-miR-579-5p) with highest accurate prediction (area under the ROC curve [AUC] = 0.72; n = 189) from the training data set. The signature stratified patients into high- and low-risk groups with significantly different survival (median 27.9 vs. 55.2 months, log-rank test, p < 0.001) in the training data set, and its risk stratification ability were validated in the test data set (median 47.4 vs. 84.4 months, log-rank test, p = 0.03) and an independent data set (median 31.0 vs. 46.0 months, log-rank test, p = 0.01). Multivariable Cox regression analysis showed that the signature was an independent prognostic factor. And the signature was proved to have a better survival prediction power than tumor–node–metastasis (TNM) stage (AUC signature = 0.72/0.64/0.62 vs. AUC TNM = 0.65/0.61/0.61; p < 0.05). Moreover, we validated the expression of these prognostic genes from the PCG-miRNA signature in Huh-7 cell by real-time polymerase chain reaction. In conclusion, we found a signature that can predict survival of HCC patients and serve as a prognostic marker for HCC.",0
"Comparing drug safety of hepatitis C therapies using post-market data. BACKGROUND: Hepatitis C affects about 3 % of the world's population. In the United States, about 3.5 million have chronic hepatitis C, and it is the leading cause of liver cancer and the most common indication for liver transplantation. In the last decades, new advances in therapy have substantially increased the cure rate of hepatitis C to more than 95% with the use of antiviral agents. However, drug safety of the new treatments remains one of the major concerns. Data from the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) and the Electronic Health Record (EHR) systems provide crucial post-market information to evaluate drug safety. Currently, quantitative evidence of drug safety of hepatitis C treatments based on post-market data are still limited, and there is also a lack of a standard statistical procedure to systematically compare drug safety across multiple drugs using FAERS and EHR. METHOD: In this study, we presented a statistical procedure to compare the difference in adverse events (AE) across multiple hepatitis C drugs using data from FAERS and EHR, and to assess the consistency of results from two data bases. Through three major steps, including descriptive comparison, testing for difference among groups, and quantification of association, the proposed method can provide a quantitative comparison on safety of multiple drugs. Specifically, we compared drugs that were approved by FDA to treat hepatitis C before 2011versus those approved after 2013. We used spontaneous AE reports submitted between 2004 to 2015 from FAERS data base and medical records between 1999 to 2015 from the Cerner health facts data base to estimate and compare the rate of AE after drug use. RESULT: We studied 30 most frequently reported AEs after treatment of hepatitis C, comparing the difference between drugs approved before 2011versus those approved after 2013. Our results showed that there was difference in rate of AE between the two groups of treatment. We reported the AEs that have significant statistical difference, and estimate the difference attributable to variation of age and gender between the two groups of drug users. Our findings are consistent with results in existing literature. Moreover, we compared the results obtained from FAERS data and EHR data, and evaluated the consistency of evidence. CONCLUSION: The proposed procedure is a general and standardized pipeline that can be used to compare and visualize drug safety among multiple drugs to support regulatory decision-makings using post-market data. We showed that there was statistically significant difference in AE rates between the new and old therapies for hepatitis C. We showed that both FAERS and EHR contained large information for research of post-market drug safety, but each has its own strength and limitations. Cautions should be taken when combining evidence from the two data resources and there is a need of more sophisticated informatics and statistical tools for evidence synthesis.",0
"Alpha7 nicotinic acetylcholine receptors and neural network synaptic transmission in human induced pluripotent stem cell-derived neurons. The α7 nicotinic acetylcholine receptor has been extensively researched as a target for treatment of cognitive impairment in Alzheimer's disease and schizophrenia. Investigation of the α7 receptor is commonly performed in animals but it is critical to increase the biologically relevance of the model systems to fully capture the physiological role of the α7 receptor in humans. For example most humans, in contrast to animals, express the hybrid gene CHRFAM7A, the product of which modulates α7 receptor activity. In the present study, we used human induced pluripotent stem cell (hiPSC) derived neurons to establish a humanized α7 model. We established a cryobank of neural stem cells (NSCs) that could reproducibly be matured into neurons expressing neuronal markers and CHRNA7 and CHRFAM7A. The neurons responded to NMDA, GABA, and acetylcholine and exhibited synchronized spontaneous calcium oscillations. Gene expression studies and application of a range of α7 positive allosteric modulators (PNU-120595, TQS, JNJ-39393406 and AF58801) together with the α7 agonist PNU-282987 during measurement of intracellular calcium levels demonstrated the presence of functional α7 receptors in matured hiPSC-derived neuronal cultures. Pharmacological α7 activation also resulted in intracellular signaling as measured by ERK 1/2 phosphorylation and c-Fos protein expression. Moreover, PNU-120596 increased the frequency of the spontaneous calcium oscillations demonstrating implication of α7 receptors in human synaptic networks activity. Overall, we show that hiPSC derived neurons are an advanced in vitro model for studying human α7 receptor pharmacology and the involvement of this receptor in cellular processes as intracellular signaling and synaptic transmission.",0
"Biometric handwriting analysis to support Parkinson's Disease assessment and grading. BACKGROUND: Handwriting represents one of the major symptom in Parkinson's Disease (PD) patients. The computer-aided analysis of the handwriting allows for the identification of promising patterns that might be useful in PD detection and rating. In this study, we propose an innovative set of features extracted by geometrical, dynamical and muscle activation signals acquired during handwriting tasks, and evaluate the contribution of such features in detecting and rating PD by means of artificial neural networks. METHODS: Eleven healthy subjects and twenty-one PD patients were enrolled in this study. Each involved subject was asked to write three different patterns on a graphic tablet while wearing the Myo Armband used to collect the muscle activation signals of the main forearm muscles. We have then extracted several features related to the written pattern, the movement of the pen and the pressure exerted with the pen and the muscle activations. The computed features have been used to classify healthy subjects versus PD patients and to discriminate mild PD patients from moderate PD patients by using an artificial neural network (ANN). RESULTS: After the training and evaluation of different ANN topologies, the obtained results showed that the proposed features have high relevance in PD detection and rating. In particular, we found that our approach both detect and rate (mild and moderate PD) with a classification accuracy higher than 90%. CONCLUSIONS: In this paper we have investigated the representativeness of a set of proposed features related to handwriting tasks in PD detection and rating. In particular, we used an ANN to classify healthy subjects and PD patients (PD detection), and to classify mild and moderate PD patients (PD rating). The implemented and tested methods showed promising results proven by the high level of accuracy, sensitivity and specificity. Such results suggest the usability of the proposed setup in clinical settings to support the medical decision about Parkinson's Disease.",1
"Improving workflow control in radiotherapy using discrete-event simulation. BACKGROUND: In radiotherapy, minimizing the time between referral and start of treatment (waiting time) is important to possibly mitigate tumor growth and avoid psychological distress in cancer patients. Radiotherapy pre-treatment workflow is driven by the scheduling of the first irradiation session, which is usually set right after consultation (pull strategy) or can alternatively be set after the pre-treatment workflow has been completed (push strategy). The objective of this study is to assess the impact of using pull and push strategies and explore alternative interventions for improving timeliness in radiotherapy. METHODS: Discrete-event simulation is used to model the patient flow of a large radiotherapy department of a Dutch hospital. A staff survey, interviews with managers, and historical data from 2017 are used to generate model inputs, in which fluctuations in patient inflow and resource availability are considered. RESULTS: A hybrid (40% pull / 60% push) strategy representing the current practice (baseline case) leads to 12% lower average waiting times and 48% fewer first appointment rebooks when compared to a full pull strategy, which in turn leads to 41% fewer patients breaching the waiting time targets. An additional scenario analysis performed on the baseline case showed that spreading consultation slots evenly throughout the week can provide a 21% reduction in waiting times. CONCLUSIONS: A 100% pull strategy allows for more patients starting treatment within the waiting time targets than a hybrid strategy, in spite of slightly longer waiting times and more first appointment rebooks. Our algorithm can be used by radiotherapy policy makers to identify the optimal balance between push and pull strategies to ensure timely treatments while providing patient-centered care adapted to their specific conditions.",0
"Curcumin Blocks Cytotoxicity of Enteroaggregative and Enteropathogenic Escherichia coli by Blocking Pet and EspC Proteolytic Release From Bacterial Outer Membrane. Pet and EspC are toxins secreted by enteroaggregative (EAEC) and enteropathogenic (EPEC) diarrheagenic Escherichia coli pathotypes, respectively. Both toxins are members of the Serine Protease Autotransporters of Enterobacteriaceae (SPATEs) family. Pet and EspC are important virulence factors that produce cytotoxic and enterotoxic effects on enterocytes. Here, we evaluated the effect of curcumin, a polyphenolic compound obtained from the rhizomes of Curcuma longa L. (Zingiberaceae) on the secretion and cytotoxic effects of Pet and EspC proteins. We found that curcumin prevents Pet and EspC secretion without affecting bacterial growth or the expression of pet and espC. Our results show that curcumin affects the release of these SPATEs from the translocation domain, thereby affecting the pathogenesis of EAEC and EPEC. Curcumin-treated EAEC and EPEC did not induce significant cell damage like the ability to disrupt the actin cytoskeleton, without affecting their characteristic adherence patterns on epithelial cells. A molecular model of docking predicted that curcumin interacts with the determinant residues Asp1018-Asp1019 and Asp1029-Asp1030 of the translocation domain required for the release of Pet and EspC, respectively. Consequently, curcumin blocks Pet and EspC cytotoxicity on epithelial cells by preventing their release from the outer membrane.",0
"Human non-REM sleep and the mean global BOLD signal. A hallmark of non-rapid eye movement (REM) sleep is the decreased brain activity as measured by global reductions in cerebral blood flow, oxygen metabolism, and glucose metabolism. It is unknown whether the blood oxygen level dependent (BOLD) signal undergoes similar changes. Here we show that, in contrast to the decreases in blood flow and metabolism, the mean global BOLD signal increases with sleep depth in a regionally non-uniform manner throughout gray matter. We relate our findings to the circulatory and metabolic processes influencing the BOLD signal and conclude that because oxygen consumption decreases proportionately more than blood flow in sleep, the resulting decrease in paramagnetic deoxyhemoglobin accounts for the increase in mean global BOLD signal.",0
"Deciphering protein glycosylation by computational integration of on-chip profiling, glycan-array data, and mass spectrometry. The difficulty in uncovering detailed information about protein glycosylation stems from the complexity of glycans and the large amount of material needed for the experiments. Here we report a method that gives information on the isomeric variants of glycans in a format compatible with analyzing low-abundance proteins. Onchip glycan modification and probing (on-chip gmap) uses sequential and parallel rounds of exoglycosidase cleavage and lectin profiling of microspots of proteins, together with algorithms that incorporate glycan-array analyses and information from mass spectrometry, when available, to computationally interpret the data. In tests on control proteins with simple or complex glycosylation, on-chip gmap accurately characterized the relative proportions of core types and terminal features of glycans. Subterminal features (monosaccharides and linkages under a terminal monosaccharide) were accurately probed using a rationally designed sequence of lectin and exoglycosidase incubations. The integration of mass information further improved accuracy in each case. An alternative use of on-chip gmap was to complement the mass spectrometry analysis of detached glycans by specifying the isomers that comprise the glycans identified by mass spectrometry. On-chip gmap provides the potential for detailed studies of glycosylation in a format compatible with clinical specimens or other low-abundance sources.",0
"Metabolic Profile of Supragingival Plaque Exposed to Arginine and Fluoride. Caries lesions develop when acid production from bacterial metabolism of dietary carbohydrates outweighs the various mechanisms that promote pH homeostasis, including bacterial alkali production. Therapies that provide arginine as a substrate for alkali production in supragingival oral biofilms have strong anticaries potential. The objective of this study was to investigate the metabolic profile of site-specific supragingival plaque in response to the use of arginine (Arg: 1.5% arginine, fluoride-free) or fluoride (F: 1,100 ppm F/NaF) toothpastes. Eighty-three adults of different caries status were recruited and assigned to treatment with Arg or F for 12 wk. Caries lesions were diagnosed using International Caries Detection and Assessment System II, and plaque samples were collected from caries-free and carious tooth surfaces. Taxonomic profiles were obtained by HOMINGS (Human Oral Microbe Identification using Next Generation Sequencing), and plaque metabolism was assessed by the levels of arginine catabolism via the arginine deiminase pathway (ADS), acidogenicity, and global metabolomics. Principal component analysis (PCA), partial least squares-discriminant analysis, analysis of variance, and random forest tests were used to distinguish metabolic profiles. Of the 509 active lesions diagnosed at baseline, 70 (14%) were inactive after 12 wk. Generalized linear model showed that enamel lesions were significantly more likely to become inactive compared to dentin lesions (P < 0.0001), but no difference was found when treatment with Arg was compared to F (P = 0.46). Arg significantly increased plaque ADS activity (P = 0.031) and plaque pH values after incubation with glucose (P = 0.001). F reduced plaque lactate production from endogenous sources (P = 0.02). PCA revealed differences between the metabolic profiles of plaque treated with Arg or F. Arg significantly affected the concentrations of 16 metabolites, including phenethylamine, agmatine, and glucosamine-6-phosphate (P < 0.05), while F affected the concentrations of 9 metabolites, including phenethylamine, N-methyl-glutamate, and agmatine (P < 0.05). The anticaries mechanisms of action of arginine and fluoride are distinct. Arginine metabolism promotes biofilm pH homeostasis, whereas fluoride is thought to enhance resistance of tooth minerals to low pH and reduce acid production by supragingival oral biofilms.",0
"Deep Learning for MR Angiography: Automated Detection of Cerebral Aneurysms. Purpose To develop and evaluate a supportive algorithm using deep learning for detecting cerebral aneurysms at time-of-flight MR angiography to provide a second assessment of images already interpreted by radiologists. Materials and Methods MR images reported by radiologists to contain aneurysms were extracted from four institutions for the period from November 2006 through October 2017. The images were divided into three data sets: training data set, internal test data set, and external test data set. The algorithm was constructed by deep learning with the training data set, and its sensitivity to detect aneurysms in the test data sets was evaluated. To find aneurysms that had been overlooked in the initial reports, two radiologists independently performed a blinded interpretation of aneurysm candidates detected by the algorithm. When there was disagreement, the final diagnosis was made in consensus. The number of newly detected aneurysms was also evaluated. Results The training data set, which provided training and validation data, included 748 aneurysms (mean size, 3.1 mm +/- 2.0 [standard deviation]) from 683 examinations; 318 of these examinations were on male patients (mean age, 63 years +/- 13) and 365 were on female patients (mean age, 64 years +/- 13). Test data were provided by the internal test data set (649 aneurysms [mean size, 4.1 mm +/- 3.2] in 521 examinations, including 177 male patients and 344 female patients with mean age of 66 years +/- 12 and 67 years +/- 13, respectively) and the external test data set (80 aneurysms [mean size, 4.1 mm +/- 2.1] in 67 examinations, including 19 male patients and 48 female patients with mean age of 63 years +/- 12 and 68 years +/- 12, respectively). The sensitivity was 91% (592 of 649) and 93% (74 of 80) for the internal and external test data sets, respectively. The algorithm improved aneurysm detection in the internal and external test data sets by 4.8% (31 of 649) and 13% (10 of 80), respectively, compared with the initial reports. Conclusion A deep learning algorithm detected cerebral aneurysms in radiologic reports with high sensitivity and improved aneurysm detection compared with the initial reports. (c) RSNA, 2018 See also the editorial by Flanders in this issue.",1
"Altered urinary amino acids in children with autism spectrum disorders. Autism spectrum disorders (ASD) affect 1% of children. Although there is no cure, early diagnosis and behavioral intervention can relieve the symptoms. The clinical heterogeneity of ASD has created a need for improved sensitive and specific laboratory diagnostic methods. Liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based analysis of the metabolome has shown great potential to uncover biomarkers for complex diseases such as ASD. Here, we used a two-step discovery–validation approach to identify potential novel metabolic biomarkers for ASD. Urine samples from 57 children with ASD and 81 matched children with typical development (TD) were analyzed by LS-MS/MS to assess differences in urinary amino acids and their metabolites (referred to as UAA indicators). A total of 63 UAA indicators were identified, of which 21 were present at significantly different levels in the urine of ASD children compared with TD children. Of these 21, the concentrations of 19 and 10 were higher and lower, respectively, in the urine of ASD children compared with TD children. Using support vector machine modeling and receiver operating characteristic curve analysis, we identified a panel of 7 UAA indicators that discriminated between the samples from ASD and TD children (lysine, 2-aminoisobutyric acid, 5-hydroxytryptamine, proline, aspartate, arginine/ornithine, and 4-hydroxyproline). Among the significantly changed pathways in ASD children were the ornithine/urea cycle (decreased levels of the excitatory amino acid aspartate [p = 2.15 × 10−10] and increased arginine/ornithine [p = 5.21 × 10−9]), tryptophan metabolism (increased levels of inhibitory 5-hydroxytryptamine p = 3.62 × 10−9), the methionine cycle (increased methionine sulfoxide [p = 1.46 × 10−10] and decreased homocysteine [p = 2.73 × 10−7]), and lysine metabolism (reduced lysine [p = 7.8 × 10−9 ], α-aminoadipic acid [p = 1.16 × 10−9], and 5-aminovaleric acid [p = 1.05 × 10−5]). Collectively, the data presented here identify a possible imbalance between excitatory and inhibitory amino acid metabolism in ASD children. The significantly altered UAA indicators could therefore be potential diagnostic biomarkers for ASD.",1
"Geometric Sketching Compactly Summarizes the Single-Cell Transcriptomic Landscape. Large-scale single-cell RNA sequencing (scRNA-seq) studies that profile hundreds of thousands of cells are becoming increasingly common, overwhelming existing analysis pipelines. Here, we describe how to enhance and accelerate single-cell data analysis by summarizing the transcriptomic heterogeneity within a dataset using a small subset of cells, which we refer to as a geometric sketch. Our sketches provide more comprehensive visualization of transcriptional diversity, capture rare cell types with high sensitivity, and reveal biological cell types via clustering. Our sketch of umbilical cord blood cells uncovers a rare subpopulation of inflammatory macrophages, which we experimentally validated. The construction of our sketches is extremely fast, which enabled us to accelerate other crucial resource-intensive tasks, such as scRNA-seq data integration, while maintaining accuracy. We anticipate our algorithm will become an increasingly essential step when sharing and analyzing the rapidly growing volume of scRNA-seq data and help enable the democratization of single-cell omics.",0
"Zerumbone, a cyclic sesquiterpene, exerts antimitotic activity in HeLa cells through tubulin binding and exhibits synergistic activity with vinblastine and paclitaxel. Objectives: The aim of this study was to elucidate the antimitotic mechanism of zerumbone and to investigate its effect on the HeLa cells in combination with other mitotic blockers. Materials and methods: HeLa cells and fluorescence microscopy were used to analyse the effect of zerumbone on cancer cell lines. Cellular internalization of zerumbone was investigated using FITC-labelled zerumbone. The interaction of zerumbone with tubulin was characterized using fluorescence spectroscopy. The Chou and Talalay equation was used to calculate the combination index. Results: Zerumbone selectively inhibited the proliferation of HeLa cells with an IC50 of 14.2 ± 0.5 μmol/L through enhanced cellular uptake compared to the normal cell line L929. It induced a strong mitotic block with cells exhibiting bipolar spindles at the IC50 and monopolar spindles at 30 μmol/L. Docking analysis indicated that tubulin is the principal target of zerumbone. In vitro studies indicated that it bound to goat brain tubulin with a Kd of 4 μmol/L and disrupted the assembly of tubulin into microtubules. Zerumbone and colchicine had partially overlapping binding site on tubulin. Zerumbone synergistically enhanced the anti-proliferative activity of vinblastine and paclitaxel through augmented mitotic block. Conclusion: Our data suggest that disruption of microtubule assembly dynamics is one of the mechanisms of the anti-cancer activity of zerumbone and it can be used in combination therapy targeting cell division.",0
"A Genome-wide Functional Signature Ontology Map and Applications to Natural Product Mechanism of Action Discovery. Gene expression signature-based inference of functional connectivity within and between genetic perturbations, chemical perturbations, and disease status can lead to the development of actionable hypotheses for gene function, chemical modes of action, and disease treatment strategies. Here, we report a FuSiOn-based genome-wide integration of hypomorphic cellular phenotypes that enables functional annotation of gene network topology, assignment of mechanistic hypotheses to genes of unknown function, and detection of cooperativity among cell regulatory systems. Dovetailing genetic perturbation data with chemical perturbation phenotypes allowed simultaneous generation of mechanism of action hypotheses for thousands of uncharacterized natural products fractions (NPFs). The predicted mechanism of actions span a broad spectrum of cellular mechanisms, many of which are not currently recognized as “druggable.” To enable use of FuSiOn as a hypothesis generation resource, all associations and analyses are available within an open source web-based GUI (http://fusion.yuhs.ac).",0
"Machine Learning Approach to Inpatient Violence Risk Assessment Using Routinely Collected Clinical Notes in Electronic Health Records. Importance: Inpatient violence remains a significant problem despite existing risk assessment methods. The lack of robustness and the high degree of effort needed to use current methods might be mitigated by using routinely registered clinical notes. Objective: To develop and validate a multivariable prediction model for assessing inpatient violence risk based on machine learning techniques applied to clinical notes written in patients' electronic health records. Design, Setting, and Participants: This prognostic study used retrospective clinical notes registered in electronic health records during admission at 2 independent psychiatric health care institutions in the Netherlands. No exclusion criteria for individual patients were defined. At site 1, all adults admitted between January 2013 and August 2018 were included, and at site 2 all adults admitted to general psychiatric wards between June 2016 and August 2018 were included. Data were analyzed between September 2018 and February 2019. Main Outcomes and Measures: Predictive validity and generalizability of prognostic models measured using area under the curve (AUC). Results: Clinical notes recorded during a total of 3189 admissions of 2209 unique individuals at site 1 (mean [SD] age, 34.0 [16.6] years; 1536 [48.2%] male) and 3253 admissions of 1919 unique individuals at site 2 (mean [SD] age, 45.9 [16.6] years; 2097 [64.5%] male) were analyzed. Violent outcome was determined using the Staff Observation Aggression Scale-Revised. Nested cross-validation was used to train and evaluate models that assess violence risk during the first 4 weeks of admission based on clinical notes available after 24 hours. The predictive validity of models was measured at site 1 (AUC = 0.797; 95% CI, 0.771-0.822) and site 2 (AUC = 0.764; 95% CI, 0.732-0.797). The validation of pretrained models in the other site resulted in AUCs of 0.722 (95% CI, 0.690-0.753) at site 1 and 0.643 (95% CI, 0.610-0.675) at site 2; the difference in AUCs between the internally trained model and the model trained on other-site data was significant at site 1 (AUC difference = 0.075; 95% CI, 0.045-0.105; P <.001) and site 2 (AUC difference = 0.121; 95% CI, 0.085-0.156; P <.001). Conclusions and Relevance: Internally validated predictions resulted in AUC values with good predictive validity, suggesting that automatic violence risk assessment using routinely registered clinical notes is possible. The validation of trained models using data from other sites corroborates previous findings that violence risk assessment generalizes modestly to different populations.",1
"Key challenges for delivering clinical impact with artificial intelligence. BACKGROUND: Artificial intelligence (AI) research in healthcare is accelerating rapidly, with potential applications being demonstrated across various domains of medicine. However, there are currently limited examples of such techniques being successfully deployed into clinical practice. This article explores the main challenges and limitations of AI in healthcare, and considers the steps required to translate these potentially transformative technologies from research to clinical practice. MAIN BODY: Key challenges for the translation of AI systems in healthcare include those intrinsic to the science of machine learning, logistical difficulties in implementation, and consideration of the barriers to adoption as well as of the necessary sociocultural or pathway changes. Robust peer-reviewed clinical evaluation as part of randomised controlled trials should be viewed as the gold standard for evidence generation, but conducting these in practice may not always be appropriate or feasible. Performance metrics should aim to capture real clinical applicability and be understandable to intended users. Regulation that balances the pace of innovation with the potential for harm, alongside thoughtful post-market surveillance, is required to ensure that patients are not exposed to dangerous interventions nor deprived of access to beneficial innovations. Mechanisms to enable direct comparisons of AI systems must be developed, including the use of independent, local and representative test sets. Developers of AI algorithms must be vigilant to potential dangers, including dataset shift, accidental fitting of confounders, unintended discriminatory bias, the challenges of generalisation to new populations, and the unintended negative consequences of new algorithms on health outcomes. CONCLUSION: The safe and timely translation of AI research into clinically validated and appropriately regulated systems that can benefit everyone is challenging. Robust clinical evaluation, using metrics that are intuitive to clinicians and ideally go beyond measures of technical accuracy to include quality of care and patient outcomes, is essential. Further work is required (1) to identify themes of algorithmic bias and unfairness while developing mitigations to address these, (2) to reduce brittleness and improve generalisability, and (3) to develop methods for improved interpretability of machine learning predictions. If these goals can be achieved, the benefits for patients are likely to be transformational.",0
"A Quantitative Severity Scale for Retinopathy of Prematurity Using Deep Learning to Monitor Disease Regression After Treatment. Importance: Retinopathy of prematurity (ROP) is a leading cause of childhood blindness worldwide, but treatment failure and disease recurrence are important causes of adverse outcomes in patients with treatment-requiring ROP (TR-ROP). Objectives: To apply an automated ROP vascular severity score obtained using a deep learning algorithm and to assess its utility for objectively monitoring ROP regression after treatment. Design, Setting, and Participants: This retrospective cohort study used data from the Imaging and Informatics in ROP consortium, which comprises 9 tertiary referral centers in North America that screen high volumes of at-risk infants for ROP. Images of 5255 clinical eye examinations from 871 infants performed between July 2011 and December 2016 were assessed for eligibility in the present study. The disease course was assessed with time across the numerous examinations for patients with TR-ROP. Infants born prematurely meeting screening criteria for ROP who developed TR-ROP and who had images captured within 4 weeks before and after treatment as well as at the time of treatment were included. Main Outcomes and Measures: The primary outcome was mean (SD) ROP vascular severity score before, at time of, and after treatment. A deep learning classifier was used to assign a continuous ROP vascular severity score, which ranged from 1 (normal) to 9 (most severe), at each examination. A secondary outcome was the difference in ROP vascular severity score among eyes treated with laser or the vascular endothelial growth factor antagonist bevacizumab. Differences between groups for both outcomes were assessed using unpaired 2-tailed t tests with Bonferroni correction. Results: Of 5255 examined eyes, 91 developed TR-ROP, of which 46 eyes met the inclusion criteria based on the available images. The mean (SD) birth weight of those patients was 653 (185) g, with a mean (SD) gestational age of 24.9 (1.3) weeks. The mean (SD) ROP vascular severity scores significantly increased 2 weeks prior to treatment (4.19 [1.75]), peaked at treatment (7.43 [1.89]), and decreased for at least 2 weeks after treatment (4.00 [1.88]) (all P < .001). Eyes requiring retreatment with laser had higher ROP vascular severity scores at the time of initial treatment compared with eyes receiving a single treatment (P < .001). Conclusions and Relevance: This quantitative ROP vascular severity score appears to consistently reflect clinical disease progression and posttreatment regression in eyes with TR-ROP. These study results may have implications for the monitoring of patients with ROP for treatment failure and disease recurrence and for determining the appropriate level of disease severity for primary treatment in eyes with aggressive disease.",1
"A deep learning model to predict a diagnosis of Alzheimer disease by using18F-FDG PET of the brain. Purpose: To develop and validate a deep learning algorithm that predicts the final diagnosis of Alzheimer disease (AD), mild cognitive impairment, or neither at fluorine 18 (18F) fluorodeoxyglucose (FDG) PET of the brain and compare its performance to that of radiologic readers. Materials and Methods: Prospective18F-FDG PET brain images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (2109 imaging studies from 2005 to 2017, 1002 patients) and retrospective independent test set (40 imaging studies from 2006 to 2016, 40 patients) were collected. Final clinical diagnosis at follow-up was recorded. Convolutional neural network of InceptionV3 architecture was trained on 90% of ADNI data set and tested on the remaining 10%, as well as the independent test set, with performance compared to radiologic readers. Model was analyzed with sensitivity, specificity, receiver operating characteristic (ROC), saliency map, and t-distributed stochastic neighbor embedding. Results: The algorithm achieved area under the ROC curve of 0.98 (95% confidence interval: 0.94, 1.00) when evaluated on predicting the final clinical diagnosis of AD in the independent test set (82% specificity at 100% sensitivity), an average of 75.8 months prior to the final diagnosis, which in ROC space outperformed reader performance (57% [four of seven] sensitivity, 91% [30 of 33] specificity; P , .05). Saliency map demonstrated attention to known areas of interest but with focus on the entire brain. Conclusion: By using fluorine 18 fluorodeoxyglucose PET of the brain, a deep learning algorithm developed for early prediction of Alzheimer disease achieved 82% specificity at 100% sensitivity, an average of 75.8 months prior to the final diagnosis.",0
"Deep learning and manual assessment show that the absolute mitotic count does not contain prognostic information in triple negative breast cancer. Purpose: The prognostic value of mitotic count for invasive breast cancer is firmly established. As yet, however, limited studies have been aimed at assessing mitotic counts as a prognostic factor for triple negative breast cancers (TNBC). Here, we assessed the prognostic value of absolute mitotic counts for TNBC, using both deep learning and manual procedures. Methods: A retrospective TNBC cohort (n = 298) was used. The absolute manual mitotic count was assessed by averaging counts from three independent observers. Deep learning was performed using a convolutional neural network on digitized H&E slides. Multivariable Cox regression models for relapse-free survival and overall survival served as baseline models. These were expanded with dichotomized mitotic counts, attempting every possible cut-off value, and evaluated by means of the c-statistic. Results: We found that per 2 mm2 averaged manual mitotic counts ranged from 1 to 187 (mean 37.6, SD 23.4), whereas automatic counts ranged from 1 to 269 (mean 57.6; SD 42.2). None of the cut-off values improved the models’ baseline c-statistic, for both manual and automatic assessments. Conclusions: Based on our results we conclude that the level of proliferation, as reflected by mitotic count, does not serve as a prognostic factor for TNBC. Therefore, TNBC patient management based on mitotic count should be discouraged.",0
"A Laboratory Medicine Best Practices Systematic Review and Meta-analysis of Nucleic Acid Amplification Tests (NAATs) and Algorithms Including NAATs for the Diagnosis of Clostridioides (Clostridium) difficile in Adults. The evidence base for the optimal laboratory diagnosis of Clostridioides (Clostridium) difficile in adults is currently unresolved due to the uncertain performance characteristics and various combinations of tests. This systematic review evaluates the diagnostic accuracy of laboratory testing algorithms that include nucleic acid amplification tests (NAATs) to detect the presence of C. difficile The systematic review and meta-analysis included eligible studies (those that had PICO [population, intervention, comparison, outcome] elements) that assessed the diagnostic accuracy of NAAT alone or following glutamate dehydrogenase (GDH) enzyme immunoassays (EIAs) or GDH EIAs plus C. difficile toxin EIAs (toxin). The diagnostic yield of NAAT for repeat testing after an initial negative result was also assessed. Two hundred thirty-eight studies met inclusion criteria. Seventy-two of these studies had sufficient data for meta-analysis. The strength of evidence ranged from high to insufficient. The uses of NAAT only, GDH-positive EIA followed by NAAT, and GDH-positive/toxin-negative EIA followed by NAAT are all recommended as American Society for Microbiology (ASM) best practices for the detection of the C. difficile toxin gene or organism. Meta-analysis of published evidence supports the use of testing algorithms that use NAAT alone or in combination with GDH or GDH plus toxin EIA to detect the presence of C. difficile in adults. There is insufficient evidence to recommend against repeat testing of the sample using NAAT after an initial negative result due to a lack of evidence of harm (i.e., financial, length of stay, or delay of treatment) as specified by the Laboratory Medicine Best Practices (LMBP) systematic review method in making such an assessment. Findings from this systematic review provide clarity to diagnostic testing strategies and highlight gaps, such as low numbers of GDH/toxin/PCR studies, in existing evidence on diagnostic performance, which can be used to guide future clinical research studies.",0
"Oxidized Low-density Lipoprotein and the Incidence of Age-related Macular Degeneration. Purpose: To examine the relationship between serum oxidized low-density lipoprotein (ox-LDL) cholesterol and the incidence of age-related macular degeneration (AMD) over a 25-year period in a sample of persons from the population-based Beaver Dam Eye Study (BDES). Design: Observational prospective cohort study. Participants: A total of 4972 people from the BDES (aged 43–84 years and living in Beaver Dam, Wisconsin in 1988) seen during at least 1 of 6 examination phases at approximately 5-year intervals between 1988 and 2016. Methods: A 50% random sample of participants (N = 2468) was selected for ox-LDL measurements. Stored frozen specimens from every examination phase were processed using an enzyme-linked immunosorbent assay from a single batch. All available intervals were included for a person, resulting in 6586 person-visits. Main Outcome Measures: Age-related macular degeneration was assessed using the Wisconsin Age-related Maculopathy Grading System, and severity was defined using a 5-step severity scale. The severity of the worse eye at each examination was used for analyses. A multi-state Markov (MSM) model was fit to simultaneously assess the ox-LDL relationship to all AMD transitions, including incidence of any AMD, incidence of late AMD, and worsening and improvement of AMD over the 25 years of the study. Results: The mean (standard deviation) level of ox-LDL was 75.3 (23.1) U/L at the baseline examination. When adjusting for age, sex, ARMS2 and CFH risk alleles, and examination phase, the ox-LDL at the beginning of a period was not statistically significantly associated with the incidence of any AMD (hazard ratio per 10 U/L ox-LDL was 1.03, 95% confidence interval 0.98,1.09). Furthermore, ox-LDL was not associated with worsening anywhere along the AMD severity scale, nor with incidence of late AMD. The lack of relationships of ox-LDL to the incidence of any AMD or worsening of AMD remained after adjustment for history of statin use, smoking status, body mass index, and history of cardiovascular disease (data not shown). Conclusions: Our findings do not provide evidence for statistically significant relationships between ox-LDL and AMD disease development or worsening of AMD.",0
"A comparison of machine learning classifiers for dementia with Lewy bodies using miRNA expression data. BACKGROUND: Dementia with Lewy bodies (DLB) is the second most common subtype of neurodegenerative dementia in humans following Alzheimer's disease (AD). Present clinical diagnosis of DLB has high specificity and low sensitivity and finding potential biomarkers of prodromal DLB is still challenging. MicroRNAs (miRNAs) have recently received a lot of attention as a source of novel biomarkers. METHODS: In this study, using serum miRNA expression of 478 Japanese individuals, we investigated potential miRNA biomarkers and constructed an optimal risk prediction model based on several machine learning methods: penalized regression, random forest, support vector machine, and gradient boosting decision tree. RESULTS: The final risk prediction model, constructed via a gradient boosting decision tree using 180 miRNAs and two clinical features, achieved an accuracy of 0.829 on an independent test set. We further predicted candidate target genes from the miRNAs. Gene set enrichment analysis of the miRNA target genes revealed 6 functional genes included in the DHA signaling pathway associated with DLB pathology. Two of them were further supported by gene-based association studies using a large number of single nucleotide polymorphism markers (BCL2L1: P = 0.012, PIK3R2: P = 0.021). CONCLUSIONS: Our proposed prediction model provides an effective tool for DLB classification. Also, a gene-based association test of rare variants revealed that BCL2L1 and PIK3R2 were statistically significantly associated with DLB.",0
"Heterogeneous network embedding enabling accurate disease association predictions. Background: It is significant to identificate complex biological mechanisms of various diseases in biomedical research. Recently, the growing generation of tremendous amount of data in genomics, epigenomics, metagenomics, proteomics, metabolomics, nutriomics, etc., has resulted in the rise of systematic biological means of exploring complex diseases. However, the disparity between the production of the multiple data and our capability of analyzing data has been broaden gradually. Furthermore, we observe that networks can represent many of the above-mentioned data, and founded on the vector representations learned by network embedding methods, entities which are in close proximity but at present do not actually possess direct links are very likely to be related, therefore they are promising candidate subjects for biological investigation. Results: We incorporate six public biological databases to construct a heterogeneous biological network containing three categories of entities (i.e., genes, diseases, miRNAs) and multiple types of edges (i.e., the known relationships). To tackle the inherent heterogeneity, we develop a heterogeneous network embedding model for mapping the network into a low dimensional vector space in which the relationships between entities are preserved well. And in order to assess the effectiveness of our method, we conduct gene-disease as well as miRNA-disease associations predictions, results of which show the superiority of our novel method over several state-of-the-arts. Furthermore, many associations predicted by our method are verified in the latest real-world dataset. Conclusions: We propose a novel heterogeneous network embedding method which can adequately take advantage of the abundant contextual information and structures of heterogeneous network. Moreover, we illustrate the performance of the proposed method on directing studies in biology, which can assist in identifying new hypotheses in biological investigation.",0
"Predicting Responders to Reslizumab after 16 Weeks of Treatment Using an Algorithm Derived from Clinical Studies of Patients with Severe Eosinophilic Asthma. RATIONALE: Reslizumab is a humanized anti-IL-5 monoclonal antibody used as add-on maintenance treatment for patients with uncontrolled eosinophilic asthma. OBJECTIVES: To predict response and nonresponse to intravenous reslizumab at 52 weeks with an algorithm we developed based on clinical indicators from pivotal clinical trials. METHODS: Patients aged 18 years and older who met Global Initiative for Asthma 4 or 5 criteria and received intravenous reslizumab (n = 321) in two trials ( www.clinicaltrials.gov identifiers, NCT01287039 and NCT01285323) were selected as the data source. A mathematical model was constructed that was based on change from baseline to 16 weeks in Asthma Control Questionnaire and Asthma Quality of Life Questionnaire scores and FEV1, and number of clinical asthma exacerbations during the year before enrollment and in the first 16 weeks of treatment, and these measures were evaluated for their ability to predict the outcome at 52 weeks: responder, nonresponder, or indeterminate. MEASUREMENTS AND MAIN RESULTS: The algorithm predicted that 276 patients would be classified as responders; in 248 (89.9%), the prediction was correct. In comparison, 26 patients were predicted to be nonresponders; 50.0% of these predictions were correct. Nineteen patients were classified as indeterminate. The algorithm had 95.4-95.5% sensitivity and 40.6-54.1% specificity. Jackknife and cross-study validation confirmed the robustness of the algorithm. CONCLUSIONS: Our algorithm enabled prediction at 16 weeks of treatment of the response to intravenous reslizumab treatment at 52 weeks, but it was not suitable for predicting nonresponse. A positive score at 16 weeks should encourage continued treatment, and a negative score should prompt close monitoring to determine whether discontinuation is warranted.",0
"SOD1 in amyotrophic lateral sclerosis development – in silico analysis and molecular dynamics of A4F and A4V variants. Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease that is characterized by the selective loss of motor neurons. Approximately 5% to 10% of patients with ALS have a family history of the disease, and approximately 20% of familial amyotrophic lateral sclerosis (fALS) cases are associated with mutations in Cu/Zn superoxide dismutase (SOD1). In this study, we evaluated the structural and functional effects of human A4F and A4V SOD1 protein mutations. We performed an in silico analysis using prediction algorithms of nonsynonymous single-nucleotide polymorphisms (nsSNPs) associated with the fALS development. Our structural conservation results show that the mutations analyzed (A4V and A4F) were in a highly conserved region. Molecular dynamics simulations using the Linux GROMACS package revealed how these mutations affect protein structure, protein stability, and aggregation. These results suggest that there might be an effect on the SOD1 function. Understanding the molecular basis of disease provides new insights useful for rational drug design and advancing our understanding of the ALS development.",0
"Data-driven Development of ROTEM and TEG Algorithms for the Management of Trauma Hemorrhage: A Prospective Observational Multicenter Study. OBJECTIVE: Developing pragmatic data-driven algorithms for management of trauma induced coagulopathy (TIC) during trauma hemorrhage for viscoelastic hemostatic assays (VHAs). BACKGROUND: Admission data from conventional coagulation tests (CCT), rotational thrombelastometry (ROTEM) and thrombelastography (TEG) were collected prospectively at 6 European trauma centers during 2008 to 2013. METHODS: To identify significant VHA parameters capable of detecting TIC (defined as INR > 1.2), hypofibrinogenemia (< 2.0 g/L), and thrombocytopenia (< 100 x10/L), univariate regression models were constructed. Area under the curve (AUC) was calculated, and threshold values for TEG and ROTEM parameters with 70% sensitivity were included in the algorithms. RESULTS: A total of, 2287 adult trauma patients (ROTEM: 2019 and TEG: 968) were enrolled. FIBTEM clot amplitude at 5 minutes (CA5) had the largest AUC and 10 mm detected hypofibrinogenemia with 70% sensitivity. The corresponding value for functional fibrinogen (FF) TEG maximum amplitude (MA) was 19 mm. Thrombocytopenia was similarly detected using the calculated threshold EXTEM-FIBTEM CA5 30 mm. The corresponding rTEG-FF TEG MA was 46 mm. TIC was identified by EXTEM CA5 41 mm, rTEG MA 64 mm (80% sensitivity). For hyperfibrinolysis, we examined the relationship between viscoelastic lysis parameters and clinical outcomes, with resulting threshold values of 85% for EXTEM Li30 and 10% for rTEG Ly30.Based on these analyses, we constructed algorithms for ROTEM, TEG, and CCTs to be used in addition to ratio driven transfusion and tranexamic acid. CONCLUSIONS: We describe a systematic approach to define threshold parameters for ROTEM and TEG. These parameters were incorporated into algorithms to support data-driven adjustments of resuscitation with therapeutics, to optimize damage control resuscitation practice in trauma.",0
"Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Computerized electrocardiogram (ECG) interpretation plays a critical role in the clinical ECG workflow(1). Widely available digital ECG data and the algorithmic paradigm of deep learning(2) present an opportunity to substantially improve the accuracy and scalability of automated ECG analysis. However, a comprehensive evaluation of an end-to-end deep learning approach for ECG analysis across a wide variety of diagnostic classes has not been previously reported. Here, we develop a deep neural network (DNN) to classify 12 rhythm classes using 91,232 single-lead ECGs from 53,549 patients who used a single-lead ambulatory ECG monitoring device. When validated against an independent test dataset annotated by a consensus committee of board-certified practicing cardiologists, the DNN achieved an average area under the receiver operating characteristic curve (ROC) of 0.97. The average F1 score, which is the harmonic mean of the positive predictive value and sensitivity, for the DNN (0.837) exceeded that of average cardiologists (0.780). With specificity fixed at the average specificity achieved by cardiologists, the sensitivity of the DNN exceeded the average cardiologist sensitivity for all rhythm classes. These findings demonstrate that an end-to-end deep learning approach can classify a broad range of distinct arrhythmias from single-lead ECGs with high diagnostic performance similar to that of cardiologists. If confirmed in clinical settings, this approach could reduce the rate of misdiagnosed computerized ECG interpretations and improve the efficiency of expert human ECG interpretation by accurately triaging or prioritizing the most urgent conditions.",0
"Quercetin protects rats from catheter-related Staphylococcus aureus infections by inhibiting coagulase activity. Coagulase (Coa) activity is essential for the virulence of Staphylococcus aureus (S aureus), one of the most important pathogenic bacteria leading to catheter-related bloodstream infections (CRBSI). We have demonstrated that the mutation of coagulase improved outcomes in disease models of S aureus CRBSI, suggesting that targeting Coa may represent a novel antiinfective strategy for CRBSI. Here, we found that quercetin, a natural compound that does not affect S aureus viability, could inhibit Coa activity. Chemical biological analysis revealed that the direct engagement of quercetin with the active site (residues Tyr187, Leu221 and His228) of Coa inhibited its activity. Furthermore, treatment with quercetin reduced the retention of bacteria on catheter surfaces, decreased the bacterial load in the kidneys and alleviated kidney abscesses in vivo. These data suggest that antiinfective therapy targeting Coa with quercetin may represent a novel strategy and provide a new leading compound with which to combat bacterial infections.",0
"Deep Learning Neural Networks Highly Predict Very Early Onset of Pluripotent Stem Cell Differentiation. Deep learning is a significant step forward for developing autonomous tasks. One of its branches, computer vision, allows image recognition with high accuracy thanks to the use of convolutional neural networks (CNNs). Our goal was to train a CNN with transmitted light microscopy images to distinguish pluripotent stem cells from early differentiating cells. We induced differentiation of mouse embryonic stem cells to epiblast-like cells and took images at several time points from the initial stimulus. We found that the networks can be trained to recognize undifferentiated cells from differentiating cells with an accuracy higher than 99%. Successful prediction started just 20 min after the onset of differentiation. Furthermore, CNNs displayed great performance in several similar pluripotent stem cell (PSC) settings, including mesoderm differentiation in human induced PSCs. Accurate cellular morphology recognition in a simple microscopic set up may have a significant impact on how cell assays are performed in the near future. In this article, Miriuka and colleagues show that deep learning convolutional neural networks can be trained to accurately classify light microscopy images of pluripotent stem cells from those of early differentiating cells, only minutes after the differentiation stimulus. These algorithms thus provide novel tools to quantitatively characterize subtle changes in cell morphology.",0
"Delirium detection using relative delta power based on 1-minute single-channel EEG: a multicentre study. BACKGROUND: Delirium is frequently unrecognised. EEG shows slower frequencies (i.e. below 4 Hz) during delirium, which might be useful in improving delirium recognition. We studied the discriminative performance of a brief single-channel EEG recording for delirium detection in an independent cohort of patients. METHODS: In this prospective, multicentre study, postoperative patients aged >/=60 yr were included (n=159). Before operation and during the first 3 postoperative days, patients underwent a 5-min EEG recording, followed by a video-recorded standardised cognitive assessment. Two or, in case of disagreement, three delirium experts classified each postoperative day based on the video and chart review. Relative delta power (1-4 Hz) was based on 1-min artifact-free EEG. The diagnostic value of the relative delta power was evaluated by the area under the receiver operating characteristic curve (AUROC), using the expert classification as the gold standard. RESULTS: Experts classified 84 (23.3%) postoperative days as either delirium or possible delirium, and 276 (76.7%) non-delirium days. The AUROC of the relative EEG delta power was 0.75 [95% confidence interval (CI) 0.69-0.82]. Exploratory analysis showed that relative power from 1 to 6 Hz had significantly higher AUROC (0.78, 95% CI 0.72-0.84, P=0.014). CONCLUSIONS: Delirium/possible delirium can be detected in older postoperative patients based on a single-channel EEG recording that can be automatically analysed. This objective detection method with a continuous scale instead of a dichotomised outcome is a promising approach for routine detection of delirium. CLINICAL TRIAL REGISTRATION: NCT02404181.",0
"Diagnosis of thyroid cancer using deep convolutional neural network models applied to sonographic images: a retrospective, multicohort, diagnostic study. BACKGROUND: The incidence of thyroid cancer is rising steadily because of overdiagnosis and overtreatment conferred by widespread use of sensitive imaging techniques for screening. This overall incidence growth is especially driven by increased diagnosis of indolent and well-differentiated papillary subtype and early-stage thyroid cancer, whereas the incidence of advanced-stage thyroid cancer has increased marginally. Thyroid ultrasound is frequently used to diagnose thyroid cancer. The aim of this study was to use deep convolutional neural network (DCNN) models to improve the diagnostic accuracy of thyroid cancer by analysing sonographic imaging data from clinical ultrasounds. METHODS: We did a retrospective, multicohort, diagnostic study using ultrasound images sets from three hospitals in China. We developed and trained the DCNN model on the training set, 131 731 ultrasound images from 17 627 patients with thyroid cancer and 180 668 images from 25 325 controls from the thyroid imaging database at Tianjin Cancer Hospital. Clinical diagnosis of the training set was made by 16 radiologists from Tianjin Cancer Hospital. Images from anatomical sites that were judged as not having cancer were excluded from the training set and only individuals with suspected thyroid cancer underwent pathological examination to confirm diagnosis. The model's diagnostic performance was validated in an internal validation set from Tianjin Cancer Hospital (8606 images from 1118 patients) and two external datasets in China (the Integrated Traditional Chinese and Western Medicine Hospital, Jilin, 741 images from 154 patients; and the Weihai Municipal Hospital, Shandong, 11 039 images from 1420 patients). All individuals with suspected thyroid cancer after clinical examination in the validation sets had pathological examination. We also compared the specificity and sensitivity of the DCNN model with the performance of six skilled thyroid ultrasound radiologists on the three validation sets. FINDINGS: Between Jan 1, 2012, and March 28, 2018, ultrasound images for the four study cohorts were obtained. The model achieved high performance in identifying thyroid cancer patients in the validation sets tested, with area under the curve values of 0.947 (95% CI 0.935-0.959) for the Tianjin internal validation set, 0.912 (95% CI 0.865-0.958) for the Jilin external validation set, and 0.908 (95% CI 0.891-0.925) for the Weihai external validation set. The DCNN model also showed improved performance in identifying thyroid cancer patients versus skilled radiologists. For the Tianjin internal validation set, sensitivity was 93.4% (95% CI 89.6-96.1) versus 96.9% (93.9-98.6; p=0.003) and specificity was 86.1% (81.1-90.2) versus 59.4% (53.0-65.6; p<0.0001). For the Jilin external validation set, sensitivity was 84.3% (95% CI 73.6-91.9) versus 92.9% (84.1-97.6; p=0.048) and specificity was 86.9% (95% CI 77.8-93.3) versus 57.1% (45.9-67.9; p<0.0001). For the Weihai external validation set, sensitivity was 84.7% (95% CI 77.0-90.7) versus 89.0% (81.9-94.0; p=0.25) and specificity was 87.8% (95% CI 81.6-92.5) versus 68.6% (60.7-75.8; p<0.0001). INTERPRETATION: The DCNN model showed similar sensitivity and improved specificity in identifying patients with thyroid cancer compared with a group of skilled radiologists. The improved technical performance of the DCNN model warrants further investigation as part of randomised clinical trials. FUNDING: The Program for Changjiang Scholars and Innovative Research Team in University in China, and National Natural Science Foundation of China.",1
"Perinodular and Intranodular Radiomic Features on Lung CT Images Distinguish Adenocarcinomas from Granulomas. Purpose To evaluate ability of radiomic (computer-extracted imaging) features to distinguish non-small cell lung cancer adenocarcinomas from granulomas at noncontrast CT. Materials and Methods For this retrospective study, screening or standard diagnostic noncontrast CT images were collected for 290 patients (mean age, 68 years; range, 18-92 years; 125 men [mean age, 67 years; range, 18-90 years] and 165 women [mean age, 68 years; range, 33-92 years]) from two institutions between 2007 and 2013. Histopathologic analysis was available for one nodule per patient. Corresponding nodule of interest was identified on axial CT images by a radiologist with manual annotation. Nodule shape, wavelet (Gabor), and texture-based (Haralick and Laws energy) features were extracted from intra- and perinodular regions. Features were pruned to train machine learning classifiers with 145 patients. In a test set of 145 patients, classifier results were compared against a convolutional neural network (CNN) and diagnostic readings of two radiologists. Results Support vector machine classifier with intranodular radiomic features achieved an area under the receiver operating characteristic curve (AUC) of 0.75 on the test set. Combining radiomics of intranodular with perinodular regions improved the AUC to 0.80. On the same test set, CNN resulted in an AUC of 0.76. Radiologist readers achieved AUCs of 0.61 and 0.60, respectively. Conclusion Radiomic features from intranodular and perinodular regions of nodules can distinguish non-small cell lung cancer adenocarcinomas from benign granulomas at noncontrast CT. (c) RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Nishino in this issue.",1
"Estimating cholera incidence with cross-sectional serology. The development of new approaches to cholera control relies on an accurate understanding of cholera epidemiology. However, most information on cholera incidence lacks laboratory confirmation and instead relies on surveillance systems reporting medically attended acute watery diarrhea. If recent infections could be identified using serological markers, cross-sectional serosurveys would offer an alternative approach to measuring incidence. Here, we used 1569 serologic samples from a cohort of cholera cases and their uninfected contacts in Bangladesh to train machine learning models to identify recent Vibrio cholerae O1 infections. We found that an individual's antibody profile contains information on the timing of V. cholerae O1 infections in the previous year. Our models using six serological markers accurately identified individuals in the Bangladesh cohort infected within the last year [cross-validated area under the curve (AUC), 93.4%; 95% confidence interval (CI), 92.1 to 94.7%], with a marginal performance decrease using models based on two markers (cross-validated AUC, 91.0%; 95% CI, 89.2 to 92.7%). We validated the performance of the two-marker model on data from a cohort of North American volunteers challenged with V. cholerae O1 (AUC range, 88.4 to 98.4%). In simulated serosurveys, our models accurately estimated annual incidence in both endemic and epidemic settings, even with sample sizes as small as 500 and annual incidence as low as two infections per 1000 individuals. Cross-sectional serosurveys may be a viable approach to estimating cholera incidence.",1
"Quantitative MRI of Diffuse Liver Disease: Current Applications and Future Directions. As radiologic technology advances, quantitative imaging is becoming more prevalent in clinical practice. This article reviews quantitative hepatic MRI, specifically involving fat and iron deposition, by demonstrating how they were iteratively improved. These iterative improvements involved incorporating more knowledge about the physiology of liver disease and MRI physics to reduce the adverse effects caused by confounding factors. The relevant foundations of MRI physics and liver pathophysiology are briefly reviewed, followed by the various improvements made by expanding on this foundational knowledge. Results from the literature are then discussed within this context, validating the improvement of these resultant methods into clinically robust and useful techniques. Fibrosis quantification, which has been more difficult to robustly perform in clinical practice, is similarly reviewed in an online appendix, with proposals for future multiparametric directions to improve performance on the basis of the insights gained from fat and iron quantification in the liver.",0
"A Hallucinogenic Serotonin-2A Receptor Agonist Reduces Visual Response Gain and Alters Temporal Dynamics in Mouse V1. Activation of serotonin-2A receptors (5-HT2ARs) is associated with hallucinations, but impacts on sensory processing are largely unknown. Michaiel et al. demonstrate that the 5-HT2AR agonist DOI strongly reduces sensory-evoked activity and disrupts temporal dynamics. These results support models of hallucinations that propose reduced bottom-up sensory drive.",0
"Added value of aortic pulse wave velocity index for the detection of coronary heart disease by elective coronary angiography. Background: Non-invasive tests leading to elective coronary angiography (CAG) have low diagnostic yield for obstructive coronary heart disease (CHD). Aortic stiffness, an independent predictor of CHD events can be easily measured by pulse wave velocity (PWV). We aimed at retrospectively evaluating the diagnostic accuracy PWV index to detect CHD in consecutive patients with suspected CHD that underwent CAG. Method: In population of 86 healthy patients with available PWV data, a theoretical PWV was derived. In different population of 62 individuals who underwent CAG for suspected CHD, PWV index was calculated as index [(measured PWV–theoretical PWV)/theoretical PWV]. Logistic regression and comparisons between ROC curves were used to add value of CAG indication performance of PWV index. Results: Out of 62, seventeen patients presented obstructive CHD and 22 patients had non-obstructive CHD. PWV index and severity of CHD were positively correlated (p < 0.0001). After applying several models that included classical CHD predictor, the higher performance to detect abnormal CAG was obtained with the combined classifier PWV index/carotid plaque with 87% sensitivity, 93% specificity, 0.92 accuracy and 0.31 threshold. To detect obstructive CAG, individual classifier PWV index presents 94% sensitivity, 91% specificity, 0.95 accuracy and 0.46 threshold. Conclusion: PWV index is individualized approach that optimizes CHD diagnostic strategies and thus might be clinically useful for reducing the rate of unnecessary invasive CAG.",0
"Accurate blood pressure during patient arm movement: The Welch Allyn Connex Spot Monitor's SureBP algorithm. Background Current blood pressure (BP) measurement guidelines specify patient requirements, including being still. Some populations of patients cannot comply. A new International Organization for Standards is being developed to test devices that claim tolerance to transport-induced motion artifacts. This study proposes the first protocol to assess BP device accuracy in the presence of patient-induced motion. Participants and methods Forty healthy volunteers (23 males) participated. The device tested was the Welch Allyn Connex Spot Monitor (CSM) using the SureBP algorithm. A reusable cuff was placed on the left arm. During inflation/deflation cycles the participant performed pronation/supination movements of the left forearm every 5 s. The CSM readings during motion were compared to the average of manual resting auscultatory estimations immediately before and after each motion cycle (bracketing). Results The CSM recorded a BP reading on the first cycle in 37 participants. It displayed a reading in all 40 participants with one repeat cycle in the other three. The mean±SD for the device minus the manual BP values was 0.9±7.3 mmHg for systolic BP and -3.4±7.9 mmHg for diastolic BP. Conclusion This study represents a proposal for an automated BP device assessment in the presence of patient-induced motion. The CSM device, which uses an inflation-based algorithm, routinely produced BP values that closely matched auscultatory values bracketed immediately before and after the motion-associated cycle. The CSM should be of significant clinical value in populations in whom resting 'still' readings are not usually feasible, such as pediatric and geriatric patients, and patients in pain from injury or illness.",0
"Deep Evolutionary Networks with Expedited Genetic Algorithms for Medical Image Denoising. Deep convolutional neural networks offer state-of-the-art performance for medical image analysis. However, their architectures are manually designed for particular problems. On the one hand, a manual designing process requires many trials to tune a large number of hyperparameters and is thus quite a time-consuming task. On the other hand, the fittest hyperparameters that can adapt to source data properties (e.g., sparsity, noisy features) are not able to be quickly identified for target data properties. For instance, the realistic noise in medical images is usually mixed and complicated, and sometimes unknown, leading to challenges in applying existing methods directly and creating effective denoising neural networks easily. In this paper, we present a Genetic Algorithm (GA)-based network evolution approach to search for the fittest genes to optimize network structures automatically. We expedite the evolutionary process through an experience-based greedy exploration strategy and transfer learning. Our evolutionary algorithm procedure has flexibility, which allows taking advantage of current state-of-the-art modules (e.g., residual blocks) to search for promising neural networks. We evaluate our framework on a classic medical image analysis task: denoising. The experimental results on computed tomography perfusion (CTP) image denoising demonstrate the capability of the method to select the fittest genes for building high-performance networks, named EvoNets. Our results outperform state-of-the-art methods consistently at various noise levels.",1
"Association of Mental Health Disorders with Health Care Utilization and Costs among Adults with Chronic Disease. Importance: A population-based study using validated algorithms to estimate the costs of treating people with chronic disease with and without mental health disorders is needed. Objective: To determine the association of mental health disorders with health care costs among people with chronic diseases. Design, Setting, and Participants: This population-based cohort study in the Canadian province of Alberta collected data from April 1, 2012, to March 31, 2015, among 991445 adults 18 years and older with a chronic disease (ie, asthma, congestive heart failure, myocardial infarction, diabetes, epilepsy, hypertension, chronic pulmonary disease, or chronic kidney disease). Data analysis was conducted from October 2017 to August 2018. Exposures: Mental health disorder (ie, depression, schizophrenia, alcohol use disorder, or drug use disorder). Main Outcomes and Measures: Resource use, mean total unadjusted and adjusted 3-year health care costs, and mean total unadjusted 3-year costs for hospitalization and emergency department visits for ambulatory care-sensitive conditions. Results: Among 991445 participants, 156296 (15.8%) had a mental health disorder. Those with no mental health disorder were older (mean [SD] age, 58.1 [17.6] years vs 55.4 [17.0] years; P <.001) and less likely to be women (50.4% [95% CI, 50.3%-50.5%] vs 57.7% [95% CI, 57.4%-58.0%]; P <.001) than those with mental health disorders. For those with a mental health disorder, mean total 3-year adjusted costs were $38250 (95% CI, $36476-$39935), and for those without a mental health disorder, mean total 3-year adjusted costs were $22280 (95% CI, $21780-$22760). Having a mental health disorder was associated with significantly higher resource use, including hospitalization and emergency department visit rates, length of stay, and hospitalization for ambulatory care-sensitive conditions. Higher resource use by patients with mental health disorders was not associated with health care presentations owing to chronic diseases compared with patients without a mental health disorder (chronic disease hospitalization rate per 1000 patient days, 0.11 [95% CI, 0.11-0.12] vs 0.06 [95% CI, 0.06-0.06]; P <.001; overall hospitalization rate per 1000 patient days, 0.88 [95% CI, 0.87-0.88] vs 0.43 [95% CI, 0.43-0.43]; P <.001). Conclusions and Relevance: This study suggests that mental health disorders are associated with substantially higher resource utilization and health care costs among patients with chronic diseases. These findings have clinical and health policy implications..",0
"Safety and immunogenicity of a vaccine for extra-intestinal pathogenic Escherichia coli (ESTELLA): a phase 2 randomised controlled trial. Background: ExPEC4V (JNJ-63871860) is a bioconjugate vaccine, containing O-antigens from Escherichia coli serotypes O1A, O2, O6A, and O25B, developed for the prevention of invasive extra-intestinal pathogenic E coli (ExPEC) disease. We aimed to assess safety, reactogenicity, and immunogenicity of ExPEC4V in healthy adults. Methods: In this phase 2 randomised, double-blind placebo-controlled study, we recruited healthy adults (≥18 years with a body-mass index of 35 kg/m2 or less) between Nov 16, 2015, and Aug 8, 2017, and randomly assigned them to receive a single dose of ExPEC4V (antigen O1A:O2:O6A:O25B content 4:4:4:4 μg [group 1]; 4:4:4:8 μg [group 2], 8:8:8:8 μg [group 3], 8:8:8:16 μg [group 4], or 16:16:16:16 μg [group 5]) or placebo. The primary objectives were evaluation of the safety, tolerability, and immunogenicity of ExPEC4V and determination of its dose-dependent immunogenicity 15 days after vaccination by ELISA in individuals who had received at least one vaccination dose. Antibody titres and safety evaluation were used to select two ExPEC4V doses for assessment up to day 360. This trial is registered at ClinicalTrials.gov, number NCT02546960. Findings: Of 848 enrolled participants, 843 (99%) received the ExPEC4V vaccine (757) or placebo (86) and were included in the safety analysis. Of 757 participants vaccinated with ExPEC4V, 222 (29%) had a solicited local adverse event and 325 (43%) had any solicited systemic adverse event, compared with 11 (13%) and 30 (35%) of 86 participants in the control group. Symptoms were mild-to-moderate. The most frequently reported solicited local adverse event was pain or tenderness (205 [27·1%] of 757 in combined ExPEC4V groups) and the most frequently reported solicited systemic adverse event was fatigue (208 [27·6%] of 757). Only 13 (2%) of 843 had a grade 3 event. At day 15, 80% or more of all participants achieved a two times or greater increase in serotype-specific IgG antibodies (except O25B at the lowest dose, 103 [72%] of 144). At day 360, 66% (95% CI 56·47–74·33) of participants in group 2 and 71% (62·13–78·95) of participants in group 4 selected for long-term follow-up maintained a two times or greater increase in serotype-specific antibody compared with baseline. Interpretation: EXPEC4V seemed well tolerated and elicited robust and functional antibody responses across all serotypes, doses, and age groups. For the two dosages evaluated (4:4:4:8 μg and 8:8:8:16 μg), the immune response persisted for 1 year. Funding: Janssen Pharmaceuticals.",0
"The promise of technology in the future of dementia care. Dementia is a leading cause of disability, and the prevalence of dementia is steadily increasing. Although people with dementia are living longer lives in the community, without adequate support for their declining physical and psychological needs, the majority of these individuals end up in nursing homes. With no cure in sight, and in the context of population ageing, we must consider how to care for these individuals in the future. Technologies that augment existing care can maintain a person comfortably in their community, maximize individual autonomy and promote social participation. However, to date, such technologies have rarely been used in dementia care. This Perspectives article highlights the need for affordable and appropriate technologies to assist future dementia care, outlines some of the technologies currently available and describes the many challenges to integration of such technologies. Finally, guidelines are suggested for the development and implementation of new technologies in dementia care.",0
"RMDL: Recalibrated multi-instance deep learning for whole slide gastric image classification. The whole slide histopathology images (WSIs) play a critical role in gastric cancer diagnosis. However, due to the large scale of WSIs and various sizes of the abnormal area, how to select informative regions and analyze them are quite challenging during the automatic diagnosis process. The multi-instance learning based on the most discriminative instances can be of great benefit for whole slide gastric image diagnosis. In this paper, we design a recalibrated multi-instance deep learning method (RMDL) to address this challenging problem. We first select the discriminative instances, and then utilize these instances to diagnose diseases based on the proposed RMDL approach. The designed RMDL network is capable of capturing instance-wise dependencies and recalibrating instance features according to the importance coefficient learned from the fused features. Furthermore, we build a large whole-slide gastric histopathology image dataset with detailed pixel-level annotations. Experimental results on the constructed gastric dataset demonstrate the significant improvement on the accuracy of our proposed framework compared with other state-of-the-art multi-instance learning methods. Moreover, our method is general and can be extended to other diagnosis tasks of different cancer types based on WSIs.",1
"HF-SENSE: an improved partially parallel imaging using a high-pass filter. BACKGROUND: One of the major limitations of MRI is its slow acquisition speed. To accelerate data acquisition, partially parallel imaging (PPI) methods have been widely used in clinical applications such as sensitivity encoding (SENSE) and generalized autocalibrating partially parallel acquisitions (GRAPPA). SENSE is a popular image-domain partially parallel imaging method, which suffers from residual aliasing artifacts when the reduction factor goes higher. Undersampling the k-space data and then reconstruct images with artificial sparsity is an efficient way to accelerate data acquisition. By exploiting artificial sparsity with a high-pass filter, an improved SENSE method is proposed in this work, termed high-pass filtered SENSE (HF-SENSE). METHODS: First, a high-pass filter was applied to the raw k-space data, the result of which was used as the inputs of sensitivity estimation and undersampling process. Second, the adaptive array coil combination method was adopted to calculate sensitivity maps on a block-by-block basis. Third, Tikhonov regularized SENSE was then used to reconstruct magnetic resonance images. Fourth, the reconstructed images were transformed into k-space data, which was filtered with the corresponding inverse filter. RESULTS: Both simulation and in vivo experiments demonstrate that HF-SENSE method significantly reduces noise level of the reconstructed images compared with SENSE. Furthermore, it is found that HF-SENSE can achieve lower normalized root-mean-square error value than SENSE. CONCLUSIONS: The proposed method explores artificial sparsity with a high-pass filter. Experiments demonstrate that the proposed HF-SENSE method can improve the image quality of SENSE reconstruction. The high-pass filter parameters can be predefined. With this image reconstruction method, high acceleration factors can be achieved, which will improve the clinical applicability of SENSE. This retrospective study (HF-SENSE: an improved partially parallel imaging using a high-pass filter) was approved by Institute Review Board of 2nd Affiliated Hospital of Zhejiang University (ethical approval number 2018-314). Participant for all images have informed consent that he knew the risks and agreed to participate in the research.",0
"Identification of esophageal cancer pathway deviation and construction of a diagnosis model using three kernel genes. The purpose of this study is to better understand the role of interleukin 35 (IL35) in esophageal carcinoma by comparing the mRNA level in Barrett's esophageal mucosa and in matched normal squamous mucosa and to understand how the diagnosis model works with two other genes: hepatocyte nuclear factor 1B (HNF1B) and cAMP responsive element binding protein 3-like 1 (CREB3L1). By comparing carcinoma tissue and normal tissue samples, we extracted all the differentially expressed mRNAs. The bioinformatics analysis resulted in the discovery of three prominent genes. Eventually, the three genes were utilized to train a deep-learning model. An additional wet experiment was conducted to validate the effect of IL35. All the differentially expressed genes were enriched into nine groups, each of which has specific biological functions. Given that the three significant genes HNF1B, CREB3L1, and IL35 as diagnostic features, a deep-learning model was constructed, reaching an accuracy of 93% in the training set and 87% in the test set. Our findings suggest that IL35, along with the other two signatures, can distinguish esophageal tumor samples from normal samples precisely.",0
"Drug Discovery and Repurposing Inhibits a Major Gut Pathogen-Derived Oncogenic Toxin. Objective: The human intestinal microbiome plays an important role in inflammatory bowel disease (IBD) and colorectal cancer (CRC) development. One of the first discovered bacterial mediators involves Bacteroides fragilis toxin (BFT, also named as fragilysin), a metalloprotease encoded by enterotoxigenic Bacteroides fragilis (ETBF) that causes barrier disruption and inflammation of the colon, leads to tumorigenesis in susceptible mice, and is enriched in the mucosa of IBD and CRC patients. Thus, targeted inhibition of BFT may benefit ETBF carrying patients. Design: By applying two complementary in silico drug design techniques, drug repositioning and molecular docking, we predicted potential BFT inhibitory compounds. Top candidates were tested in vitro on the CRC epithelial cell line HT29/c1 for their potential to inhibit key aspects of BFT activity, being epithelial morphology changes, E-cadherin cleavage (a marker for barrier function) and increased IL-8 secretion. Results: The primary bile acid and existing drug chenodeoxycholic acid (CDCA), currently used for treating gallstones, cerebrotendinous xanthomatosis, and constipation, was found to significantly inhibit all evaluated cell responses to BFT exposure. The inhibition of BFT resulted from a direct interaction between CDCA and BFT, as confirmed by an increase in the melting temperature of the BFT protein in the presence of CDCA. Conclusion: Together, our results show the potential of in silico drug discovery to combat harmful human and microbiome-derived proteins and more specifically suggests a potential for retargeting CDCA to inhibit the pro-oncogenic toxin BFT.",0
"The Extracellular RNA Communication Consortium: Establishing Foundational Knowledge and Technologies for Extracellular RNA Research. The Extracellular RNA Communication Consortium (ERCC) was launched to accelerate progress in the new field of extracellular RNA (exRNA) biology and to establish whether exRNAs and their carriers, including extracellular vesicles (EVs), can mediate intercellular communication and be utilized for clinical applications. Phase 1 of the ERCC focused on exRNA/EV biogenesis and function, discovery of exRNA biomarkers, development of exRNA/EV-based therapeutics, and construction of a robust set of reference exRNA profiles for a variety of biofluids. Here, we present progress by ERCC investigators in these areas, and we discuss collaborative projects directed at development of robust methods for EV/exRNA isolation and analysis and tools for sharing and computational analysis of exRNA profiling data.",0
"Forecasting one-day-forward wellness conditions for community-dwelling elderly with single lead short electrocardiogram signals. BACKGROUND: The accelerated growth of elderly population is creating a heavy burden to the healthcare system in many developed countries and regions. Electrocardiogram (ECG) analysis has been recognized as effective approach to cardiovascular disease diagnosis and widely utilized for monitoring personalized health conditions. METHOD: In this study, we present a novel approach to forecasting one-day-forward wellness conditions for community-dwelling elderly by analyzing single lead short ECG signals acquired from a station-based monitoring device. More specifically, exponentially weighted moving-average (EWMA) method is employed to eliminate the high-frequency noise from original signals at first. Then, Fisher-Yates normalization approach is used to adjust the self-evaluated wellness score distribution since the scores among different individuals are skewed. Finally, both deep learning-based and traditional machine learning-based methods are utilized for building wellness forecasting models. RESULTS: The experiment results show that the deep learning-based methods achieve the best fitted forecasting performance, where the forecasting accuracy and F value are 93.21% and 91.98% respectively. The deep learning-based methods, with the merit of non-hand-crafted engineering, have superior wellness forecasting performance towards the competitive traditional machine learning-based methods. CONCLUSION: The developed approach in this paper is effective in wellness forecasting for community-dwelling elderly, which can provide insights in terms of implementing a cost-effective approach to informing healthcare provider about health conditions of elderly in advance and taking timely interventions to reduce the risk of malignant events.",1
"Comparison of conventional and Si-photomultiplier-based PET systems for image quality and diagnostic performance. BACKGROUND: A new generation of positron emission tomography with computed tomography (PET-CT) was recently introduced using silicon (Si) photomultiplier (PM)-based technology. Our aim was to compare the image quality and diagnostic performance of a SiPM-based PET-CT (Discovery MI; GE Healthcare, Milwaukee, WI, USA) with a time-of-flight PET-CT scanner with a conventional PM detector (Gemini TF; Philips Healthcare, Cleveland, OH, USA), including reconstruction algorithms per vendor's recommendations. METHODS: Imaging of the National Electrical Manufacturers Association IEC body phantom and 16 patients was carried out using 1.5 min/bed for the Discovery MI PET-CT and 2 min/bed for the Gemini TF PET-CT. Images were analysed for recovery coefficients for the phantom, signal-to-noise ratio in the liver, standardized uptake values (SUV) in lesions, number of lesions and metabolic TNM classifications in patients. RESULTS: In phantom, the correct (> 90%) activity level was measured for spheres ≥17 mm for Discovery MI, whereas the Gemini TF reached a correct measured activity level for the 37-mm sphere. In patient studies, metabolic TNM classification was worse using images obtained from the Discovery MI compared those obtained from the Gemini TF in 4 of 15 patients. A trend toward more malignant, inflammatory and unclear lesions was found using images acquired with the Discovery MI compared with the Gemini TF, but this was not statistically significant. Lesion-to-blood-pool SUV ratios were significantly higher in images from the Discovery MI compared with the Gemini TF for lesions smaller than 1 cm (p < 0.001), but this was not the case for larger lesions (p = 0.053). The signal-to-noise ratio in the liver was similar between platforms (p = 0.52). Also, shorter acquisition times were possible using the Discovery MI, with preserved signal-to-noise ratio in the liver. CONCLUSIONS: Image quality was better with Discovery MI compared to conventional Gemini TF. Although no gold standard was available, the results indicate that the new PET-CT generation will provide potentially better diagnostic performance.",0
"Hypolipidemic effect of novel 2,5-bis(4-hydroxybenzylidenamino)-1,3,4-thiadiazole as potential peroxisome proliferation-activated receptor-α agonist in acute hyperlipidemic rat model. The development of new antihyperlipidemic agents with higher potency and lower side effects is of high priority. In this study, 1,3,4 thiadiazole Schiff base derivatives were synthesized as potential peroxisome proliferation-activated receptor-α (PPARα) agonists and characterized using elemental analysis, FTIR, 1H-NMR, 13C-NMR and mass spectroscopy and then tested for their hypolipidemic activity in Triton WR-1339-induced acute hyperlipidemic rat model in comparison with bezafibrate. The compounds showed significant hypolipidemic activity. Induced fit docking showed that the compounds are potential activators of PPARα with binding scores − 8.00 Kcal/mol for 2,5-bis(4-hydroxybenzylidenamino)-1,3,4-thiadiazole. PCR array analysis showed an increase in the expression of several genes involved in lipid metabolism through mitochondrial fatty acid β oxidation and are part of PPARα signaling pathway including Acsm3, Fabp4 and Hmgcs1. Gene expression of Lrp12 and Lrp1b involved in LDL uptake by liver cells and Cyp7a1 involved in cholesterol catabolism were also found to be upregulated.",0
"Automated Abdominal Segmentation of CT Scans for Body Composition Analysis Using Deep Learning. Purpose To develop and evaluate a fully automated algorithm for segmenting the abdomen from CT to quantify body composition. Materials and Methods For this retrospective study, a convolutional neural network based on the U-Net architecture was trained to perform abdominal segmentation on a data set of 2430 two-dimensional CT examinations and was tested on 270 CT examinations. It was further tested on a separate data set of 2369 patients with hepatocellular carcinoma (HCC). CT examinations were performed between 1997 and 2015. The mean age of patients was 67 years; for male patients, it was 67 years (range, 29-94 years), and for female patients, it was 66 years (range, 31-97 years). Differences in segmentation performance were assessed by using two-way analysis of variance with Bonferroni correction. Results Compared with reference segmentation, the model for this study achieved Dice scores (mean +/- standard deviation) of 0.98 +/- 0.03, 0.96 +/- 0.02, and 0.97 +/- 0.01 in the test set, and 0.94 +/- 0.05, 0.92 +/- 0.04, and 0.98 +/- 0.02 in the HCC data set, for the subcutaneous, muscle, and visceral adipose tissue compartments, respectively. Performance met or exceeded that of expert manual segmentation. Conclusion Model performance met or exceeded the accuracy of expert manual segmentation of CT examinations for both the test data set and the hepatocellular carcinoma data set. The model generalized well to multiple levels of the abdomen and may be capable of fully automated quantification of body composition metrics in three-dimensional CT examinations. (c) RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Chang in this issue.",1
"Immune Infiltration Profiling in Nonsmall Cell Lung Cancer and Their Clinical Significance: Study Based on Gene Expression Measurements. Immune cell infiltration is associated with the prognosis of cancer. This study focused on the immune infiltration profiling and their association with survival outcome in nonsmall cell lung cancer (NSCLC). Research data were obtained from the Gene Expression Omnibus and The Cancer Genome Atlas databases. CIBERSORT algorithm was applied to assess the relative proportions of 22 kinds of immune cells. Log-rank test was performed to compare the survival outcome of patients with different proportions of immune cells. The estimated hazard ratios were presented with forest plot. Multivariate Cox regression analysis was conducted to estimate the adjusted associations between different types of infiltrating immune cells and survival prognosis controlling for other clinical features and confounders. With the CIBERSORT approach, we assessed the proportions of 22 infiltrating immune cells of 2050 cases with NSCLC. By conducting survival analysis, we found different survival outcomes among cases with different proportions of certain types of infiltrating immune cells. Among the cell subsets investigated, plasma cells (hazard ratio [HR] = 0.775, 95% confidence interval [CI]: 0.669-0.898) and regulatory T cells (HR = 1.258, 95% CI: 1.091-1.451) were associated with survival outcome of NSCLC patients controlling for other covariates. Subgroup analysis suggested a good consistency and robustness of our results. Our findings might provide useful information for prognosis prediction and cellular study in NSCLC.",0
"Beyond Performance Metrics: Automatic Deep Learning Retinal OCT Analysis Reproduces Clinical Trial Outcome. PURPOSE: To validate the efficacy of a fully automatic, deep learning-based segmentation algorithm beyond conventional performance metrics by measuring the primary outcome of a clinical trial for macular telangiectasia type 2 (MacTel2). DESIGN: Evaluation of diagnostic test or technology. PARTICIPANTS: A total of 92 eyes from 62 participants with MacTel2 from a phase 2 clinical trial (NCT01949324) randomized to 1 of 2 treatment groups METHODS: The ellipsoid zone (EZ) defect areas were measured on spectral domain OCT images of each eye at 2 time points (baseline and month 24) by a fully automatic, deep learning-based segmentation algorithm. The change in EZ defect area from baseline to month 24 was calculated and analyzed according to the clinical trial protocol. MAIN OUTCOME MEASURE: Difference in the change in EZ defect area from baseline to month 24 between the 2 treatment groups. RESULTS: The difference in the change in EZ defect area from baseline to month 24 between the 2 treatment groups measured by the fully automatic segmentation algorithm was 0.072+/-0.035 mm(2) (P = 0.021). This was comparable to the outcome of the clinical trial using semiautomatic measurements by expert readers, 0.065+/-0.033 mm(2) (P = 0.025). CONCLUSIONS: The fully automatic segmentation algorithm was as accurate as semiautomatic expert segmentation to assess EZ defect areas and was able to reliably reproduce the statistically significant primary outcome measure of the clinical trial. This approach, to validate the performance of an automatic segmentation algorithm on the primary clinical trial end point, provides a robust gauge of its clinical applicability.",1
"Peroxisome protein import recapitulated in Xenopus egg extracts. Peroxisomes import their luminal proteins from the cytosol. Most substrates contain a C-terminal Ser-Lys-Leu (SKL) sequence that is recognized by the receptor Pex5. Pex5 binds to peroxisomes via a docking complex containing Pex14, and recycles back into the cytosol following its mono-ubiquitination at a conserved Cys residue. The mechanism of peroxisome protein import remains incompletely understood. Here, we developed an in vitro import system based on Xenopus egg extracts. Import is dependent on the SKL motif in the substrate and on the presence of Pex5 and Pex14, and is sustained by ATP hydrolysis. A protein lacking an SKL sequence can be coimported, providing strong evidence for import of a folded protein. The conserved cysteine in Pex5 is not essential for import or to clear import sites for subsequent rounds of translocation. This new in vitro assay will be useful for further dissecting the mechanism of peroxisome protein import.",0
"Real-time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy: a multicentre, case-control, diagnostic study. BACKGROUND: Upper gastrointestinal cancers (including oesophageal cancer and gastric cancer) are the most common cancers worldwide. Artificial intelligence platforms using deep learning algorithms have made remarkable progress in medical imaging but their application in upper gastrointestinal cancers has been limited. We aimed to develop and validate the Gastrointestinal Artificial Intelligence Diagnostic System (GRAIDS) for the diagnosis of upper gastrointestinal cancers through analysis of imaging data from clinical endoscopies. METHODS: This multicentre, case-control, diagnostic study was done in six hospitals of different tiers (ie, municipal, provincial, and national) in China. The images of consecutive participants, aged 18 years or older, who had not had a previous endoscopy were retrieved from all participating hospitals. All patients with upper gastrointestinal cancer lesions (including oesophageal cancer and gastric cancer) that were histologically proven malignancies were eligible for this study. Only images with standard white light were deemed eligible. The images from Sun Yat-sen University Cancer Center were randomly assigned (8:1:1) to the training and intrinsic verification datasets for developing GRAIDS, and the internal validation dataset for evaluating the performance of GRAIDS. Its diagnostic performance was evaluated using an internal and prospective validation set from Sun Yat-sen University Cancer Center (a national hospital) and additional external validation sets from five primary care hospitals. The performance of GRAIDS was also compared with endoscopists with three degrees of expertise: expert, competent, and trainee. The diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of GRAIDS and endoscopists for the identification of cancerous lesions were evaluated by calculating the 95% CIs using the Clopper-Pearson method. FINDINGS: 1 036 496 endoscopy images from 84 424 individuals were used to develop and test GRAIDS. The diagnostic accuracy in identifying upper gastrointestinal cancers was 0.955 (95% CI 0.952-0.957) in the internal validation set, 0.927 (0.925-0.929) in the prospective set, and ranged from 0.915 (0.913-0.917) to 0.977 (0.977-0.978) in the five external validation sets. GRAIDS achieved diagnostic sensitivity similar to that of the expert endoscopist (0.942 [95% CI 0.924-0.957] vs 0.945 [0.927-0.959]; p=0.692) and superior sensitivity compared with competent (0.858 [0.832-0.880], p<0.0001) and trainee (0.722 [0.691-0.752], p<0.0001) endoscopists. The positive predictive value was 0.814 (95% CI 0.788-0.838) for GRAIDS, 0.932 (0.913-0.948) for the expert endoscopist, 0.974 (0.960-0.984) for the competent endoscopist, and 0.824 (0.795-0.850) for the trainee endoscopist. The negative predictive value was 0.978 (95% CI 0.971-0.984) for GRAIDS, 0.980 (0.974-0.985) for the expert endoscopist, 0.951 (0.942-0.959) for the competent endoscopist, and 0.904 (0.893-0.916) for the trainee endoscopist. INTERPRETATION: GRAIDS achieved high diagnostic accuracy in detecting upper gastrointestinal cancers, with sensitivity similar to that of expert endoscopists and was superior to that of non-expert endoscopists. This system could assist community-based hospitals in improving their effectiveness in upper gastrointestinal cancer diagnoses. FUNDING: The National Key R&D Program of China, the Natural Science Foundation of Guangdong Province, the Science and Technology Program of Guangdong, the Science and Technology Program of Guangzhou, and the Fundamental Research Funds for the Central Universities.",1
"An augmented reality microscope with real-time artificial intelligence integration for cancer diagnosis. The microscopic assessment of tissue samples is instrumental for the diagnosis and staging of cancer, and thus guides therapy. However, these assessments demonstrate considerable variability and many regions of the world lack access to trained pathologists. Though artificial intelligence (AI) promises to improve the access and quality of healthcare, the costs of image digitization in pathology and difficulties in deploying AI solutions remain as barriers to real-world use. Here we propose a cost-effective solution: the augmented reality microscope (ARM). The ARM overlays AI-based information onto the current view of the sample in real time, enabling seamless integration of AI into routine workflows. We demonstrate the utility of ARM in the detection of metastatic breast cancer and the identification of prostate cancer, with latency compatible with real-time use. We anticipate that the ARM will remove barriers towards the use of AI designed to improve the accuracy and efficiency of cancer diagnosis.",1
"Longitudinal engagement trajectories and risk of death among new ART starters in Zambia: A group-based multi-trajectory analysis. BACKGROUND: Retention in HIV treatment must be improved to advance the HIV response, but research to characterize gaps in retention has focused on estimates from single time points and population-level averages. These approaches do not assess the engagement patterns of individual patients over time and fail to account for both their dynamic nature and the heterogeneity between patients. We apply group-based trajectory analysis-a special application of latent class analysis to longitudinal data-among new antiretroviral therapy (ART) starters in Zambia to identify groups defined by engagement patterns over time and to assess their association with mortality. METHODS AND FINDINGS: We analyzed a cohort of HIV-infected adults who newly started ART between August 1, 2013, and February 1, 2015, across 64 clinics in Zambia. We performed group-based multi-trajectory analysis to identify subgroups with distinct trajectories in medication possession ratio (MPR, a validated adherence metric based on pharmacy refill data) over the past 3 months and loss to follow-up (LTFU, >90 days late for last visit) among patients with at least 180 days of observation time. We used multinomial logistic regression to identify baseline factors associated with belonging to particular trajectory groups. We obtained Kaplan-Meier estimates with bootstrapped confidence intervals of the cumulative incidence of mortality stratified by trajectory group and performed adjusted Poisson regression to estimate adjusted incidence rate ratios (aIRRs) for mortality by trajectory group. Inverse probability weights were applied to all analyses to account for updated outcomes ascertained from tracing a random subset of patients lost to follow-up as of July 31, 2015. Overall, 38,879 patients (63.3% female, median age 35 years [IQR 29-41], median enrollment CD4 count 280 cells/mul [IQR 146-431]) were included in our cohort. Analyses revealed 6 trajectory groups among the new ART starters: (1) 28.5% of patients demonstrated consistently high adherence and retention; (2) 22.2% showed early nonadherence but consistent retention; (3) 21.6% showed gradually decreasing adherence and retention; (4) 8.6% showed early LTFU with later reengagement; (5) 8.7% had early LTFU without reengagement; and (6) 10.4% had late LTFU without reengagement. Identified groups exhibited large differences in survival: after adjustment, the ""early LTFU with reengagement"" group (aIRR 3.4 [95% CI 1.2-9.7], p = 0.019), the ""early LTFU"" group (aIRR 6.4 [95% CI 2.5-16.3], p < 0.001), and the ""late LTFU"" group (aIRR 4.7 [95% CI 2.0-11.3], p = 0.001) had higher rates of mortality as compared to the group with consistently high adherence/retention. Limitations of this study include using data observed after baseline to identify trajectory groups and to classify patients into these groups, excluding patients who died or transferred within the first 180 days, and the uncertain generalizability of the data to current care standards. CONCLUSIONS: Among new ART starters in Zambia, we observed 6 patient subgroups that demonstrated distinctive engagement trajectories over time and that were associated with marked differences in the subsequent risk of mortality. Further efforts to develop tailored intervention strategies for different types of engagement behaviors, monitor early engagement to identify higher-risk patients, and better understand the determinants of these heterogeneous behaviors can help improve care delivery and survival in this population.",0
"Interaction and molecular dynamics simulation study of Osimertinib (AstraZeneca 9291) anticancer drug with the EGFR kinase domain in native protein and mutated L844V and C797S. Background: Targeted therapy is a novel, promising approach to anticancer treatment that endeavors to overcome drug resistance to traditional chemotherapies. Patients with the L858R mutation in epidermal growth factor receptor (EGFR) respond to the first generation tyrosine kinase inhibitors (TKIs); however, after one year of treatment, they may become resistant. The T790M mutation is the most probable cause for drug resistance. Third generation drugs, including Osimertinib (AZD9291), are more effective against T790M and other sensitive mutations. Osimertinib is effective against the L844V mutation, has conditional effectiveness for the L718Q mutation, and is ineffective for the Cys797Ser (C797S) mutation. Cells that have both the T790M and C797 mutations are more resistant to third generation drugs. Although research has shown that Osimertinib is an effective treatment for EGFR L844V cells, this has not been shown for cells that have the C797S mutation. This molecular mechanism has not been well-studied. Methods: In the present study, we used the GROMACS software for molecular dynamics simulation to identify interactions between Osimertinib and the kinase part of EGFR in L844V and C797S mutants. Results: We evaluated native EGFR protein and the L844V and C797S mutations’ docking and binding energy, kI, intermolecular, internal, and torsional energy parameters. Osimertinib was effective for the EGFR L844V mutation, but not for EGFR C797S. All simulations were validated by root-mean-square deviation (RMSD), root-mean square fluctuation (RMSF), and radius of gyration (ROG). Conclusion: According to our computational simulation, the results supported the experimental models and, therefore, could confirm and predict the molecular mechanism of drug efficacy.",0
"Nimbolide protects against endotoxin-induced acute respiratory distress syndrome by inhibiting TNF-α mediated NF-κB and HDAC-3 nuclear translocation. Acute respiratory distress syndrome (ARDS) is characterized by an excessive acute inflammatory response in lung parenchyma, which ultimately leads to refractory hypoxemia. One of the earliest abnormalities seen in lung injury is the elevated levels of inflammatory cytokines, among them, the soluble tumor necrosis factor (TNF-α) has a key role, which exerts cytotoxicity in epithelial and endothelial cells thus exacerbates edema. The bacterial lipopolysaccharide (LPS) was used both in vitro (RAW 264.7, THP-1, MLE-12, A549, and BEAS-2B) and in vivo (C57BL/6 mice), as it activates a plethora of overlapping inflammatory signaling pathways involved in ARDS. Nimbolide is a chemical constituent of Azadirachta indica, which contains multiple biological properties, while its role in ARDS is elusive. Herein, we have investigated the protective effects of nimbolide in abrogating the complications associated with ARDS. We showed that nimbolide markedly suppressed the nitrosative-oxidative stress, inflammatory cytokines, and chemokines expression by suppressing iNOS, myeloperoxidase, and nitrotyrosine expression. Moreover, nimbolide mitigated the migration of neutrophils and mast cells whilst normalizing the LPS-induced hypothermia. Also, nimbolide modulated the expression of epigenetic regulators with multiple HDAC inhibitory activity by suppressing the nuclear translocation of NF-κB and HDAC-3. We extended our studies using molecular docking studies, which demonstrated a strong interaction between nimbolide and TNF-α. Additionally, we showed that treatment with nimbolide increased GSH, Nrf-2, SOD-1, and HO-1 protein expression; concomitantly abrogated the LPS-triggered TNF-α, p38 MAPK, mTOR, and GSK-3β protein expression. Collectively, these results indicate that TNF-α-regulated NF-κB and HDAC-3 crosstalk was ameliorated by nimbolide with promising anti-nitrosative, antioxidant, and anti-inflammatory properties in LPS-induced ARDS.",0
"Automated segmentation of macular edema in OCT using deep neural networks. Macular edema is an eye disease that can affect visual acuity. Typical disease symptoms include subretinal fluid (SRF) and pigment epithelium detachment (PED). Optical coherence tomography (OCT) has been widely used for diagnosing macular edema because of its non-invasive and high resolution properties. Segmentation for macular edema lesions from OCT images plays an important role in clinical diagnosis. Many computer-aided systems have been proposed for the segmentation. Most traditional segmentation methods used in these systems are based on low-level hand-crafted features, which require significant domain knowledge and are sensitive to the variations of lesions. To overcome these shortcomings, this paper proposes to use deep neural networks (DNNs) together with atrous spatial pyramid pooling (ASPP) to automatically segment the SRF and PED lesions. Lesions-related features are first extracted by DNNs, then processed by ASPP which is composed of multiple atrous convolutions with different fields of view to accommodate the various scales of the lesions. Based on ASPP, a novel module called stochastic ASPP (sASPP) is proposed to combat the co-adaptation of multiple atrous convolutions. A large OCT dataset provided by a competition platform called ""AI Challenger"" are used to train and evaluate the proposed model. Experimental results demonstrate that the DNNs together with ASPP achieve higher segmentation accuracy compared with the state-of-the-art method. The stochastic operation added in sASPP is empirically verified as an effective regularization method that can alleviate the overfitting problem and significantly reduce the validation error.",1
"Molecular docking, dynamics, and pharmacology studies on bexarotene as an agonist of ligand-activated transcription factors, retinoid X receptors. Retinoid X receptors (RXRs) belong to the nuclear receptor superfamily, and upon ligand activation, these receptors control gene transcription via either homodimerization with themselves or heterodimerization with the partner-nuclear receptor. The protective effects of RXRs and RXR agonists have been reported in several neurodegenerative diseases, including in the retina. This study was aimed to prioritize compounds from natural and synthetic origin retinoids as potential RXR agonists by molecular docking and molecular dynamic simulation strategies. The docking studies indicated bexarotene as a lead compound that can activate various RXR receptor isoforms (α, β, and γ) and has a strong binding affinity to the receptor protein than retinoic acid, which is known as a natural endogenous RXR agonist. Dynamic simulation studies confirmed that the hydrogen bonding and hydrophobic interactions were highly stable in all the three isoforms of the RXR-bexarotene complex. To further validate the significance of the RXR receptor in neurons, in vitro pharmacological treatment of neuronal SH-SY5Y cells with bexarotene was performed. In vitro data from SH-SY5Y cells confirmed that bexarotene activated RXR-simulated neurite outgrowth significantly. We conclude that bexarotene could be potentially used as an exogenous activator of RXRs and emerge as a good drug target for several neurodegenerative disorders.",0
"Electroacupuncture Facilitates the Integration of Neural Stem Cell-Derived Neural Network with Transected Rat Spinal Cord. In this article, Y.S. Zeng, Y. Ding, and colleagues show that EA treatment can reinforce the survival, neuronal differentiation, and synaptic connections of donor neurons in injured spinal cord by activating the NT-3/TRKC/AKT pathway. Moreover, the combinational therapy fosters host axonal regeneration into the injury/graft site to rebuild the synaptic connections with grafted NN, and improves nerve conduction of the spinal cord as well as locomotor function of paralyzed hindlimbs.",0
"Pregnancy-Adapted YEARS Algorithm for Diagnosis of Suspected Pulmonary Embolism. BACKGROUND: Pulmonary embolism is one of the leading causes of maternal death in the Western world. Because of the low specificity and sensitivity of the d-dimer test, all pregnant women with suspected pulmonary embolism undergo computed tomographic (CT) pulmonary angiography or ventilation-perfusion scanning, both of which involve radiation exposure to the mother and fetus. Whether a pregnancy-adapted algorithm could be used to safely avoid diagnostic imaging in pregnant women with suspected pulmonary embolism is unknown. METHODS: In a prospective study involving pregnant women with suspected pulmonary embolism, we assessed three criteria from the YEARS algorithm (clinical signs of deep-vein thrombosis, hemoptysis, and pulmonary embolism as the most likely diagnosis) and measured the d-dimer level. Pulmonary embolism was ruled out if none of the three criteria were met and the d-dimer level was less than 1000 ng per milliliter or if one or more of the three criteria were met and the d-dimer level was less than 500 ng per milliliter. Adaptation of the YEARS algorithm for pregnant women involved compression ultrasonography for women with symptoms of deep-vein thrombosis; if the results were positive (i.e., a clot was present), CT pulmonary angiography was not performed. All patients in whom pulmonary embolism had not been ruled out underwent CT pulmonary angiography. The primary outcome was the incidence of venous thromboembolism at 3 months. The secondary outcome was the proportion of patients in whom CT pulmonary angiography was not indicated to safely rule out pulmonary embolism. RESULTS: A total of 510 women were screened, of whom 12 (2.4%) were excluded. Pulmonary embolism was diagnosed in 20 patients (4.0%) at baseline. During follow-up, popliteal deep-vein thrombosis was diagnosed in 1 patient (0.21%; 95% confidence interval [CI], 0.04 to 1.2); no patient had pulmonary embolism. CT pulmonary angiography was not indicated, and thus was avoided, in 195 patients (39%; 95% CI, 35 to 44). The efficiency of the algorithm was highest during the first trimester of pregnancy and lowest during the third trimester; CT pulmonary angiography was avoided in 65% of patients who began the study in the first trimester and in 32% who began the study in the third trimester. CONCLUSIONS: Pulmonary embolism was safely ruled out by the pregnancy-adapted YEARS diagnostic algorithm across all trimesters of pregnancy. CT pulmonary angiography was avoided in 32 to 65% of patients. (Funded by Leiden University Medical Center and 17 other participating hospitals; Artemis Netherlands Trial Register number, NL5726.).",0
"In vitro cell migration, invasion, and adhesion assays: From cell imaging to data analysis. Cell migration is a key procedure involved in many biological processes including embryological development, tissue formation, immune defense or inflammation, and cancer progression. How physical, chemical, and molecular aspects can affect cell motility is a challenge to understand migratory cells behavior. In vitro assays are excellent approaches to extrapolate to in vivo situations and study live cells behavior. Here we present four in vitro protocols that describe step-by-step cell migration, invasion and adhesion strategies and their corresponding image data quantification. These current protocols are based on two-dimensional wound healing assays (comparing traditional pipette tip-scratch assay vs. culture insert assay), 2D individual cell-tracking experiments by live cell imaging and three-dimensional spreading and transwell assays. All together, they cover different phenotypes and hallmarks of cell motility and adhesion, providing orthogonal information that can be used either individually or collectively in many different experimental setups. These optimized protocols will facilitate physiological and cellular characterization of these processes, which may be used for fast screening of specific therapeutic cancer drugs for migratory function, novel strategies in cancer diagnosis, and for assaying new molecules involved in adhesion and invasion metastatic properties of cancer cells.",0
"Scoring of Coronary Artery Disease Characteristics on Coronary CT Angiograms by Using Machine Learning. Background Coronary CT angiography contains prognostic information but the best method to extract these data remains unknown. Purpose To use machine learning to develop a model of vessel features to discriminate between patients with and without subsequent death or cardiovascular events. Performance was compared with that of conventional scores. Materials and Methods Coronary CT angiography was analyzed by radiologists into four features for each of 16 coronary segments. Four machine learning model types were explored. Five conventional vessel scores were computed for comparison including the Coronary Artery Disease Reporting and Data System (CAD-RADS) score. The National Death Index was retrospectively queried from January 2004 through December 2015. Outcomes were all-cause mortality, coronary heart disease deaths, and coronary deaths or nonfatal myocardial infarctions. Score performance was assessed by using area under the receiver operating characteristic curve (AUC). Results Between February 2004 and November 2009, 6892 patients (4452 men [mean age +/- standard deviation, 51 years +/- 11] and 2440 women [mean age, 57 years +/- 12]) underwent coronary CT angiography (median follow-up, 9.0 years; interquartile range, 8.2-9.8 years). There were 380 deaths of all causes, 70 patients died of coronary artery disease, and 43 patients reported nonfatal myocardial infarctions. For all-cause mortality, the AUC was 0.77 (95% confidence interval: 0.76, 0.77) for machine learning (k-nearest neighbors) versus 0.72 (95% confidence interval: 0.72, 0.72) for CAD-RADS (P < .001). For coronary artery heart disease deaths, AUC was 0.85 (95% confidence interval: 0.84, 0.85) for machine learning versus 0.79 (95% confidence interval: 0.78, 0.80) for CAD-RADS (P < .001). When deciding whether to start statins, if the choice is made to tolerate treating 45 patients to be sure to include one patient who will later die of coronary disease, the use of the machine learning score ensures that 93% of patients with events will be administered the drug; if CAD-RADS is used, only 69% will be treated. Conclusion Compared with Coronary Artery Disease Reporting and Data System and other scores, machine learning methods better discriminated patients who subsequently experienced an adverse event from those who did not. (c) RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Schoepf and Tesche in this issue.",1
"MRI-based radiomics of rectal cancer: preoperative assessment of the pathological features. BACKGROUND: This study aimed to evaluate the significance of MRI-based radiomics model derived from high-resolution T2-weighted images (T2WIs) in predicting tumor pathological features of rectal cancer. METHODS: A total of 152 patients with rectal cancer who underwent surgery without any neoadjuvant therapy between March 2017 and September 2018 were included retrospectively. The patients were scanned using a 3-T magnetic resonance imaging, and high-resolution T2WIs were obtained. Lesions were delineated, and 1029 radiomics features were extracted. Least absolute shrinkage and selection operator was used to select features, and multilayer perceptron (MLP), logistic regression (LR), support vector machine (SVM), decision tree (DT), random forest (RF), and K-nearest neighbor (KNN) were trained using fivefold cross-validation to build a prediction model. The diagnostic performance of the prediction models was assessed using the receiver operating characteristic curves. RESULTS: A total of 1029 features were extracted, and 15, 11, and 11 features were selected to predict the degree of differentiation, T stage, and N stage, respectively. The best performance of the radiomics model for the degree of differentiation, T stage, and N stage was obtained by SVM [area under the curve (AUC), 0.862; 95% confidence interval (CI), 0.750-0.967; sensitivity, 83.3%; specificity, 85.0%], MLP (AUC, 0.809; 95% CI, 0.690-0.905; sensitivity, 76.2%; specificity, 74.1%), and RF (AUC, 0.746; 95% CI, 0.622-0.872; sensitivity, 79.3%; specificity, 72.2%). CONCLUSION: This study demonstrated that the high-resolution T2WI-based radiomics model could serve as pretreatment biomarkers in predicting pathological features of rectal cancer.",1
"Identification of diterpenoid compounds that interfere with Fli-1 DNA binding to suppress leukemogenesis. The ETS transcription factor Fli-1 controls the expression of genes involved in hematopoiesis including cell proliferation, survival, and differentiation. Dysregulation of Fli-1 induces hematopoietic and solid tumors, rendering it an important target for therapeutic intervention. Through high content screens of a library of chemicals isolated from medicinal plants in China for inhibitors of a Fli-1 transcriptional reporter cells, we hereby report the identification of diterpenoid-like compounds that strongly inhibit Fli-1 transcriptional activity. These agents suppressed the growth of erythroleukemic cells by inducing apoptosis and differentiation. They also inhibited survival and proliferation of B-cell leukemic cell lines as well as primary B-cell lymphocytic leukemia (B-CLL) isolated from 7 patients. Moreover, these inhibitors blocked leukemogenesis in a mouse model of erythroleukemia, in which Fli-1 is the driver of tumor initiation. Computational docking analysis revealed that the diterpenoid-like compounds bind with high affinity to nucleotide residues in a pocket near the major groove within the DNA-binding sites of Fli-1. Functional inhibition of Fli-1 by these compounds triggered its further downregulation through miR-145, whose promoter is normally repressed by Fli-1. These results uncover the importance of Fli-1 in leukemogenesis, a Fli-1-miR145 autoregulatory loop and new anti-Fli-1 diterpenoid agents for the treatment of diverse hematological malignancies overexpressing this transcription factor.",0
"Dynamic Organellar Maps for Spatial Proteomics. Eukaryotic cells are highly compartmentalized and protein subcellular localization critically influences protein function. Identification of the subcellular localizations of proteins and their translocation events upon perturbation has mostly been confined to targeted studies or laborious microscopy-based methods. Here we describe a systematic mass spectrometry-based method for spatial proteomics. The approach uses simple fractionation profiling and has two applications: Firstly it can be used to infer subcellular protein localization on a proteome-wide scale, resulting in a protein map of the cell. Secondly, the method permits identification of changes in protein localization, by comparing maps made under different conditions, as a tool for unbiased systems cell biology. © 2018 by John Wiley & Sons, Inc.",0
"SingleCellNet: A Computational Tool to Classify Single Cell RNA-Seq Data Across Platforms and Across Species. Single-cell RNA-seq has emerged as a powerful tool in diverse applications, from determining the cell-type composition of tissues to uncovering regulators of developmental programs. A near-universal step in the analysis of single-cell RNA-seq data is to hypothesize the identity of each cell. Often, this is achieved by searching for combinations of genes that have previously been implicated as being cell-type specific, an approach that is not quantitative and does not explicitly take advantage of other single-cell RNA-seq studies. Here, we describe our tool, SingleCellNet, which addresses these issues and enables the classification of query single-cell RNA-seq data in comparison to reference single-cell RNA-seq data. SingleCellNet compares favorably to other methods in sensitivity and specificity, and it is able to classify across platforms and species. We highlight SingleCellNet's utility by classifying previously undetermined cells, and by assessing the outcome of a cell fate engineering experiment. A major obstacle in analyzing single-cell RNA-seq data is determining the identity of each cell. Often this process is time-consuming, error prone, and lacking in quantitative rigor. We have addressed this challenge by developing SingleCellNet (SCN), which provides a quantitative classification of single-cell RNA-seq data. SCN compares favorably to other methods in sensitivity and specificity. One of the major advantages of SCN is that it is possible to use it to classify cells across platforms and across species.",0
"DeepSeeNet: A Deep Learning Model for Automated Classification of Patient-based Age-related Macular Degeneration Severity from Color Fundus Photographs. PURPOSE: In assessing the severity of age-related macular degeneration (AMD), the Age-Related Eye Disease Study (AREDS) Simplified Severity Scale predicts the risk of progression to late AMD. However, its manual use requires the time-consuming participation of expert practitioners. Although several automated deep learning systems have been developed for classifying color fundus photographs (CFP) of individual eyes by AREDS severity score, none to date has used a patient-based scoring system that uses images from both eyes to assign a severity score. DESIGN: DeepSeeNet, a deep learning model, was developed to classify patients automatically by the AREDS Simplified Severity Scale (score 0-5) using bilateral CFP. PARTICIPANTS: DeepSeeNet was trained on 58 402 and tested on 900 images from the longitudinal follow-up of 4549 participants from AREDS. Gold standard labels were obtained using reading center grades. METHODS: DeepSeeNet simulates the human grading process by first detecting individual AMD risk factors (drusen size, pigmentary abnormalities) for each eye and then calculating a patient-based AMD severity score using the AREDS Simplified Severity Scale. MAIN OUTCOME MEASURES: Overall accuracy, specificity, sensitivity, Cohen's kappa, and area under the curve (AUC). The performance of DeepSeeNet was compared with that of retinal specialists. RESULTS: DeepSeeNet performed better on patient-based classification (accuracy = 0.671; kappa = 0.558) than retinal specialists (accuracy = 0.599; kappa = 0.467) with high AUC in the detection of large drusen (0.94), pigmentary abnormalities (0.93), and late AMD (0.97). DeepSeeNet also outperformed retinal specialists in the detection of large drusen (accuracy 0.742 vs. 0.696; kappa 0.601 vs. 0.517) and pigmentary abnormalities (accuracy 0.890 vs. 0.813; kappa 0.723 vs. 0.535) but showed lower performance in the detection of late AMD (accuracy 0.967 vs. 0.973; kappa 0.663 vs. 0.754). CONCLUSIONS: By simulating the human grading process, DeepSeeNet demonstrated high accuracy with increased transparency in the automated assignment of individual patients to AMD risk categories based on the AREDS Simplified Severity Scale. These results highlight the potential of deep learning to assist and enhance clinical decision-making in patients with AMD, such as early AMD detection and risk prediction for developing late AMD. DeepSeeNet is publicly available on https://github.com/ncbi-nlp/DeepSeeNet.",1
"Association of Prepubertal and Postpubertal Exposure to Childhood Maltreatment with Adult Amygdala Function. Importance: Abnormalities in amygdala response to threatening faces have been observed in anxiety disorders, autism, bipolar disorder, depression, posttraumatic stress disorder, and schizophrenia. Abnormally hyperactive and hypoactive responses have typically been associated with anxiety and inhibition vs risk taking and inappropriate social behaviors. Maltreatment is a major risk factor for most of these disorders and is associated with abnormal amygdala function. Objective: To identify the type and age of exposure to childhood maltreatment that are associated with hyperactive and hypoactive amygdala responses in young adulthood. Design, Setting, and Participants: Data collection for this retrospective cohort study took place from November 8, 2010, to August 23, 2012. Data analyses were conducted from September 20, 2012, to June 27, 2018. Participants were recruited from the urban and suburban Boston vicinity without diagnostic restrictions based on exposure history. Exposures: The Maltreatment and Abuse Chronology of Exposure (MACE) scale was used to retrospectively assess type and age of exposure to childhood maltreatment. Main Outcomes and Measures: Activation and pattern information functional magnetic resonance imaging were used to assess bilateral amygdala response to angry and fearful faces vs neutral faces or shapes, and sensitive exposure periods were identified using cross-validated artificial intelligence predictive analytics (50 averaged randomized iterations with training on 63.3% and testing on 36.7% of the sample). Results: Of the 202 participants (mean [SD] age, 23.2 [1.7] years; 118 [58.4%] female), 52 (25.7%) reported no exposure to maltreatment and 150 (74.3%) reported exposure to 1 or more maltreatment types. Eight participants (15.1%) with a MACE score of 0 and 51 (34.2%) with a MACE score of 1 or higher had a history of major depression (odds ratio, 2.40; 95% CI, 1.05-6.06; P =.03); 8 unexposed participants (15.1%) and 46 with MACE scores of 1 or higher (30.9%) had a history of 1 or more anxiety disorders (odds ratio, 2.45; 95% CI, 1.03-6.50; P =.03). Retrospective self-report of physical maltreatment between 3 and 6 years of age and peer emotional abuse at 13 and 15 years were associated with amygdala activation to emotional faces vs shapes. Early exposure was associated with blunted response (β = -0.17, P <.001), whereas later exposure was associated with augmented response (β = 0.16, P <.001). Prepubertal vs postpubertal maltreatment was associated with an opposite response on the voxelwise response pattern in clustering stimuli of the same type (eg, mean [SD] emotional ellipse areas for physical maltreatment at age 4 years vs nonverbal emotional abuse at 13 years: 1.41 [1.05] vs 0.25 [0.10], P <.001) and in distinguishing between stimuli of different types (eg, mean [SD] emotional vs neutral faces distance for peer emotional abuse at age 6 years vs 13 years: 1.89 [0.75] vs 0.80 [0.39], P <.001). Conclusions and Relevance: The findings suggest that prepubertal vs postpubertal developmental differences in the association between maltreatment and amygdala response to threatening or salient stimuli exist. Understanding the role of adversity in different sensitive exposure periods and the potential adaptive significance of attenuated vs enhanced amygdala response may help explain why maltreatment may be a risk factor for many different disorders and foster creation of targeted interventions to preempt the emergence of psychopathology in at-risk youths..",0
"A graph-based algorithm for estimating clonal haplotypes of tumor sample from sequencing data. Background: Haplotype phasing is an important step in many bioinformatics workflows. In cancer genomics, it is suggested that reconstructing the clonal haplotypes of a tumor sample could facilitate a comprehensive understanding of its clonal architecture and further provide valuable reference in clinical diagnosis and treatment. However, the sequencing data is an admixture of reads sampled from different clonal haplotypes, which complicates the computational problem by exponentially increasing the solution-space and leads the existing algorithms to an unacceptable time-/space- complexity. In addition, the evolutionary process among clonal haplotypes further weakens those algorithms by bringing indistinguishable candidate solutions. Results: To improve the algorithmic performance of phasing clonal haplotypes, in this article, we propose MixSubHap, which is a graph-based computational pipeline working on cancer sequencing data. To reduce the computation complexity, MixSubHap adopts three bounding strategies to limit the solution space and filter out false positive candidates. It first estimates the global clonal structure by clustering the variant allelic frequencies on sampled point mutations. This offers a priori on the number of clonal haplotypes when copy-number variations are not considered. Then, it utilizes a greedy extension algorithm to approximately find the longest linkage of the locally assembled contigs. Finally, it incorporates a read-depth stripping algorithm to filter out false linkages according to the posterior estimation of tumor purity and the estimated percentage of each sub-clone in the sample. A series of experiments are conducted to verify the performance of the proposed pipeline. Conclusions: The results demonstrate that MixSubHap is able to identify about 90% on average of the preset clonal haplotypes under different simulation configurations. Especially, MixSubHap is robust when decreasing the mutation rates, in which cases the longest assembled contig could reach to 10kbps, while the accuracy of assigning a mutation to its haplotype still keeps more than 60% on average. MixSubHap is considered as a practical algorithm to reconstruct clonal haplotypes from cancer sequencing data. The source codes have been uploaded and maintained at https://github.com/YixuanWang1120/MixSubHap for academic use only.",0
"Integrating shortest dependency path and sentence sequence into a deep learning framework for relation extraction in clinical text. BACKGROUND: Extracting relations between important clinical entities is critical but very challenging for natural language processing (NLP) in the medical domain. Researchers have applied deep learning-based approaches to clinical relation extraction; but most of them consider sentence sequence only, without modeling syntactic structures. The aim of this study was to utilize a deep neural network to capture the syntactic features and further improve the performances of relation extraction in clinical notes. METHODS: We propose a novel neural approach to model shortest dependency path (SDP) between target entities together with the sentence sequence for clinical relation extraction. Our neural network architecture consists of three modules: (1) sentence sequence representation module using bidirectional long short-term memory network (Bi-LSTM) to capture the features in the sentence sequence; (2) SDP representation module implementing the convolutional neural network (CNN) and Bi-LSTM network to capture the syntactic context for target entities using SDP information; and (3) classification module utilizing a fully-connected layer with Softmax function to classify the relation type between target entities. RESULTS: Using the 2010 i2b2/VA relation extraction dataset, we compared our approach with other baseline methods. Our experimental results show that the proposed approach achieved significant improvements over comparable existing methods, demonstrating the effectiveness of utilizing syntactic structures in deep learning-based relation extraction. The F-measure of our method reaches 74.34% which is 2.5% higher than the method without using syntactic features. CONCLUSIONS: We propose a new neural network architecture by modeling SDP along with sentence sequence to extract multi-relations from clinical text. Our experimental results show that the proposed approach significantly improve the performances on clinical notes, demonstrating the effectiveness of syntactic structures in deep learning-based relation extraction.",1
"Simultaneous detection of genotype and phenotype enables rapid and accurate antibiotic susceptibility determination. Multidrug resistant organisms are a serious threat to human health(1,2). Fast, accurate antibiotic susceptibility testing (AST) is a critical need in addressing escalating antibiotic resistance, since delays in identifying multidrug resistant organisms increase mortality(3,4) and use of broad-spectrum antibiotics, further selecting for resistant organisms. Yet current growth-based AST assays, such as broth microdilution(5), require several days before informing key clinical decisions. Rapid AST would transform the care of patients with infection while ensuring that our antibiotic arsenal is deployed as efficiently as possible. Growth-based assays are fundamentally constrained in speed by doubling time of the pathogen, and genotypic assays are limited by the ever-growing diversity and complexity of bacterial antibiotic resistance mechanisms. Here we describe a rapid assay for combined genotypic and phenotypic AST through RNA detection, GoPhAST-R, that classifies strains with 94-99% accuracy by coupling machine learning analysis of early antibiotic-induced transcriptional changes with simultaneous detection of key genetic resistance determinants to increase accuracy of resistance detection, facilitate molecular epidemiology and enable early detection of emerging resistance mechanisms. This two-pronged approach provides phenotypic AST 24-36 h faster than standard workflows, with <4 h assay time on a pilot instrument for hybridization-based multiplexed RNA detection implemented directly from positive blood cultures.",1
"Application of a multi-material artifact reduction algorithm in a wide-detector CT in the evaluation of the portal venous angiography of postoperative TIPS and embolization. Objective: To assess the effect of monochromatic images and metal artifact reduction (MAR) on the image quality of spectral CT portal venous angiography in patients with operation of after the performing transjugular intrahepatic portosystemic stent shunt(TIPS) and embolization. Methods: From December 2017 to April 2018, the examination data of 28 patients with portal hypertension due to cirrhosis who underwent portal vein angiography 1 month after TIPS and embolization were prospectively collected. After spectral CT scanning in revolution CT, the monochromatic energy levels(60 keV, 120 keV), 60 keV + 120 keV, 120kV-like + 120 keV fused images combined with MAR algorithm were reconstructed. Quantitative parameters such as image artifact index (AI) and qualitative visual evaluation scores were recorded and compared. Results: The 120 keV monochromatic images showed the lowest AI value(30.8±8.5, 18.2±4.3) and highest metal artifacts reduction effect. The 60 keV monochromatic images showed the highest AI value (57.3±15.7, 32.1±7.9) and the lowest metal artifacts reduction effect. The AI value of 60 keV + 120 keV fused images was lower than that of 60 keV images(26.2%, 24.7%). The difference of AI value between each group was statistically significant(all P<0.05). The interobserver agreement in the subjective image scores was moderate with kappa value of 0.824. The overall image quality score of 60 keV + 120 keV fused image and the noise score of 120 kV-like+120 keV were higher than the remaining groups. The differences of the subjective scores among each group were statistically significant(all P<0.05). Conclusion: The spectral CT with MAR algorithm can effectively improve the image quality of portal vein angiography after the TIPS and embolization therapy and the 60 keV + 120 keV fused images can eliminate artifacts and ensure a clear display of blood vessels.",0
"Evaluating global and local sequence alignment methods for comparing patient medical records. BACKGROUND: Sequence alignment is a way of arranging sequences (e.g., DNA, RNA, protein, natural language, financial data, or medical events) to identify the relatedness between two or more sequences and regions of similarity. For Electronic Health Records (EHR) data, sequence alignment helps to identify patients of similar disease trajectory for more relevant and precise prognosis, diagnosis and treatment of patients. METHODS: We tested two cutting-edge global sequence alignment methods, namely dynamic time warping (DTW) and Needleman-Wunsch algorithm (NWA), together with their local modifications, DTW for Local alignment (DTWL) and Smith-Waterman algorithm (SWA), for aligning patient medical records. We also used 4 sets of synthetic patient medical records generated from a large real-world EHR database as gold standard data, to objectively evaluate these sequence alignment algorithms. RESULTS: For global sequence alignments, 47 out of 80 DTW alignments and 11 out of 80 NWA alignments had superior similarity scores than reference alignments while the rest 33 DTW alignments and 69 NWA alignments had the same similarity scores as reference alignments. Forty-six out of 80 DTW alignments had better similarity scores than NWA alignments with the rest 34 cases having the equal similarity scores from both algorithms. For local sequence alignments, 70 out of 80 DTWL alignments and 68 out of 80 SWA alignments had larger coverage and higher similarity scores than reference alignments while the rest DTWL alignments and SWA alignments received the same coverage and similarity scores as reference alignments. Six out of 80 DTWL alignments showed larger coverage and higher similarity scores than SWA alignments. Thirty DTWL alignments had the equal coverage but better similarity scores than SWA. DTWL and SWA received the equal coverage and similarity scores for the rest 44 cases. CONCLUSIONS: DTW, NWA, DTWL and SWA outperformed the reference alignments. DTW (or DTWL) seems to align better than NWA (or SWA) by inserting new daily events and identifying more similarities between patient medical records. The evaluation results could provide valuable information on the strengths and weakness of these sequence alignment methods for future development of sequence alignment methods and patient similarity-based studies.",0
"MRI-based radiomics signature for tumor grading of rectal carcinoma using random forest model. The present study aimed to construct prospective models for tumor grading of rectal carcinoma by using magnetic resonance (MR)-based radiomics features. A set of 118 patients with rectal carcinoma was analyzed. After imbalance-adjustments of the data using Synthetic Minority Oversampling Technique (SMOTE), the final data set was randomized into the training set and validation set at the ratio of 3:1. The radiomics features were captured from manually segmented lesion of magnetic resonance imaging (MRI). The most related radiomics features were selected using the random forest model by calculating the Gini importance of initial extracted characteristics. A random forest classifier model was constructed using the top important features. The classifier model performance was evaluated via receive operator characteristic curve and area under the curve (AUC). A total of 1,131 radiomics features were extracted from segmented lesion. The top 50 most important features were selected to construct a random forest classifier model. The AUC values of grade 1, 2, 3, and 4 for training set were 0.918, 0.822, 0.775, and 1.000, respectively, and the corresponding AUC values for testing set were 0.717, 0.683, 0.690, and 0.827 separately. The developed feature selection method and machine learning-based prediction models using radiomics features of MRI show a relatively acceptable performance in tumor grading of rectal carcinoma and could distinguish the tumor subjects from the healthy ones, which is important for the prognosis of cancer patients.",0
"The Kidney Failure Risk Equation for prediction of end stage renal disease in UK primary care: An external validation and clinical impact projection cohort study. BACKGROUND: The Kidney Failure Risk Equation (KFRE) uses the 4 variables of age, sex, urine albumin-to-creatinine ratio (ACR), and estimated glomerular filtration rate (eGFR) in individuals with chronic kidney disease (CKD) to predict the risk of end stage renal disease (ESRD), i.e., the need for dialysis or a kidney transplant, within 2 and 5 years. Currently, national guideline writers in the UK and other countries are evaluating the role of the KFRE in renal referrals from primary care to secondary care, but the KFRE has had limited external validation in primary care. The study's objectives were therefore to externally validate the KFRE's prediction of ESRD events in primary care, perform model recalibration if necessary, and assess its projected impact on referral rates to secondary care renal services. METHODS AND FINDINGS: Individuals with 2 or more Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) eGFR values < 60 ml/min/1.73 m2 more than 90 days apart and a urine ACR or protein-to-creatinine ratio measurement between 1 December 2004 and 1 November 2016 were included in the cohort. The cohort included 35,539 (5.6%) individuals (57.5% female, mean age 75.9 years, median CKD-EPI eGFR 51 ml/min/1.73 m2, median ACR 3.2 mg/mmol) from a total adult practice population of 630,504. Overall, 176 (0.50%) and 429 (1.21%) ESRD events occurred within 2 and 5 years, respectively. Median length of follow-up was 4.7 years (IQR 2.8 to 6.6). Model discrimination was excellent for both 2-year (C-statistic 0.932, 95% CI 0.909 to 0.954) and 5-year (C-statistic 0.924, 95% 0.909 to 0.938) ESRD prediction. The KFRE overpredicted risk in lower (<20%) risk groups. Reducing the model's baseline risk improved calibration for both 2- and 5-year risk for lower risk groups, but led to some underprediction of risk in higher risk groups. Compared to current criteria, using referral criteria based on a KFRE-calculated 5-year ESRD risk of >/=5% and/or an ACR of >/=70 mg/mmol reduced the number of individuals eligible for referral who did not develop ESRD, increased the likelihood of referral eligibility in those who did develop ESRD, and referred the latter at a younger age and with a higher eGFR. The main limitation of the current study is that the cohort is from one region of the UK and therefore may not be representative of primary care CKD care in other countries. CONCLUSIONS: In this cohort, the recalibrated KFRE accurately predicted the risk of ESRD at 2 and 5 years in primary care. Its introduction into primary care for referrals to secondary care renal services may lead to a reduction in unnecessary referrals, and earlier referrals in those who go on to develop ESRD. However, further validation studies in more diverse cohorts of the clinical impact projections and suggested referral criteria are required before the latter can be clinically implemented.",0
"Computer-aided assessment of liver fibrosis progression in patients with chronic hepatitis B: an exploratory research. Objective: To establish automatic liver fibrosis classification models by using traditional machine learning and deep learning methods and preliminaryly evaluate the efficiency. Methods: Gray scale ultrasound images and corresponding elastic images of 354 patients, 247 males and 107 females, mean age (54±12) years undergoing partial hepatectomy in Zhongshan Hospital of Fudan University from November 2014 to January 2016 were enrolled in this study. By using traditional machine learning and deep learning methods, an automatic classification model of liver fibrosis stages (S0 to S4) were established through feature extraction and classification of ultrasound image data sets and the accuracy in different classification categories of each model were calculated, by using liver biopsy as the reference standard. Results: Pathological examination showed 73 cases in pathological stage S0, 40 cases in S1, 49 cases in S2, 41 cases in S3, and 151 cases in S4. The traditional machine classification model based on support vector machine (SVM) classifier and sparse representation classifier and the deep learning classification model based on LeNet-5 neural network, their accuracy rates in the two categories (S0/S1/S2 and S3/S4) were 89.8%, 91.8% and 90.7% respectively; the accuracy rates in the three categories (S0/S1 and S2/S3 and S4) were 75.3%, 79.4% and 82.8% respectively; the accuracy in the three categories (S0 and S1/S2/S3 and S4) were 79.3%, 82.7% and 87.2% respectively. Conclusions: Computer-aided assessment of liver fibrosis progression in patients with chronic hepatitis B has a high accuracy, and can achieve a more detailed classification. This method is expected to be applied in the non-invasive evaluation of liver fibrosis in patients with hepatitis B in clinical work in the future.",0
"Simplified clinical algorithm for identifying patients eligible for same-day HIV treatment initiation (SLATE): Results from an individually randomized trial in South Africa and Kenya. BACKGROUND: The World Health Organization recommends ""same-day"" initiation of antiretroviral therapy (ART) for HIV patients who are eligible and ready. Identifying efficient, safe, and feasible procedures for determining same-day eligibility and readiness is now a priority. The Simplified Algorithm for Treatment Eligibility (SLATE) study evaluated a clinical algorithm that allows healthcare workers to determine eligibility for same-day treatment and to initiate ART at the patient's first clinic visit. METHODS AND FINDINGS: SLATE was an individually randomized trial at three outpatient clinics in urban settlements in Johannesburg, South Africa and three hospital clinics in western Kenya. Adult, nonpregnant, HIV-positive, ambulatory patients presenting for any HIV care, including HIV testing, but not yet on ART were enrolled and randomized to the SLATE algorithm arm or standard care. The SLATE algorithm used four screening tools-a symptom self-report, medical history questionnaire, physical examination, and readiness assessment-to ascertain eligibility for same-day initiation or refer for further care. Follow-up was by record review, and analysis was conducted by country. We report primary outcomes of 1) ART initiation </=28 days and 2) initiation </=28 days and retention in care </=8 months of enrollment. From March 7, 2017 to April 17, 2018, we enrolled 600 patients (median [IQR] age 34 [29-40] and CD4 count 286 [128-490]; 63% female) in South Africa and 477 patients in Kenya (median [IQR] age 35 [29-43] and CD4 count 283 [117-541]; 58% female). In the intervention arm, 78% of patients initiated </=28 days in South Africa, compared to 68% in the standard arm (risk difference [RD] [95% confidence interval (CI)] 10% [3%-17%]); in Kenya, 94% of intervention-arm patients initiated </=28 days compared to 89% in the standard arm (6% [0.5%-11%]). By 8 months in South Africa, 161/298 (54%) intervention-arm patients had initiated and were retained, compared to 146/302 (48%) in the standard arm (6% [(2% to 14%]). By 8 months in Kenya, the corresponding retention outcomes were identical in both arms (137/240 [57%] of intervention-arm patients and 136/237 [57%] of standard-arm patients). Limitations of the trial included limited geographic representativeness, exclusion of patients too ill to participate, missing viral load data, greater study fidelity to the algorithm than might be achieved in standard care, and secular changes in standard care over the course of the study. CONCLUSIONS: In South Africa, the SLATE algorithm increased uptake of ART within 28 days by 10% and showed a numerical increase (6%) in retention at 8 months. In Kenya, the algorithm increased uptake of ART within 28 days by 6% but found no difference in retention at 8 months. Eight-month retention was poor in both arms and both countries. These results suggest that a simple structured algorithm for same-day treatment initiation procedures is feasible and can increase and accelerate ART uptake but that early retention on treatment remains problematic. TRIAL REGISTRATION: Clinicaltrials.gov NCT02891135, registered September 1, 2016. First participant enrolled March 6, 2017 in South Africa and July 13, 2017 in Kenya.",0
"Survival outcome prediction in cervical cancer: Cox models vs deep-learning model. BACKGROUND: Historically, the Cox proportional hazard regression model has been the mainstay for survival analyses in oncologic research. The Cox proportional hazard regression model generally is used based on an assumption of linear association. However, it is likely that, in reality, there are many clinicopathologic features that exhibit a nonlinear association in biomedicine. OBJECTIVE: The purpose of this study was to compare the deep-learning neural network model and the Cox proportional hazard regression model in the prediction of survival in women with cervical cancer. STUDY DESIGN: This was a retrospective pilot study of consecutive cases of newly diagnosed stage I-IV cervical cancer from 2000-2014. A total of 40 features that included patient demographics, vital signs, laboratory test results, tumor characteristics, and treatment types were assessed for analysis and grouped into 3 feature sets. The deep-learning neural network model was compared with the Cox proportional hazard regression model and 3 other survival analysis models for progression-free survival and overall survival. Mean absolute error and concordance index were used to assess the performance of these 5 models. RESULTS: There were 768 women included in the analysis. The median age was 49 years, and the majority were Hispanic (71.7%). The majority of tumors were squamous (75.3%) and stage I (48.7%). The median follow-up time was 40.2 months; there were 241 events for recurrence and progression and 170 deaths during the follow-up period. The deep-learning model showed promising results in the prediction of progression-free survival when compared with the Cox proportional hazard regression model (mean absolute error, 29.3 vs 316.2). The deep-learning model also outperformed all the other models, including the Cox proportional hazard regression model, for overall survival (mean absolute error, Cox proportional hazard regression vs deep-learning, 43.6 vs 30.7). The performance of the deep-learning model further improved when more features were included (concordance index for progression-free survival: 0.695 for 20 features, 0.787 for 36 features, and 0.795 for 40 features). There were 10 features for progression-free survival and 3 features for overall survival that demonstrated significance only in the deep-learning model, but not in the Cox proportional hazard regression model. There were no features for progression-free survival and 3 features for overall survival that demonstrated significance only in the Cox proportional hazard regression model, but not in the deep-learning model. CONCLUSION: Our study suggests that the deep-learning neural network model may be a useful analytic tool for survival prediction in women with cervical cancer because it exhibited superior performance compared with the Cox proportional hazard regression model. This novel analytic approach may provide clinicians with meaningful survival information that potentially could be integrated into treatment decision-making and planning. Further validation studies are necessary to support this pilot study.",1
"Recurrent inference machines for reconstructing heterogeneous MRI data. Deep learning allows for accelerated magnetic resonance image (MRI) reconstruction, thereby shortening measurement times. Rather than using sparsifying transforms, a prerequisite in Compressed Sensing (CS), suitable MRI prior distributions are learned from data. In clinical practice, both the underlying anatomy as well as image acquisition settings vary. For this reason, deep neural networks must be able to reapply what they learn across different measurement conditions. We propose to use Recurrent Inference Machines (RIM) as a framework for accelerated MRI reconstruction. RIMs solve inverse problems in an iterative and recurrent inference procedure by repeatedly reassessing the state of their reconstruction, and subsequently making incremental adjustments to it in accordance with the forward model of accelerated MRI. RIMs learn the inferential process of reconstructing a given signal, which, in combination with the use of internal states as part of their recurrent architecture, makes them less dependent on learning the features pertaining to the source of the signal itself. This gives RIMs a low tendency to overfit, and a high capacity to generalize to unseen types of data. We demonstrate this ability with respect to anatomy by reconstructing brain and knee scans, as well as other MRI acquisition settings, by reconstructing scans of different contrast and resolution, at different field strength, subjected to varying acceleration levels. We show that RIMs outperform CS not only with respect to quality metrics, but also according to a rating given by an experienced neuroradiologist in a double blinded experiment. Finally, we show with qualitative results that our model can be applied to prospectively under-sampled raw data, as acquired by pre-installed acquisition protocols.",0
"Mapping Spatiotemporal Microproteomics Landscape in Experimental Model of Traumatic Brain Injury Unveils a link to Parkinson’s Disease. Traumatic brain injury (TBI) represents a major health concerns with no clinically-approved FDA drug available for therapeutic intervention. Several genomics and neuroproteomics studies have been employed to decipher the underlying pathological mechanisms involved that can serve as potential neurotherapeutic targets and unveil a possible underlying relation of TBI to other secondary neurological disorders. In this work, we present a novel high throughput systems biology approach using a spatially resolved microproteomics platform conducted on different brain regions in an experimental rat model of moderate of controlled cortical injury (CCI) at a temporal pattern postinjury (1 day, 3 days, 7 days, and 10 days). Mapping the spatiotemporal landscape of signature markers in TBI revealed an overexpression of major protein families known to be implicated in Parkinson’s disease (PD) such as GPR158, HGMB1, synaptotagmin and glutamate decarboxylase in the ipsilateral substantia nigra. In silico bioinformatics docking experiments indicated the potential correlation between TBI and PD through alpha-synuclein. In an in vitro model, stimulation with palmitoylcarnitine triggered an inflammatory response in macrophages and a regeneration processes in astrocytes which also further confirmed the in vivo TBI proteomics data. Taken together, this is the first study to assess the microproteomics landscape in TBI, mainly in the substantia nigra, thus revealing a potential predisposition for PD or Parkinsonism post-TBI.",0
"Transcriptomic and Single-Cell Analysis of the Murine Parotid Gland. The salivary complex of mammals consists of 3 major pairs of glands: the parotid, submandibular, and sublingual glands. While the 3 glands share similar functional properties, such as saliva secretion, their differences are largely based on the types of secretions they produce. While recent studies have begun to shed light on the underlying molecular differences among the glands, few have examined the global transcriptional repertoire over various stages of gland maturation. To better elucidate the molecular nature of the parotid gland, we have performed RNA sequencing to generate comprehensive and global gene expression profiles of this gland at different stages of maturation. Our transcriptomic characterization and hierarchical clustering analysis with adult organ RNA sequencing data sets has identified a number of molecular players and pathways that are relevant for parotid gland biology. Moreover, our detailed analysis has revealed a unique parotid gland-specific gene signature that may represent important players that could impart parotid gland-specific biological properties. To complement our transcriptomic studies, we have performed single-cell RNA sequencing to map the transcriptomes of parotid epithelial cells. Interrogation of the single-cell transcriptomes revealed the degree of molecular and cellular heterogeneity of the various epithelial cell types within the parotid gland. Moreover, we uncovered a mixed-lineage population of cells that may reflect molecular priming of differentiation potentials. Overall our comprehensive studies provide a powerful tool for the discovery of novel molecular players important in parotid gland biology.",0
"Multiple-correlation similarity for block-matching based fast CT to ultrasound registration in liver interventions. In this work we present a fast approach to perform registration of computed tomography to ultrasound volumes for image guided intervention applications. The method is based on a combination of block-matching and outlier rejection. The block-matching uses a correlation based multimodal similarity metric, where the intensity and the gradient of the computed tomography images along with the ultrasound volumes are the input images to find correspondences between blocks in the computed tomography and the ultrasound volumes. A variance and octree based feature point-set selection method is used for selecting distinct and evenly spread point locations for block-matching. Geometric consistency and smoothness criteria are imposed in an outlier rejection step to refine the block-matching results. The block-matching results after outlier rejection are used to determine the affine transformation between the computed tomography and the ultrasound volumes. Various experiments are carried out to assess the optimal performance and the influence of parameters on accuracy and computational time of the registration. A leave-one-patient-out cross-validation registration error of 3.6 mm is achieved over 29 datasets, acquired from 17 patients.",0
"Development and Validation of a Deep Learning System to Detect Glaucomatous Optic Neuropathy Using Fundus Photographs. Importance: A deep learning system (DLS) that could automatically detect glaucomatous optic neuropathy (GON) with high sensitivity and specificity could expedite screening for GON. Objective: To establish a DLS for detection of GON using retinal fundus images and glaucoma diagnosis with convoluted neural networks (GD-CNN) that has the ability to be generalized across populations. Design, Setting, and Participants: In this cross-sectional study, a DLS for the classification of GON was developed for automated classification of GON using retinal fundus images obtained from the Chinese Glaucoma Study Alliance, the Handan Eye Study, and online databases. The researchers selected 241 032 images were selected as the training dataset. The images were entered into the databases on June 9, 2009, obtained on July 11, 2018, and analyses were performed on December 15, 2018. The generalization of the DLS was tested in several validation datasets, which allowed assessment of the DLS in a clinical setting without exclusions, testing against variable image quality based on fundus photographs obtained from websites, evaluation in a population-based study that reflects a natural distribution of patients with glaucoma within the cohort and an additive dataset that has a diverse ethnic distribution. An online learning system was established to transfer the trained and validated DLS to generalize the results with fundus images from new sources. To better understand the DLS decision-making process, a prediction visualization test was performed that identified regions of the fundus images utilized by the DLS for diagnosis. Exposures: Use of a deep learning system. Main Outcomes and Measures: Area under the receiver operating characteristics curve (AUC), sensitivity and specificity for DLS with reference to professional graders. Results: From a total of 274 413 fundus images initially obtained from CGSA, 269 601 images passed initial image quality review and were graded for GON. A total of 241 032 images (definite GON 29 865 [12.4%], probable GON 11 046 [4.6%], unlikely GON 200 121 [83%]) from 68 013 patients were selected using random sampling to train the GD-CNN model. Validation and evaluation of the GD-CNN model was assessed using the remaining 28 569 images from CGSA. The AUC of the GD-CNN model in primary local validation datasets was 0.996 (95% CI, 0.995-0.998), with sensitivity of 96.2% and specificity of 97.7%. The most common reason for both false-negative and false-positive grading by GD-CNN (51 of 119 [46.3%] and 191 of 588 [32.3%]) and manual grading (50 of 113 [44.2%] and 183 of 538 [34.0%]) was pathologic or high myopia. Conclusions and Relevance: Application of GD-CNN to fundus images from different settings and varying image quality demonstrated a high sensitivity, specificity, and generalizability for detecting GON. These findings suggest that automated DLS could enhance current screening programs in a cost-effective and time-efficient manner.",1
"Mitigation of T-cell dependent immunogenicity by reengineering factor VIIa analogue. Vatreptacog alfa (VA), a recombinant activated human factor VII (rFVIIa) variant with 3 amino acid substitutions, was developed to provide increased procoagulant activity in hemophilia patients with inhibitors to factor VIII or factor IX. In phase 3 clinical trials, changes introduced during the bioengineering of VA resulted in the development of undesired anti-drug antibodies in some patients, leading to the termination of a potentially promising therapeutic protein product. Here, we use preclinical biomarkers associated with clinical immunogenicity to validate our deimmunization strategy applied to this bioengineered rFVIIa analog. The reengineered rFVIIa analog variants retained increased intrinsic thrombin generation activity but did not elicit T-cell responses in peripheral blood mononuclear cells isolated from 50 HLA typed subjects representing the human population. Our algorithm, rational immunogenicity determination, offers a broadly applicable deimmunizing strategy for bioengineered proteins.",0
"Performance of a deep learning model vs human reviewers in grading endoscopic disease severity of patients with ulcerative colitis. IMPORTANCE Assessing endoscopic disease severity in ulcerative colitis (UC) is a key element in determining therapeutic response, but its use in clinical practice is limited by the requirement for experienced human reviewers. OBJECTIVE To determine whether deep learning models can grade the endoscopic severity of UC as well as experienced human reviewers. DESIGN, SETTING, AND PARTICIPANTS In this diagnostic study, retrospective grading of endoscopic images using the 4-levelMayo subscorewas performed by 2 independent reviewers with score discrepancies adjudicated by a third reviewer. Using 16 514 images from 3082 patients with UC who underwent colonoscopy at a single tertiary care referral center in the United States between January 1, 2007, and December 31, 2017, a 159-layer convolutional neural network (CNN) was constructed as a deep learning model to train and categorize images into 2 clinically relevant groups: Remission (Mayo subscore 0 or 1) and moderate to severe disease (Mayo subscore, 2 or 3). Ninety percent of the cohort was used to build the model and 10% was used to test it; the process was repeated 10 times. A set of 30 full-motion colonoscopy videos, unseen by the model, was then used for external validation to mimic real-world application. MAIN OUTCOMES AND MEASURES Model performance was assessed using area under the receiver operating curve (AUROC), sensitivity and specificity, positive predictive value (PPV), and negative predictive value (NPV). Kappa statistics (κ) were used to measure agreement of the CNN relative to adjudicated human reference cores. RESULTS The authors included 16 514 images from 3082 unique patients (median [IQR] age, 41.3 [26.1-61.8] years, 1678 [54.4%] female), with 3980 images (24.1%) classified as moderate-to-severe disease by the adjudicated reference score. The CNN was excellent for distinguishing endoscopic remission from moderate-to-severe disease with an AUROC of 0.966 (95%CI, 0.967-0.972); a PPV of 0.87 (95%CI, 0.85-0.88) with a sensitivity of 83.0% (95%CI, 80.8%-85.4%) and specificty of 96.0%(95%CI, 95.1%-97.1%); and NPV of 0.94 (95%CI, 0.93-0.95).Weighted κ agreement between the CNN and the adjudicated reference score was also good for identifying exactMayo subscores (κ = 0.84; 95%CI, 0.83-0.86) and was similar to the agreement between experienced reviewers (κ = 0.86; 95%CI, 0.85-0.87). Applying the CNN to entire colonoscopy videos had similar accuracy for identifying moderate to severe disease (AUROC, 0.97; 95%CI, 0.963-0.969). CONCLUSIONS AND RELEVANCE This study found that deep learning model performance was similar to experienced human reviewers in grading endoscopic severity of UC. Given its scalability, this approach could improve the use of colonoscopy for UC in both research and routine practice.",1
"Emerging Applications of Artificial Intelligence in Neuro-Oncology. Due to the exponential growth of computational algorithms, artificial intelligence (AI) methods are poised to improve the precision of diagnostic and therapeutic methods in medicine. The field of radiomics in neuro-oncology has been and will likely continue to be at the forefront of this revolution. A variety of AI methods applied to conventional and advanced neuro-oncology MRI data can already delineate infiltrating margins of diffuse gliomas, differentiate pseudoprogression from true progression, and predict recurrence and survival better than methods used in daily clinical practice. Radiogenomics will also advance our understanding of cancer biology, allowing noninvasive sampling of the molecular environment with high spatial resolution and providing a systems-level understanding of underlying heterogeneous cellular and molecular processes. By providing in vivo markers of spatial and molecular heterogeneity, these AI-based radiomic and radiogenomic tools have the potential to stratify patients into more precise initial diagnostic and therapeutic pathways and enable better dynamic treatment monitoring in this era of personalized medicine. Although substantial challenges remain, radiologic practice is set to change considerably as AI technology is further developed and validated for clinical use.",0
"Artificial Intelligence and Machine Learning in Anesthesiology. Commercial applications of artificial intelligence and machine learning have made remarkable progress recently, particularly in areas such as image recognition, natural speech processing, language translation, textual analysis, and self-learning. Progress had historically languished in these areas, such that these skills had come to seem ineffably bound to intelligence. However, these commercial advances have performed best at single-task applications in which imperfect outputs and occasional frank errors can be tolerated.The practice of anesthesiology is different. It embodies a requirement for high reliability, and a pressured cycle of interpretation, physical action, and response rather than any single cognitive act. This review covers the basics of what is meant by artificial intelligence and machine learning for the practicing anesthesiologist, describing how decision-making behaviors can emerge from simple equations. Relevant clinical questions are introduced to illustrate how machine learning might help solve them-perhaps bringing anesthesiology into an era of machine-assisted discovery.",0
"The binding affinity of PTPN13's tandem PDZ2/3 domain is allosterically modulated. Background: Protein tyrosine phosphatase PTPN13, also known as PTP-BL in mice, is a large multi-domain non-transmembrane scaffolding protein with a molecular mass of 270 kDa. It is involved in the regulation of several cellular processes such as cytokinesis and actin-cytoskeletal rearrangement. The modular structure of PTPN13 consists of an N-terminal KIND domain, a FERM domain, and five PDZ domains, followed by a C-terminal protein tyrosine phosphatase domain. PDZ domains are among the most abundant protein modules and they play a crucial role in signal transduction of protein networks. Results: Here, we have analysed the binding characteristics of the isolated PDZ domains 2 and 3 from PTPN13 and compared them to the tandem domain PDZ2/3, which interacts with 12 C-terminal residues of the tumour suppressor protein of APC, using heteronuclear multidimensional NMR spectroscopy. Furthermore, we could show for the first time that PRK2 is a weak binding partner of PDZ2 and we demonstrate that the presence of PDZ3 alters the binding affinity of PDZ2 for APC, suggesting an allosteric effect and thereby modulating the binding characteristics of PDZ2. A HADDOCK-based molecular model of the PDZ2/3 tandem domain from PTPN13 supports these results. Conclusions: Our study of tandem PDZ2/3 in complex with APC suggests that the interaction of PDZ3 with PDZ2 induces an allosteric modulation within PDZ2 emanating from the back of the domain to the ligand binding site. Thus, the modified binding preference of PDZ2 for APC could be explained by an allosteric effect and provides further evidence for the pivotal function of PDZ2 in the PDZ123 domain triplet within PTPN13.",0
"A space-variant visual pathway model for data efficient deep learning. We present an investigation into adopting a model of the retino-cortical mapping, found in biological visual systems, to improve the efficiency of image analysis using Deep Convolutional Neural Nets (DCNNs) in the context of robot vision and egocentric perception systems. This work has now enabled DCNNs to process input images approaching one million pixels in size, in real time, using only consumer grade graphics processor (GPU) hardware in a single pass of the DCNN.",1
"Characterization of Parkinson's disease using blood-based biomarkers: A multicohort proteomic analysis. BACKGROUND: Parkinson's disease (PD) is a progressive neurodegenerative disease affecting about 5 million people worldwide with no disease-modifying therapies. We sought blood-based biomarkers in order to provide molecular characterization of individuals with PD for diagnostic confirmation and prediction of progression. METHODS AND FINDINGS: In 141 plasma samples (96 PD, 45 neurologically normal control [NC] individuals; 45.4% female, mean age 70.0 years) from a longitudinally followed Discovery Cohort based at the University of Pennsylvania (UPenn), we measured levels of 1,129 proteins using an aptamer-based platform. We modeled protein plasma concentration (log10 of relative fluorescence units [RFUs]) as the effect of treatment group (PD versus NC), age at plasma collection, sex, and the levodopa equivalent daily dose (LEDD), deriving first-pass candidate protein biomarkers based on p-value for PD versus NC. These candidate proteins were then ranked by Stability Selection. We confirmed findings from our Discovery Cohort in a Replication Cohort of 317 individuals (215 PD, 102 NC; 47.9% female, mean age 66.7 years) from the multisite, longitudinally followed National Institute of Neurological Disorders and Stroke Parkinson's Disease Biomarker Program (PDBP) Cohort. Analytical approach in the Replication Cohort mirrored the approach in the Discovery Cohort: each protein plasma concentration (log10 of RFU) was modeled as the effect of group (PD versus NC), age at plasma collection, sex, clinical site, and batch. Of the top 10 proteins from the Discovery Cohort ranked by Stability Selection, four associations were replicated in the Replication Cohort. These blood-based biomarkers were bone sialoprotein (BSP, Discovery false discovery rate [FDR]-corrected p = 2.82 x 10-2, Replication FDR-corrected p = 1.03 x 10-4), osteomodulin (OMD, Discovery FDR-corrected p = 2.14 x 10-2, Replication FDR-corrected p = 9.14 x 10-5), aminoacylase-1 (ACY1, Discovery FDR-corrected p = 1.86 x 10-3, Replication FDR-corrected p = 2.18 x 10-2), and growth hormone receptor (GHR, Discovery FDR-corrected p = 3.49 x 10-4, Replication FDR-corrected p = 2.97 x 10-3). Measures of these proteins were not significantly affected by differences in sample handling, and they did not change comparing plasma samples from 10 PD participants sampled both on versus off dopaminergic medication. Plasma measures of OMD, ACY1, and GHR differed in PD versus NC but did not differ between individuals with amyotrophic lateral sclerosis (ALS, n = 59) versus NC. In the Discovery Cohort, individuals with baseline levels of GHR and ACY1 in the lowest tertile were more likely to progress to mild cognitive impairment (MCI) or dementia in Cox proportional hazards analyses adjusting for age, sex, and disease duration (hazard ratio [HR] 2.27 [95% CI 1.04-5.0, p = 0.04] for GHR, and HR 3.0 [95% CI 1.24-7.0, p = 0.014] for ACY1). GHR's association with cognitive decline was confirmed in the Replication Cohort (HR 3.6 [95% CI 1.20-11.1, p = 0.02]). The main limitations of this study were its reliance on the aptamer-based platform for protein measurement and limited follow-up time available for some cohorts. CONCLUSIONS: In this study, we found that the blood-based biomarkers BSP, OMD, ACY1, and GHR robustly associated with PD across multiple clinical sites. Our findings suggest that biomarkers based on a peripheral blood sample may be developed for both disease characterization and prediction of future disease progression in PD.",0
"Identification of a novel glycolysis-related gene signature that can predict the survival of patients with lung adenocarcinoma. Lung cancer is one of the most malignant cancers worldwide, and lung adenocarcinoma (LUAD) is the most common histologic subtype. Thousands of biomarkers related to the survival and prognosis of patients with this cancer type have been investigated through database mining; however, the prediction effect of a single gene biomarker is not satisfactorily specific or sensitive. Thus, the present study aimed to develop a novel gene signature of prognostic values for patients with LUAD. Using a data-mining method, we performed expression profiling of 1145 mRNAs in large cohorts with LUAD (n = 511) from The Cancer Genome Atlas database. Using the Gene Set Enrichment Analysis, we selected 198 genes related to GLYCOLYSIS, which is the most important enrichment gene set. Moreover, these genes were identified using Cox proportional regression modeling. We established a risk score staging system to predict the outcome of patients with LUAD and subsequently identified four genes (AGRN, AKR1A1, DDIT4, and HMMR) that were closely related to the prognosis of patients with LUAD. The identified genes allowed us to classify patients into the high-risk group (with poor outcome) and low-risk group (with better outcome). Compared with other clinical factors, the risk score has a better performance in predicting the outcome of patients with LUAD, particularly in the early stage of LUAD. In conclusion, we developed a four-gene signature related to glycolysis by utilizing the Cox regression model and a risk staging model for LUAD, which might prove valuable for the clinical management of patients with LUAD.",0
"Context-sensitive network analysis identifies food metabolites associated with Alzheimer's disease: An exploratory study. Background: Diet plays an important role in Alzheimer's disease (AD) initiation, progression and outcomes. Previous studies have shown individual food-derived substances may have neuroprotective or neurotoxic effects. However, few works systematically investigate the role of food and food-derived metabolites on the development and progression of AD. Methods: In this study, we systematically investigated 7569 metabolites and identified AD-associated food metabolites using a novel network-based approach. We constructed a context-sensitive network to integrate heterogeneous chemical and genetic data, and to model context-specific inter-relationships among foods, metabolites, human genes and AD. Results: Our metabolite prioritization algorithm ranked 59 known AD-associated food metabolites within top 4.9%, which is significantly higher than random expectation. Interestingly, a few top-ranked food metabolites were specifically enriched in herbs and spices. Pathway enrichment analysis shows that these top-ranked herb-and-spice metabolites share many common pathways with AD, including the amyloid processing pathway, which is considered as a hallmark in AD-affected brains and has pathological roles in AD development. Conclusions: Our study represents the first unbiased systems approach to characterizing the effects of food and food-derived metabolites in AD pathogenesis. Our ranking approach prioritizes the known AD-associated food metabolites, and identifies interesting relationships between AD and the food group ""herbs and spices"". Overall, our study provides intriguing evidence for the role of diet, as an important environmental factor, in AD etiology.",0
"MILD-Net: Minimal information loss dilated network for gland instance segmentation in colon histology images. The analysis of glandular morphology within colon histopathology images is an important step in determining the grade of colon cancer. Despite the importance of this task, manual segmentation is laborious, time-consuming and can suffer from subjectivity among pathologists. The rise of computational pathology has led to the development of automated methods for gland segmentation that aim to overcome the challenges of manual segmentation. However, this task is non-trivial due to the large variability in glandular appearance and the difficulty in differentiating between certain glandular and non-glandular histological structures. Furthermore, a measure of uncertainty is essential for diagnostic decision making. To address these challenges, we propose a fully convolutional neural network that counters the loss of information caused by max-pooling by re-introducing the original image at multiple points within the network. We also use atrous spatial pyramid pooling with varying dilation rates for preserving the resolution and multi-level aggregation. To incorporate uncertainty, we introduce random transformations during test time for an enhanced segmentation result that simultaneously generates an uncertainty map, highlighting areas of ambiguity. We show that this map can be used to define a metric for disregarding predictions with high uncertainty. The proposed network achieves state-of-the-art performance on the GlaS challenge dataset and on a second independent colorectal adenocarcinoma dataset. In addition, we perform gland instance segmentation on whole-slide images from two further datasets to highlight the generalisability of our method. As an extension, we introduce MILD-Net(+) for simultaneous gland and lumen segmentation, to increase the diagnostic power of the network.",1
"Motion artifact recognition and quantification in coronary CT angiography using convolutional neural networks. Excellent image quality is a primary prerequisite for diagnostic non-invasive coronary CT angiography. Artifacts due to cardiac motion may interfere with detection and diagnosis of coronary artery disease and render subsequent treatment decisions more difficult. We propose deep-learning-based measures for coronary motion artifact recognition and quantification in order to assess the diagnostic reliability and image quality of coronary CT angiography images. More specifically, the application, steering and evaluation of motion compensation algorithms can be triggered by these measures. A Coronary Motion Forward Artifact model for CT data (CoMoFACT) is developed and applied to clinical cases with excellent image quality to introduce motion artifacts using simulated motion vector fields. The data required for supervised learning is generated by the CoMoFACT from 17 prospectively ECG-triggered clinical cases with controlled motion levels on a scale of 0-10. Convolutional neural networks achieve an accuracy of 93.3%+/-1.8% for the classification task of separating motion-free from motion-perturbed coronary cross-sectional image patches. The target motion level is predicted by a corresponding regression network with a mean absolute error of 1.12+/-0.07. Transferability and generalization capabilities are demonstrated by motion artifact measurements on eight additional CCTA cases with real motion artifacts.",1
"miR-148-3p and miR-152-3p synergistically regulate prostate cancer progression via repressing KLF4. Background: miR-148-3p and miR-152-3p as the tumor suppressors have been reported in various cancer types. Our study is aimed to discuss the synergistic effect of miR-148-3p and miR-152-3p in prostate cancer (PCa). Methods: Bioinformatics algorithm and luciferase reporter assays were used to verify whether miR-148-3p and 152-3p could bind with the 3′-untranslated region (3′-UTR) of Kruppel-like factor 4 (KLF4). PCa cell growth in vivo was analyzed using the mouse xenograft tumor model. Results: miR-148-3p and miR-152-3p were reduced in PCa tumor tissues. Moreover, the protein expression of KLF4 was increased in PCa tissues. The 3′-UTR of KLF4 contained the conserved binding sites with miR-148-3p and miR-152-3p. The mimics or inhibitors of miR-148-3p and/or miR-152-3p could downregulated or upregulated KLF4 expression, respectively. miR-148-3p and miR-152-3p-induced PCa cell growth inhibition were observed both in vivo and in vitro. KLF4 overexpression had the ability to neutralize the antitumor effect of miR-148-3p/152-3p in vivo and in vitro. Conclusion: miR-148-3p/152-3p family could serve as tumor suppressors in PCa via repressing KLF4.",0
"Multi-omic Profiling Reveals Dynamics of the Phased Progression of Pluripotency. Cell signaling underlies transcriptional and/or epigenetic control of a vast majority of cell fate decisions during early embryonic development. Yet, the sequence of molecular and signaling events underlying pre- to post-implantation epiblast differentiation remains poorly understood. Through comprehensive mapping of the proteome, phosphoproteome, transcriptome, and epigenome of embryonic stem cells transitioning from naive to primed pluripotency, Yang et al. shed new insights on the multi-layered control of the phased progression of pluripotency.",0
"Identifying Extrinsic versus Intrinsic Drivers of Variation in Cell Behavior in Human iPSC Lines from Healthy Donors. Large cohorts of human induced pluripotent stem cells (iPSCs) from healthy donors are a potentially powerful tool for investigating the relationship between genetic variants and cellular behavior. Here, we integrate high content imaging of cell shape, proliferation, and other phenotypes with gene expression and DNA sequence datasets from over 100 human iPSC lines. By applying a dimensionality reduction approach, Probabilistic Estimation of Expression Residuals (PEER), we extracted factors that captured the effects of intrinsic (genetic concordance between different cell lines from the same donor) and extrinsic (cell responses to different fibronectin concentrations) conditions. We identify genes that correlate in expression with intrinsic and extrinsic PEER factors and associate outlier cell behavior with genes containing rare deleterious non-synonymous SNVs. Our study, thus, establishes a strategy for examining the genetic basis of inter-individual variability in cell behavior. Cell behavior reflects both the intrinsic state of the cell and extrinsic signals it receives from its microenvironment. By integrating genomic, gene expression, and cell biology datasets from a large number of human iPSCs from healthy donors, Vigilante et al. show how genetic variation contributes to phenotypic variation.",0
"Detecting virus integration sites based on multiple related sequencing data by VirTect. Background: Since tumor often has a high level of intra-tumor heterogeneity, multiple tumor samples from the same patient at different locations or different time points are often sequenced to study tumor intra-heterogeneity or tumor evolution. In virus-related tumors such as human papillomavirus- and Hepatitis B Virus-related tumors, virus genome integrations can be critical driving events. It is thus important to investigate the integration sites of the virus genomes. Currently, a few algorithms for detecting virus integration sites based on high-throughput sequencing have been developed, but their insufficient performance in their sensitivity, specificity and computational complexity hinders their applications in multiple related tumor sequencing. Results: We develop VirTect for detecting virus integration sites simultaneously from multiple related-sample data. This algorithm is mainly based on the joint analysis of short reads spanning breakpoints of integration sites from multiple samples. To achieve high specificity and breakpoint accuracy, a local precise sandwich alignment algorithm is used. Simulation and real data analyses show that, compared with other algorithms, VirTect is significantly more sensitive and has a similar or lower false discovery rate. Conclusions: VirTect can provide more accurate breakpoint position and is computationally much more efficient in terms both memory requirement and computational time.",0
"The association between deep learning approach and case based learning. Being medical students, and having experienced different learning approaches ourselves, here, we discuss and critically analyse the importance of the deep learning approach that Chonkar et al. have presented, alongside emphasizing Case Based Learning, and their roles in life long medical learning.",0
"Molecular Basis for Ligand Modulation of a Mammalian Voltage-Gated Ca2+ Channel. The L-type voltage-gated Ca2+ (Cav) channels are modulated by various compounds exemplified by 1,4-dihydropyridines (DHP), benzothiazepines (BTZ), and phenylalkylamines (PAA), many of which have been used for characterizing channel properties and for treatment of hypertension and other disorders. Here, we report the cryoelectron microscopy (cryo-EM) structures of Cav1.1 in complex with archetypal antagonistic drugs, nifedipine, diltiazem, and verapamil, at resolutions of 2.9 Ã…, 3.0 Ã…, and 2.7 Ã…, respectively, and with a DHP agonist Bay K 8644 at 2.8 Ã…. Diltiazem and verapamil traverse the central cavity of the pore domain, directly blocking ion permeation. Although nifedipine and Bay K 8644 occupy the same fenestration site at the interface of repeats III and IV, the coordination details support previous functional observations that Bay K 8644 is less favored in the inactivated state. These structures elucidate the modes of action of different Cav ligands and establish a framework for structure-guided drug discovery. A view on how both agonists and antagonists interact with a voltage-gated calcium channel opens up avenues for understanding channel gating and new ligand design.",0
"From Machine to Machine: An OCT-Trained Deep Learning Algorithm for Objective Quantification of Glaucomatous Damage in Fundus Photographs. PURPOSE: Previous approaches using deep learning (DL) algorithms to classify glaucomatous damage on fundus photographs have been limited by the requirement for human labeling of a reference training set. We propose a new approach using quantitative spectral-domain (SD) OCT data to train a DL algorithm to quantify glaucomatous structural damage on optic disc photographs. DESIGN: Cross-sectional study. PARTICIPANTS: A total of 32 820 pairs of optic disc photographs and SD OCT retinal nerve fiber layer (RNFL) scans from 2312 eyes of 1198 participants. METHODS: The sample was divided randomly into validation plus training (80%) and test (20%) sets, with randomization performed at the patient level. A DL convolutional neural network was trained to assess optic disc photographs and predict SD OCT average RNFL thickness. MAIN OUTCOME MEASURES: The DL algorithm performance was evaluated in the test sample by evaluating correlation and agreement between the predictions and actual SD OCT measurements. We also assessed the ability to discriminate eyes with glaucomatous visual field loss from healthy eyes with area under the receiver operating characteristic (ROC) curves. RESULTS: The mean prediction of average RNFL thickness from all 6292 optic disc photographs in the test set was 83.3+/-14.5 mum, whereas the mean average RNFL thickness from all corresponding SD OCT scans was 82.5+/-16.8 mum (P = 0.164). There was a very strong correlation between predicted and observed RNFL thickness values (Pearson r = 0.832; R(2) = 69.3%; P < 0.001), with mean absolute error of the predictions of 7.39 mum. The area under the ROC curves for discriminating glaucomatous from healthy eyes with the DL predictions and actual SD OCT average RNFL thickness measurements were 0.944 (95% confidence interval [CI], 0.912-0.966) and 0.940 (95% CI, 0.902-0.966), respectively (P = 0.724). CONCLUSIONS: We introduced a novel DL approach to assess fundus photographs and provide quantitative information about the amount of neural damage that can be used to diagnose and stage glaucoma. In addition, training neural networks to predict SD OCT data objectively represents a new approach that overcomes limitations of human labeling and could be useful in other areas of ophthalmology.",1
"Machine-learning-based patient-specific prediction models for knee osteoarthritis. Osteoarthritis (OA) is an extremely common musculoskeletal disease. However, current guidelines are not well suited for diagnosing patients in the early stages of disease and do not discriminate patients for whom the disease might progress rapidly. The most important hurdle in OA management is identifying and classifying patients who will benefit most from treatment. Further efforts are needed in patient subgrouping and developing prediction models. Conventional statistical modelling approaches exist; however, these models are limited in the amount of information they can adequately process. Comprehensive patient-specific prediction models need to be developed. Approaches such as data mining and machine learning should aid in the development of such models. Although a challenging task, technology is now available that should enable subgrouping of patients with OA and lead to improved clinical decision-making and precision medicine.",0
"Identifying Epistasis in Cancer Genomes: A Delicate Affair. Recent studies of the tumor genome seek to identify cancer pathways as groups of genes in which mutations are epistatic with one another or, specifically, ""mutually exclusive."" Here, we show that most mutations are mutually exclusive not due to pathway structure but to interactions with disease subtype and tumor mutation load. In particular, many cancer driver genes are mutated preferentially in tumors with few mutations overall, causing mutations in these cancer genes to appear mutually exclusive with numerous others. Researchers should view current epistasis maps with caution until we better understand the multiple cause-and-effect relationships among factors such as tumor subtype, positive selection for mutations, and gross tumor characteristics including mutational signatures and load.",0
"Protein modeling to assess the pathogenicity of rare variants of SERPINA1 in patients suspected of having Alpha 1 Antitrypsin Deficiency. Background: Alpha 1 Antitrypsin (AAT) is a key serum proteinase inhibitor encoded by SERPINA1. Sequence variants of the gene can cause Alpha 1 Antitrypsin Deficiency (AATD), a condition associated with lung and liver disease. The majority of AATD cases are caused by the 'Z' and 'S' variants - single-nucleotide variations (SNVs) that result in amino acid substitutions of E342K and E264V. However, SERPINA1 is highly polymorphic, with numerous potentially clinically relevant variants reported. Novel variants continue to be discovered, and without reports of pathogenicity, it can be difficult for clinicians to determine the best course of treatment. Methods: We assessed the utility of next-generation sequencing (NGS) and predictive computational analysis to guide the diagnosis of patients suspected of having AATD. Blood samples on serum separator cards were submitted to the DNA1 Advanced Screening Program (Biocerna LLC, Fulton, Maryland, USA) by physicians whose patients were suspected of having AATD. Laboratory analyses included quantification of serum AAT levels, qualitative analysis by isoelectric focusing, and targeted genotyping and NGS of the SERPINA1 gene. Molecular modeling software UCSF Chimera (University College of San Francisco, CA) was used to visualize the positions of amino acid changes as a result of rare/novel SNVs. Predictive software was used to assess the potential pathogenicity of these variants; methods included a support vector machine (SVM) program, PolyPhen-2 (Harvard University, Cambridge, MA), and FoldX (Centre for Genomic Regulation, Barcelona, Spain). Results: Samples from 23 patients were analyzed; 21 rare/novel sequence variants were identified by NGS, including splice variants (n = 2), base pair deletions (n = 1), stop codon insertions (n = 2), and SNVs (n = 16). Computational modeling of protein structures caused by the novel SNVs showed that 8 were probably deleterious, and two were possibly deleterious. For the majority of probably/possibly deleterious SNVs (I50N, P289S, M385T, M221T, D341V, V210E, P369H, V333M and A142D), the mechanism is probably via disruption of the packed hydrophobic core of AAT. Several deleterious variants occurred in combination with more common deficiency alleles, resulting in very low AAT levels. Conclusions: NGS and computational modeling are useful tools that can facilitate earlier, more precise diagnosis, and consideration for AAT therapy in AATD.",0
"Diagnostic Accuracy of Community-Based Diabetic Retinopathy Screening With an Offline Artificial Intelligence System on a Smartphone. Importance: Offline automated analysis of retinal images on a smartphone may be a cost-effective and scalable method of screening for diabetic retinopathy; however, to our knowledge, assessment of such an artificial intelligence (AI) system is lacking. Objective: To evaluate the performance of Medios AI (Remidio), a proprietary, offline, smartphone-based, automated system of analysis of retinal images, to detect referable diabetic retinopathy (RDR) in images taken by a minimally trained health care worker with Remidio Non-Mydriatic Fundus on Phone, a smartphone-based, nonmydriatic retinal camera. Referable diabetic retinopathy is defined as any retinopathy more severe than mild diabetic retinopathy, with or without diabetic macular edema. Design, Setting, and Participants: This prospective, cross-sectional, population-based study took place from August 2018 to September 2018. Patients with diabetes mellitus who visited various dispensaries administered by the Municipal Corporation of Greater Mumbai in Mumbai, India, on a particular day were included. Interventions: Three fields of the fundus (the posterior pole, nasal, and temporal fields) were photographed. The images were analyzed by an ophthalmologist and the AI system. Main Outcomes and Measures: To evaluate the sensitivity and specificity of the offline automated analysis system in detecting referable diabetic retinopathy on images taken on the smartphone-based, nonmydriatic retinal imaging system by a health worker. Results: Of 255 patients seen in the dispensaries, 231 patients (90.6%) consented to diabetic retinopathy screening. The major reasons for not participating were unwillingness to wait for screening and the blurring of vision that would occur after dilation. Images from 18 patients were deemed ungradable by the ophthalmologist and hence were excluded. In the remaining participants (110 female patients [51.6%] and 103 male patients [48.4%]; mean [SD] age, 53.1 [10.3] years), the sensitivity and specificity of the offline AI system in diagnosing referable diabetic retinopathy were 100.0% (95% CI, 78.2%-100.0%) and 88.4% (95% CI, 83.2%-92.5%), respectively, and in diagnosing any diabetic retinopathy were 85.2% (95% CI, 66.3%-95.8%) and 92.0% (95% CI, 97.1%-95.4%), respectively, compared with ophthalmologist grading using the same images. Conclusions and Relevance: These pilot study results show promise in the use of an offline AI system in community screening for referable diabetic retinopathy with a smartphone-based fundus camera. The use of AI would enable screening for referable diabetic retinopathy in remote areas where services of an ophthalmologist are unavailable. This study was done on patients with diabetes who were visiting a dispensary that provides curative services to the population at the primary level. A study with a larger sample size may be needed to extend the results to general population screening, however.",1
"Comparison of Machine Learning Optimal Classification Trees with the Pediatric Emergency Care Applied Research Network Head Trauma Decision Rules. Importance: Computed tomographic (CT) scanning is the standard for the rapid diagnosis of intracranial injury, but it is costly and exposes patients to ionizing radiation. The Pediatric Emergency Care Applied Research Network (PECARN) rules for identifying children with minor head trauma who are at very low risk of clinically important traumatic brain injury (ciTBI) are widely used to triage CT imaging. Objective: To examine whether optimal classification trees (OCTs), which are novel machine-learning classifiers, improve on PECARN rules' predictive accuracy. Design, Setting, and Participants: A secondary analysis of prospective, publicly available data on emergency department visits for head trauma used by the PECARN group to develop their tool was conducted to derive OCT-based prediction rules for ciTBI in a development cohort and compare their predictive performance vs the PECARN rules in a validation cohort among children who were younger than 2 years and 2 years or older. Data on 42412 children with head trauma and without severely altered mental status who were examined between June 1, 2004, and September 30, 2006, were gathered from 25 emergency departments in North America participating in PECARN. Data analysis was conducted from September 15, 2016, to December 18, 2018. Main Outcomes and Measures: The outcome was ciTBI, with predictive performance measured by estimating the sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, and negative likelihood ratio for the OCT and the PECARN rules. The OCT and PECARN rules' performance was compared by estimating ratios for each measure. Results: Of the 42412 children (15 996 [37.7%] girls) included in the analysis, 10718 were younger than 2 years (25.3%; mean [SD] age, 11.6 [0.6] months) and 31694 were 2 years or older (74.7%; age, 9.1 [4.9] years). Compared with PECARN rules, OCTs misclassified 0 vs 1 child with ciTBI in the younger and 10 vs 9 children with ciTBI in the older cohort, and correctly identified more children with very low risk of ciTBI in the younger (7605 vs 5701) and older (20 594 vs 18 134) cohorts. In the validation cohorts, compared with the PECARN rules, the OCTs had statistically significantly better specificity (in the younger cohort: 69.3%; 95% CI, 67.4%-71.2% vs 52.8%; 95% CI, 50.8%-54.9%; in the older cohort: 65.6%; 95% CI, 64.5%-66.8% vs 57.6%; 95% CI, 56.4%-58.8%), positive predictive value (odds ratios, 1.54; 95% CI, 1.36-1.74 and 1.23; 95% CI, 1.17-1.30, in younger and older children, respectively), and positive likelihood ratio (risk ratios, 1.54; 95% CI, 1.36-1.74 and 1.23; 95% CI, 1.17-1.30, in younger and older children, respectively). There were no statistically significant differences in the sensitivity, negative predictive value, and negative likelihood ratio between the 2 sets of rules. Conclusions and Relevance: If implemented, OCTs may help reduce the number of unnecessary CT scans, without missing more patients with ciTBI than the PECARN rules..",1
"Predictors of perioperative complications in paediatric cranial vault reconstruction surgery: a multicentre observational study from the Pediatric Craniofacial Collaborative Group. BACKGROUND: The current incidence of major complications in paediatric craniofacial surgery in North America has not been accurately defined. In this report, the Pediatric Craniofacial Collaborative Group evaluates the incidence and determines the independent predictors of major perioperative complications using a multicentre database. METHODS: The Pediatric Craniofacial Surgery Perioperative Registry was queried for subjects undergoing complex cranial vault reconstruction surgery over a 5-year period. Major perioperative complications were identified through a structured a priori consensus process. Logistic regression was applied to identify predictors of a major perioperative complication with bootstrapping to evaluate discrimination accuracy and provide internal validity of the multivariable model. RESULTS: A total of 1814 patients from 33 institutions in the US and Canada were analysed; 15% were reported to have a major perioperative complication. Multivariable predictors included ASA physical status 3 or 4 (P=0.005), craniofacial syndrome (P=0.008), antifibrinolytic administered (P=0.003), blood product transfusion >50 ml kg(-1) (P<0.001), and surgery duration over 5 h (P<0.001). Bootstrapping indicated that the predictive algorithm had good internal validity and excellent discrimination and model performance. A perioperative complication was estimated to increase the hospital length of stay by an average of 3 days (P<0.001). CONCLUSIONS: The predictive algorithm can be used as a prognostic tool to risk stratify patients and thereby potentially reduce morbidity and mortality. Craniofacial teams can utilise these predictors of complications to identify high-risk patients. Based on this information, further prospective quality improvement initiatives may decrease complications, and reduce morbidity and mortality.",0
"High-sensitivity cardiac troponin assays: finally ready for prime time?. High-sensitivity cardiac troponin (hs-cTn) assays facilitate the ruling-out of myocardial infarction (MI) but identify a high number of patients with elevated troponin levels but without MI. Consequently, the term myocardial injury was included in the latest universal definition of MI. In the High-STEACS trial, use of a hs-cTnI assay was safe but had no prognostic benefit.",0
"An algorithm for learning shape and appearance models without annotations. This paper presents a framework for automatically learning shape and appearance models for medical (and certain other) images. The algorithm was developed with the aim of eventually enabling distributed privacy-preserving analysis of brain image data, such that shared information (shape and appearance basis functions) may be passed across sites, whereas latent variables that encode individual images remain secure within each site. These latent variables are proposed as features for privacy-preserving data mining applications. The approach is demonstrated qualitatively on the KDEF dataset of 2D face images, showing that it can align images that traditionally require shape and appearance models trained using manually annotated data (manually defined landmarks etc.). It is applied to the MNIST dataset of handwritten digits to show its potential for machine learning applications, particularly when training data is limited. The model is able to handle ""missing data"", which allows it to be cross-validated according to how well it can predict left-out voxels. The suitability of the derived features for classifying individuals into patient groups was assessed by applying it to a dataset of over 1900 segmented T1-weighted MR images, which included images from the COBRE and ABIDE datasets.",1
"High-content phenotypic assay for proliferation of human iPSC-derived cardiomyocytes identifies L-type calcium channels as targets. Over 5 million people in the United States suffer from heart failure, due to the limited ability to regenerate functional cardiac tissue. One potential therapeutic strategy is to enhance proliferation of resident cardiomyocytes. However, phenotypic screening for therapeutic agents is challenged by the limited ability of conventional markers to discriminate between cardiomyocyte proliferation and endoreplication (e.g. polyploidy and multinucleation). Here, we developed a novel assay that combines automated live-cell microscopy and image processing algorithms to discriminate between proliferation and endoreplication by quantifying changes in the number of nuclei, changes in the number of cells, binucleation, and nuclear DNA content. We applied this assay to further prioritize hits from a primary screen for DNA synthesis, identifying 30 compounds that enhance proliferation of human induced pluripotent stem cell-derived cardiomyocytes. Among the most active compounds from the phenotypic screen are clinically approved L-type calcium channel blockers from multiple chemical classes whose activities were confirmed across different sources of human induced pluripotent stem cell-derived cardiomyocytes. Identification of compounds that stimulate human cardiomyocyte proliferation may provide new therapeutic strategies for heart failure.",0
"Atrial fibrillation classification based on convolutional neural networks. BACKGROUND: The global age-adjusted mortality rate related to atrial fibrillation (AF) registered a rapid growth in the last four decades, i.e., from 0.8 to 1.6 and 0.9 to 1.7 per 100,000 for men and women during 1990-2010, respectively. In this context, this study uses convolutional neural networks for classifying (diagnosing) AF, employing electrocardiogram data in a general hospital. METHODS: Data came from Anam Hospital in Seoul, Korea, with 20,000 unique patients (10,000 normal sinus rhythm and 10,000 AF). 30 convolutional neural networks were applied and compared for the diagnosis of the normal sinus rhythm vs. AF condition: 6 Alex networks with 5 convolutional layers, 3 fully connected layers and the number of kernels changing from 3 to 256; and 24 residual networks with the number of residuals blocks (or kernels) varying from 8 to 2 (or 64 to 2). RESULTS: In terms of the accuracy, the best Alex network was one with 24 initial kernels (i.e., kernels in the first layer), 5,268,818 parameters and the training time of 89 s (0.997), while the best residual network was one with 6 residual blocks, 32 initial kernels, 248,418 parameters and the training time of 253 s (0.999). In general, the performance of the residual network improved as the number of its residual blocks (its depth) increased. CONCLUSION: For AF diagnosis, the residual network might be a good model with higher accuracy and fewer parameters than its Alex-network counterparts.",1
"A Deep Learning Approach for Automated Detection of Geographic Atrophy from Color Fundus Photographs. PURPOSE: To assess the utility of deep learning in the detection of geographic atrophy (GA) from color fundus photographs and to explore potential utility in detecting central GA (CGA). DESIGN: A deep learning model was developed to detect the presence of GA in color fundus photographs, and 2 additional models were developed to detect CGA in different scenarios. PARTICIPANTS: A total of 59 812 color fundus photographs from longitudinal follow-up of 4582 participants in the Age-Related Eye Disease Study (AREDS) dataset. Gold standard labels were from human expert reading center graders using a standardized protocol. METHODS: A deep learning model was trained to use color fundus photographs to predict GA presence from a population of eyes with no AMD to advanced AMD. A second model was trained to predict CGA presence from the same population. A third model was trained to predict CGA presence from the subset of eyes with GA. For training and testing, 5-fold cross-validation was used. For comparison with human clinician performance, model performance was compared with that of 88 retinal specialists. MAIN OUTCOME MEASURES: Area under the curve (AUC), accuracy, sensitivity, specificity, and precision. RESULTS: The deep learning models (GA detection, CGA detection from all eyes, and centrality detection from GA eyes) had AUCs of 0.933-0.976, 0.939-0.976, and 0.827-0.888, respectively. The GA detection model had accuracy, sensitivity, specificity, and precision of 0.965 (95% confidence interval [CI], 0.959-0.971), 0.692 (0.560-0.825), 0.978 (0.970-0.985), and 0.584 (0.491-0.676), respectively, compared with 0.975 (0.971-0.980), 0.588 (0.468-0.707), 0.982 (0.978-0.985), and 0.368 (0.230-0.505) for the retinal specialists. The CGA detection model had values of 0.966 (0.957-0.975), 0.763 (0.641-0.885), 0.971 (0.960-0.982), and 0.394 (0.341-0.448). The centrality detection model had values of 0.762 (0.725-0.799), 0.782 (0.618-0.945), 0.729 (0.543-0.916), and 0.799 (0.710-0.888). CONCLUSIONS: A deep learning model demonstrated high accuracy for the automated detection of GA. The AUC was noninferior to that of human retinal specialists. Deep learning approaches may also be applied to the identification of CGA. The code and pretrained models are publicly available at https://github.com/ncbi-nlp/DeepSeeNet.",1
"A clinical text classification paradigm using weak supervision and deep representation. BACKGROUND: Automatic clinical text classification is a natural language processing (NLP) technology that unlocks information embedded in clinical narratives. Machine learning approaches have been shown to be effective for clinical text classification tasks. However, a successful machine learning model usually requires extensive human efforts to create labeled training data and conduct feature engineering. In this study, we propose a clinical text classification paradigm using weak supervision and deep representation to reduce these human efforts. METHODS: We develop a rule-based NLP algorithm to automatically generate labels for the training data, and then use the pre-trained word embeddings as deep representation features for training machine learning models. Since machine learning is trained on labels generated by the automatic NLP algorithm, this training process is called weak supervision. We evaluat the paradigm effectiveness on two institutional case studies at Mayo Clinic: smoking status classification and proximal femur (hip) fracture classification, and one case study using a public dataset: the i2b2 2006 smoking status classification shared task. We test four widely used machine learning models, namely, Support Vector Machine (SVM), Random Forest (RF), Multilayer Perceptron Neural Networks (MLPNN), and Convolutional Neural Networks (CNN), using this paradigm. Precision, recall, and F1 score are used as metrics to evaluate performance. RESULTS: CNN achieves the best performance in both institutional tasks (F1 score: 0.92 for Mayo Clinic smoking status classification and 0.97 for fracture classification). We show that word embeddings significantly outperform tf-idf and topic modeling features in the paradigm, and that CNN captures additional patterns from the weak supervision compared to the rule-based NLP algorithms. We also observe two drawbacks of the proposed paradigm that CNN is more sensitive to the size of training data, and that the proposed paradigm might not be effective for complex multiclass classification tasks. CONCLUSION: The proposed clinical text classification paradigm could reduce human efforts of labeled training data creation and feature engineering for applying machine learning to clinical text classification by leveraging weak supervision and deep representation. The experimental experiments have validated the effectiveness of paradigm by two institutional and one shared clinical text classification tasks.",1
"Building a tobacco user registry by extracting multiple smoking behaviors from clinical notes. BACKGROUND: Usage of structured fields in Electronic Health Records (EHRs) to ascertain smoking history is important but fails in capturing the nuances of smoking behaviors. Knowledge of smoking behaviors, such as pack year history and most recent cessation date, allows care providers to select the best care plan for patients at risk of smoking attributable diseases. METHODS: We developed and evaluated a health informatics pipeline for identifying complete smoking history from clinical notes in EHRs. We utilized 758 patient-visit notes (from visits between 03/28/2016 and 04/04/2016) from our local EHR in addition to a public dataset of 502 clinical notes from the 2006 i2b2 Challenge to assess the performance of this pipeline. We used a machine-learning classifier to extract smoking status and a comprehensive set of text processing regular expressions to extract pack years and cessation date information from these clinical notes. RESULTS: We identified smoking status with an F1 score of 0.90 on both the i2b2 and local data sets. Regular expression identification of pack year history in the local test set was 91.7% sensitive and 95.2% specific, but due to variable context the pack year extraction was incomplete in 25% of cases, extracting packs per day or years smoked only. Regular expression identification of cessation date was 63.2% sensitive and 94.6% specific. CONCLUSIONS: Our work indicates that the development of an EHR-based Smokers' Registry containing information relating to smoking behaviors, not just status, from free-text clinical notes using an informatics pipeline is feasible. This pipeline is capable of functioning in external EHRs, reducing the amount of time and money needed at the institute-level to create a Smokers' Registry for improved identification of patient risk and eligibility for preventative and early detection services.",1
"Protopanaxadiol inhibits epithelial–mesenchymal transition of hepatocellular carcinoma by targeting STAT3 pathway. Diol-type ginsenosides, such as protopanaxadiol (PPD), exhibit antioxidation, anti-inflammation, and antitumor effects. However, the antitumor effect of these ginsenosides and the mechanism of PPD remain unclear. In this work, the antitumor effects of several derivatives, including PPD, Rg5, Rg3, Rh2, and Rh3, were evaluated in five different cancer cell lines. PPD demonstrated the best inhibitory effects on the proliferation and migration of the five cancer cell lines, especially the hepatocellular carcinoma (HCC) cell lines. Therefore, the mechanism of action of PPD in HCC cells was elucidated. PPD inhibited the proliferation, migration, and invasion ability of HepG2 and PLC/PRF/5 cells in a dose-dependent manner. Western blot and immunofluorescence assay showed that PPD can alter the expression of epithelial–mesenchymal transition markers, increase E-cadherin expression, and decrease vimentin expression. Docking and biacore experiments revealed that STAT3 is the target protein of PPD, which formed hydrogen bonds with Gly583/Leu608/Tyr674 at the SH2 domain of STAT3. PPD inhibited the phosphorylation of STAT3 and its translocation from the cytosol to the nucleus, thereby inhibiting the expression of Twist1. PPD also inhibited tumor volume and tumor lung metastasis in PLC/PRF/5 xenograft model. In conclusion, PPD can inhibit the proliferation and metastasis of HCC cells through the STAT3/Twist1 pathway.",0
"Heuristic bias in stem cell biology. When studying purified hematopoietic stem cells, the urge for mechanisms and reductionist approaches appears to be overwhelming. The prime focus of the field has recently been on the study of highly purified hematopoietic stem cells using various lineage and stem cell-specific markers, all of which adequately and conveniently fit the established hierarchical stem cell model. This methodology is tainted with bias and has led to incomplete conclusions. Much of our own work has shown that the purified hematopoietic stem cell, which has been so heavily studied, is not representative of the total population of hematopoietic stem cells and that rather than functioning within a hierarchical model of expansion the true hematopoietic stem cell is one that is actively cycling through various differentiation potentials within a dynamic continuum. Additional work with increased emphasis on studying whole populations and direct mechanistic studies to these populations is needed. Furthermore, the most productive studies may well be mechanistic at the cellular or tissue levels. Lastly, the application of robust machine learning algorithms may provide insight into the dynamic variability and flux of stem cell fate and differentiation potential.",0
"Single-Cell Heterogeneity Analysis and CRISPR Screen Identify Key β-Cell-Specific Disease Genes. Fang et al. found that β cells from healthy, obese, and diabetic donors have a distinct cellular heterogeneity pattern, which allows sensitive identification of disease signature genes from a small number of donors. Combined with results from a genome-wide CRISPR screen, they further annotated signature genes with insulin regulatory functions.",0
"Comparison of Artificial Intelligence Techniques to Evaluate Performance of a Classifier for Automatic Grading of Prostate Cancer From Digitized Histopathologic Images. Importance: Proper evaluation of the performance of artificial intelligence techniques in the analysis of digitized medical images is paramount for the adoption of such techniques by the medical community and regulatory agencies. Objectives: To compare several cross-validation (CV) approaches to evaluate the performance of a classifier for automatic grading of prostate cancer in digitized histopathologic images and compare the performance of the classifier when trained using data from 1 expert and multiple experts. Design, Setting, and Participants: This quality improvement study used tissue microarray data (333 cores) from 231 patients who underwent radical prostatectomy at the Vancouver General Hospital between June 27, 1997, and June 7, 2011. Digitized images of tissue cores were annotated by 6 pathologists for 4 classes (benign and Gleason grades 3, 4, and 5) between December 12, 2016, and October 5, 2017. Patches of 192 µm2 were extracted from these images. There was no overlap between patches. A deep learning classifier based on convolutional neural networks was trained to predict a class label from among the 4 classes (benign and Gleason grades 3, 4, and 5) for each image patch. The classification performance was evaluated in leave-patches-out CV, leave-cores-out CV, and leave-patients-out 20-fold CV. The analysis was performed between November 15, 2018, and January 1, 2019. Main Outcomes and Measures: The classifier performance was evaluated by its accuracy, sensitivity, and specificity in detection of cancer (benign vs cancer) and in low-grade vs high-grade differentiation (Gleason grade 3 vs grades 4-5). The statistical significance analysis was performed using the McNemar test. The agreement level between pathologists and the classifier was quantified using a quadratic-weighted κ statistic. Results: On 333 tissue microarray cores from 231 participants with prostate cancer (mean [SD] age, 63.2 [6.3] years), 20-fold leave-patches-out CV resulted in mean (SD) accuracy of 97.8% (1.2%), sensitivity of 98.5% (1.0%), and specificity of 97.5% (1.2%) for classifying benign patches vs cancerous patches. By contrast, 20-fold leave-patients-out CV resulted in mean (SD) accuracy of 85.8% (4.3%), sensitivity of 86.3% (4.1%), and specificity of 85.5% (7.2%). Similarly, 20-fold leave-cores-out CV resulted in mean (SD) accuracy of 86.7% (3.7%), sensitivity of 87.2% (4.0%), and specificity of 87.7% (5.5%). Results of McNemar tests showed that the leave-patches-out CV accuracy, sensitivity, and specificity were significantly higher than those for both leave-patients-out CV and leave-cores-out CV. Similar results were observed for classifying low-grade cancer vs high-grade cancer. When trained on a single expert, the overall agreement in grading between pathologists and the classifier ranged from 0.38 to 0.58; when trained using the majority vote among all experts, it was 0.60. Conclusions and Relevance: Results of this study suggest that in prostate cancer classification from histopathologic images, patch-wise CV and single-expert training and evaluation may lead to a biased estimation of classifier's performance. To allow reproducibility and facilitate comparison between automatic classification methods, studies in the field should evaluate their performance using patient-based CV and multiexpert data. Some of these conclusions may be generalizable to other histopathologic applications and to other applications of machine learning in medicine.",1
"Structure of a Signaling Cannabinoid Receptor 1-G Protein Complex. Cannabis elicits its mood-enhancing and analgesic effects through the cannabinoid receptor 1 (CB1), a G protein-coupled receptor (GPCR) that signals primarily through the adenylyl cyclase-inhibiting heterotrimeric G protein Gi. Activation of CB1-Gi signaling pathways holds potential for treating a number of neurological disorders and is thus crucial to understand the mechanism of Gi activation by CB1. Here, we present the structure of the CB1-Gi signaling complex bound to the highly potent agonist MDMB-Fubinaca (FUB), a recently emerged illicit synthetic cannabinoid infused in street drugs that have been associated with numerous overdoses and fatalities. The structure illustrates how FUB stabilizes the receptor in an active state to facilitate nucleotide exchange in Gi. The results compose the structural framework to explain CB1 activation by different classes of ligands and provide insights into the G protein coupling and selectivity mechanisms adopted by the receptor.",0
"A Molecular Signature in Blood Reveals a Role for p53 in Regulating Malaria-Induced Inflammation. The mechanisms that protect from febrile malaria remain unclear. Tran et al. applied a systems-based approach to a longitudinal pediatric study to identify immune signatures that associate with control of malaria fever and parasitemia, revealing that p53 upregulation in monocytes attenuates malaria-induced inflammation and predicts protection from fever.",0
"Utility of machine learning algorithms in assessing patients with a systemic right ventricle. To investigate the utility of novel deep learning (DL) algorithms in recognizing transposition of the great arteries (TGA) after atrial switch procedure or congenitally corrected TGA (ccTGA) based on routine transthoracic echocardiograms. In addition, the ability of DL algorithms for delineation and segmentation of the systemic ventricle was evaluated. Methods and results: In total, 132 patients (92 TGA and atrial switch and 40 with ccTGA; 60% male, age 38.3 ± 12.1 years) and 67 normal controls (57% male, age 48.5 ± 17.9 years) with routine transthoracic examinations were included. Convolutional neural networks were trained to classify patients by underlying diagnosis and a U-Net design was used to automatically segment the systemic ventricle. Convolutional networks were build based on over 100 000 frames of an apical four-chamber or parasternal short-axis view to detect underlying diagnoses. The DL algorithm had an overall accuracy of 98.0% in detecting the correct diagnosis. The U-Net architecture model correctly identified the systemic ventricle in all individuals and achieved a high performance in segmenting the systemic right or left ventricle (Dice metric between 0.79 and 0.88 depending on diagnosis) when compared with human experts. Conclusion: Our study demonstrates the potential of machine learning algorithms, trained on routine echocardiographic datasets to detect underlying diagnosis in complex congenital heart disease. Automated delineation of the ventricular area was also feasible. These methods may in future allow for the longitudinal, objective, and automated assessment of ventricular function.",1
"Machine-learning based radiogenomics analysis of MRI features and metagenes in glioblastoma multiforme patients with different survival time. Background: This study aimed to examine multi-dimensional MRI features’ predictability on survival outcome and associations with differentially expressed Genes (RNA Sequencing) in groups of glioblastoma multiforme (GBM) patients. Methods: Radiomics features were extracted from segmented lesions of T2-FLAIR MRI data of 137 GBM patients. Radiomics features include intensity, shape and textural features in seven classes were included in the analysis. Patients were divided into two groups depending on their survival time (shorter or longer than 1-year survival). Four different machine learning algorithms were implemented to construct the prediction models. Features with top importance (importance >0.04) were selected to construct the prediction model using the model with the best performance. The interactions between image features and genomics were then analysed with Pearson's correlation analysis. Results: The GBDT model with 72 features with highest importance had the highest accuracy of 0.81 on both short and long survival time classes, and the area under the curve (AUC) of the receiver operative characteristic (ROC) of the short and long survival time class were 0.79 and 0.81. Six metagenes showed significant interactive effect (P < 0.05), and Pearson's correlation analysis revealed that three of these metagenes (TIMP1, ROS1 EREG) showed moderate (0.3 < |r| < 0.5) or high correlation (|r| > 0.5) with image features. Conclusion: Radiogenomics analysis shows that MRI features are predictive of survival outcomes, and image features are highly associated with selective metagenes. Radiogenomics analysis is a useful method for optimizing clinical diagnosis and selecting effective treatments.",0
"The Metastable XBP1u Transmembrane Domain Defines Determinants for Intramembrane Proteolysis by Signal Peptide Peptidase. Unspliced XBP1 mRNA encodes XBP1u, the transcriptionally inert variant of the unfolded protein response (UPR) transcription factor XBP1s. XBP1u targets its mRNA-ribosome-nascent-chain-complex to the endoplasmic reticulum (ER) to facilitate UPR activation and prevents overactivation. Yet, its membrane association is controversial. Here, we use cell-free translocation and cellular assays to define a moderately hydrophobic stretch in XBP1u that is sufficient to mediate insertion into the ER membrane. Mutagenesis of this transmembrane (TM) region reveals residues that facilitate XBP1u turnover by an ER-associated degradation route that is dependent on signal peptide peptidase (SPP). Furthermore, the impact of these mutations on TM helix dynamics was assessed by residue-specific amide exchange kinetics, evaluated by a semi-automated algorithm. Based on our results, we suggest that SPP-catalyzed intramembrane proteolysis of TM helices is not only determined by their conformational flexibility, but also by side-chain interactions near the scissile peptide bond with the enzyme's active site.",0
"Identifying clinically important COPD sub-types using data-driven approaches in primary care population based electronic health records. BACKGROUND: COPD is a highly heterogeneous disease composed of different phenotypes with different aetiological and prognostic profiles and current classification systems do not fully capture this heterogeneity. In this study we sought to discover, describe and validate COPD subtypes using cluster analysis on data derived from electronic health records. METHODS: We applied two unsupervised learning algorithms (k-means and hierarchical clustering) in 30,961 current and former smokers diagnosed with COPD, using linked national structured electronic health records in England available through the CALIBER resource. We used 15 clinical features, including risk factors and comorbidities and performed dimensionality reduction using multiple correspondence analysis. We compared the association between cluster membership and COPD exacerbations and respiratory and cardiovascular death with 10,736 deaths recorded over 146,466 person-years of follow-up. We also implemented and tested a process to assign unseen patients into clusters using a decision tree classifier. RESULTS: We identified and characterized five COPD patient clusters with distinct patient characteristics with respect to demographics, comorbidities, risk of death and exacerbations. The four subgroups were associated with 1) anxiety/depression; 2) severe airflow obstruction and frailty; 3) cardiovascular disease and diabetes and 4) obesity/atopy. A fifth cluster was associated with low prevalence of most comorbid conditions. CONCLUSIONS: COPD patients can be sub-classified into groups with differing risk factors, comorbidities, and prognosis, based on data included in their primary care records. The identified clusters confirm findings of previous clustering studies and draw attention to anxiety and depression as important drivers of the disease in young, female patients.",1
"Recent advances in Swedish and Spanish medical entity recognition in clinical texts using deep neural approaches. BACKGROUND: Text mining and natural language processing of clinical text, such as notes from electronic health records, requires specific consideration of the specialized characteristics of these texts. Deep learning methods could potentially mitigate domain specific challenges such as limited access to in-domain tools and data sets. METHODS: A bi-directional Long Short-Term Memory network is applied to clinical notes in Spanish and Swedish for the task of medical named entity recognition. Several types of embeddings, both generated from in-domain and out-of-domain text corpora, and a number of generation and combination strategies for embeddings have been evaluated in order to investigate different input representations and the influence of domain on the final results. RESULTS: For Spanish, a micro averaged F1-score of 75.25 was obtained and for Swedish, the corresponding score was 76.04. The best results for both languages were achieved using embeddings generated from in-domain corpora extracted from electronic health records, but embeddings generated from related domains were also found to be beneficial. CONCLUSIONS: A recurrent neural network with in-domain embeddings improved the medical named entity recognition compared to shallow learning methods, showing this combination to be suitable for entity recognition in clinical text for both languages.",1
"WP1130 reveals USP24 as a novel target in T-cell acute lymphoblastic leukemia. Background: T-cell acute lymphoblastic leukemia (T-ALL) is a lymphoid malignancy caused by the oncogenic transformation of immature T-cell progenitors with poor outcomes. WP1130 has shown potent activity against a variety of cancer but whether WP1130 has anti-T-ALL activity is not clear. USP24, one target of WP1130, is one of the largest deubiquitinases and its detailed mechanism is poorly understood. The aim of this study was to explore whether WP1130 could suppress T-ALL and the role of USP24 in T-ALL. Methods: Molecular docking and cellular thermal shift assay were performed to determine whether and how WP1130 directly interact with USP24. Mitochondrial transmembrane potential assay was measured via Rhodamine 123 staining. USP24 was reactivated using the deactivated CRISPR-associated protein 9 (dCas9)-synergistic activation mediator (SAM) system. The in vivo results were examined by tumor xenografts in NOD-SCID mice. All statistical analyses were performed with the SPSS software package. Results: WP1130 treatment decreased the viability and induces apoptosis of T-ALL cells both in vitro and in vivo. Furthermore, we demonstrated that knockdown of USP24 but not USP9X could significantly induce growth inhibition and apoptosis of T-ALL cells. Oncomine database showed that USP24 expression was upregulated in T-ALL samples and Kaplan-Meier results indicated that the USP24 was negatively but USP9X was positively associated with survival in T-ALL patients. Additionally, we proposed that WP1130 directly interacts with the activity site pocket of USP24 in T-ALL cells, which leads to the decrease of its substrates Mcl-1. Mechanistically, WP1130 induces apoptosis by accelerating the collapse of mitochondrial transmembrane potential via USP24-Mcl-1 axis. Conclusions: Altogether, using WP1130 as a chemical probe, we demonstrate that USP24 but not USP9X is a novel target in T-ALL cells. Moreover, we uncovered that WP1130 induces apoptosis by accelerating the collapse of mitochondrial transmembrane potential via USP24-Mcl-1 axis. These results provide that USP24-Mcl-1 axis may represent a novel strategy in the treatment of T-ALL and WP1130 is a promising lead compound for developing anti-T-ALL drugs.",0
"Assessing Nasal Soft-Tissue Envelope Thickness for Rhinoplasty: Normative Data and a Predictive Algorithm. Importance: Preoperative assessment of nasal soft-tissue envelope (STE) thickness is an important component of rhinoplasty that presently lacks validated tools. Objective: To measure and assess the distribution of nasal STE thickness in a large patient population and to determine if facial plastic surgery clinicians can predict nasal STE thickness based on visual examination of the nose. Design, Setting, and Participants: This retrospective review and prospective assessment of 190 adult patients by 4 expert raters was conducted at an academic tertiary referral center. The patients had high-resolution maxillofacial computed tomography (CT) scans and standardized facial photographs on file and did not have a history of nasal fracture, septal perforation, rhinoplasty, or other surgery or medical conditions altering nasal form. Data were analyzed in March 2019. Main Outcomes and Measures: Measure nasal STE thickness at defined anatomic subsites using high-resolution CT scans. Measure expert-predicted nasal STE thickness based on visual examination of the nose using a scale from 0 (thinnest) to 100 (thickest). Results: Of the 190 patients, 78 were women and the mean (SD) age was 45 (17) years. The nasal STE was thickest at the sellion (mean [SD]) (6.7 [1.7] mm), thinnest at the rhinion (2.1 [0.7] mm), thickened over the supratip (4.8 [1.0] mm) and nasal tip (3.1 [0.6] mm), and thinned over the columella (2.6 [0.4] mm). In the study population, nasal STE thickness followed a nearly normal distribution for each measured subsite, with the majority of patients in a medium thickness range. Comparison of predicted and actual nasal STE thickness showed that experts could accurately predict nasal STE thickness, with the highest accuracy at the nasal tip (r, 0.73; prediction accuracy, 91%). A strong positive correlation was noted among the experts' STE estimates (r, 0.83-0.89), suggesting a high level of agreement between individual raters. Conclusions and Relevance: There is variable thickness of the nasal STE, which influences the external nasal contour and rhinoplasty outcomes. With visual analysis of the nose, experts can agree on and predict nasal STE thickness, with the highest accuracy at the nasal tip. These data can aid in preoperative planning for rhinoplasty, allowing implementation of preoperative, intraoperative, and postoperative strategies to optimize the nasal STE, which may ultimately improve patient outcomes and satisfaction.NA.",0
"Epigenomic signatures underpin the axonal regenerative ability of dorsal root ganglia sensory neurons. Axonal injury results in regenerative success or failure, depending on whether the axon lies in the peripheral or the CNS, respectively. The present study addresses whether epigenetic signatures in dorsal root ganglia discriminate between regenerative and non-regenerative axonal injury. Chromatin immunoprecipitation for the histone 3 (H3) post-translational modifications H3K9ac, H3K27ac and H3K27me3; an assay for transposase-accessible chromatin; and RNA sequencing were performed in dorsal root ganglia after sciatic nerve or dorsal column axotomy. Distinct histone acetylation and chromatin accessibility signatures correlated with gene expression after peripheral, but not central, axonal injury. DNA-footprinting analyses revealed new transcriptional regulators associated with regenerative ability. Machine-learning algorithms inferred the direction of most of the gene expression changes. Neuronal conditional deletion of the chromatin remodeler CCCTC-binding factor impaired nerve regeneration, implicating chromatin organization in the regenerative competence. Altogether, the present study offers the first epigenomic map providing insight into the transcriptional response to injury and the differential regenerative ability of sensory neurons.",1
"Identification of diagnostic biomarker in patients with gestational diabetes mellitus based on transcriptome-wide gene expression and pattern recognition. Gestational diabetes mellitus (GDM) is becoming a growing threat for all pregnancies. In this study, we set up an automatic screening method combining both transcriptomic databases and support vector machine (SVM)-based pattern recognition to select biomarkers that can be used in predicting and preventing GDM for gravidas. We screened 63 samples (32 GDM samples and 31 normal controls) in GEO database for the GDM-specific biomarkers. Differentially expressed genes between patients with GDM and normal controls were picked out using edgeR package. Enrichment analysis was performed using database for annotation, visualization, and integrated discovery. The regulatory gene network was constructed based on the KEGG pathway database. Genes in the hub of the network were selected as specific biomarkers of GDM and further validated through document investigation. Finally, the GDM prediction model was verified using the SVMs. In total, 189 probes corresponding to 69 genes that differentially expressed between GDM and controls were screened out by edgeR package. Nineteen pathways were clustered by KEGG enrichment analysis and were integrated into a regulatory network containing 572 nodes and 1874 edges. The intersection of 50 hub genes extracted from the network and 69 differential genes picked out by edgeR was a collection of six genes, including members of HLA superfamily. In the SVM model, the six genes had a good capacity of predicting GDM in both the training data set (area under curve [AUC] is 0.781) and the testing data set (AUC is 0.710) and had been reported to be associated with GDM. We found that the collection of six genes can be potentially applied as a biomarker for GDM diagnosis.",0
"Cyclin D-Cdk4,6 Drives Cell-Cycle Progression via the Retinoblastoma Protein's C-Terminal Helix. The cyclin-dependent kinases Cdk4 and Cdk6 form complexes with D-type cyclins to drive cell proliferation. A well-known target of cyclin D-Cdk4,6 is the retinoblastoma protein Rb, which inhibits cell-cycle progression until its inactivation by phosphorylation. However, the role of Rb phosphorylation by cyclin D-Cdk4,6 in cell-cycle progression is unclear because Rb can be phosphorylated by other cyclin-Cdks, and cyclin D-Cdk4,6 has other targets involved in cell division. Here, we show that cyclin D-Cdk4,6 docks one side of an alpha-helix in the Rb C terminus, which is not recognized by cyclins E, A, and B. This helix-based docking mechanism is shared by the p107 and p130 Rb-family members across metazoans. Mutation of the Rb C-terminal helix prevents its phosphorylation, promotes G1 arrest, and enhances Rb's tumor suppressive function. Our work conclusively demonstrates that the cyclin D-Rb interaction drives cell division and expands the diversity of known cyclin-based protein docking mechanisms. Precise timing of cell-cycle transitions relies on regulation of the activity and specificity of cyclin-dependent kinases. Topacio et al. show that the G1 cyclin-Cdk complex cyclin D-Cdk4,6 targets its well-known substrate, the retinoblastoma protein Rb, through recognition of a C-terminal alpha-helix and demonstrate that this specific cyclin-Cdk-substrate interaction drives cell proliferation.",0
"Interaction with hyaluronan matrix and miRNA cargo as contributors for in vitro potential of mesenchymal stem cell-derived extracellular vesicles in a model of human osteoarthritic synoviocytes. Background: Osteoarthritis (OA) is the most prevalent joint disease, and to date, no options for effective tissue repair and restoration are available. With the aim of developing new therapies, the impact of mesenchymal stem cells (MSCs) has been explored, and the efficacy of MSCs started to be deciphered. A strong paracrine capacity relying on both secreted and vesicle-embedded (EVs) protein or nucleic acid-based factors has been proposed as the principal mechanism that contributes to tissue repair. This work investigated the mechanism of internalization of extracellular vesicles (EVs) released by adipose-derived MSCs (ASCs) and the role of shuttled miRNAs in the restoration of homeostasis in an in vitro model of human fibroblast-like synoviocytes (FLSs) from OA patients. Methods: ASC-EVs were isolated by differential centrifugation and validated by flow cytometry and nanoparticle tracking analysis. ASC-EVs with increased hyaluronan (HA) receptor CD44 levels were obtained culturing ASCs on HA-coated plastic surfaces. OA FLSs with intact or digested HA matrix were co-cultured with fluorescent ASC-EVs, and incorporation scored by flow cytometry and ELISA. ASC-EV complete miRNome was deciphered by high-throughput screening. In inflamed OA FLSs, genes and pathways potentially regulated by ASC-EV miRNA were predicted by bioinformatics. OA FLSs stimulated with IL-1β at physiological levels (25 pg/mL) were treated with ASC-EVs, and expression of inflammation and OA-related genes was measured by qRT-PCR over a 10-day time frame with modulated candidates verified by ELISA. Results: The data showed that HA is involved in ASC-EV internalization in FLSs. Indeed, both removal of HA matrix presence on FLSs and modulation of CD44 levels on EVs affected their recruitment. Bioinformatics analysis of EV-embedded miRNAs showed their ability to potentially regulate the main pathways strictly associated with synovial inflammation in OA. In this frame, ASC-EVs reduced the expression of pro-inflammatory cytokines and chemokines in a chronic model of FLS inflammation. Conclusions: Given their ability to affect FLS behavior in a model of chronic inflammation through direct interaction with HA matrix and miRNA release, ASC-EVs confirm their role as a novel therapeutic option for osteoarthritic joints.",0
"Single-Cell Transcriptomic Analyses of Cell Fate Transitions during Human Cardiac Reprogramming. Direct cellular reprogramming provides a powerful platform to study cell plasticity and dissect mechanisms underlying cell fate determination. Here, we report a single-cell transcriptomic study of human cardiac (hiCM) reprogramming that utilizes an analysis pipeline incorporating current data normalization methods, multiple trajectory prediction algorithms, and a cell fate index calculation we developed to measure reprogramming progression. These analyses revealed hiCM reprogramming-specific features and a decision point at which cells either embark on reprogramming or regress toward their original fibroblast state. In combination with functional screening, we found that immune-response-associated DNA methylation is required for hiCM induction and validated several downstream targets of reprogramming factors as necessary for productive hiCM reprograming. Collectively, this single-cell transcriptomics study provides detailed datasets that reveal molecular features underlying hiCM determination and rigorous analytical pipelines for predicting cell fate conversion. Zhou et al. performed single-cell RNA sequencing to unravel molecular features of human cardiac reprogramming. They identified a “decision” point where cells either reprogram or regress to initial fate. Further, progression of reprogramming was quantitatively assessed by their developed “cell fate index,” which could be used for studying other biological processes.",0
"Detection of medical text semantic similarity based on convolutional neural network. BACKGROUND: Imaging examinations, such as ultrasonography, magnetic resonance imaging and computed tomography scans, play key roles in healthcare settings. To assess and improve the quality of imaging diagnosis, we need to manually find and compare the pre-existing reports of imaging and pathology examinations which contain overlapping exam body sites from electrical medical records (EMRs). The process of retrieving those reports is time-consuming. In this paper, we propose a convolutional neural network (CNN) based method which can better utilize semantic information contained in report texts to accelerate the retrieving process. METHODS: We included 16,354 imaging and pathology report-pairs from 1926 patients who admitted to Shanghai Tongren Hospital and had ultrasonic examinations between 1st May 2017 and 31st July 2017. We adapted the CNN model to calculate the similarities among the report-pairs to identify target report-pairs with overlapping body sites, and compared the performance with other six conventional models, including keyword mapping, latent semantic analysis (LSA), latent Dirichlet allocation (LDA), Doc2Vec, Siamese long short term memory (LSTM) and a model based on named entity recognition (NER). We also utilized graph embedding method to enhance the word representation by capturing the semantic relations information from medical ontologies. Additionally, we used LIME algorithm to identify which features (or words) are decisive for the prediction results and improved the model interpretability. RESULTS: Experiment results showed that our CNN model gained significant improvement compared to all other conventional models on area under the receiver operating characteristic (AUROC), precision, recall and F1-score in our test dataset. The AUROC of our CNN models gained approximately 3-7% improvement. The AUROC of CNN model with graph-embedding and ontology based medical concept vectors was 0.8% higher than the model with randomly initialized vectors and 1.5% higher than the one with pre-trained word vectors. CONCLUSION: Our study demonstrates that CNN model with pre-trained medical concept vectors could accurately identify target report-pairs with overlapping body sites and potentially accelerate the retrieving process for imaging diagnosis quality measurement.",1
"Automatic segmentation of prostate MRI using convolutional neural networks: Investigating the impact of network architecture on the accuracy of volume measurement and MRI-ultrasound registration. Convolutional neural networks (CNNs) have recently led to significant advances in automatic segmentations of anatomical structures in medical images, and a wide variety of network architectures are now available to the research community. For applications such as segmentation of the prostate in magnetic resonance images (MRI), the results of the PROMISE12 online algorithm evaluation platform have demonstrated differences between the best-performing segmentation algorithms in terms of numerical accuracy using standard metrics such as the Dice score and boundary distance. These small differences in the segmented regions/boundaries outputted by different algorithms may potentially have an unsubstantial impact on the results of downstream image analysis tasks, such as estimating organ volume and multimodal image registration, which inform clinical decisions. This impact has not been previously investigated. In this work, we quantified the accuracy of six different CNNs in segmenting the prostate in 3D patient T2-weighted MRI scans and compared the accuracy of organ volume estimation and MRI-ultrasound (US) registration errors using the prostate segmentations produced by different networks. Networks were trained and tested using a set of 232 patient MRIs with labels provided by experienced clinicians. A statistically significant difference was found among the Dice scores and boundary distances produced by these networks in a non-parametric analysis of variance (p < 0.001 and p < 0.001, respectively), where the following multiple comparison tests revealed that the statistically significant difference in segmentation errors were caused by at least one tested network. Gland volume errors (GVEs) and target registration errors (TREs) were then estimated using the CNN-generated segmentations. Interestingly, there was no statistical difference found in either GVEs or TREs among different networks, (p=0.34 and p=0.26, respectively). This result provides a real-world example that these networks with different segmentation performances may potentially provide indistinguishably adequate registration accuracies to assist prostate cancer imaging applications. We conclude by recommending that the differences in the accuracy of downstream image analysis tasks that make use of data output by automatic segmentation methods, such as CNNs, within a clinical pipeline should be taken into account when selecting between different network architectures, in addition to reporting the segmentation accuracy.",1
"CATARACTS: Challenge on automatic tool annotation for cataRACT surgery. Surgical tool detection is attracting increasing attention from the medical image analysis community. The goal generally is not to precisely locate tools in images, but rather to indicate which tools are being used by the surgeon at each instant. The main motivation for annotating tool usage is to design efficient solutions for surgical workflow analysis, with potential applications in report generation, surgical training and even real-time decision support. Most existing tool annotation algorithms focus on laparoscopic surgeries. However, with 19 million interventions per year, the most common surgical procedure in the world is cataract surgery. The CATARACTS challenge was organized in 2017 to evaluate tool annotation algorithms in the specific context of cataract surgery. It relies on more than nine hours of videos, from 50 cataract surgeries, in which the presence of 21 surgical tools was manually annotated by two experts. With 14 participating teams, this challenge can be considered a success. As might be expected, the submitted solutions are based on deep learning. This paper thoroughly evaluates these solutions: in particular, the quality of their annotations are compared to that of human interpretations. Next, lessons learnt from the differential analysis of these solutions are discussed. We expect that they will guide the design of efficient surgery monitoring tools in the near future.",1
"Identification of natural compound inhibitors against PfDXR: A hybrid structure-based molecular modeling approach and molecular dynamics simulation studies. In the present contribution, multicomplex-based pharmacophore studies were carried out on the structural proteome of Plasmodium falciparum 1-deoxy-D-xylulose-5-phosphate reductoisomerase. Among the constructed models, a representative model with complementary features, accountable for the inhibition was used as a primary filter for the screening of database molecules. Auxiliary evaluations of the screened molecules were performed via drug-likeness and molecular docking studies. Subsequently, the stability of the docked inhibitors was envisioned by molecular dynamics simulations, principle component analysis, and molecular mechanics-Poisson-Boltzmann surface area-based free binding energy calculations. The stability assessment of the hits was done by comparing with the reference (beta-substituted fosmidomycin analog, LC5) to prioritize more potent candidates. All the complexes showed stable dynamic behavior while three of them displayed higher binding free energy compared with the reference. The work resulted in the identification of the compounds with diverse scaffolds, which could be used as initial leads for the design of novel PfDXR inhibitors.",0
"Maternal exposure to imazalil disrupts the endocrine system in F1 generation mice. The fungicide imazalil (IMZ), an AR antagonist, has been linked to endocrine disruption in animals. Here, adult female C57BL/6 mice were administered IMZ through their drinking water at levels of 0, 0.025‰ and 0.25‰ during the gestation and lactation periods (the exposed females are marked as F0, and the offspring are marked as F1). Then, we evaluated the physiological, biochemical and gene expression levels in mice after maternal IMZ exposure. The genes involved in sex hormone receptors, cholesterol synthesis and T synthesis were generally inhibited, and the serum total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C) levels were also decreased in the F0 generation female mice. In addition, after F0 IMZ exposure, ovarian androgen receptor (AR) expression was significantly inhibited, and the androgen levels in the serum increased significantly. This may lead to the appearance of progressive virilization during pregnancy. This phenomenon leads to an aromatase deficiency in the F1 generation mice, which results in a decrease in androgen conversion into estrogen and androgen accumulation. In addition, the mRNA expression of key genes and the serum TC, HDL-C, and LDL-C levels increased in the F1 generation after maternal exposure to IMZ. In addition, testicular TC and LDL-C levels also decreased in the F1 generation male mice. Molecular docking analysis revealed that key hydrogen bonds were formed by nitrogen atoms of the imidazole bonds with Trp751 of the ARs. Our data suggests that maternal IMZ exposure could induce endocrine disruption in the next generation of mice.",0
"A clinical algorithm to diagnose differences of sex development. The diagnosis and management of children born with ambiguous genitalia is challenging for clinicians. Such differences of sex development (DSDs) are congenital conditions in which chromosomal, gonadal, or anatomical sex is atypical. The aetiology of DSDs is very heterogenous and a precise diagnosis is essential for management of genetic, endocrine, surgical, reproductive, and psychosocial issues. In this Review, we outline a step-by-step approach, compiled in a diagnostic algorithm, for the clinical assessment and molecular diagnosis of a patient with ambiguity of the external genitalia on initial presentation. We appraise established and emerging technologies and their effect on diagnosis, and discuss current controversies.",0
"Micro-Net: A unified model for segmentation of various objects in microscopy images. Object segmentation and structure localization are important steps in automated image analysis pipelines for microscopy images. We present a convolution neural network (CNN) based deep learning architecture for segmentation of objects in microscopy images. The proposed network can be used to segment cells, nuclei and glands in fluorescence microscopy and histology images after slight tuning of input parameters. The network trains at multiple resolutions of the input image, connects the intermediate layers for better localization and context and generates the output using multi-resolution deconvolution filters. The extra convolutional layers which bypass the max-pooling operation allow the network to train for variable input intensities and object size and make it robust to noisy data. We compare our results on publicly available data sets and show that the proposed network outperforms recent deep learning algorithms.",0
"Exploring specific prognostic biomarkers in triple-negative breast cancer. Lacking of both prognostic biomarkers and therapeutic targets, triple-negative breast cancer (TNBC) underscores pivotal needs to uncover novel biomarkers and viable therapies. MicroRNAs have broad biological functions in cancers and may serve as ideal biomarkers. In this study, by data mining of the Cancer Genome Atlas database, we screened out 4 differentially-expressed microRNAs (DEmiRNAs) between TNBC and normal samples: miR-135b-5p, miR-9-3p, miR-135b-3p and miR-455-5p. They were specially correlated with the prognosis of TNBC but not non-TNBC. The weighted correlation network analysis (WGCNA) for potential target genes of 3 good prognosis-related DEmiRNAs (miR-135b-5p, miR-9-3p, miR-135b-3p) identified 4 hub genes with highly positive correlation with TNBC subtype: FOXC1, BCL11A, FAM171A1 and RGMA. The targeting relationships between miR-9-3p and FOXC1/FAM171A1, miR-135b-3p and RGMA were validated by dual-luciferase reporter assays. Importantly, the regulatory functions of 4 DEmiRNAs and 3 verified target genes on cell proliferation and migration were explored in TNBC cell lines. In conclusion, we shed lights on these 4 DEmiRNAs (miR-135b-5p, miR-9-3p, miR-135b-3p, miR-455-5p) and 3 hub genes (FOXC1, FAM171A1, RGMA) as specific prognostic biomarkers and promising therapeutic targets for TNBC.",0
"Implementation of machine learning algorithms to create diabetic patient re-admission profiles. BACKGROUND: Machine learning is a branch of Artificial Intelligence that is concerned with the design and development of algorithms, and it enables today's computers to have the property of learning. Machine learning is gradually growing and becoming a critical approach in many domains such as health, education, and business. METHODS: In this paper, we applied machine learning to the diabetes dataset with the aim of recognizing patterns and combinations of factors that characterizes or explain re-admission among diabetes patients. The classifiers used include Linear Discriminant Analysis, Random Forest, k-Nearest Neighbor, Naïve Bayes, J48 and Support vector machine. RESULTS: Of the 100,000 cases, 78,363 were diabetic and over 47% were readmitted.Based on the classes that models produced, diabetic patients who are more likely to be readmitted are either women, or Caucasians, or outpatients, or those who undergo less rigorous lab procedures, treatment procedures, or those who receive less medication, and are thus discharged without proper improvements or administration of insulin despite having been tested positive for HbA1c. CONCLUSION: Diabetic patients who do not undergo vigorous lab assessments, diagnosis, medications are more likely to be readmitted when discharged without improvements and without receiving insulin administration, especially if they are women, Caucasians, or both.",1
"Understanding the importance of key risk factors in predicting chronic bronchitic symptoms using a machine learning approach. BACKGROUND: Chronic respiratory symptoms involving bronchitis, cough and phlegm in children are underappreciated but pose a significant public health burden. Efforts for prevention and management could be supported by an understanding of the relative importance of determinants, including environmental exposures. Thus, we aim to develop a prediction model for bronchitic symptoms. METHODS: Schoolchildren from the population-based southern California Children's Health Study were visited annually from 2003 to 2012. Bronchitic symptoms over the prior 12 months were assessed by questionnaire. A gradient boosting model was fit using groups of risk factors (including traffic/air pollution exposures) for all children and by asthma status. Training data consisted of one observation per participant in a random study year (for 50% of participants). Validation data consisted of: (1) a random (later) year in the same participants (within-participant); (2) a random year in participants excluded from the training data (across-participant). RESULTS: At baseline, 13.2% of children had asthma and 18.1% reported bronchitic symptoms. Models performed similarly within- and across-participant. Previous year symptoms/medication use provided much of the predictive ability (across-participant area under the receiver operating characteristic curve (AUC): 0.76 vs 0.78 for all risk factors, in all participants). Traffic/air pollution exposures added modestly to prediction as did body mass index percentile, age and parent stress. CONCLUSIONS: Regardless of asthma status, previous symptoms were the most important predictors of current symptoms. Traffic/air pollution variables contribute modest predictive information, but impact large populations. Methods proposed here could be generalized to personalized exacerbation predictions in future longitudinal studies to support targeted prevention efforts.",1
"Deep Learning for Diagnosis of Chronic Myocardial Infarction on Nonenhanced Cardiac Cine MRI. Background Renal impairment is common in patients with coronary artery disease and, if severe, late gadolinium enhancement (LGE) imaging for myocardial infarction (MI) evaluation cannot be performed. Purpose To develop a fully automatic framework for chronic MI delineation via deep learning on non-contrast material-enhanced cardiac cine MRI. Materials and Methods In this retrospective single-center study, a deep learning model was developed to extract motion features from the left ventricle and delineate MI regions on nonenhanced cardiac cine MRI collected between October 2015 and March 2017. Patients with chronic MI, as well as healthy control patients, had both nonenhanced cardiac cine (25 phases per cardiac cycle) and LGE MRI examinations. Eighty percent of MRI examinations were used for the training data set and 20% for the independent testing data set. Chronic MI regions on LGE MRI were defined as ground truth. Diagnostic performance was assessed by analysis of the area under the receiver operating characteristic curve (AUC). MI area and MI area percentage from nonenhanced cardiac cine and LGE MRI were compared by using the Pearson correlation, paired t test, and Bland-Altman analysis. Results Study participants included 212 patients with chronic MI (men, 171; age, 57.2 years +/- 12.5) and 87 healthy control patients (men, 42; age, 43.3 years +/- 15.5). Using the full cardiac cine MRI, the per-segment sensitivity and specificity for detecting chronic MI in the independent test set was 89.8% and 99.1%, respectively, with an AUC of 0.94. There were no differences between nonenhanced cardiac cine and LGE MRI analyses in number of MI segments (114 vs 127, respectively; P = .38), per-patient MI area (6.2 cm(2) +/- 2.8 vs 5.5 cm(2) +/- 2.3, respectively; P = .27; correlation coefficient, r = 0.88), and MI area percentage (21.5% +/- 17.3 vs 18.5% +/- 15.4; P = .17; correlation coefficient, r = 0.89). Conclusion The proposed deep learning framework on nonenhanced cardiac cine MRI enables the confirmation (presence), detection (position), and delineation (transmurality and size) of chronic myocardial infarction. However, future larger-scale multicenter studies are required for a full validation. Published under a CC BY 4.0 license. Online supplemental material is available for this article. See also the editorial by Leiner in this issue.",1
"Nuclear stress bodies: Interaction of its components in oncogenic regulation. Oncogenesis involves continuous genetic alterations that lead to compromised cellular integrity and immortal cell fate. The cells remain under excessive stress due to endo- and exogenous influences. Human Satellite III long noncoding RNA (SatIII lncRNA) is a key regulator of the global cellular stress response, although its function is poorly explained in cancers. The principal regulator of cancer meshwork is tumor protein p53, which if altered may result in chemoresistance. The heat shock factor 1 (HSF1) being a common molecule between the oncogenic control and global cellular stress acts as an oncogene as well as transcribes SatIII upon heat shock. This prompted us to determine the structure of SatIII RNA and establish the association between SatIII-HSF1-p53. We determined the most stable structure of SatIII RNA with the least energy of − 115.7 kcal/mol. Also, we observed a possible interaction of p53 with SatIII and HSF1 using support vector machine (SVM) algorithm for predicting RNA-protein interaction (RPI). Further, we employ the STRING database to understand if p53 is an interacting component of the nuclear stress bodies (nSBs). A precise inference was drawn from molecular docking which confirmed the interaction of SatIII-HSF1-p53, where a mutated p53 resulted in an altered DNA-binding property with the SatIII molecule. This study being first of its kind infers p53 to be a possible integral component of the nSBs, which may regulate cellular stress response during cancer progression in the presence of HSF1 and SatIII. An extended research on the regulations of SatIII and p53 may open new avenues in the field of apoptosis in cancer and the early approach of molecular targeting.",0
"A validated natural language processing algorithm for brain imaging phenotypes from radiology reports in UK electronic health records. BACKGROUND: Manual coding of phenotypes in brain radiology reports is time consuming. We developed a natural language processing (NLP) algorithm to enable automatic identification of brain imaging in radiology reports performed in routine clinical practice in the UK National Health Service (NHS). METHODS: We used anonymized text brain imaging reports from a cohort study of stroke/TIA patients and from a regional hospital to develop and test an NLP algorithm. Two experts marked up text in 1692 reports for 24 cerebrovascular and other neurological phenotypes. We developed and tested a rule-based NLP algorithm first within the cohort study, and further evaluated it in the reports from the regional hospital. RESULTS: The agreement between expert readers was excellent (Cohen's κ =0.93) in both datasets. In the final test dataset (n = 700) in unseen regional hospital reports, the algorithm had very good performance for a report of any ischaemic stroke [sensitivity 89% (95% CI:81-94); positive predictive value (PPV) 85% (76-90); specificity 100% (95% CI:0.99-1.00)]; any haemorrhagic stroke [sensitivity 96% (95% CI: 80-99), PPV 72% (95% CI:55-84); specificity 100% (95% CI:0.99-1.00)]; brain tumours [sensitivity 96% (CI:87-99); PPV 84% (73-91); specificity: 100% (95% CI:0.99-1.00)] and cerebral small vessel disease and cerebral atrophy (sensitivity, PPV and specificity all > 97%). We obtained few reports of subarachnoid haemorrhage, microbleeds or subdural haematomas. In 110,695 reports from NHS Tayside, atrophy (n = 28,757, 26%), small vessel disease (15,015, 14%) and old, deep ischaemic strokes (10,636, 10%) were the commonest findings. CONCLUSIONS: An NLP algorithm can be developed in UK NHS radiology records to allow identification of cohorts of patients with important brain imaging phenotypes at a scale that would otherwise not be possible.",1
"Social determinants of health in relation to firearm-related homicides in the United States: A nationwide multilevel cross-sectional study. Background Gun violence has shortened the average life expectancy of Americans, and better knowledge about the root causes of gun violence is crucial to its prevention. While some empirical evidence exists regarding the impacts of social and economic factors on violence and firearm homicide rates, to the author's knowledge, there has yet to be a comprehensive and comparative lagged, multilevel investigation of major social determinants of health in relation to firearm homicides and mass shootings. Methods and findings This study used negative binomial regression models and geolocated gun homicide incident data from January 1, 2015, to December 31, 2015, to explore and compare the independent associations of key state-, county-, and neighborhood-level social determinants of health-social mobility, social capital, income inequality, racial and economic segregation, and social spending-with neighborhood firearm-related homicides and mass shootings in the United States, accounting for relevant state firearm laws and a variety of state, county, and neighborhood (census tract [CT]) characteristics. Latitude and longitude coordinates on firearm-related deaths were previously collected by the Gun Violence Archive, and then linked by the British newspaper The Guardian to CTs according to 2010 Census geographies. The study population consisted of all 74,134 CTs as defined for the 2010 Census in the 48 states of the contiguous US. The final sample spanned 70,579 CTs, containing an estimated 314,247,908 individuals, or 98% of the total US population in 2015. The analyses were based on 13,060 firearm-related deaths in 2015, with 11,244 non-mass shootings taking place in 8,673 CTs and 141 mass shootings occurring in 138 CTs. For area-level social determinants, lag periods of 3 to 17 years were examined based on existing theory, empirical evidence, and data availability. County-level institutional social capital (levels of trust in institutions), social mobility, income inequality, and public welfare spending exhibited robust relationships with CT-level gun homicide rates and the total numbers of combined non-mass and mass shooting homicide incidents and non-mass shooting homicide incidents alone. A 1-standard deviation (SD) increase in institutional social capital was linked to a 19% reduction in the homicide rate (incidence rate ratio [IRR] = 0.81, 95% CI 0.73-0.91, p < 0.001) and a 17% decrease in the number of firearm homicide incidents (IRR = 0.83, 95% CI 0.73-0.95, p = 0.01). Upward social mobility was related to a 25% reduction in the gun homicide rate (IRR = 0.75, 95% CI 0.66-0.86, p < 0.001) and a 24% decrease in the number of homicide incidents (IRR = 0.76, 95% CI 0.67-0.87, p < 0.001). Meanwhile, 1-SD increases in the neighborhood percentages of residents in poverty and males living alone were associated with 26%-27% and 12% higher homicide rates, respectively. Study limitations include possible residual confounding by factors at the individual/household level, and lack of disaggregation of gun homicide data by gender and race/ethnicity. Conclusions This study finds that the rich-poor gap, level of citizens' trust in institutions, economic opportunity, and public welfare spending are all related to firearm homicide rates in the US. Further establishing the causal nature of these associations and modifying these social determinants may help to address the growing gun violence epidemic and reverse recent life expectancy declines among Americans.",0
"Rethinking drug design in the artificial intelligence era. Artificial intelligence (AI) tools are increasingly being applied in drug discovery. While some protagonists point to vast opportunities potentially offered by such tools, others remain sceptical, waiting for a clear impact to be shown in drug discovery projects. The reality is probably somewhere in-between these extremes, yet it is clear that AI is providing new challenges not only for the scientists involved but also for the biopharma industry and its established processes for discovering and developing new medicines. This article presents the views of a diverse group of international experts on the 'grand challenges' in small-molecule drug discovery with AI and the approaches to address them.",0
"Substantial Cardiovascular Morbidity in Adults With Lower-Complexity Congenital Heart Disease. BACKGROUND: Although lower-complexity cardiac malformations constitute the majority of adult congenital heart disease (ACHD), the long-term risks of adverse cardiovascular events and relationship with conventional risk factors in this population are poorly understood. We aimed to quantify the risk of adverse cardiovascular events associated with lower-complexity ACHD that is unmeasured by conventional risk factors. METHODS: A multitiered classification algorithm was used to select individuals with lower-complexity ACHD and individuals without ACHD for comparison among >500 000 British adults in the UK Biobank. ACHD diagnoses were subclassified as isolated aortic valve and noncomplex defects. Time-to-event analyses were conducted for the primary end points of fatal or nonfatal acute coronary syndrome, ischemic stroke, heart failure, and atrial fibrillation and a secondary combined end point for major adverse cardiovascular events. Maximum follow-up time for the study period was 22 years with retrospectively and prospectively collected data from the UK Biobank. RESULTS: We identified 2006 individuals with lower-complexity ACHD and 497 983 unexposed individuals in the UK Biobank (median age at enrollment, 58 [interquartile range, 51-63] years). Of the ACHD-exposed group, 59% were male, 51% were current or former smokers, 30% were obese, and 69%, 41%, and 7% were diagnosed or treated for hypertension, hyperlipidemia, and diabetes mellitus, respectively. After adjustment for 12 measured cardiovascular risk factors, ACHD remained strongly associated with the primary end points, with hazard ratios ranging from 2.0 (95% CI, 1.5-2.8; P<0.001) for acute coronary syndrome to 13.0 (95% CI, 9.4-18.1; P<0.001) for heart failure. ACHD-exposed individuals with </=2 cardiovascular risk factors had a 29% age-adjusted incidence rate of major adverse cardiovascular events, in contrast to 13% in individuals without ACHD with >/=5 risk factors. CONCLUSIONS: Individuals with lower-complexity ACHD had a higher burden of adverse cardiovascular events relative to the general population that was unaccounted for by conventional cardiovascular risk factors. These findings highlight the need for closer surveillance of patients with mild to moderate ACHD and further investigation into management and mechanisms of cardiovascular risk unique to this growing population of high-risk adults.",0
"Ferulic acid inhibits interleukin 17-dependent expression of nodal pathogenic mediators in fibroblast-like synoviocytes of rheumatoid arthritis. Interleukin 17 (IL-17), a proinflammatory cytokine produced by T helper (Th) 17 cells, potentially controls fibroblast-like synoviocytes (FLS)-mediated disease activity of rheumatoid arthritis (RA) via IL-17/ IL-17 receptor type A (IL-17RA)/signal transducer and activator of transcription 3 (STAT-3) signaling cascade. This has suggested that targeting IL-17 signaling could serve as an important strategy to treat FLS-mediated RA progression. Ferulic acid (FA), a key polyphenol, attenuates the development of gouty arthritis and cancer through its anti-inflammatory effects, but its therapeutic efficiency on IL-17 signaling in FLS-mediated RA pathogenesis remains unknown. In the current study, FA markedly inhibited the IL-17-mediated expression of its specific transmembrane receptor IL-17RA in FLS isolated from adjuvant-induced arthritis (AA) rats. Importantly, FA dramatically suppressed the IL-17-mediated expression of toll-like receptor 3 (TLR-3), cysteine-rich angiogenic inducer 61 (Cyr61), IL-23, granulocyte-macrophage colony stimulating factor (GM-CSF) in AA-FLS via the inhibition of IL-17/IL-17RA/STAT-3 signaling cascade. In addition, FA significantly decreased the formation of osteoclast cells and bone resorption potential in a coculture system consisting of IL-17 treated AA-FLS and rat bone marrow derived monocytes/macrophages. Furthermore, FA remarkably inhibited the IL-17-mediated expression of receptor activator of nuclear factor κ-Β ligand (RANKL) and increased the expression of osteoprotegerin (OPG) in AA-FLS via the regulation of IL-17/IL-17RA/STAT-3 signaling cascade. The therapeutic efficiency of FA on IL-17 signaling was further confirmed by knockdown of IL-17RA using small interfering RNA or blocking of STAT-3 activation with S3I-201. The molecular docking analysis revealed that FA manifests significant ligand efficiency toward IL-17RA, STAT-3, IL-23, and RANKL proteins. This study provides new evidence that FA can be used as a potential therapeutic agent for inhibiting IL-17-mediated disease severity and bone erosion in RA.",0
"A Decision Analysis of Follow-up and Treatment Algorithms for Nonsolid Pulmonary Nodules. Purpose To evaluate management strategies and treatment options for patients with ground-glass nodules (GGNs) by using decision-analysis models. Materials and Methods A simulation was developed for 1 000 000 hypothetical patients with GGNs undergoing follow-up per the Lung Imaging Reporting and Data System (Lung-RADS) recommendations. The initial age range was 55-75 years (mean, 64 years). Nodules could grow and develop solid components over time. Clinically significant malignancy rates were calibrated to data from the National Lung Screening Trial. Annual versus 3-year-interval follow-up of Lung-RADS category 2 nodules was compared, and different treatment strategies were tested (stereotactic body radiation therapy, surgery, and no therapy). Results Overall, 2.3% (22 584 of 1 000 000) of nodules were clinically significant malignancies; 6.3% (62 559 of 1 000 000) of nodules were treated. Only 30% (18 668 of 62 559) of Lung-RADS category 4B or 4X nodules were clinically significant malignancies. The risk of clinically significant malignancy for persistent nonsolid nodules after baseline was higher than Lung-RADS estimates for categories 2 and 3 (3% vs <1% and 1%-2%, respectively). Overall survival (OS) at 10 years was 72% (527 827 of 737 306; 95% confidence interval [CI]: 71%, 72%) with annual follow-up and 71% (526 507 of 737 306; 95% CI: 71%, 72%) with 3-year-interval follow-up (P < .01). At 10 years, OS among patients whose nodules progressed to Lung-RADS category 4B or 4X was 80% after radiation therapy (49 945 of 62 559; 95% CI: 80%, 80%), 79% after surgery (49 139 of 62 559; 95% CI: 78%, 79%), and 74% after no therapy (46 512 of 62 559; 95% CI: 74%, 75%) (P < .01). Conclusion Simulation modeling suggests that the follow-up interval for evaluating ground-glass nodules can be increased from 1 year to 3 years with minimal change in outcomes. Stereotactic body radiation therapy demonstrated the best outcomes compared with lobectomy and with no therapy for nonsolid nodules. (c) RSNA, 2018 Online supplemental material is available for this article.",0
"Using a machine learning algorithm to predict acute graft-versus-host disease following allogeneic transplantation. Acute graft-versus-host disease (aGVHD) is 1 of the critical complications that often occurs following allogeneic hematopoietic stem cell transplantation (HSCT). Thus far, various types of prediction scores have been created using statistical calculations. The primary objective of this study was to establish and validate the machine learning–dependent index for predicting aGVHD. This was a retrospective cohort study that involved analyzing databases of adult HSCT patients in Japan. The alternating decision tree (ADTree) machine learning algorithm was applied to develop models using the training cohort (70%). The ADTree algorithm was confirmed using the hazard model on data from the validation cohort (30%). Data from 26 695 HSCT patients transplanted from allogeneic donors between 1992 and 2016 were included in this study. The cumulative incidence of aGVHD was 42.8%. Of >40 variables considered, 15 were adapted into a model for aGVHD prediction. The model was tested in the validation cohort, and the incidence of aGVHD was clearly stratified according to the categorized ADTree scores; the cumulative incidence of aGVHD was 29.0% for low risk and 58.7% for high risk (hazard ratio, 2.57). Predicting scores for aGVHD also demonstrated the link between the risk of development aGVHD and overall survival after HSCT. The machine learning algorithms produced clinically reasonable and robust risk stratification scores. The relatively high reproducibility and low impacts from the interactions among the variables indicate that the ADTree algorithm, along with the other data-mining approaches, may provide tools for establishing risk score.",1
"A computational method to characterize the missense mutations in the catalytic domain of GAA protein causing Pompe disease. Pompe disease is an autosomal recessive lysosomal storage disease caused by acid α-glucosidase (GAA) deficiency, resulting in intralysosomal accumulation of glycogen, including cardiac, skeletal, and smooth muscle cells. The GAA gene is located on chromosome 17 (17q25.3), the GAA protein consists of 952 amino acids; of which 378 amino acids (347-726) falls within the catalytic domain of the protein and comprises of active sites (518 and 521) and binding sites (404, 600, 616, and 674). In this study, we used several computational tools to classify the missense mutations in the catalytic domain of GAA for their pathogenicity and stability. Eight missense mutations (R437C, G478R, N573H, Y575S, G605D, V642D, L705P, and L712P) were predicted to be pathogenic and destabilizing to the protein structure. These mutations were further subjected to phenotyping analysis using SNPeffect 4.0 to predict the chaperone binding sites and structural stability of the protein. The mutations R437C and G478R were found to compromise the chaperone-binding activity with GAA. Molecular docking analysis revealed that the G478R mutation to be more significant and hinders binding to the DNJ (Miglustat) compared with the R437C. Further molecular dynamic analysis for the two mutations demonstrated that the G478R mutation was acquired higher deviation, fluctuation, and lower compactness with decreased intramolecular hydrogen bonds compared to the mutant R437C. These data are expected to serve as a platform for drug design against Pompe disease and will serve as an ultimate tool for variant classification and interpretations.",0
"Training recurrent neural networks robust to incomplete data: Application to Alzheimer's disease progression modeling. Disease progression modeling (DPM) using longitudinal data is a challenging machine learning task. Existing DPM algorithms neglect temporal dependencies among measurements, make parametric assumptions about biomarker trajectories, do not model multiple biomarkers jointly, and need an alignment of subjects' trajectories. In this paper, recurrent neural networks (RNNs) are utilized to address these issues. However, in many cases, longitudinal cohorts contain incomplete data, which hinders the application of standard RNNs and requires a pre-processing step such as imputation of the missing values. Instead, we propose a generalized training rule for the most widely used RNN architecture, long short-term memory (LSTM) networks, that can handle both missing predictor and target values. The proposed LSTM algorithm is applied to model the progression of Alzheimer's disease (AD) using six volumetric magnetic resonance imaging (MRI) biomarkers, i.e., volumes of ventricles, hippocampus, whole brain, fusiform, middle temporal gyrus, and entorhinal cortex, and it is compared to standard LSTM networks with data imputation and a parametric, regression-based DPM method. The results show that the proposed algorithm achieves a significantly lower mean absolute error (MAE) than the alternatives with p<0.05 using Wilcoxon signed rank test in predicting values of almost all of the MRI biomarkers. Moreover, a linear discriminant analysis (LDA) classifier applied to the predicted biomarker values produces a significantly larger area under the receiver operating characteristic curve (AUC) of 0.90vs. at most 0.84 with p<0.001 using McNemar's test for clinical diagnosis of AD. Inspection of MAE curves as a function of the amount of missing data reveals that the proposed LSTM algorithm achieves the best performance up until more than 74% missing values. Finally, it is illustrated how the method can successfully be applied to data with varying time intervals. This paper shows that built-in handling of missing values in training an LSTM network benefits the application of RNNs in neurodegenerative disease progression modeling in longitudinal cohorts.",1
"Ethics of Artificial Intelligence in Radiology: Summary of the Joint European and North American Multisociety Statement. This is a condensed summary of an international multisociety statement on ethics of artificial intelligence (AI) in radiology produced by the ACR, European Society of Radiology, RSNA, Society for Imaging Informatics in Medicine, European Society of Medical Imaging Informatics, Canadian Association of Radiologists, and American Association of Physicists in Medicine. AI has great potential to increase efficiency and accuracy throughout radiology, but it also carries inherent pitfalls and biases. Widespread use of AI-based intelligent and autonomous systems in radiology can increase the risk of systemic errors with high consequence and highlights complex ethical and societal issues. Currently, there is little experience using AI for patient care in diverse clinical settings. Extensive research is needed to understand how to best deploy AI in clinical practice. This statement highlights our consensus that ethical use of AI in radiology should promote well-being, minimize harm, and ensure that the benefits and harms are distributed among stakeholders in a just manner. We believe AI should respect human rights and freedoms, including dignity and privacy. It should be designed for maximum transparency and dependability. Ultimate responsibility and accountability for AI remains with its human designers and operators for the foreseeable future. The radiology community should start now to develop codes of ethics and practice for AI that promote any use that helps patients and the common good and should block use of radiology data and algorithms for financial gain without those two attributes. This article is a simultaneous joint publication in Radiology, Journal of the American College of Radiology, Canadian Association of Radiologists Journal, and Insights into Imaging. Published under a CC BY-NC-ND 4.0 license. Online supplemental material is available for this article.",0
"Computational design and evaluation of β-sheet breaker peptides for destabilizing Alzheimer's amyloid-β42 protofibrils. The β-sheet breaker (BSB) peptides interfere with amyloid fibril assembly and used as therapeutic agents in the treatment of Alzheimer's disease (AD). In this regard, a simple yet effective in silico screening methodology was applied in the present study to evaluate a potential 867 pentapeptide library based on known BSB peptide, LPFFD, for destabilizing Aβ42 protofibrils. The molecular docking based virtual screening was used to filter out pentapeptides having binding affinities stronger than LPFFD. In the next step, binding free energies of the top 10 pentapeptides were evaluated using the MM-PBSA method. The residue-wise binding free energy analysis reveals that two pentapeptides, PVFFE, and PPFYE, bind to the surface of Aβ42 protofibril and another pentapeptide, PPFFE, bind in the core region of Aβ42 protofibril. By employing molecular dynamics simulation as a post filter for the top-hit peptides from MM-PBSA, the pentapeptides, PPFFE, PVFFE, and PPFYE, have been identified as potential BSB peptides for destabilizing Aβ42 protofibril structure. The conformational microstate analysis, a significant decrease in the β-sheet content of Aβ42 protofibril, a loss in the total number of hydrogen bonds in Aβ42 protofibril, Asp23-Lys28 salt bridge destabilization and analysis of the free energy surfaces highlight Aβ42 protofibril structure destabilization in presence of pentapeptides. Among three top-hit pentapeptides, PPFFE displayed the most potent Aβ42 protofibril destabilization effect that shifted the energy minima toward lowest value of β-sheet content as well as lowest number of hydrogen bonds in Aβ42 protofibril. The in silico screening workflow presented in the study highlight an alternative tool for designing novel peptides with enhanced BSB ability as potential therapeutic agents for AD.",0
"Discovery of potent necroptosis inhibitors targeting RIPK1 kinase activity for the treatment of inflammatory disorder and cancer metastasis. Necroptosis is a form of regulated necrosis controlled by receptor-interacting kinase 1 (RIPK1 or RIP1), RIPK3 (RIP3), and pseudokinase mixed lineage kinase domain-like protein (MLKL). Increasing evidence suggests that necroptosis is closely associated with pathologies including inflammatory diseases, neurodegenerative diseases, and cancer metastasis. Herein, we discovered the small-molecule PK6 and its derivatives as a novel class of necroptosis inhibitors that directly block the kinase activity of RIPK1. Optimization of PK6 led to PK68, which has improved efficacy for the inhibition of RIPK1-dependent necroptosis, with an EC50 of around 14–22 nM in human and mouse cells. PK68 efficiently blocks cellular activation of RIPK1, RIPK3, and MLKL upon necroptosis stimuli. PK68 displays reasonable selectivity for inhibition of RIPK1 kinase activity and favorable pharmacokinetic properties. Importantly, PK68 provides strong protection against TNF-α-induced systemic inflammatory response syndrome in vivo. Moreover, pre-treatment of PK68 significantly represses metastasis of both melanoma cells and lung carcinoma cells in mice. Together, our study demonstrates that PK68 is a potent and selective inhibitor of RIPK1 and also highlights its great potential for use in the treatment of inflammatory disorders and cancer metastasis.",0
"Challenges related to artificial intelligence research in medical imaging and the importance of image analysis competitions. In recent years, there has been enormous interest in applying artificial intelligence (AI) to radiology. Although some of this interest may have been driven by exaggerated expectations that the technology can outperform radiologists in some tasks, there is a growing body of evidence that illustrates its limitations in medical imaging. The true potential of the technique probably lies somewhere in the middle, and AI will ultimately play a key role in medical imaging in the future. The limitless power of computers makes AI an ideal candidate to provide the standardization, consistency, and dependability needed to support radiologists in their mission to provide excellent patient care. However, important roadblocks currently limit the expansion of this field in medical imaging. This article reviews some of the challenges and potential solutions to advance the field forward, with focus on the experience gained by hosting image-based competitions.",0
"Weakly supervised mitosis detection in breast histopathology images using concentric loss. Developing new deep learning methods for medical image analysis is a prevalent research topic in machine learning. In this paper, we propose a deep learning scheme with a novel loss function for weakly supervised breast cancer diagnosis. According to the Nottingham Grading System, mitotic count plays an important role in breast cancer diagnosis and grading. To determine the cancer grade, pathologists usually need to manually count mitosis from a great deal of histopathology images, which is a very tedious and time-consuming task. This paper proposes an automatic method for detecting mitosis. We regard the mitosis detection task as a semantic segmentation problem and use a deep fully convolutional network to address it. Different from conventional training data used in semantic segmentation system, the training label of mitosis data is usually in the format of centroid pixel, rather than all the pixels belonging to a mitosis. The centroid label is a kind of weak label, which is much easier to annotate and can save the effort of pathologists a lot. However, technically this weak label is not sufficient for training a mitosis segmentation model. To tackle this problem, we expand the single-pixel label to a novel label with concentric circles, where the inside circle is a mitotic region and the ring around the inside circle is a ""middle ground"". During the training stage, we do not compute the loss of the ring region because it may have the presence of both mitotic and non-mitotic pixels. This new loss termed as ""concentric loss"" is able to make the semantic segmentation network be trained with the weakly annotated mitosis data. On the generated segmentation map from the segmentation model, we filter out low confidence and obtain mitotic cells. On the challenging ICPR 2014 MITOSIS dataset and AMIDA13 dataset, we achieve a 0.562 F-score and 0.673 F-score respectively, outperforming all previous approaches significantly. On the latest TUPAC16 dataset, we obtain a F-score of 0.669, which is also the state-of-the-art result. The excellent results quantitatively demonstrate the effectiveness of the proposed mitosis segmentation network with the concentric loss. All of our code has been made publicly available at https://github.com/ChaoLi977/SegMitos_mitosis_detection.",1
"Artificial Intelligence Classification of Central Visual Field Patterns in Glaucoma. PURPOSE: To quantify the central visual field (VF) loss patterns in glaucoma using artificial intelligence. DESIGN: Retrospective study. PARTICIPANTS: VFs of 8712 patients with 13 951 Humphrey 10-2 test results from 13 951 eyes for cross-sectional analyses, and 824 patients with at least 5 reliable 10-2 test results at 6-month intervals or more from 1191 eyes for longitudinal analyses. METHODS: Total deviation values were used to determine the central VF patterns using the most recent 10-2 test results. A 24-2 VF within a 3-month window of the 10-2 tests was used to stage eyes into mild, moderate, or severe functional loss using the Hodapp-Anderson-Parrish scale at baseline. Archetypal analysis was applied to determine the central VF patterns. Cross-validation was performed to determine the optimal number of patterns. Stepwise regression was applied to select the optimal feature combination of global indices, average baseline decomposition coefficients from central VFs archetypes, and other factors to predict central VF mean deviation (MD) slope based on the Bayesian information criterion (BIC). MAIN OUTCOME MEASURES: The central VF patterns stratified by severity stage based on 24-2 test results and a model to predict the central VF MD change over time using baseline test results. RESULTS: From cross-sectional analysis, 17 distinct central VF patterns were determined for the 13 951 eyes across the spectrum of disease severity. These central VF patterns could be divided into isolated superior loss, isolated inferior loss, diffuse loss, and other loss patterns. Notably, 4 of the 5 patterns of diffuse VF loss preserved the less vulnerable inferotemporal zone, whereas they lost most of the remaining more vulnerable zone described by the Hood model. Inclusion of coefficients from central VF archetypical patterns strongly improved the prediction of central VF MD slope (BIC decrease, 35; BIC decrease of >6 indicating strong prediction improvement) than using only the global indices of 2 baseline VF results. Eyes with baseline VF results with more superonasal and inferonasal loss were more likely to show worsening MD over time. CONCLUSIONS: We quantified central VF patterns in glaucoma, which were used to improve the prediction of central VF worsening compared with using only global indices.",0
"Biomarkers identify the Binswanger type of vascular cognitive impairment. Binswanger’s disease is a form of subcortical ischemic vascular disease (SIVD-BD) with extensive white matter changes. To test the hypothesis that biomarkers could improve classification of SIVD-BD, we recruited 62 vascular cognitive impairment and dementia (VCID) patients. Multimodal biomarkers were collected at entry into the study based on clinical and neuropsychological testing, multimodal magnetic resonance imaging (MRI), and cerebrospinal fluid (CSF) analysis. The patients’ diagnoses were confirmed by long-term follow-up, and they formed a “training set” to test classification methods, including (1) subcortical ischemic vascular disease score (SIVDS), (2) exploratory factor analysis (EFA), (3) logistic regression (LR), and (4) random forest (RF). A subsequently recruited cohort of 43 VCID patients with provisional diagnoses were used as a “test” set to calculate the probability of SIVD-BD based on biomarkers obtained at entry. We found that N-acetylaspartate (NAA) on proton magnetic resonance spectroscopy (1H-MRS) was the best variable for classification, followed by matrix metalloproteinase-2 in CSF and blood–brain barrier permeability on MRI. Both LR and RF performed better in diagnosing SIVD-BD than either EFA or SIVDS. Two-year follow-up of provisional diagnosis patients confirmed the accuracy of statistically derived classifications. We propose that biomarker-based classification methods could diagnose SIVD-BD patients earlier, facilitating clinical trials.",1
"Improving reference prioritisation with PICO recognition. BACKGROUND: Machine learning can assist with multiple tasks during systematic reviews to facilitate the rapid retrieval of relevant references during screening and to identify and extract information relevant to the study characteristics, which include the PICO elements of patient/population, intervention, comparator, and outcomes. The latter requires techniques for identifying and categorising fragments of text, known as named entity recognition. METHODS: A publicly available corpus of PICO annotations on biomedical abstracts is used to train a named entity recognition model, which is implemented as a recurrent neural network. This model is then applied to a separate collection of abstracts for references from systematic reviews within biomedical and health domains. The occurrences of words tagged in the context of specific PICO contexts are used as additional features for a relevancy classification model. Simulations of the machine learning-assisted screening are used to evaluate the work saved by the relevancy model with and without the PICO features. Chi-squared and statistical significance of positive predicted values are used to identify words that are more indicative of relevancy within PICO contexts. RESULTS: Inclusion of PICO features improves the performance metric on 15 of the 20 collections, with substantial gains on certain systematic reviews. Examples of words whose PICO context are more precise can explain this increase. CONCLUSIONS: Words within PICO tagged segments in abstracts are predictive features for determining inclusion. Combining PICO annotation model into the relevancy classification pipeline is a promising approach. The annotations may be useful on their own to aid users in pinpointing necessary information for data extraction, or to facilitate semantic search.",1
"Development and validation of GMI signature based random survival forest prognosis model to predict clinical outcome in acute myeloid leukemia. Background: Acute myeloid leukemia (AML) is a disease with marked molecular heterogeneity and a high early death rate. Our aim was to investigate an integrated Gene expression, Mirna and miRNA-mRNA Interactions (GMI) signature for improving risk stratification of AML. Methods: We identified differentially expressed genes by pooling a large number of 861 human AML patients and 75 normal cases. We then used miRWalk to identify the functional miRNA-mRNA regulatory module. The GMI signature based random survival forest (RSF) prognosis model was developed from training data set and evaluated in independent patient cohorts from The Cancer Genome Atlas (TCGA) dataset (N = 147). Univariate and multivariate Cox proportional hazards regression analyses were applied to evaluate the prognostic value of GMI signature. Results: We identified 139 differentially expressed genes between normal and abnormal AML samples. We discovered the functional miRNA-mRNA regulatory module which participate in the network of cancer progression. We named 23 differentially expressed genes and 16 validated target miRNAs as the GMI signature. The RSF model-based scores separated independent patient cohorts into two groups with significantly different overall survival (C-index = 0.59, hazard ratio [HR], 2.12; 95% confidence interval [CI], 1.11-4.03; p = 0.019). Similar results were obtained with reversed training and testing datasets (C-index = 0.58, hazard ratio [HR], 2.08; 95% confidence interval [CI], 1.02-4.24; p = 0.038). The GMI signature score contributed more information about recurrence than standard clinical covariates. Conclusion: The GMI signature based RSF prognosis model not only reflects regulatory relationships from identified miRNA-mRNA module but also informs patient prognosis. While in the TCGA data set the GMI signature score contributed additional information about recurrence in comparison to standard clinical covariates, further studies are needed to determine its clinical significance.",0
"Early temporal characteristics of elderly patient cognitive impairment in electronic health records. BACKGROUND: The aging population has led to an increase in cognitive impairment (CI) resulting in significant costs to patients, their families, and society. A research endeavor on a large cohort to better understand the frequency and severity of CI is urgent to respond to the health needs of this population. However, little is known about temporal trends of patient health functions (i.e., activity of daily living [ADL]) and how these trends are associated with the onset of CI in elderly patients. Also, the use of a rich source of clinical free text in electronic health records (EHRs) to facilitate CI research has not been well explored. The aim of this study is to characterize and better understand early signals of elderly patient CI by examining temporal trends of patient ADL and analyzing topics of patient medical conditions in clinical free text using topic models. METHODS: The study cohort consists of physician-diagnosed CI patients (n = 1,435) and cognitively unimpaired (CU) patients (n = 1,435) matched by age and sex, selected from patients 65 years of age or older at the time of enrollment in the Mayo Clinic Biobank. A corpus analysis was performed to examine the basic statistics of event types and practice settings where the physician first diagnosed CI. We analyzed the distribution of ADL in three different age groups over time before the development of CI. Furthermore, we applied three different topic modeling approaches on clinical free text to examine how patients' medical conditions change over time when they were close to CI diagnosis. RESULTS: The trajectories of ADL deterioration became steeper in CI patients than CU patients approximately 1 to 1.5 year(s) before the actual physician diagnosis of CI. The topic modeling showed that the topic terms were mostly correlated and captured the underlying semantics relevant to CI when approaching to CI diagnosis. CONCLUSIONS: There exist notable differences in temporal trends of basic and instrumental ADL between CI and CU patients. The trajectories of certain individual ADL, such as bathing and responsibility of own medication, were closely associated with CI development. The topic terms obtained by topic modeling methods from clinical free text have a potential to show how CI patients' conditions evolve and reveal overlooked conditions when they close to CI diagnosis.",0
"Passenger Hotspot Mutations in Cancer. Current statistical models for assessing hotspot significance do not properly account for variation in site-specific mutability, thereby yielding many false-positives. We thus (i) detail a Log-normal-Poisson (LNP) background model that accounts for this variability in a manner consistent with models of mutagenesis; (ii) use it to show that passenger hotspots arise from all common mutational processes; and (iii) apply it to a ∼10,000-patient cohort to nominate driver hotspots with far fewer false-positives compared with conventional methods. Overall, we show that many cancer hotspot mutations recurring at the same genomic site across multiple tumors are actually passenger events, recurring at inherently mutable genomic sites under no positive selection.",0
"Network Topologies That Can Achieve Dual Function of Adaptation and Noise Attenuation. Many signaling systems execute adaptation under circumstances that require noise attenuation. Here, we identify an intrinsic trade-off existing between sensitivity and noise attenuation in the three-node networks. We demonstrate that although fine-tuning timescales in three-node adaptive networks can partially mediate this trade-off in this context, it prolongs adaptation time and imposes unrealistic parameter constraints. By contrast, four-node networks can effectively decouple adaptation and noise attenuation to achieve dual function without a trade-off, provided that these functions are executed sequentially. We illustrate ideas in seven biological examples, including Dictyostelium discoideum chemotaxis and the p53 signaling network and find that adaptive networks are often associated with a noise attenuation module. Our approach may be applicable to finding network design principles for other dual and multiple functions.",0
"Hierarchical-Clustering, Scaffold-Mining Exercises and Dynamics Simulations for Effectual Inhibitors Against Lipid-A Biosynthesis of Helicobacter pylori. Introduction: Treatment failures of standard regimens and new strains egression are due to the augmented drug resistance conundrum. These confounding factors now became the drug designers spotlight to implement therapeutics against Helicobacter pylori strains and to safeguard infected victims with devoid of adverse drug reactions. Thereby, to navigate the chemical space for medicine, paramount vital drug target opting considerations should be imperative. The study is therefore aimed to develop potent therapeutic variants against an insightful extrapolative, common target LpxC as a follow-up to previous studies. Methods: We explored the relationships between existing inhibitors and novel leads at the scaffold level in an appropriate conformational plasticity for lead-optimization campaign. Hierarchical-clustering and shape-based screening against an in-house library of > 21 million compounds resulted in panel of 11,000 compounds. Rigid-receptor docking through virtual screening cascade, quantum-polarized-ligand, induced-fit dockings, post-docking processes and system stability assessments were performed. Results: After docking experiments, an enrichment performance unveiled seven ranked actives better binding efficiencies with Zinc-binding potency than substrate and in-actives (decoy-set) with ROC (1.0) and area under accumulation curve (0.90) metrics. Physics-based membrane permeability accompanied ADME/T predictions and long-range dynamic simulations of 250 ns chemical time have depicted good passive diffusion with no toxicity of leads and sustained consistency of lead1-LpxC in the physiological milieu respectively. Conclusions: In the study, as these static outcomes obtained from this approach competed with the substrate and existing ligands in binding affinity estimations as well as positively correlated from different aspects of predictions, which could facilitate promiscuous new chemical entities against H. pylori.",0
"Global Dimensions of Plant Virus Diseases: Current Status and Future Perspectives. Viral diseases provide a major challenge to twenty-first century agriculture worldwide. Climate change and human population pressures are driving rapid alterations in agricultural practices and cropping systems that favor destructive viral disease outbreaks. Such outbreaks are strikingly apparent in subsistence agriculture in food-insecure regions. Agricultural globalization and international trade are spreading viruses and their vectors to new geographical regions with unexpected consequences for food production and natural ecosystems. Due to the varying epidemiological characteristics of diverent viral pathosystems, there is no one-size-fits-all approach toward mitigating negative viral disease impacts on diverse agroecological production systems. Advances in scientific understanding of virus pathosystems, rapid technological innovation, innovative communication strategies, and global scientific networks provide opportunities to build epidemiologic intelligence of virus threats to crop production and global food security. A paradigm shift toward deploying integrated, smart, and eco-friendly strategies is required to advance virus disease management in diverse agricultural cropping systems.",0
"Using parallelized incremental meta-docking can solve the conformational sampling issue when docking large ligands to proteins. Background: Docking large ligands, and especially peptides, to protein receptors is still considered a challenge in computational structural biology. Besides the issue of accurately scoring the binding modes of a protein-ligand complex produced by a molecular docking tool, the conformational sampling of a large ligand is also often considered a challenge because of its underlying combinatorial complexity. In this study, we evaluate the impact of using parallelized and incremental paradigms on the accuracy and performance of conformational sampling when docking large ligands. We use five datasets of protein-ligand complexes involving ligands that could not be accurately docked by classical protein-ligand docking tools in previous similar studies. Results: Our computational evaluation shows that simply increasing the amount of conformational sampling performed by a protein-ligand docking tool, such as Vina, by running it for longer is rarely beneficial. Instead, it is more efficient and advantageous to run several short instances of this docking tool in parallel and group their results together, in a straightforward parallelized docking protocol. Even greater accuracy and efficiency are achieved by our parallelized incremental meta-docking tool, DINC, showing the additional benefits of its incremental paradigm. Using DINC, we could accurately reproduce the vast majority of the protein-ligand complexes we considered. Conclusions: Our study suggests that, even when trying to dock large ligands to proteins, the conformational sampling of the ligand should no longer be considered an issue, as simple docking protocols using existing tools can solve it. Therefore, scoring should currently be regarded as the biggest unmet challenge in molecular docking.",0
"Optomotor Swimming in Larval Zebrafish Is Driven by Global Whole-Field Visual Motion and Local Light-Dark Transitions. Kist and Portugues use reverse correlation in an optomotor behavioral assay in larval zebrafish to identify the stereotypic filter that elicits swimming. It consists of a forward-moving local light-dark transition alongside global whole-field motion. The luminance profile strongly affects behavioral parameters, and filter-specific activity is spread across the brain.",0
"CALDER: Inferring Phylogenetic Trees from Longitudinal Tumor Samples. Longitudinal DNA sequencing of cancer patients yields insight into how tumors evolve over time or in response to treatment. However, sequencing data from bulk tumor samples often have considerable ambiguity in clonal composition, complicating the inference of ancestral relationships between clones. We introduce Cancer Analysis of Longitudinal Data through Evolutionary Reconstruction (CALDER), an algorithm to infer phylogenetic trees from longitudinal bulk DNA sequencing data. CALDER explicitly models a longitudinally observed phylogeny incorporating constraints that longitudinal sampling imposes on phylogeny reconstruction. We show on simulated bulk tumor data that longitudinal constraints substantially reduce ambiguity in phylogeny reconstruction and that CALDER outperforms existing methods that do not leverage this longitudinal information. On real data from two chronic lymphocytic leukemia patients, we find that CALDER reconstructs more plausible and parsimonious phylogenies than existing methods, with CALDER phylogenies containing fewer tumor clones per sample. CALDER's use of longitudinal information will be advantageous in further studies of tumor heterogeneity and evolution.",0
"Identifying Smoking Environments from Images of Daily Life with Deep Learning. Importance: Environments associated with smoking increase a smoker's craving to smoke and may provoke lapses during a quit attempt. Identifying smoking risk environments from images of a smoker's daily life provides a basis for environment-based interventions. Objective: To apply a deep learning approach to the clinically relevant identification of smoking environments among settings that smokers encounter in daily life. Design, Setting, and Participants: In this cross-sectional study, 4902 images of smoking (n = 2457) and nonsmoking (n = 2445) locations were photographed by 169 smokers from Durham, North Carolina, and Pittsburgh, Pennsylvania, areas from 2010 to 2016. These images were used to develop a probabilistic classifier to predict the location type (smoking or nonsmoking location), thus relating objects and settings in daily environments to established smoking patterns. The classifier combines a deep convolutional neural network with an interpretable logistic regression model and was trained and evaluated via nested cross-validation with participant-wise partitions (ie, out-of-sample prediction). To contextualize model performance, images taken by 25 randomly selected participants were also classified by smoking cessation experts. As secondary validation, craving levels reported by participants when viewing unfamiliar environments were compared with the model's predictions. Data analysis was performed from September 2017 to May 2018. Main Outcomes and Measures: Classifier performance (accuracy and area under the receiver operating characteristic curve [AUC]), comparison with 4 smoking cessation experts, contribution of objects and settings to smoking environment status (standardized model coefficients), and correlation with participant-reported craving. Results: Of 169 participants, 106 (62.7%) were from Durham (53 [50.0%] female; mean [SD] age, 41.4 [12.0] years) and 63 (37.3%) were from Pittsburgh (31 [51.7%] female; mean [SD] age, 35.2 [13.8] years). A total of 4902 images were available for analysis, including 3386 from Durham (mean [SD], 31.9 [1.3] images per participant) and 1516 from Pittsburgh (mean [SD], 24.1 [0.5] images per participant). Images were evenly split between the 2 classes, with 2457 smoking images (50.1%) and 2445 nonsmoking images (49.9%). The final model discriminated smoking vs nonsmoking environments with a mean (SD) AUC of 0.840 (0.024) (accuracy [SD], 76.5% [1.6%]). A model trained only with images from Durham participants effectively classified images from Pittsburgh participants (AUC, 0.757; accuracy, 69.2%), and a model trained only with images from Pittsburgh participants effectively classified images from Durham participants (AUC, 0.821; accuracy, 75.0%), suggesting good generalizability between geographic areas. Only 1 expert's performance was a statistically significant improvement compared with the classifier (μ =.05). Median self-reported craving was significantly correlated with model-predicted smoking environment status (ρ = 0.894; P =.003). Conclusions and Relevance: In this study, features of daily environments predicted smoking vs nonsmoking status consistently across participants. The findings suggest that a deep learning approach can identify environments associated with smoking, can predict the probability that any image of daily life represents a smoking environment, and can potentially trigger environment-based interventions. This work demonstrates a framework for predicting how daily environments may influence target behaviors or symptoms that may have broad applications in mental and physical health..",1
"Assessment and Management of Patients at Risk for Suicide: Synopsis of the 2019 U.S. Department of Veterans Affairs and U.S. Department of Defense Clinical Practice Guidelines. Description: In May 2019, the U.S. Department of Veterans Affairs (VA) and U.S. Department of Defense (DoD) approved an update to the 2013 joint clinical practice guideline for assessing and managing patients who are at risk for suicide. This guideline provides health care providers with a framework by which to screen for, evaluate, treat, and manage the individual needs and preferences of VA and DoD patients who may be at risk for suicide. Methods: In January 2018, the VA/DoD Evidence-Based Practice Work Group convened to develop a joint VA/DoD guideline including clinical stakeholders and conforming to the National Academy of Medicine's tenets for trustworthy clinical practice guidelines. The guideline panel drafted key questions, systematically searched and evaluated the literature through April 2018, created algorithms, and advanced 22 recommendations in accordance with the GRADE (Grading of Recommendations Assessment, Development and Evaluation) system. Recommendations: This synopsis, which includes 3 clinical practice algorithms, summarizes the key recommendations of the guideline related to screening and evaluation, risk management and treatment, and other management methods. Risk management and treatment recommendations address both pharmacologic and nonpharmacologic approaches for patients with suicidal ideation and behavior. Other management methods address lethal means safety (such as restricting access to firearms, poisons, and medications and installing barriers to prevent jumping from lethal heights) and population health strategies.",0
"Species-Level Salivary Microbial Indicators of Well-Resolved Periodontitis: A Preliminary Investigation. Objective: To profile the salivary microbiomes of a Hong Kong Chinese cohort at a species-level resolution and determine species that discriminated clinically resolved periodontitis from periodontally healthy cases. Methods: Salivary microbiomes of 35 Hong Kong Chinese subjects' under routine supportive dental care were analyzed. All subjects had been treated for any dental caries or periodontal disease with all restorative treatment completed at least 1 year ago and had ≤3 residual pockets. They were categorized based on a past diagnosis of chronic periodontitis into “healthy” (H) or “periodontitis” (P) categories. Unstimulated whole saliva was collected, genomic DNA was isolated, and high throughput Illumina MiSeq sequencing of 16S rRNA (V3-V4) gene amplicons was performed. The sequences were assigned taxonomy at the species level by using a BLASTN based algorithm that used a combined reference database of HOMD RefSeqV14.51, HOMD RefSeqExtended V1.1 and GreenGeneGold. Species-level OTUs were subjected to downstream analysis in QIIME and R. For P and H group comparisons, community diversity measures were compared, differentially abundant species were determined using DESeq2, and disease indicator species were determined using multi-level pattern analysis within the R package “indicspecies.” Results: P subjects were significantly older than H subjects (p = 0.003) but not significantly different in their BOP scores (p = 0.82). No significant differences were noted in alpha diversity measures after adjusting for age, gender, and BOP or in the beta diversity estimates. Four species; Treponema sp. oral taxon 237, TM7 sp. Oral Taxon A56, Prevotella sp. oral taxon 314, Prevotella sp. oral taxon 304, and Capnocytophaga leadbetteri were significantly more abundant in P than in the H group. Indicator species analysis showed 7 significant indicators species of P group. Fusobacterium sp oral taxon 370 was the sole positive indicator of P group (positive predictive value = 0.9, p = 0.04). Significant indicators of the H category were Leptotrichia buccalis, Corynebacterium matruchotii, Leptotrichia hofstadii, and Streptococcus intermedius. Conclusion: This exploratory study showed salivary microbial species could discriminate treated, well-maintained chronic periodontitis from healthy controls with similar gingival inflammation levels. The findings suggest that certain salivary microbiome features may identify periodontitis-susceptible individuals despite clinical disease resolution.",0
"Validation of an algorithmic nutritional approach in children undergoing chemotherapy for cancer. Background: Undernutrition impacts clinical outcome adversely in children with cancer. This study aimed to validate a nutritional algorithm with specific application to the low- and middle-income country (LMIC) setting. Procedure: Fifty children with a new diagnosis of cancer were enrolled in this randomized interventional study. Weight, height/length, and mid-upper-arm circumference (MUAC) were measured at baseline. The study arm was administered nutritional care as per the algorithm and the control arm received the institutional standard of care. Weight was monitored regularly and MUAC was repeated after 3 months. Children were classified based on weight for height if <2 years of age or body mass index if ≥2 years, as normal, wasted, and severely wasted. The algorithmic approach comprised administration of oral supplements, nasogastric feeds, and/or parenteral nutrition based on objective assessment of the nutritional status. Results: Fifty patients were analyzed (study: 25, control: 25). Four in the study arm (16%) and six in the control arm (24%) had wasting at baseline. MUAC was <5th percentile in 15 (60%) and 13 (52%) patients in the study and control arms, respectively. At the end of 3 months, the median increment in weight was 0.8 kg (interquartile range [IQR]: –0.02; 2.00) and 0.0 kg (IQR: –0.70; 1.25) in the study and control arms, respectively (P =.153). The median increment in MUAC was 1.20 cm (IQR: 0.10; 2.30) and 0.00 cm (IQR: –0.50; 1.10) in the study and control arms, respectively (P =.020). Conclusions: The application of an algorithm designed for use in LMICs resulted in significant improvement in nutritional status, as measured by MUAC.",0
"Multimodal hyper-connectivity of functional networks using functionally-weighted LASSO for MCI classification. Recent works have shown that hyper-networks derived from blood-oxygen-level-dependent (BOLD) fMRI, where an edge (called hyper-edge) can be connected to more than two nodes, are effective biomarkers for MCI classification. Although BOLD fMRI is a high temporal resolution fMRI approach to assess alterations in brain networks, it cannot pinpoint to a single correlation of neuronal activity since BOLD signals are composite. In contrast, arterial spin labeling (ASL) is a lower temporal resolution fMRI technique for measuring cerebral blood flow (CBF) that can provide quantitative, direct brain network physiology measurements. This paper proposes a novel sparse regression algorithm for inference of the integrated hyper-connectivity networks from BOLD fMRI and ASL fMRI. Specifically, a least absolution shrinkage and selection operator (LASSO) algorithm, which is constrained by the functional connectivity derived from ASL fMRI, is employed to estimate hyper-connectivity for characterizing BOLD-fMRI-based functional interaction among multiple regions. An ASL-derived functional connectivity is constructed by using an Ultra-GroupLASSO-UOLS algorithm, where the combination of ultra-least squares (ULS) criterion with a group LASSO (GroupLASSO) algorithm is applied to detect the topology of ASL-based functional connectivity networks, and then an ultra-orthogonal least squares (UOLS) algorithm is used to estimate the connectivity strength. By combining the complementary characterization conveyed by rs-fMRI and ASL fMRI, our multimodal hyper-networks demonstrated much better discriminative characteristics than either the conventional pairwise connectivity networks or the unimodal hyper-connectivity networks. Experimental results on publicly available ADNI dataset demonstrate that the proposed method outperforms the existing single modality based sparse functional connectivity inference methods.",1
"Single-Cell RNA-Seq of the Developing Cardiac Outflow Tract Reveals Convergent Development of the Vascular Smooth Muscle Cells. Cardiac outflow tract (OFT) is a major hotspot for congenital heart diseases. A thorough understanding of the cellular diversity, transitions, and regulatory networks of normal OFT development is essential to decipher the etiology of OFT malformations. We performed single-cell transcriptomic sequencing of 55,611 mouse OFT cells from three developmental stages that generally correspond to the early, middle, and late stages of OFT remodeling and septation. Known cellular transitions, such as endothelial-to-mesenchymal transition, have been recapitulated. In particular, we identified convergent development of the vascular smooth muscle cell (VSMC) lineage where intermediate cell subpopulations were found to be involved in either myocardial-to-VSMC trans-differentiation or mesenchymal-to-VSMC transition. Finally, we uncovered transcriptional regulators potentially governing cellular transitions. Our study provides a single-cell reference map of cell states for normal OFT development and paves the way for further studies of the etiology of OFT malformations at the single-cell level. Liu et al. present single-cell transcriptomes of over 50,000 cells from the developing cardiac outflow tract in mice. They identify convergent development of the vascular smooth muscle cell (VSMC) lineage, with these cells arising either by a myocardial-to-VSMC trans-differentiation or mesenchymal-to-VSMC transition.",0
"Attention-Deficit/Hyperactivity Disorder Symptoms Are Associated With Lower Adaptive Behavior Skills in Children With Autism. Objective: To investigate the predictive power of comorbid attention-deficit/hyperactivity disorder (ADHD) symptoms on adaptive behavior skills in children who have an autism specrum disorder (ASD) diagnosis. Method: This case-control study recruited 347 children from specialty clinics, primary care, and the community. Linear regression was used to test whether ADHD Rating Scale, Fourth Edition, scores of autistic children associated with poorer adaptive behavior scores, after controlling for the effects of age, intelligence, sex, and ASD symptom severity. Adaptive behaviors were measured with the Vineland Adaptive Behavior Scales, Second Edition. Subsequent analyses tested this relation in a subset of the ASD sample with subclinical ADHD symptoms (n = 179) and another with teacher ratings (n = 153). Prior relations between age with adaptive behaviors and ADHD symptoms were replicated and age was explored as a moderator. Results: ADHD symptoms predicted poor adaptive behavior scores in the full ASD sample (caregiver ratings, ΔR2 = 0.033–0.119; teacher ratings, ΔR2 = 0.113–0.163) and in the subset with subclinical ADHD symptoms (caregiver ratings, ΔR2= 0.023–0.030; teacher ratings, ΔR2 = 0.097–0.159) after controlling for confounds. Age correlated negatively with ADHD symptoms (r = −0.21) and adaptive behaviors (−0.17 < r < −0.39) in the full ASD sample. Age did not moderate the effect of ADHD symptoms on adaptive behaviors. Conclusion: ADHD symptoms predict poorer adaptive behavior for autistic children across settings, even for children with subclinical co-occurring ADHD symptoms. Findings support a Research Domain Criteria framework that behavioral impairments and functional outcome measures exist along a continuum.",0
"LOX-1, the common therapeutic target in hypercholesterolemia: A new perspective of antiatherosclerotic action of aegeline. Background. Lectin-like oxidized low-density lipoprotein receptor-1 (LOX-1) is the major receptor for oxidized low-density lipoprotein (Ox-LDL) in the aorta of aged rats. Ox-LDL initiates LOX-1 activation in the endothelium of lipid-accumulating sites of both animal and human subjects of hypercholesterolemia. Targeting LOX-1 may provide a novel diagnostic strategy towards hypercholesterolemia and vascular diseases. Hypothesis. This study was planned to address whether aegeline (AG) could bind to LOX-1 with a higher affinity and modulate the uptake of Ox-LDL in hypercholesterolemia. Study Design. Thirty-six Wistar rats were divided into six groups. The pathology group rats were fed with high-cholesterol diet (HCD) for 45 days, and the treatment group rats were fed with HCD and aegeline/atorvastatin (AV) for the last 30 days. In vivo and in vitro experiments were carried out to assay the markers of atherosclerosis like Ox-LDL and LOX-1 levels. Histopathological examination was performed. Oil Red O staining was carried out in the IC-21 cell line. Docking studies were performed. Results. AG administration effectively brought down the lipid levels induced by HCD. The lowered levels of Ox-LDL and LOX-1 in AG-administered rats deem it to be a potent antihypercholesterolemic agent. Compared to AV, AG had a pronounced effect in downregulating the expression of lipids evidenced by Oil Red O staining. AG binds with LOX-1 at a higher affinity validated by docking. Conclusion. This study validates AG to be an effective stratagem in bringing down the lipid stress induced by HCD and can be deemed as an antihypercholesterolemic agent.",0
"Combination of dihydromyricetin and ondansetron strengthens antiproliferative efficiency of adriamycin in K562/ADR through downregulation of SORCIN: A new strategy of inhibiting P-glycoprotein. Though the advancement of chemotherapy drugs alleviates the progress of cancer, long-term therapy with anticancer agents gradually leads to acquired multidrug resistance (MDR), which limits the survival outcomes in patients. It was shown that dihydromyricetin (DMY) could partly reverse MDR by suppressing P-glycoprotein (P-gp) and soluble resistance-related calcium-binding protein (SORCIN) independently. To reverse MDR more effectively, a new strategy was raised, that is, circumventing MDR by the coadministration of DMY and ondansetron (OND), a common antiemetic drug, during cancer chemotherapy. Meanwhile, the interior relation between P-gp and SORCIN was also revealed. The combination of DMY and OND strongly enhanced antiproliferative efficiency of adriamycin (ADR) because of the increasing accumulation of ADR in K562/ADR-resistant cell line. DMY could downregulate the expression of SORCIN and P-gp via the ERK/Akt pathways, whereas OND could not. In addition, it was proved that SORCIN suppressed ERK and Akt to inhibit P-gp by the silence of SORCIN, however, not vice versa. Finally, the combination of DMY, OND, and ADR led to G2/M cell cycle arrest and apoptosis via resuming P53 function and restraining relevant proteins expression. These fundamental findings provided a promising approach for further treatment of MDR.",0
"A new era: artificial intelligence and machine learning in prostate cancer. Artificial intelligence (AI) - the ability of a machine to perform cognitive tasks to achieve a particular goal based on provided data - is revolutionizing and reshaping our health-care systems. The current availability of ever-increasing computational power, highly developed pattern recognition algorithms and advanced image processing software working at very high speeds has led to the emergence of computer-based systems that are trained to perform complex tasks in bioinformatics, medical imaging and medical robotics. Accessibility to 'big data' enables the 'cognitive' computer to scan billions of bits of unstructured information, extract the relevant information and recognize complex patterns with increasing confidence. Computer-based decision-support systems based on machine learning (ML) have the potential to revolutionize medicine by performing complex tasks that are currently assigned to specialists to improve diagnostic accuracy, increase efficiency of throughputs, improve clinical workflow, decrease human resource costs and improve treatment choices. These characteristics could be especially helpful in the management of prostate cancer, with growing applications in diagnostic imaging, surgical interventions, skills training and assessment, digital pathology and genomics. Medicine must adapt to this changing world, and urologists, oncologists, radiologists and pathologists, as high-volume users of imaging and pathology, need to understand this burgeoning science and acknowledge that the development of highly accurate AI-based decision-support applications of ML will require collaboration between data scientists, computer researchers and engineers.",0
"Metabolic Diversity in Human Non-Small Cell Lung Cancer Cells. Intermediary metabolism in cancer cells is regulated by diverse cell-autonomous processes, including signal transduction and gene expression patterns, arising from specific oncogenotypes and cell lineages. Although it is well established that metabolic reprogramming is a hallmark of cancer, we lack a full view of the diversity of metabolic programs in cancer cells and an unbiased assessment of the associations between metabolic pathway preferences and other cell-autonomous processes. Here, we quantified metabolic features, mostly from the 13C enrichment of molecules from central carbon metabolism, in over 80 non-small cell lung cancer (NSCLC) cell lines cultured under identical conditions. Because these cell lines were extensively annotated for oncogenotype, gene expression, protein expression, and therapeutic sensitivity, the resulting database enables the user to uncover new relationships between metabolism and these orthogonal processes. Metabolic reprogramming influences therapeutic sensitivity in cancer, but the scope of metabolic diversity among cancer cells is unknown. Chen et al. characterized metabolic phenotypes in over 80 non-small cell lung cancer cell lines and then used genomics, transcriptomics, proteomics, and therapeutic sensitivities to uncover relationships between metabolism and orthogonal processes.",0
"Task representations in neural networks trained to perform many cognitive tasks. The brain has the ability to flexibly perform many tasks, but the underlying mechanism cannot be elucidated in traditional experimental and modeling studies designed for one task at a time. Here, we trained single network models to perform 20 cognitive tasks that depend on working memory, decision making, categorization, and inhibitory control. We found that after training, recurrent units can develop into clusters that are functionally specialized for different cognitive processes, and we introduce a simple yet effective measure to quantify relationships between single-unit neural representations of tasks. Learning often gives rise to compositionality of task representations, a critical feature for cognitive flexibility, whereby one task can be performed by recombining instructions for other tasks. Finally, networks developed mixed task selectivity similar to recorded prefrontal neurons after learning multiple tasks sequentially with a continual-learning technique. This work provides a computational platform to investigate neural representations of many cognitive tasks.",1
"Exploring the effect of inhibitor AKB-9778 on VE-PTP by molecular docking and molecular dynamics simulation. Diabetic macular edema, also known as diabetic eye disease, is mainly caused by the overexpression of vascular endothelial protein tyrosine phosphatase (VE-PTP) at hypoxia/ischemic. AKB-9778 is a known VE-PTP inhibitor that can effectively interact with the active site of VE-PTP to inhibit the activity of VE-PTP. However, the binding pattern of VE-PTP with AKB-9778 and the dynamic implications of AKB-9778 on VE-PTP system at the molecular level are poorly understood. Through molecular docking, it was found that the AKB-9778 was docked well in the binding pocket of VE-PTP by the interactions of hydrogen bond and Van der Waals. Furthermore, after molecular dynamic simulations on VE-PTP system and VE-PTP AKB-9778 system, a series of postdynamic analyses found that the flexibility and conformation of the active site undergone an obvious transition after VE-PTP binding with AKB-9778. Moreover, by constructing the RIN, it was found that the different interactions in the active site were the detailed reasons for the conformational differences between these two systems. Thus, the finding here might provide a deeper understanding of AKB-9778 as VE-PTP Inhibitor.",0
"Matrine is a novel inhibitor of the TMEM16A chloride channel with antilung adenocarcinoma effects. Calcium-activated chloride channels (CaCCs) are ion channels with key roles in physiological processes. They are abnormally expressed in various cancers, including esophageal squamous cell cancer, head and neck squamous cell carcinoma, colorectal cancer, and gastrointestinal stromal tumors. The CaCC component TMEM16A/ANO1 was recently shown to be overexpressed in lung adenocarcinoma cells and may serve as a tumorigenic protein. In this study, we determined that matrine is a potent TMEM16A inhibitor that exerts anti-lung adenocarcinoma effects. Patch clamp experiments showed that matrine inhibited TMEM16A current in a concentration-dependent manner with an IC 50 of 27.94 ± 4.78 μM. Molecular simulation and site-directed mutation experiments demonstrated that the matrine-sensitive sites of the TMEM16A channel involve the amino acids Y355, F411, and F415. Results of cell viability and wound healing assays showed that matrine significantly inhibited the proliferation and migration of LA795 cells, which exhibit high TMEM16A expression. In contrast, matrine has only weak inhibitory effect on CCD-19Lu and HeLa cells lacking TMEM16A expression. Matrine-induced effects on the proliferation and migration of LA795 cells were abrogated upon shRNA-mediated TMEM16A knockdown in LA795 cells. Finally, in vivo experiments demonstrated that matrine dramatically inhibited the growth of lung adenocarcinoma xenograft tumors in mice but did not affect mouse body weight. Collectively, these data indicate that matrine is an effective and safe TMEM16A inhibitor and that TMEM16A is the target of matrine anti-lung adenocarcinoma activity. These findings provide new insight for the development of novel treatments for lung adenocarcinoma.",0
"Clinical text classification with rule-based features and knowledge-guided convolutional neural networks. BACKGROUND: Clinical text classification is an fundamental problem in medical natural language processing. Existing studies have cocnventionally focused on rules or knowledge sources-based feature engineering, but only a limited number of studies have exploited effective representation learning capability of deep learning methods. METHODS: In this study, we propose a new approach which combines rule-based features and knowledge-guided deep learning models for effective disease classification. Critical Steps of our method include recognizing trigger phrases, predicting classes with very few examples using trigger phrases and training a convolutional neural network (CNN) with word embeddings and Unified Medical Language System (UMLS) entity embeddings. RESULTS: We evaluated our method on the 2008 Integrating Informatics with Biology and the Bedside (i2b2) obesity challenge. The results demonstrate that our method outperforms the state-of-the-art methods. CONCLUSION: We showed that CNN model is powerful for learning effective hidden features, and CUIs embeddings are helpful for building clinical text representations. This shows integrating domain knowledge into CNN models is promising.",1
"Detecting early-warning signals of influenza outbreak based on dynamic network marker. The seasonal outbreaks of influenza infection cause globally respiratory illness, or even death in all age groups. Given early-warning signals preceding the influenza outbreak, timely intervention such as vaccination and isolation management effectively decrease the morbidity. However, it is usually a difficult task to achieve the real-time prediction of influenza outbreak due to its complexity intertwining both biological systems and social systems. By exploring rich dynamical and high-dimensional information, our dynamic network marker/biomarker (DNM/DNB) method opens a new way to identify the tipping point prior to the catastrophic transition into an influenza pandemics. In order to detect the early-warning signals before the influenza outbreak by applying DNM method, the historical information of clinic hospitalization caused by influenza infection between years 2009 and 2016 were extracted and assembled from public records of Tokyo and Hokkaido, Japan. The early-warning signal, with an average of 4-week window lead prior to each seasonal outbreak of influenza, was provided by DNM-based on the hospitalization records, providing an opportunity to apply proactive strategies to prevent or delay the onset of influenza outbreak. Moreover, the study on the dynamical changes of hospitalization in local district networks unveils the influenza transmission dynamics or landscape in network level.",0
"Identification and characterization of potential membrane-bound molecular drug targets of methicillin-resistant Staphylococcus aureus using in silico approaches. Aim. To identify novel putative drug targets of methicillin-resistant S. aureus (MRSA) through subtractive proteome analysis. Methods. Identification of non-homologous proteins in the human proteome, search of MRSA essential genes and evaluation of drug target novelty were performed using a protein BLAST server. Unique metabolic pathways identification was carried out using data and tools from KEGG (Kyoto Encyclopedia of Genes and Genomes). Prediction of sub-cellular proteins localization was performed using combination of PSORT v. 3.0.2, CELLO v. 2.5, iLoc-Gpos, and Pred-Lipo tools. Homology modeling was performed using SWISS-MODEL, Phyre2, I-TASSER web-servers and the MODELLER software. Results. Proteomes of six annotated methicillin-resistant strains: MRSA ATCC BAA-1680, H-EMRSA-15, LA MRSA ST398, MRSA 252, MRSA ST772, UTSW MRSA 55 were initially analyzed. The proteome analysis of the MRSA strains in several consequent steps allowed to identify two molecular targets: diadenylate cyclase and D-alanyl-lipoteichoic acid biosynthesis (DltB) protein which meet the requirements of being essential, membrane-bound, non-homologous to human proteome, involved in unique metabolic pathways and new in terms of not having approved drugs. Using the homology modeling approach, we have built three-dimensional structures of these proteins and predicted their ligand-binding sites. Conclusions. We used classical bioinformatics approaches to identify two molecular targets of MRSA:diadenylate cyclase and DltB which can be used for further rational drug design in order to find novel therapeutic agents for treatment of multidrug resistant staphylococcal infection.",0
"EEG-based image classification via a region-level stacked bi-directional deep learning framework. BACKGROUND: As a physiological signal, EEG data cannot be subjectively changed or hidden. Compared with other physiological signals, EEG signals are directly related to human cortical activities with excellent temporal resolution. After the rapid development of machine learning and artificial intelligence, the analysis and calculation of EEGs has made great progress, leading to a significant boost in performances for content understanding and pattern recognition of brain activities across the areas of both neural science and computer vision. While such an enormous advance has attracted wide range of interests among relevant research communities, EEG-based classification of brain activities evoked by images still demands efforts for further improvement with respect to its accuracy, generalization, and interpretation, yet some characters of human brains have been relatively unexplored. METHODS: We propose a region-level stacked bi-directional deep learning framework for EEG-based image classification. Inspired by the hemispheric lateralization of human brains, we propose to extract additional information at regional level to strengthen and emphasize the differences between two hemispheres. The stacked bi-directional long short-term memories are used to capture the dynamic correlations hidden from both the past and the future to the current state in EEG sequences. RESULTS: Extensive experiments are carried out and our results demonstrate the effectiveness of our proposed framework. Compared with the existing state-of-the-arts, our framework achieves outstanding performances in EEG-based classification of brain activities evoked by images. In addition, we find that the signals of Gamma band are not only useful for achieving good performances for EEG-based image classification, but also play a significant role in capturing relationships between the neural activations and the specific emotional states. CONCLUSIONS: Our proposed framework provides an improved solution for the problem that, given an image used to stimulate brain activities, we should be able to identify which class the stimuli image comes from by analyzing the EEG signals. The region-level information is extracted to preserve and emphasize the hemispheric lateralization for neural functions or cognitive processes of human brains. Further, stacked bi-directional LSTMs are used to capture the dynamic correlations hidden in EEG data. Extensive experiments on standard EEG-based image classification dataset validate that our framework outperforms the existing state-of-the-arts under various contexts and experimental setups.",1
"Assessment of Deep Generative Models for High-Resolution Synthetic Retinal Image Generation of Age-Related Macular Degeneration. Importance: Deep learning (DL) used for discriminative tasks in ophthalmology, such as diagnosing diabetic retinopathy or age-related macular degeneration (AMD), requires large image data sets graded by human experts to train deep convolutional neural networks (DCNNs). In contrast, generative DL techniques could synthesize large new data sets of artificial retina images with different stages of AMD. Such images could enhance existing data sets of common and rare ophthalmic diseases without concern for personally identifying information to assist medical education of students, residents, and retinal specialists, as well as for training new DL diagnostic models for which extensive data sets from large clinical trials of expertly graded images may not exist. Objective: To develop DL techniques for synthesizing high-resolution realistic fundus images serving as proxy data sets for use by retinal specialists and DL machines. Design, Setting, and Participants: Generative adversarial networks were trained on 133 821 color fundus images from 4613 study participants from the Age-Related Eye Disease Study (AREDS), generating synthetic fundus images with and without AMD. We compared retinal specialists' ability to diagnose AMD on both real and synthetic images, asking them to assess image gradability and testing their ability to discern real from synthetic images. The performance of AMD diagnostic DCNNs (referable vs not referable AMD) trained on either all-real vs all-synthetic data sets was compared. Main Outcomes and Measures: Accuracy of 2 retinal specialists (T.Y.A.L. and K.D.P.) for diagnosing and distinguishing AMD on real vs synthetic images and diagnostic performance (area under the curve) of DL algorithms trained on synthetic vs real images. Results: The diagnostic accuracy of 2 retinal specialists on real vs synthetic images was similar. The accuracy of diagnosis as referable vs nonreferable AMD compared with certified human graders for retinal specialist 1 was 84.54% (error margin, 4.06%) on real images vs 84.12% (error margin, 4.16%) on synthetic images and for retinal specialist 2 was 89.47% (error margin, 3.45%) on real images vs 89.19% (error margin, 3.54%) on synthetic images. Retinal specialists could not distinguish real from synthetic images, with an accuracy of 59.50% (error margin, 3.93%) for retinal specialist 1 and 53.67% (error margin, 3.99%) for retinal specialist 2. The DCNNs trained on real data showed an area under the curve of 0.9706 (error margin, 0.0029), and those trained on synthetic data showed an area under the curve of 0.9235 (error margin, 0.0045). Conclusions and Relevance: Deep learning-synthesized images appeared to be realistic to retinal specialists, and DCNNs achieved diagnostic performance on synthetic data close to that for real images, suggesting that DL generative techniques hold promise for training humans and machines.",1
"A novel optical tissue clearing protocol for mouse skeletal muscle to visualize endplates in their tissue context. Neuromuscular junctions (NMJs) mediate skeletal muscle contractions and play an important role in several neuromuscular disorders when their morphology and function are compromised. However, due to their small size and sparse distribution throughout the comparatively large, inherently opaque muscle tissue the analysis of NMJ morphology has been limited to teased fiber preparations, longitudinal muscle sections, and flat muscles. Consequently, whole mount analyses of NMJ morphology, numbers, their distribution, and assignment to a given muscle fiber have also been impossible to determine in muscle types that are frequently used in experimental paradigms. This impossibility is exacerbated by the lack of optical tissue clearing techniques that are compatible with clear and persistent NMJ stains. Here, we present MYOCLEAR, a novel and highly reproducible muscle tissue clearing protocol. Based on hydrogel-based tissue clearing methods, this protocol permits the labeling and detection of all NMJs in adult hindleg extensor digitorum longus muscles from wildtype and diseased mice. The method is also applicable to adult mouse diaphragm muscles and can be used for different staining agents, including toxins, lectins, antibodies, and nuclear dyes. It will be useful in understanding the distribution, morphological features, and muscle tissue context of NMJs in hindleg muscle whole mounts for biomedical and basic research.",0
"Artificial intelligence in digital pathology - new tools for diagnosis and precision oncology. In the past decade, advances in precision oncology have resulted in an increased demand for predictive assays that enable the selection and stratification of patients for treatment. The enormous divergence of signalling and transcriptional networks mediating the crosstalk between cancer, stromal and immune cells complicates the development of functionally relevant biomarkers based on a single gene or protein. However, the result of these complex processes can be uniquely captured in the morphometric features of stained tissue specimens. The possibility of digitizing whole-slide images of tissue has led to the advent of artificial intelligence (AI) and machine learning tools in digital pathology, which enable mining of subvisual morphometric phenotypes and might, ultimately, improve patient management. In this Perspective, we critically evaluate various AI-based computational approaches for digital pathology, focusing on deep neural networks and 'hand-crafted' feature-based methodologies. We aim to provide a broad framework for incorporating AI and machine learning tools into clinical oncology, with an emphasis on biomarker development. We discuss some of the challenges relating to the use of AI, including the need for well-curated validation datasets, regulatory approval and fair reimbursement strategies. Finally, we present potential future opportunities for precision oncology.",0
"Attributable Failure of First-line Cancer Treatment and Incremental Costs Associated With Smoking by Patients With Cancer. Importance: Previous studies have shown that continued smoking among patients with cancer can increase overall and cancer-specific mortality, risk for second primary cancer, and risk for toxic effects of cancer treatment. To our knowledge, there have been no efforts to estimate additional costs for cancer treatment attributed to smoking. Objective: To model attributable incremental costs of subsequent cancer treatment associated with continued smoking by patients with cancer. Design, Setting, and Participants: For this economic evaluation, a model was developed in 2018 using data from a 2014 US Surgeon General's report that considered expected failure rates of first-line cancer treatment in nonsmoking patients, smoking prevalence, odds ratio of first-line cancer treatment failure attributed to smoking compared with nonsmoking, and cost of cancer treatment after failure of first-line cancer treatment. Main Outcomes and Measures: Attributable failures of first-line cancer treatment and incremental cost for subsequent treatment associated with continued smoking among patients with cancer. Results: Attributable treatment failures were higher under conditions in which high first-line cure rates were expected in nonsmoking patients compared with conditions in which low cure rates were expected. Peak attributable failures occurred under the conditions in which expected cure rates among nonsmoking patients ranged from 50% to 65%. Under the conditions of a 30% expected treatment failure rate among nonsmoking patients, 20% smoking prevalence, 60% increased risk of failure of first-line cancer treatment, and $100 000 mean added cost of treating a first-line cancer treatment failure, the additional incremental cost per 1000 total patients was estimated to be $2.1 million, reflecting an additional cost of $10 678 per smoking patient. Extrapolation of cost to 1.6 million patients with cancer diagnosed annually reflects a potential $3.4 billion in incremental cost. Conclusions and Relevance: The findings suggest that continued smoking among patients with cancer and the increase in attributable first-line cancer treatment failure is associated with significant incremental costs for subsequent cancer treatments. Additional work appears to be needed to identify optimal methods to mitigate these incremental costs.",0
"Mechanism of imipenem resistance in metallo-β-lactamases expressing pathogenic bacterial spp. and identification of potential inhibitors: An in silico approach. The World Health Organization reports that millions of people around the world are infected with antibiotic-resistant bacteria. Such resistance is more common in Pseudomonas aeruginosa, Acinetobacter baumannii, and Klebsiella pneumoniae strains because of the expression of the metallo-β-lactamases (MBLs) namely Imipenemase (IMP)-1, IMP-2, New Delhi metallo-β-lactamases-, Verona imipenemase (VIM)-4, VIM-5, and VIM-7. We did an in silico analysis to understand the resistance mechanism of imipenem at the structural level. Our modeling studies reveal that the VIM-4-imipenem complex has highest binding energy and forms a stable complex as indicated by a consensus score (C-score) value of 5.44. The intense interaction between the substrate and the β-lactamases leads to the increased hydrolysis of the substrate resulting in rapid hydrolysis of the antibiotic imipenem by VIM-4. Virtual screening of compounds from the ZINC database targeting VIM-4 was done, and we found compound ZINC44608383 as the high binding energy compound with the C-score value of 5.58. This compound could be exploited for inhibitor design and development. The current study helps us to understand the resistance mechanism of imipenem in MBL-expressing strains. Also, we have identified a probable inhibitor for VIM-4. We believe that our results will be useful for researchers in designing potent inhibitors for VIM-4.",0
"Integration of transthoracic focused cardiac ultrasound in the diagnostic algorithm for suspected acute aortic syndromes. AIMS: The diagnosis of acute aortic syndromes (AASs) is challenging and requires integrated strategies. Transthoracic focused cardiac ultrasound (FoCUS) is endorsed by guidelines as a first-line/triage tool allowing rapid bedside assessment of the aorta. However, the performance of FoCUS in the European Society of Cardiology-recommended workup of AASs awaits validation. METHODS AND RESULTS: This was a prespecified subanalysis of the ADvISED multicentre prospective study. Patients with suspected AAS underwent FoCUS for detection of direct/indirect signs of AAS. Clinical probability assessment was performed with the aortic dissection detection risk score (ADD-RS). Case adjudication was based on advanced imaging, surgery, autopsy, or 14-day follow-up. An AAS was diagnosed in 146 (17.4%) of 839 patients. Presence of direct FoCUS signs had a sensitivity and specificity of 45.2% [95% confidence interval (CI) 37-53.6%] and 97.4% (95% CI 95.9-98.4%), while presence of any FoCUS sign had a sensitivity and specificity of 89% (95% CI 82.8-93.6%) and 74.5% (95% CI 71-77.7%) for AAS. The additive value of FoCUS was most evident within low clinical probability (ADD-RS </=1). Herein, direct FoCUS signs were identified in 40 (4.8%) patients (P < 0.001), including 29 with AAS. ADD-RS </=1 plus negative FoCUS for AAS rule-out had a sensitivity of 93.8% (95% CI 88.6-97.1%) and a failure rate of 1.9% (95% CI 0.9-3.6%). Addition of negative D-dimer led to a failure rate of 0% (95% CI 0-1.2%). CONCLUSION: FoCUS has additive value in the workup of AASs. Direct FoCUS signs can rapidly identify patients requiring advanced imaging despite low clinical probability. In integrated bundles, negative FoCUS is useful for rule-out of AASs.",0
"Accurate and rapid screening model for potential diabetes mellitus. BACKGROUND: Prediction or early diagnosis of diabetes is crucial for populations with high risk of diabetes. METHODS: In this study, we assessed the ability of five popular classifiers (J48, AdaboostM1, SMO, Bayes Net, and Naïve Bayes) to identify individuals with diabetes based on nine non-invasive and easily obtained clinical features, including age, gender, body mass index (BMI), hypertension, history of cardiovascular disease or stroke, family history of diabetes, physical activity, work stress, and salty food preference. A total of 4205 data entries were obtained from annual physical examination reports for adults in the Shengjing Hospital of China Medical University during January-April 2017. Weka data mining software was used to identify the best algorithm for diabetes classification. RESULTS: The results indicate that decision tree classifier J48 has the best performance (accuracy = 0.9503, precision = 0.950, recall = 0.950, F-measure = 0.948, and AUC = 0.964). The decision tree structure shows that age is the most significant feature, followed by family history of diabetes, work stress, BMI, salty food preference, physical activity, hypertension, gender, and history of cardiovascular disease or stroke. CONCLUSIONS: Our study shows that decision tree analyses can be applied to screen individuals for early diabetes risk without the need for invasive tests. This procedure will be particularly useful in developing regions with high epidemiological risk and poor socioeconomic status, and enable clinical practitioners to rapidly screen patients for increased risk of diabetes. The key features in the tree structure could further facilitate diabetes prevention through targeted community interventions, which can potentially improve early diabetes diagnosis and reduce burdens on the healthcare system.",0
"Combinatorial screening algorithm to engineer multiepitope subunit vaccine targeting human T-lymphotropic virus-1 infection. Human T-lymphotropic virus (HTLV), the first human retrovirus has been discovered which is known to cause the age-old assassinating disease HTLV-1 associated myelopathy. Cancer caused by this virus is adult T cell leukemia/lymphoma which targets 10–20 million throughout the world. The effect of this virus extends to the fact that it causes chronic disease to the spinal cord resulting in loss of sensation and further causes blood cancer. So, to overcome the complications, we designed a subunit vaccine by the assimilation of B-cell, cytotoxic T-lymphocyte, and helper T-lymphocyte epitopes. The epitopes were joined together along with adjuvant and linkers and a vaccine was fabricated which was further subjected to 3D modeling. The physiochemical properties, allergenicity, and antigenicity were evaluated. Molecular docking and dynamics were performed with the obtained 3D model against toll like receptor (TLR-3) immune receptor. Lastly, in silico cloning was performed to ensure the expression of the designed vaccine in pET28a(+) expression vector. The future prospects of the study entailed the in vitro and in vivo experimental analysis for evaluating the immune response of the designed vaccine construct.",0
"An approach for transgender population information extraction and summarization from clinical trial text. BACKGROUND: Gender information frequently exists in the eligibility criteria of clinical trial text as essential information for participant population recruitment. Particularly, current eligibility criteria text contains the incompleteness and ambiguity issues in expressing transgender population, leading to difficulties or even failure of transgender population recruitment in clinical trial studies. METHODS: A new gender model is proposed for providing comprehensive transgender requirement specification. In addition, an automated approach is developed to extract and summarize gender requirements from unstructured text in accordance with the gender model. This approach consists of: 1) the feature extraction module, and 2) the feature summarization module. The first module identifies and extracts gender features using heuristic rules and automatically-generated patterns. The second module summarizes gender requirements by relation inference. RESULTS: Based on 100,134 clinical trials from ClinicalTrials.gov , our approach was compared with 20 commonly applied machine learning methods. It achieved a macro-averaged precision of 0.885, a macro-averaged recall of 0.871 and a macro-averaged F1-measure of 0.878. The results illustrated that our approach outperformed all baseline methods in terms of both commonly used metrics and macro-averaged metrics. CONCLUSIONS: This study presented a new gender model aiming for specifying the transgender requirement more precisely. We also proposed an approach for gender information extraction and summarization from unstructured clinical text to enhance transgender-related clinical trial population recruitment. The experiment results demonstrated that the approach was effective in transgender criteria extraction and summarization.",1
"Digital health at fifteen: more human (more needed). There is growing appreciation that the success of digital health - whether digital tools, digital interventions or technology-based change strategies - is linked to the extent to which human factors are considered throughout design, development and implementation. A shift in focus to individuals as users and consumers of digital health highlights the capacity of the field to respond to secular developments, such as the adoption of person-centred care and consumer health technologies. We argue that this project is not only incomplete, but is fundamentally 'uncompletable' in the face of a highly dynamic landscape of both technological and human challenges. These challenges include the effects of consumerist, technology-supported care on care delivery, the rapid growth of digital users in low-income and middle-income countries and the impacts of machine learning. Digital health research will create most value by retaining a clear focus on the role of human factors in maximising health benefit, by helping health systems to anticipate and understand the person-centred effects of technology changes and by advocating strongly for the autonomy, rights and safety of consumers.",0
"In silico evidence of direct interaction between statins and β-amyloid. Introduction: Aggregation of amyloid-β (Aβ) peptides represents a crucial step in the pathogenesis of Alzheimer disease (AD). Compelling evidence from preclinical studies has established that statins may reduce amyloidogenesis and Aβ-mediated neurodegeneration, supporting a potential role of statin treatment in the prevention of AD. Different statins have been shown to interfere indirectly with Aβ production and clearance through either cholesterol-dependent or cholesterol-independent mechanisms. However, whether there may be a direct interaction between statins and Aβ metabolism is still unclear. Materials and methods: To test the possible direct interaction between statins and Aβ, we performed an in silico study by testing the orientation of different ligands, including statins and sulindac (the standard ligand of Aβ), in the Aβ active site using molecular operating environment (MOE) software. Results: Docking experiments showed that all the tested statins could directly interact with Aβ protofibrils. Among statins, pitavastatin had the strongest interaction with Aβ (pki = 7.66), followed by atorvastatin (pk i = 7.63), rosuvastatin (pk i = 6.99), fluvastatin (pk i =6.96), pravastatin (pk i = 6.46), lovastatin (pk i = 6.37), and simvastatin (pk i = 5.90). According to the above-mentioned results, pitavastatin, atorvastatin, rosuvastatin, and fluvastatin had a stronger binding to Aβ compared with the standard ligand sulindac (pk i = 6.62). Conclusion: This study showed a direct interaction between statins and Aβ protofibrils, which may underlie the protective role of this widely used class of drugs against amyloidogenesis and Aβ-mediated neurodegeneration.",0
"Machine Learning-Based Prediction of Clinical Outcomes for Children During Emergency Department Triage. Importance: While machine learning approaches may enhance prediction ability, little is known about their utility in emergency department (ED) triage. Objectives: To examine the performance of machine learning approaches to predict clinical outcomes and disposition in children in the ED and to compare their performance with conventional triage approaches. Design, Setting, and Participants: Prognostic study of ED data from the National Hospital Ambulatory Medical Care Survey from January 1, 2007, through December 31, 2015. A nationally representative sample of 52 037 children aged 18 years or younger who presented to the ED were included. Data analysis was performed in August 2018. Main Outcomes and Measures: The outcomes were critical care (admission to an intensive care unit and/or in-hospital death) and hospitalization (direct hospital admission or transfer). In the training set (70% random sample), using routinely available triage data as predictors (eg, demographic characteristics and vital signs), we derived 4 machine learning-based models: lasso regression, random forest, gradient-boosted decision tree, and deep neural network. In the test set (the remaining 30% of the sample), we measured the models' prediction performance by computing C statistics, prospective prediction results, and decision curves. These machine learning models were built for each outcome and compared with the reference model using the conventional triage classification information. Results: Of 52 037 eligible ED visits by children (median [interquartile range] age, 6 [2-14] years; 24 929 [48.0%] female), 163 (0.3%) had the critical care outcome and 2352 (4.5%) had the hospitalization outcome. For the critical care prediction, all machine learning approaches had higher discriminative ability compared with the reference model, although the difference was not statistically significant (eg, C statistics of 0.85 [95% CI, 0.78-0.92] for the deep neural network vs 0.78 [95% CI, 0.71-0.85] for the reference; P = .16), and lower number of undertriaged critically ill children in the conventional triage levels 3 to 5 (urgent to nonurgent). For the hospitalization prediction, all machine learning approaches had significantly higher discrimination ability (eg, C statistic, 0.80 [95% CI, 0.78-0.81] for the deep neural network vs 0.73 [95% CI, 0.71-0.75] for the reference; P < .001) and fewer overtriaged children who did not require inpatient management in the conventional triage levels 1 to 3 (immediate to urgent). The decision curve analysis demonstrated a greater net benefit of machine learning models over ranges of clinical thresholds. Conclusions and Relevance: Machine learning-based triage had better discrimination ability to predict clinical outcomes and disposition, with reduction in undertriaging critically ill children and overtriaging children who are less ill.",1
"Cost effectiveness of chimeric antigen receptor T-cell therapy in multiply relapsed or refractory adult large B-cell lymphoma. PURPOSE Two anti-CD19 chimeric antigen receptor T-cell (CAR-T) therapies are approved for diffuse large B-cell lymphoma, axicabtagene ciloleucel (axi-cel) and tisagenlecleucel; each costs $373,000. We evaluated their cost effectiveness. METHODS We used a decision analytic Markov model informed by recent multicenter, single-arm trials to evaluate axi-cel and tisagenlecleucel in multiply relapsed/refractory, adult, diffuse large B-cell lymphoma from a US health payer perspective over a lifetime horizon. Under a range of plausible long-term effectiveness assumptions, each therapy was compared with salvage chemoimmunotherapy regimens and stem-cell transplantation. Main outcomes were undiscounted life years, discounted lifetime costs, discounted quality-adjusted life years (QALYs), and incremental cost-effectiveness ratio (3% annual discount rate). Sensitivity analyses explored uncertainty. RESULTS In an optimistic scenario, assuming a 40% 5-year progression-free survival (PFS), axi-cel increased life expectancy by 8.2 years at $129,000/QALY gained (95% uncertainty interval, $90,000 to $219,000). At a 30% 5-year PFS, improvements in life expectancy were more modest (6.4 years) and expensive ($159,000/QALY gained [95% uncertainty interval, $105,000 to $284,000]). In an optimistic scenario, assuming a 35% 5-year PFS, tisagenlecleucel increased life expectancy by 4.6 years at $168,000/QALY gained (95% uncertainty interval, $105,000 to $414,000/QALY). At a 25% 5-year PFS, improvements in life expectancy were smaller (3.4 years) and more expensive ($223,000/QALY gained [95% uncertainty interval, $123,000 to $1,170,000/QALY]). Administering CAR-T to all indicated patients would increase US health care costs by approximately $10 billion over 5 years. Price reductions to $250,000 and $200,000, respectively, or payment only for initial complete response (at current prices) would allow axi-cel and tisagenlecleucel to cost less than $150,000/QALY, even at 25% PFS. CONCLUSION At 2018 prices, it is possible that both CAR-T therapies meet a less than $150,000/QALY threshold. This depends on long-term outcomes compared with chemoimmunotherapy and stem-cell transplantation, which are uncertain. Widespread adoption would substantially increase non-Hodgkin lymphoma health care costs. Price reductions or payment for initial response would improve cost effectiveness, even with modest long-term outcomes.",0
"Hypoxia Regulated Gene Network in Glioblastoma Has Special Algebraic Topology Structures and Revealed Communications Involving Warburg Effect and Immune Regulation. Hypoxia regulated genes (HRGs) formed a complex molecular interaction network (MINW), contributing to many aspects of glioblastoma (GBM) tumor biology. However, little is known about the intrinsic structures of the HRGs–MINW, mainly due to a lack of analysis tools to decipher MINWs. By introducing general hyper-geometric distribution, we obtained a statistically reliable gene set of HRGs (SR-HRGs) from several datasets. Next, MINWs were reconstructed from several independent GBM expression datasets. Algebraic topological analysis was performed to quantitatively analyze the amount of equivalence classes of cycles in various dimensions by calculating the Betti numbers. Persistent homology analysis of a filtration of growing networks was further performed to examine robust topological structures in the network by investigating the Betti curves, life length of the cycles. Random networks with the same number of node and edge and degree distribution were produced as controls. As a result, GBM–HRGs–MINWs reconstructed from different datasets exhibited great consistent Betti curves to each other, which were significantly different from that of random networks. Furthermore, HRGs–MINWs reconstructed from normal brain expression datasets exhibited topological structures significantly different from that of GBM–HRGs–MINWs. Analysis of cycles in GBM–HRGs–MINWs revealed genes that had clinical implications, and key parts of the cycles were also identified in reconstructed protein–protein interaction networks. In addition, the cycles are composed by genes involved in the Warburg effect, immune regulation, and angiogenesis. In summary, GBM–HRGs–MINWs contained abundant molecular interacting cycles in different dimensions, which are composed by genes involved in multiple programs essential for the tumorigenesis of GBM, revealing novel interaction diagrams in GBM and providing novel potential therapeutic targets.",0
"GM-CSF and CXCR4 define a T helper cell signature in multiple sclerosis. Cytokine dysregulation is a central driver of chronic inflammatory diseases such as multiple sclerosis (MS). Here, we sought to determine the characteristic cellular and cytokine polarization profile in patients with relapsing-remitting multiple sclerosis (RRMS) by high-dimensional single-cell mass cytometry (CyTOF). Using a combination of neural network-based representation learning algorithms, we identified an expanded T helper cell subset in patients with MS, characterized by the expression of granulocyte-macrophage colony-stimulating factor and the C-X-C chemokine receptor type 4. This cellular signature, which includes expression of very late antigen 4 in peripheral blood, was also enriched in the central nervous system of patients with relapsing-remitting multiple sclerosis. In independent validation cohorts, we confirmed that this cell population is increased in patients with MS compared with other inflammatory and non-inflammatory conditions. Lastly, we also found the population to be reduced under effective disease-modifying therapy, suggesting that the identified T cell profile represents a specific therapeutic target in MS.",0
"Determination of the Gene Regulatory Network of a Genome-Reduced Bacterium Highlights Alternative Regulation Independent of Transcription Factors. Here, we determined the relative importance of different transcriptional mechanisms in the genome-reduced bacterium Mycoplasma pneumoniae, by employing an array of experimental techniques under multiple genetic and environmental perturbations. Of the 143 genes tested (21% of the bacterium's annotated proteins), only 55% showed an altered phenotype, highlighting the robustness of biological systems. We identified nine transcription factors (TFs) and their targets, representing 43% of the genome, and 16 regulators that indirectly affect transcription. Only 20% of transcriptional regulation is mediated by canonical TFs when responding to perturbations. Using a Random Forest, we quantified the non-redundant contribution of different mechanisms such as supercoiling, metabolic control, RNA degradation, and chromosome topology to transcriptional changes. Model-predicted gene changes correlate well with experimental data in 95% of the tested perturbations, explaining up to 70% of the total variance when also considering noise. This analysis highlights the importance of considering non-TF-mediated regulation when engineering bacteria. We have achieved a comprehensive reconstruction of a gene regulatory network in a genome-reduced bacterium, Mycoplasma pneumoniae. With this network, we observed that a large part of transcription regulation is determined by non-canonical factors such as DNA supercoiling, riboswitches, and genome organization or RNA-mediated regulation. This analysis highlights the importance of considering non-transcription-factor (TF)-mediated regulation when engineering bacteria.",0
"Deep learning-based classification of mesothelioma improves prediction of patient outcome. Malignant mesothelioma (MM) is an aggressive cancer primarily diagnosed on the basis of histological criteria(1). The 2015 World Health Organization classification subdivides mesothelioma tumors into three histological types: epithelioid, biphasic and sarcomatoid MM. MM is a highly complex and heterogeneous disease, rendering its diagnosis and histological typing difficult and leading to suboptimal patient care and decisions regarding treatment modalities(2). Here we have developed a new approach-based on deep convolutional neural networks-called MesoNet to accurately predict the overall survival of mesothelioma patients from whole-slide digitized images, without any pathologist-provided locally annotated regions. We validated MesoNet on both an internal validation cohort from the French MESOBANK and an independent cohort from The Cancer Genome Atlas (TCGA). We also demonstrated that the model was more accurate in predicting patient survival than using current pathology practices. Furthermore, unlike classical black-box deep learning methods, MesoNet identified regions contributing to patient outcome prediction. Strikingly, we found that these regions are mainly located in the stroma and are histological features associated with inflammation, cellular diversity and vacuolization. These findings suggest that deep learning models can identify new features predictive of patient survival and potentially lead to new biomarker discoveries.",1
"A panel of Transcription factors identified by data mining can predict the prognosis of head and neck squamous cell carcinoma. Background: Transcription factors (TFs) are responsible for the regulation of various activities related to cancer like cell proliferation, invasion, and migration. It is thought that, the measurement of TFs levels could assist in developing strategies for diagnosis and prognosis of cancer detection. However, due to lack of effective genome-wide tests, this cannot be carried out in clinical settings. Methods: A complete assessment of RNA-seq data in samples of a head and neck squamous cell carcinoma (HNSCC) cohort in The Cancer Genome Atlas (TCGA) database was carried out. From the expression data of six TFs, a risk score model was developed and further validated in the GSE41613 and GSE65858 series. Potential functional roles were identified for the six TFs via gene set enrichment analysis. Results: Based on our multi-TF signature, patients are stratified into high- and low-risk groups with significant variations in overall survival (OS) (median survival 2.416 vs. 5.934 years, log-rank test P < 0.001). The sensitivity and specificity evaluation of our multi-TF for 3-year OS in TCGA, GSE41613 and GSE65858 was 0.707, 0.679 and 0.605, respectively, demonstrating good reproducibility and robustness for predicting overall survival of HNSCC patients. Through multivariate Cox regression analyses (MCRA) and stratified analyses, we confirmed that the predictive capability of this risk score (RS) was not dependent on any of other factors like clinicopathological parameters. Conclusions: With the help of a RS obtained from a panel of TFs expression signatures, effective OS prediction and stratification of HNSCC patients can be carried out.",0
"How to diagnose heart failure with preserved ejection fraction: the HFA-PEFF diagnostic algorithm: a consensus recommendation from the Heart Failure Association (HFA) of the European Society of Cardiology (ESC). Making a firm diagnosis of chronic heart failure with preserved ejection fraction (HFpEF) remains a challenge. We recommend a new stepwise diagnostic process, the 'HFA-PEFF diagnostic algorithm'. Step 1 (P=Pre-test assessment) is typically performed in the ambulatory setting and includes assessment for HF symptoms and signs, typical clinical demographics (obesity, hypertension, diabetes mellitus, elderly, atrial fibrillation), and diagnostic laboratory tests, electrocardiogram, and echocardiography. In the absence of overt non-cardiac causes of breathlessness, HFpEF can be suspected if there is a normal left ventricular ejection fraction, no significant heart valve disease or cardiac ischaemia, and at least one typical risk factor. Elevated natriuretic peptides support, but normal levels do not exclude a diagnosis of HFpEF. The second step (E: Echocardiography and Natriuretic Peptide Score) requires comprehensive echocardiography and is typically performed by a cardiologist. Measures include mitral annular early diastolic velocity (e'), left ventricular (LV) filling pressure estimated using E/e', left atrial volume index, LV mass index, LV relative wall thickness, tricuspid regurgitation velocity, LV global longitudinal systolic strain, and serum natriuretic peptide levels. Major (2 points) and Minor (1 point) criteria were defined from these measures. A score >/=5 points implies definite HFpEF; </=1 point makes HFpEF unlikely. An intermediate score (2-4 points) implies diagnostic uncertainty, in which case Step 3 (F1: Functional testing) is recommended with echocardiographic or invasive haemodynamic exercise stress tests. Step 4 (F2: Final aetiology) is recommended to establish a possible specific cause of HFpEF or alternative explanations. Further research is needed for a better classification of HFpEF.",0
"Automatic brain labeling via multi-atlas guided fully convolutional networks. Multi-atlas-based methods are commonly used for MR brain image labeling, which alleviates the burdening and time-consuming task of manual labeling in neuroimaging analysis studies. Traditionally, multi-atlas-based methods first register multiple atlases to the target image, and then propagate the labels from the labeled atlases to the unlabeled target image. However, the registration step involves non-rigid alignment, which is often time-consuming and might lack high accuracy. Alternatively, patch-based methods have shown promise in relaxing the demand for accurate registration, but they often require the use of hand-crafted features. Recently, deep learning techniques have demonstrated their effectiveness in image labeling, by automatically learning comprehensive appearance features from training images. In this paper, we propose a multi-atlas guided fully convolutional network (MA-FCN) for automatic image labeling, which aims at further improving the labeling performance with the aid of prior knowledge from the training atlases. Specifically, we train our MA-FCN model in a patch-based manner, where the input data consists of not only a training image patch but also a set of its neighboring (i.e., most similar) affine-aligned atlas patches. The guidance information from neighboring atlas patches can help boost the discriminative ability of the learned FCN. Experimental results on different datasets demonstrate the effectiveness of our proposed method, by significantly outperforming the conventional FCN and several state-of-the-art MR brain labeling methods.",1
"Entropy of mitochondrial DNA circulating in blood is associated with hepatocellular carcinoma. Background: Ultra-Deep Sequencing (UDS) enabled identification of specific changes in human genome occurring in malignant tumors, with current approaches calling for the detection of specific mutations associated with certain cancers. However, such associations are frequently idiosyncratic and cannot be generalized for diagnostics. Mitochondrial DNA (mtDNA) has been shown to be functionally associated with several cancer types. Here, we study the association of intra-host mtDNA diversity with Hepatocellular Carcinoma (HCC). Results: UDS mtDNA exome data from blood of patients with HCC (n = 293) and non-cancer controls (NC, n = 391) were used to: (i) measure the genetic heterogeneity of nucleotide sites from the entire population of intra-host mtDNA variants rather than to detect specific mutations, and (ii) apply machine learning algorithms to develop a classifier for HCC detection. Average total entropy of HCC mtDNA is 1.24-times lower than of NC mtDNA (p = 2.84E-47). Among all polymorphic sites, 2.09% had a significantly different mean entropy between HCC and NC, with 0.32% of the HCC mtDNA sites having greater (p < 0.05) and 1.77% of the sites having lower mean entropy (p < 0.05) as compared to NC. The entropy profile of each sample was used to further explore the association between mtDNA heterogeneity and HCC by means of a Random Forest (RF) classifier The RF-classifier separated 232 HCC and 232 NC patients with accuracy of up to 99.78% and average accuracy of 92.23% in the 10-fold cross-validation. The classifier accurately separated 93.08% of HCC (n = 61) and NC (n = 159) patients in a validation dataset that was not used for the RF parameter optimization. Conclusions: Polymorphic sites contributing most to the mtDNA association with HCC are scattered along the mitochondrial genome, affecting all mitochondrial genes. The findings suggest that application of heterogeneity profiles of intra-host mtDNA variants from blood may help overcome barriers associated with the complex association of specific mutations with cancer, enabling the development of accurate, rapid, inexpensive and minimally invasive diagnostic detection of cancer.",0
"Pseudogene DUXAP10 can be used as a diagnostic and prognostic biomarker in human cancers. The pseudogene DUXAP10 is overexpressed in numerous types of human cancers. However, the diagnostic and prognostic value of DUXAP10 in cancers has yet to be characterized. PubMed, EMBASE, Web of Science, the Cancer Genome Atlas (TCGA), and Gene Expression Omnibus databases were comprehensively searched in this study. A total of 50 studies comprising 11,292 patients were collected in this integrated analysis. DUXAP10 was confirmed to be significantly overexpressed in various human cancers (p <.05). Summary receiver operating characteristic (SROC) curve analysis was implemented, which indicated that DUXAP10 was a potential diagnostic biomarker for human cancers (area under the curve [AUC] of SROC curve = 0.81 [0.77–0.84]; pooled sensitivity = 0.69 [0.62–0.75]; pooled specificity = 0.81 [0.73–0.87]). In addition, hazard ratios (HRs) with 95% confidence intervals (CIs) were obtained to evaluate the association of DUXAP10 expression with overall survival (OS) time of cancer patients. Outcomes of meta-analysis suggested that upregulation of DUXAP10 was closely associated with poor OS (pooled HR = 1.11 [1.03–1.18]). Our study revealed that the pseudogene DUXAP10 was upregulated in multiple types of cancers and could be a potential biomarker with good diagnostic and prognostic value for human cancers.",0
"Deep-learning model for predicting 30-day postoperative mortality. BACKGROUND: Postoperative mortality occurs in 1-2% of patients undergoing major inpatient surgery. The currently available prediction tools using summaries of intraoperative data are limited by their inability to reflect shifting risk associated with intraoperative physiological perturbations. We sought to compare similar benchmarks to a deep-learning algorithm predicting postoperative 30-day mortality. METHODS: We constructed a multipath convolutional neural network model using patient characteristics, co-morbid conditions, preoperative laboratory values, and intraoperative numerical data from patients undergoing surgery with tracheal intubation at a single medical centre. Data for 60 min prior to a randomly selected time point were utilised. Model performance was compared with a deep neural network, a random forest, a support vector machine, and a logistic regression using predetermined summary statistics of intraoperative data. RESULTS: Of 95 907 patients, 941 (1%) died within 30 days. The multipath convolutional neural network predicted postoperative 30-day mortality with an area under the receiver operating characteristic curve of 0.867 (95% confidence interval [CI]: 0.835-0.899). This was higher than that for the deep neural network (0.825; 95% CI: 0.790-0.860), random forest (0.848; 95% CI: 0.815-0.882), support vector machine (0.836; 95% CI: 0.802-870), and logistic regression (0.837; 95% CI: 0.803-0.871). CONCLUSIONS: A deep-learning time-series model improves prediction compared with models with simple summaries of intraoperative data. We have created a model that can be used in real time to detect dynamic changes in a patient's risk for postoperative mortality.",1
Publisher Correction: Artificial intelligence aims to improve cancer screenings in Kenya. An amendment to this paper has been published and can be accessed via a link at the top of the paper.,0
"Multi-omics biomarker pipeline reveals elevated levels of protein-glutamine gammaglutamyltransferase 4 in seminal plasma of prostate cancer patients. Seminal plasma, because of its proximity to prostate, is a promising fluid for biomarker discovery and noninvasive diagnostics. In this study, we investigated if seminal plasma proteins could increase diagnostic specificity of detecting primary prostate cancer and discriminate between high- and low-grade cancers. To select 147 most promising biomarker candidates, we combined proteins identified through five independent experimental or data mining approaches: tissue transcriptomics, seminal plasma proteomics, cell line secretomics, tissue specificity, and androgen regulation. A rigorous biomarker development pipeline based on selected reaction monitoring assays was designed to evaluate the most promising candidates. As a result, we qualified 76, and verified 19 proteins in seminal plasma of 67 negative biopsy and 152 prostate cancer patients. Verification revealed a prostate-specific, secreted and androgen-regulated protein-glutamine gamma-glutamyltransferase 4 (TGM4), which predicted prostate cancer on biopsy and outperformed age and serum Prostate-Specific Antigen (PSA). A machine-learning approach for data analysis provided improved multi-marker combinations for diagnosis and prognosis. In the independent verification set measured by an in-house immunoassay, TGM4 protein was upregulated 3.7-fold (p = 0.006) and revealed AUC = 0.66 for detecting prostate cancer on biopsy for patients with serum PSA >4 ng/ml and age >50. Very low levels of TGM4 (120 pg/ml) were detected in blood serum. Collectively, our study demonstrated rigorous evaluation of one of the remaining and not well-explored prostate-specific proteins within the medium-abundance proteome of seminal plasma. Performance of TGM4 warrants its further investigation within the distinct genomic subtypes and evaluation for the inclusion into emerging multi-biomarker panels.",0
"Prioritization of SNPs in y+LAT-1 culpable of Lysinuric protein intolerance and their mutational impacts using protein-protein docking and molecular dynamics simulation studies. Lysinuric protein intolerance (LPI) is a rare, yet inimical, genetic disorder characterized by the paucity of essential dibasic amino acids in the cells. Amino acid transporter y+LAT-1 interacts with 4F2 cell-surface antigen heavy chain to transport the required dibasic amino acids. Mutation in y+LAT-1 is rumored to cause LPI. However, the underlying pathological mechanism is unknown, and, in this analysis, we investigate the impact of point mutation in y+LAT-1's interaction with 4F2 cell-surface antigen heavy chain in causing LPI. Using an efficient and extensive computational pipeline, we have isolated M50K and L334R single-nucleotide polymorphisms to be the most deleterious mutations in y+LAT-1s. Docking of mutant y+LAT-1 with 4F2 cell-surface antigen heavy chain showed decreased interaction compared with native y+LAT-1. Further, molecular dynamic simulation analysis reveals that the protein molecules increase in size, become more flexible, and alter their secondary structure upon mutation. We believe that these conformational changes because of mutation could be the reason for decreased interaction with 4F2 cell-surface antigen heavy chain causing LPI. Our analysis gives pathological insights about LPI and helps researchers to better understand the disease mechanism and develop an effective treatment strategy.",0
"Pristimerin attenuates cell proliferation of uveal melanoma cells by inhibiting insulin-like growth factor-1 receptor and its downstream pathways. Uveal melanoma (UM) has a high mortality rate due to liver metastasis. The insulin-like growth factor-1 receptor (IGF-1R) is highly expressed in UM and has been shown to be associated with hepatic metastases. Targeting IGF signalling may be considered as a promising approach to inhibit the process of metastatic UM cells. Pristimerin (PRI) has been demonstrated to inhibit the growth of several cancer cells, but its role and underlying mechanisms in the IGF-1-induced UM cell proliferation are largely unknown. The present study examined the anti-proliferative effect of PRI on UM cells and its possible role in IGF-1R signalling transduction. MTT and clonogenic assays were used to determine the role of PRI in the proliferation of UM cells. Flow cytometry was performed to detect the effect of PRI on the cell cycle distribution of UM cells. Western blotting was carried out to assess the effects of PRI and IGF-1 on the IGF-1R phosphorylation and its downstream targets. The results indicated that IGF-1 promoted the UM cell proliferation and improved the level of IGF-1R phosphorylation, whereas PRI attenuated the effect of IGF-1. Interestingly, PRI could not only induce the G1 phase accumulation and reduce the G2 phase induced by IGF-1, but also could stimulate the expression of p21 and inhibit the expression of cyclin D1. Besides, PRI could attenuate the phosphorylations of Akt, mTOR and ERK1/2 induced by IGF-1. Furthermore, the molecular docking study also demonstrated that PRI had potential inhibitory effects on IGF-1R. Taken together, these results indicated that PRI could inhibit the proliferation of UM cells through down-regulation of phosphorylated IGF-1R and its downstream signalling.",0
"Temporal indexing of medical entity in Chinese clinical notes. BACKGROUND: The goal of temporal indexing is to select an occurred time or time interval for each medical entity in clinical notes, so that all medical entities can be indexed on a united timeline, which could assist the understanding of clinical notes and the further application of medical entities. Some temporal relation shared tasks for the medical entity in English clinical notes have been organized in the past few years, such as the 2012 i2b2 NLP challenge, 2015 and 2016 clinical TempEval challenges. In these tasks, many heuristics rule-based and machine learning-based systems have been developed. In recent years, the deep neural network models have shown great potential on many problems including the relation classification. METHODS: In this paper, we propose a recurrent convolutional neural network (RNN-CNN) model for the temporal indexing task, which consists of four layers: input layer - generates representation for each context word of medical entities or temporal expressions; LSTM (long-short term memory) layer - learns the context information of each word in a sentence and outputs a new word representation sequence; CNN layer - extracts meaningful features from a sentence and outputs a new representation for medical entity or temporal expression; Output layer - takes the representations of medical entity, temporal expression and relation features as input and classifies the temporal relation. Finally, the time or time interval for each medical entity can be directly selected according to the probability of each temporal relation predicted by above model. RESULTS: To investigate the performance of our RNN-CNN model for the temporal indexing task, several baseline methods were also employed, such as the rule-based, support vector machine (SVM), convolutional neural network (CNN) and recurrent neural network (RNN) methods. Experiments conducted on a manually annotated corpus (including 563 clinical notes with 12,611 medical entities and 4006 temporal expressions) show that RNN-CNN model achieves the best F1-score of 75.97% for temporal relation classification and the best accuracy of 71.96% for temporal indexing. CONCLUSIONS: Neural network methods perform much better than the traditional rule-based and SVM-based method, which can capture more semantic information from the context of medical entities and temporal expressions. Besides, all our methods perform much better for the accurate time indexing than the time interval indexing, so how to improve the performance for time interval indexing will be the main focus in our future work.",1
"GAS: A genetic atlas selection strategy in multi-atlas segmentation framework. Multi-Atlas based Segmentation (MAS) algorithms have been successfully applied to many medical image segmentation tasks, but their success relies on a large number of atlases and good image registration performance. Choosing well-registered atlases for label fusion is vital for an accurate segmentation. This choice becomes even more crucial when the segmentation involves organs characterized by a high anatomical and pathological variability. In this paper, we propose a new genetic atlas selection strategy (GAS) that automatically chooses the best subset of atlases to be used for segmenting the target image, on the basis of both image similarity and segmentation overlap. More precisely, the key idea of GAS is that if two images are similar, the performances of an atlas for segmenting each image are similar. Since the ground truth of each atlas is known, GAS first selects a predefined number of similar images to the target, then, for each one of them, finds a near-optimal subset of atlases by means of a genetic algorithm. All these near-optimal subsets are then combined and used to segment the target image. GAS was tested on single-label and multi-label segmentation problems. In the first case, we considered the segmentation of both the whole prostate and of the left ventricle of the heart from magnetic resonance images. Regarding multi-label problems, the zonal segmentation of the prostate into peripheral and transition zone was considered. The results showed that the performance of MAS algorithms statistically improved when GAS is used.",0
"Improvement of fully automated airway segmentation on volumetric computed tomographic images using a 2.5 dimensional convolutional neural net. We propose a novel airway segmentation method in volumetric chest computed tomography (CT) and evaluate its performance on multiple datasets. The segmentation is performed voxel-by-voxel by a 2.5D convolutional neural net (2.5D CNN) trained in a supervised manner. To enhance the accuracy of the segmented airway tree, we simultaneously took three adjacent slices in each of the orthogonal directions including axial, sagittal, and coronal and fine-tuned the parameters that influence the tree length and the number of leakage. The gold standard of airway segmentation was generated by a semi-automated method using AVIEW. The 2.5D CNN was trained and evaluated on a subset of inspiratory thoracic CT scans taken from the Korean obstructive lung disease study, which includes normal subjects and chronic obstructive pulmonary disease patients. The reliability and further practicality of our proposed method was demonstrated in multiple datasets. In eight test datasets collected by the same imaging protocol, the percentage detected tree length, false positive rate, and Dice similarity coefficient of our method were 92.16%, 7.74%, and 0.8997+/-0.0892, respectively. In 20 test datasets of the EXACT'09 challenge, the percentage detected tree length was 60.1% and the false positive rate was 4.56%. Our fully automated (end-to-end) segmentation method could be applied in radiologic practice.",1
"Repetitive motion compensation for real time intraoperative video processing. In this paper, we present a motion compensation algorithm dedicated to video processing during neurosurgery. After craniotomy, the brain surface undergoes a repetitive motion due to the cardiac pulsation. This motion as well as potential video camera motion prevent accurate video analysis. We propose a dedicated motion model where the brain deformation is described using a linear basis learned from a few initial frames of the video. As opposed to other works using linear basis for the flow, the camera motion is explicitly accounted in the transformation model. Despite the nonlinear nature of our model, all the motion parameters are robustly estimated all at once, using only one singular value decomposition (SVD), making our procedure computationally efficient. A Lagrangian specification of the flow field ensures the stability of the method. Experiments on in vivo data are presented to evaluate the capacity of the method to cope with occlusion or camera motion. The method we propose satisfies the intraoperative constraints: it is robust to surgical tools occlusions, it works in real time, and it is able to handle large camera viewpoint changes.",0
"Molecular modeling investigation of the potential mechanism for phytochemical-induced skin collagen biosynthesis by inhibition of the protein phosphatase 1 holoenzyme. The most prominent feature of UV-induced photoaged skin is decreased type 1 procollagen. Increase of the TGF-β/Smad signaling through inhibition of the TβRI dephosphorylation by the GADD34–PP1c phosphatase complex represents a promising strategy for the increase in type 1 collagen production and prevention of UV-induced skin photoaging. In this study, the molecular docking and dynamics simulations, and pharmacophore modeling method were run to investigate a possible binding site as well as binding modes between apigenin, daidzein, asiaticoside, obovatol, and astragaloside IV and PP1c. Through docking study, the possible binding site for these phytochemicals was predicted as the hydrophobic (PP1–substrate binding) groove. The result indicates that PP1 is the significant target of these compounds. Moreover, the 20,000-ps MD simulations present that the binding locations and modes predicted by the docking have been slightly changed considering that the MD simulations proffer more reliable details upon the protein–ligand recognition. The MM-GBSA binding free energy calculations and pharmacophore modeling rationally identify that the highly hydrophobic surfaces/pockets at close proximity of the catalytic core are the most favorable binding locations of the herbal compounds, and that some experimental facts upon a possible mechanism of increase in collagen biosynthesis can be explained. The present study theoretically offers the reliable binding target of the herbal compounds, and therefore helps to understanding the action mechanism for natural small molecules that enhance collagen production.",0
"Phase I/II Trial of a Combination of Anti-CD3/CD7 Immunotoxins for Steroid-Refractory Acute Graft-versus-Host Disease. Effective therapies for treating patients with steroid-refractory acute graft-versus-host-disease (SR-aGVHD), particularly strategies that reduce the duration of immunosuppression following remission, are urgently needed. The investigated immunotoxin combination consists of a mixture of anti-CD3 and anti-CD7 antibodies separately conjugated to recombinant ricin A (CD3/CD7-IT), which induces in vivo depletion of T cells and natural killer (NK) cells and suppresses T cell receptor activation. We conducted a phase I/II trial to examine the safety and efficacy of CD3/CD7-IT in 20 patients with SR-aGVHD; 17 of these patients (85%) had severe SR-aGVHD, and all 20 patients had visceral organ involvement, including 18 (90%) with gastrointestinal (GI) involvement and 5 (25%) with liver involvement. A validated 2-biomarker algorithm classified the majority of patients (11 of 20) as high risk. On day 28 after the start of CD3/CD7-IT therapy, the overall response rate was 60% (12 of 20), with 10 patients (50%) achieving a complete response. The 6-month overall survival rate was 60% (12 of 20), including 64% (7 of 11) classified as high risk by biomarkers. The 1-week course of treatment with CD3/CD7-IT caused profound but transient depletion of T cells and NK cells, followed by rapid recovery of the immune system with a diverse TCR Vβ repertoire, and preservation of Epstein-Barr virus- and cytomegalovirus-specific T cell clones. Furthermore, our results indicate that CD3/CD7-IT appeared to be safe and well tolerated, with a relatively low prevalence of manageable and reversible adverse events, primarily worsening of hypoalbuminemia, microangiopathy, and thrombocytopenia. These encouraging results suggest that CD3/CD7-IT may improve patient outcomes in patients with SR-aGVHD.",0
"A low-cost vision system based on the analysis of motor features for recognition and severity rating of Parkinson's Disease. BACKGROUND: Assessment and rating of Parkinson's Disease (PD) are commonly based on the medical observation of several clinical manifestations, including the analysis of motor activities. In particular, medical specialists refer to the MDS-UPDRS (Movement Disorder Society - sponsored revision of Unified Parkinson's Disease Rating Scale) that is the most widely used clinical scale for PD rating. However, clinical scales rely on the observation of some subtle motor phenomena that are either difficult to capture with human eyes or could be misclassified. This limitation motivated several researchers to develop intelligent systems based on machine learning algorithms able to automatically recognize the PD. Nevertheless, most of the previous studies investigated the classification between healthy subjects and PD patients without considering the automatic rating of different levels of severity. METHODS: In this context, we implemented a simple and low-cost clinical tool that can extract postural and kinematic features with the Microsoft Kinect v2 sensor in order to classify and rate PD. Thirty participants were enrolled for the purpose of the present study: sixteen PD patients rated according to MDS-UPDRS and fourteen healthy paired subjects. In order to investigate the motor abilities of the upper and lower body, we acquired and analyzed three main motor tasks: (1) gait, (2) finger tapping, and (3) foot tapping. After preliminary feature selection, different classifiers based on Support Vector Machine (SVM) and Artificial Neural Networks (ANN) were trained and evaluated for the best solution. RESULTS: Concerning the gait analysis, results showed that the ANN classifier performed the best by reaching 89.4% of accuracy with only nine features in diagnosis PD and 95.0% of accuracy with only six features in rating PD severity. Regarding the finger and foot tapping analysis, results showed that an SVM using the extracted features was able to classify healthy subjects versus PD patients with great performances by reaching 87.1% of accuracy. The results of the classification between mild and moderate PD patients indicated that the foot tapping features were the most representative ones to discriminate (81.0% of accuracy). CONCLUSIONS: The results of this study have shown how a low-cost vision-based system can automatically detect subtle phenomena featuring the PD. Our findings suggest that the proposed tool can support medical specialists in the assessment and rating of PD patients in a real clinical scenario.",1
"Effects of Bakuchiol on chondrocyte proliferation via the PI3K-Akt and ERK1/2 pathways mediated by the estrogen receptor for promotion of the regeneration of knee articular cartilage defects. Objectives: Cartilaginous tissue degradation occurs because of the lack of survival of chondrocytes. Here, we ascertained whether bakuchiol (BAK) has the capability of activating chondrocyte proliferation. Materials and methods: The effect of BAK on the proliferation of rat chondrocytes at a concentration of 10 and 20 µmol/L was investigated. The molecular mechanisms involving target binding and signalling pathways were elucidated by RNA-sequencing, qPCR, molecular docking and Western blotting. Matrigel mixed with bakuchiol was implanted locally into rat knee articular cartilage defects to verify the activation of chondrocytes due to bakuchiol in vivo. Results: Bakuchiol implantation resulted in the activation of rat chondrocyte proliferation in a dose-dependent manner. RNA-sequencing revealed 107 differentially expressed genes (DEGs) with 75 that were up-regulated and 32 that were down-regulated, indicating increased activation of the PI3K-Akt and cell cycle pathways. Activation of the phosphorylation of Akt, ERK1/2 and their inhibitors blocked the proliferative effect of bakuchiol treatment, confirming its direct involvement in these signal transduction pathways. Molecular docking and siRNA silencing revealed that estrogen receptor-α (ERα) was the target of bakuchiol in terms of its cell proliferative effect via PI3K activation. Two weeks after implantation of bakuchiol, the appearance and physiological structure of the articular cartilage was more integrated with abundant chondrocytes and cartilage matrix compared to that of the control. Conclusions: Bakuchiol demonstrated significant bioactivity towards chondrocyte proliferation via the PI3K-Akt and ERK1/2 pathways mediated by estrogen receptor activation and exhibited enhanced promotion of the remodelling of injured cartilage.",0
"Extracting health-related causality from twitter messages using natural language processing. BACKGROUND: Twitter messages (tweets) contain various types of topics in our daily life, which include health-related topics. Analysis of health-related tweets would help us understand health conditions and concerns encountered in our daily lives. In this paper we evaluate an approach to extracting causalities from tweets using natural language processing (NLP) techniques. METHODS: Lexico-syntactic patterns based on dependency parser outputs are used for causality extraction. We focused on three health-related topics: ""stress"", ""insomnia"", and ""headache."" A large dataset consisting of 24 million tweets are used. RESULTS: The results show the proposed approach achieved an average precision between 74.59 to 92.27% in comparisons with human annotations. CONCLUSIONS: Manual analysis on extracted causalities in tweets reveals interesting findings about expressions on health-related topic posted by Twitter users.",1
"Lesional and perilesional tissue characterization by automated image processing in a novel gyrencephalic animal model of peracute intracerebral hemorrhage. Intracerebral hemorrhage (ICH) is an important stroke subtype, but preclinical research is limited by a lack of translational animal models. Large animal models are useful to comparatively investigate key pathophysiological parameters in human ICH. To (i) establish an acute model of moderate ICH in adult sheep and (ii) an advanced neuroimage processing pipeline for automatic brain tissue and hemorrhagic lesion determination; 14 adult sheep were assigned for stereotactically induced ICH into cerebral white matter under physiological monitoring. Six hours after ICH neuroimaging using 1.5T MRI including structural as well as perfusion and diffusion, weighted imaging was performed before scarification and subsequent neuropathological investigation including immunohistological staining. Controlled, stereotactic application of autologous blood caused a space-occupying intracerebral hematoma of moderate severity, predominantly affecting white matter at 5 h post-injection. Neuroimage post-processing including lesion probability maps enabled automatic quantification of structural alterations including perilesional diffusion and perfusion restrictions. Neuropathological and immunohistological investigation confirmed perilesional vacuolation, axonal damage, and perivascular blood as seen after human ICH. The model and imaging platform reflects key aspects of human ICH and enables future translational research on hematoma expansion/evacuation, white matter changes, hematoma evacuation, and other aspects.",0
"Deep learning for pollen allergy surveillance from twitter in Australia. BACKGROUND: The paper introduces a deep learning-based approach for real-time detection and insights generation about one of the most prevalent chronic conditions in Australia - Pollen allergy. The popular social media platform is used for data collection as cost-effective and unobtrusive alternative for public health monitoring to complement the traditional survey-based approaches. METHODS: The data was extracted from Twitter based on pre-defined keywords (i.e. 'hayfever' OR 'hay fever') throughout the period of 6 months, covering the high pollen season in Australia. The following deep learning architectures were adopted in the experiments: CNN, RNN, LSTM and GRU. Both default (GloVe) and domain-specific (HF) word embeddings were used in training the classifiers. Standard evaluation metrics (i.e. Accuracy, Precision and Recall) were calculated for the results validation. Finally, visual correlation with weather variables was performed. RESULTS: The neural networks-based approach was able to correctly identify the implicit mentions of the symptoms and treatments, even unseen previously (accuracy up to 87.9% for GRU with GloVe embeddings of 300 dimensions). CONCLUSIONS: The system addresses the shortcomings of the conventional machine learning techniques with manual feature-engineering that prove limiting when exposed to a wide range of non-standard expressions relating to medical concepts. The case-study presented demonstrates an application of 'black-box' approach to the real-world problem, along with its internal workings demonstration towards more transparent, interpretable and reproducible decision-making in health informatics domain.",1
"A Phage Protein Aids Bacterial Symbionts in Eukaryote Immune Evasion. Phages are increasingly recognized as important members of host-associated microbiomes, with a vast genomic diversity. The new frontier is to understand how phages may affect higher order processes, such as in the context of host-microbe interactions. Here, we use marine sponges as a model to investigate the interplay between phages, bacterial symbionts, and eukaryotic hosts. Using viral metagenomics, we find that sponges, although massively filtering seawater, harbor species-specific and even individually unique viral signatures that are taxonomically distinct from other environments. We further discover a symbiont phage-encoded ankyrin-domain-containing protein, which is widely spread in phages of many host-associated contexts including human. We confirm in macrophage infection assays that the ankyrin protein (ANKp) modulates the eukaryotic host immune response against bacteria. We predict that the role of ANKp in nature is to facilitate coexistence in the tripartite interplay between phages, symbionts, and sponges and possibly many other host-microbe associations.",0
"Impact of SNPs interplay across the locus of MBL2, between MBL and Dectin-1 gene, on women's risk of developing recurrent vulvovaginal infections. Background: Human mannose binding lectin (MBL) and dendritic cell-associated C-type lectin-1 (Dectin-1) are the two prototypical PRRs of innate immunity, whose direct role in recurrent vulvovaginal infections (RVVI) defense has been defined. Previously, MBL insufficiency was proposed as a possible risk factor for the rapid progression of RVVI while, Dectin-1 was found to be playing an active role in the defense. However, the complete genetic bases for the observed low MBL levels are still lacking as our previous studies in harmony with others demonstrated the un-expected genotype-phenotype patterns. This suggested the presence of unidentified regulatory variants that may modulate sMBL levels and risk of RVVI. Therefore, the present study was designed for more inclusive locus-wide MBL2 analysis and for the possible non-linear interaction analysis of two PRRs that may impact RVVI susceptibility. Methods: The present study has extended the previous findings by investigating (1) the role of chosen additional SNPs falling in the 5′ near region relating to sMBL levels and RVVI susceptibility, using polymerase chain reaction-restriction fragment length polymorphism, (2) interactions among SNPs within gene by comprehensive locus-wide haplotype analyses of two MBL2 blocks, (3) gene-gene interaction analyses between two PRRs, using multifactor dimensionality reduction. Results: rs11003124-G, rs7084554-C, rs36014597-G, and rs11003123-A were observed as the minor alleles in the representative North Indian cohort. RVVI cases and its types showed an appreciably high frequency of C allele, its homozygosity and heterozygosity, explaining the observed dominant mode of inheritance of rs7084554 polymorphism in contributing 1.81 fold risk of RVVI. The rs36014597 polymorphism showed the overdominant mode of inheritance, which further depicts that the carrier of a heterozygous genotype of this polymorphism had more extreme phenotype than either of its homozygous carriers in developing 4.07 fold risk of RVVI. sMBL levels significantly varied for rs11003124, rs36014597 and rs11003123 polymorphisms in bacterial vaginosis, while for rs7084554 polymorphism in mixed infection. Independent analysis of 5′ and 3′ haplotype blocks suggested the risk-modifying effect of all the 5′ additional variants, Y/X secretor polymorphism and 3′-UTR SNP i.e. rs10824792. Combined 5′/3′ haplotype analyses depicted the importance of rs36014597; an additional 5′ variant, Y/X and rs10824792 polymorphisms from both the blocks in regulating sMBL levels and RVVI risk. Three gene-gene interaction models involving uni-variant, bi-variant and tri-variant appeared as significant predictors of RVVI risk with cross-validation consistency of 10/10, 9/10 and 5/10, respectively. Conclusions: The study presented a low-cost reproducible screening design for additional 5′ variants i.e. rs11003124, rs7084554, rs36014597 and rs11003123 of MBL2 that can act as markers of susceptibility for RVVI or any other diseases. Two additional 5′ variants of MBL2 i.e. rs7084554 and rs36014597 were suggested as novel molecular markers that may contribute to RVVI risk by varying sMBL levels. Variants of two blocks were found to have more of a combined effect than the independent effect in modulating RVVI susceptibility and sMBL levels. The study presented weak synergistic interaction between MBL2 and CLEC7A in association with RVVI risk. The preliminary data will establish the foundation for the investigation of within gene and between genes interaction analyses towards RVVI susceptibility.",0
"In-silico identification of small molecules targeting H-Ras and in-vitro cytotoxicity with caspase-mediated apoptosis in carcinoma cells. H-Ras oncogene plays a critical role in the transformation of normal cells to a malignant phenotype through constitutive activation of the GTP bound protein leading to uncontrolled cell proliferation in several human cancers. Thus, H-Ras oncoprotein serves as an excellent target for anticancer drug discovery. To identify novel H-Ras inhibitors, we performed structure-based virtual screening of the Maybridge HitFinderâ„¢ library using Schrodinger suite. Thirty ligands from the chemical library were identified as they showed preferential in silico binding initially to H-Ras proteins with Gly12Val, Gly13Asp, and Gly12Val-Gly13Asp mutations. Absorption, distribution, metabolism, excretion, and toxicity profile confirmed drug-like properties of the compounds. Three representative molecules were tested for antiproliferative effect on T24 urinary bladder carcinoma cell line, MCF-7 breast cancer cell line and HDF-7 normal dermal fibroblast cells using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay. Two compounds (Cmpds) showed antiproliferative activity exclusively in the cancer cell lines with minimal effect on the control HDF-7 cells. The effect of compound treatment on cell cycle progression, assessed by propidium iodide (PI) staining, depicted increased arrest of T24 cell line in the sub G1 phase. Further, Annexin-V PI dual staining and pan caspase inhibitor Z-VAD-fmk indicated caspase-dependent apoptotic activity of Cmpds 1 and 3. Our findings demonstrate caspase-dependent apoptotic activity of Cmpds 1 and 3 selectively against Gly12Val mutated T24 cancer cell line implicating a potential for treatment of bladder cancer. We envisage that these molecules may be promising candidates with potential therapeutic value in H-Ras mutation-associated cancers.",0
"A randomized controlled trial of suicide prevention training for primary care providers: a study protocol. BACKGROUND: Suicide is a national public health crisis and a critical patient safety issue. It is the 10th leading cause of death overall and the second leading cause of death among adolescents and young adults (15-34 years old). Research shows 80% of youth who died by suicide saw their primary care provider within the year of their death. It is imperative that primary care providers develop the knowledge and skills to talk with patients about distress and suicidal thoughts, and to assess and respond in the context of the ongoing patient - primary care provider relationship. METHODS: This study examines the effectiveness of simulation on suicide prevention training for providers-in-training by comparing two conditions: 1) a control group that receives online teaching on suicide prevention in primary care via brief online videos and 2) an experimental group that includes the same online teaching videos plus two standardized patient (SP) interactions (face-to-face and telehealth, presentation randomized). All SP interactions are video-recorded. The primary analysis is a comparison of the two groups' suicide prevention skills using an SP ""test case"" at 6-month follow-up. DISCUSSION: The primary research question examines the impact of practice (through SP simulation) over and above online teaching alone on suicide prevention skills demonstrated at follow-up. We will assess moderators of outcomes, differences among SP simulations (i.e., face-to-face vs. telehealth modalities), and whether the experimental group's suicide prevention skills improve over the three SP experiences. TRIAL REGISTRATION: The study was registered on Clinical Trials Registry ( clinicaltrials.gov ) on December 14, 2016. The Trial Registration Number is NCT02996344 .",0
"Selection of reference miRNAs for relative quantification in buffalo (Bubalus bubalis) blastocysts produced by hand-made cloning and in vitro fertilization. Very low birth rate and a high incidence of abnormalities in offspring born from cloned embryos, which have limited the application of cloning technology on a wide scale, are believed to be because of incomplete or aberrant nuclear reprogramming. MicroRNAs (miRNAs) are involved in regulating a wide range of biological processes including reprogramming and embryonic development. Selection of suitable reference miRNAs is critical for normalization of data for accurate relative quantification of miRNAs by quantitative real-time polymerase chain reaction (qRT-PCR), which is currently the most widely used technique for quantifying miRNAs. This study was aimed at identification of reference miRNAs suitable for normalization of qRT-PCR data from blastocyst-stage buffalo embryos produced by handmade cloning and in vitro fertilization (IVF). RNA isolated from cloned and IVF blastocysts was subjected to next-generation sequencing based on which, 12 highly and most consistently expressed miRNAs, which included miR-92a, miR-423, miR-151, Let-7a, miR-103a, miR-93, miR-16b, miR-25, miR-30e, miR-101, miR-127, and miR-197, were selected as candidates for identification of suitable reference miRNAs using three statistical algorithms namely geNorm, NormFinder, and BestKeeper. Based on consensus of the three algorithms, the combination of miRNAs found to be suitable as reference miRNAs were miR-127 and miR-103 for IVF blastocysts; miR-92a and miR-103 for cloned blastocysts, and miR-103, miR-423, and miR-93 across both IVF and cloned blastocysts. The data of this study can be very useful in miRNA expression analysis of blastocyst-stage cloned and IVF embryos.",0
"Ultra-Low-Dose (18)F-Florbetaben Amyloid PET Imaging Using Deep Learning with Multi-Contrast MRI Inputs. Purpose To reduce radiotracer requirements for amyloid PET/MRI without sacrificing diagnostic quality by using deep learning methods. Materials and Methods Forty data sets from 39 patients (mean age +/- standard deviation [SD], 67 years +/- 8), including 16 male patients and 23 female patients (mean age, 66 years +/- 6 and 68 years +/- 9, respectively), who underwent simultaneous amyloid (fluorine 18 [(18)F]-florbetaben) PET/MRI examinations were acquired from March 2016 through October 2017 and retrospectively analyzed. One hundredth of the raw list-mode PET data were randomly chosen to simulate a low-dose (1%) acquisition. Convolutional neural networks were implemented with low-dose PET and multiple MR images (PET-plus-MR model) or with low-dose PET alone (PET-only) as inputs to predict full-dose PET images. Quality of the synthesized images was evaluated while Bland-Altman plots assessed the agreement of regional standard uptake value ratios (SUVRs) between image types. Two readers scored image quality on a five-point scale (5 = excellent) and determined amyloid status (positive or negative). Statistical analyses were carried out to assess the difference of image quality metrics and reader agreement and to determine confidence intervals (CIs) for reading results. Results The synthesized images (especially from the PET-plus-MR model) showed marked improvement on all quality metrics compared with the low-dose image. All PET-plus-MR images scored 3 or higher, with proportions of images rated greater than 3 similar to those for the full-dose images (-10% difference [eight of 80 readings], 95% CI: -15%, -5%). Accuracy for amyloid status was high (71 of 80 readings [89%]) and similar to intrareader reproducibility of full-dose images (73 of 80 [91%]). The PET-plus-MR model also had the smallest mean and variance for SUVR difference to full-dose images. Conclusion Simultaneously acquired MRI and ultra-low-dose PET data can be used to synthesize full-dose-like amyloid PET images. (c) RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Catana in this issue.",1
"Ontology-based venous thromboembolism risk assessment model developing from medical records. BACKGROUND: Padua linear model is widely used for the risk assessment of venous thromboembolism (VTE), a common but preventable complication for inpatients. However, genetic and environmental differences between Western and Chinese population limit the validity of Padua model in Chinese patients. Medical records which contain rich information about disease progression, are useful in mining new risk factors related to Chinese VTE patients. Furthermore, machine learning (ML) methods provide new opportunities to build precise risk prediction model by automatic selection of risk factors based on original medical records. METHODS: Medical records of 3,106 inpatients including 224 VTE patients were collected and various types of ontologies were integrated to parse unstructured text. A workflow of ontology-based VTE risk prediction model, that combines natural language processing (NLP) and machine learning (ML) technologies, was proposed. Firstly ontology terms were extracted from medical records, then sorted according to their calculated weights. Next importance of each term in the unit of section was evaluated and finally a ML model was built based on a subset of terms. Four ML methods were tested, and the best model was decided by comparing area under the receiver operating characteristic curve (AUROC). RESULTS: Medical records were first split into different sections and subsequently, terms from each section were sorted by their weights calculated by multiple types of information. Greedy selection algorithm was used to obtain significant sections and terms. Top terms in each section were selected to construct patients' distributed representations by word embedding technique. Using top 300 terms of two important sections, namely the 'Progress Note' section and 'Admitting Diagnosis' section, the model showed relatively better predictive performance. Then ML model which utilizes a subset of terms from two sections, about 110 terms, achieved the best AUC score, of 0.973 ± 0.006, which was significantly better compared to the Padua's performance of 0.791 ± 0.022. Terms found by the model showed their potential to help clinicians explore new risk factors. CONCLUSIONS: In this study, a new VTE risk assessment model based on ontologies extraction from raw medical records is developed and its performance is verified on real clinical dataset. Results of selected terms can help clinicians to discover meaningful risk factors.",1
"(-)-Epigallocatechin-3-gallate derivatives combined with cisplatin exhibit synergistic inhibitory effects on non-small-cell lung cancer cells. Background: Non-small-cell lung cancer (NSCLC) is the leading cause of cancer-related death worldwide. The inhibition of epidermal growth factor receptor (EGFR) signaling by tyrosine kinase inhibitors or monoclonal antibodies plays a key role in NSCLC treatment. Unfortunately, these treatment strategies are limited by eventual resistance and cell lines with differential EGFR status. Therefore, new therapeutic strategies for NSCLC are urgently required. Methods: To improve the stability and absorption of (-)-epigallocatechin-3-gallate (EGCG), we synthesized a series of EGCG derivatives. The antitumor activities of EGCG derivatives with or without cisplatin were investigated in vitro and vivo. Cell proliferation, cell cycle distribution and apoptosis were measured in NSCLC cell lines and in vivo in a NCI-H441 xenograft model. Results: We found that the EGCG derivatives inhibited cell viability and colony formation, caused cell cycle redistribution, and induced apoptosis. More importantly, the combination of the EGCG derivative and cisplatin led to increased growth inhibition, caused cell cycle redistribution, and enhanced the apoptosis rate compared to either compound alone. Consistent with the experiments in vitro, EGCG derivatives plus cisplatin significantly reduced tumor growth. Conclusions: The combination treatment was found to inhibit the EGFR signaling pathway and decrease the expression of p-EGFR, p-AKT, and p-ERK in vitro and vivo. Our results suggest that compound 3 is a novel potential compound for NSCLC patients.",0
"Discovery of Small Molecules that Activate RNA Methylation through Cooperative Binding to the METTL3-14-WTAP Complex Active Site. Chemical modifications of RNA provide an additional, epitranscriptomic, level of control over cellular functions. N-6-methylated adenosines (m6As) are found in several types of RNA, and their amounts are regulated by methyltransferases and demethylases. One of the most important enzymes catalyzing generation of m6A on mRNA is the trimer N-6-methyltransferase METTL3-14-WTAP complex. Its activity has been linked to such critical biological processes as cell differentiation, proliferation, and death. We used in silico-based discovery to identify small-molecule ligands that bind to METTL3-14-WTAP and determined experimentally their binding affinity and kinetics, as well as their effect on enzymatic function. We show that these ligands serve as activators of the METTL3-14-WTAP complex. The methyltransferase complex METTL3-14-WTAP catalyzes generation of m6A on mRNA. Selberg et al. report the in silico discovery and experimental characterization of small-molecule compounds with exceptionally high binding efficiencies to METTL3-14-WTAP. Remarkably, these compounds act as enzyme activators and lead to increased m6A levels in RNA.",0
"Evolving Images for Visual Neurons Using a Deep Generative Network Reveals Coding Principles and Neuronal Preferences. What specific features should visual neurons encode, given the infinity of real-world images and the limited number of neurons available to represent them? We investigated neuronal selectivity in monkey inferotemporal cortex via the vast hypothesis space of a generative deep neural network, avoiding assumptions about features or semantic categories. A genetic algorithm searched this space for stimuli that maximized neuronal firing. This led to the evolution of rich synthetic images of objects with complex combinations of shapes, colors, and textures, sometimes resembling animals or familiar people, other times revealing novel patterns that did not map to any clear semantic category. These results expand our conception of the dictionary of features encoded in the cortex, and the approach can potentially reveal the internal representations of any system whose input can be captured by a generative model.",0
"Current progress in CRISPR-based diagnostic platforms. The CRISPR-Cas system is a key technology for genome editing and regulation in a wide range of organisms and cell types. Recently, CRISPR-Cas–based diagnostic platform has shown idealistic properties for pathogen detection. Integrating the CRISPR-Cas platform along with lateral flow system allows rapid, sensitive, specific, cheap, and reliable diagnostic. It has the potential to be in frontline for not only pathogen detection during the epidemic outbreak, but also cancer, and genetic diseases.",0
"A Comparison between the Compass Fundus Perimeter and the Humphrey Field Analyzer. PURPOSE: To evaluate relative diagnostic precision and test-retest variability of 2 devices, the Compass (CMP, CenterVue, Padova, Italy) fundus perimeter and the Humphrey Field Analyzer (HFA, Zeiss, Dublin, CA), in detecting glaucomatous optic neuropathy (GON). DESIGN: Multicenter, cross-sectional, case-control study. PARTICIPANTS: We sequentially enrolled 499 patients with glaucoma and 444 normal subjects to analyze relative precision. A separate group of 44 patients with glaucoma and 54 normal subjects was analyzed to assess test-retest variability. METHODS: One eye of recruited subjects was tested with the index tests: HFA (Swedish interactive thresholding algorithm [SITA] standard strategy) and CMP (Zippy Estimation by Sequential Testing [ZEST] strategy), 24-2 grid. The reference test for GON was specialist evaluation of fundus photographs or OCT, independent of the visual field (VF). For both devices, linear regression was used to calculate the sensitivity decrease with age in the normal group to compute pointwise total deviation (TD) values and mean deviation (MD). We derived 5% and 1% pointwise normative limits. The MD and the total number of TD values below 5% (TD 5%) or 1% (TD 1%) limits per field were used as classifiers. MAIN OUTCOME MEASURES: We used partial receiver operating characteristic (pROC) curves and partial area under the curve (pAUC) to compare the diagnostic precision of the devices. Pointwise mean absolute deviation and Bland-Altman plots for the mean sensitivity (MS) were computed to assess test-retest variability. RESULTS: Retinal sensitivity was generally lower with CMP, with an average mean difference of 1.85+/-0.06 decibels (dB) (mean +/- standard error, P < 0.001) in healthy subjects and 1.46+/-0.05 dB (mean +/- standard error, P < 0.001) in patients with glaucoma. Both devices showed similar discriminative power. The MD metric had marginally better discrimination with CMP (pAUC difference +/- standard error, 0.019+/-0.009, P = 0.035). The 95% limits of agreement for the MS were reduced by 13% in CMP compared with HFA in participants with glaucoma and by 49% in normal participants. Mean absolute deviation was similar, with no significant differences. CONCLUSIONS: Relative diagnostic precision of the 2 devices is equivalent. Test-retest variability of MS for CMP was better than for HFA.",0
"Microglial Morphometric Parameters Correlate With the Expression Level of IL-1β, and Allow Identifying Different Activated Morphotypes. Microglia are the resident macrophages in the brain. Traditionally, two forms of microglia have been described: one considered as a resting/surveillant state in which cells have a highly branched morphology, and another considered as an activated state in which they acquire a de-ramified or amoeboid form. However, many studies describe intermediate microglial morphologies which emerge during pathological processes. Since microglial form and function are closely related, it is of interest to correlate microglial morphology with the extent of its activation. To address this issue, we used a rat model of neuroinflammation consisting in a single injection of the enzyme neuraminidase (NA) within the lateral ventricle. Sections from NA-injected animals were co-immunolabeled with the microglial marker IBA1 and the cytokine IL-1β, which highlight features of the cell’s shape and inflammatory activation, respectively. Activated (IL-1β positive) microglial cells were sampled from the dorsal hypothalamus nearby the third ventricle. Images of single microglial cells were processed in two different ways to obtain (1) an accurate measure of the level of expression of IL-1β (indicating the degree of activation), and (2) a set of 15 morphological parameters to quantitatively and objectively describe the cell’s shape. A simple regression analysis revealed a dependence of most of the morphometric parameters on IL-1β expression, demonstrating that the morphology of microglial cells changes progressively with the degree of activation. Moreover, a hierarchical cluster analysis pointed out four different morphotypes of activated microglia, which are characterized not only by morphological parameters values, but also by specific IL-1β expression levels. Thus, these results demonstrate in an objective manner that the activation of microglial cells is a gradual process, and correlates with their morphological change. Even so, it is still possible to categorize activated cells according to their morphometric parameters, each category presenting a different activation degree. The physiological relevance of those activated morphotypes is an issue worth to be assessed in the future.",0
"Drug diffusion along an intact mammalian cochlea. Intratympanic drug administration depends on the ability of drugs to pass through the round window membrane (RW) at the base of the cochlea and diffuse from this location to the apex. While the RW permeability for many different drugs can be promoted, passive diffusion along the narrowing spiral of the cochlea is limited. Earlier measurements of the distribution of marker ions, corticosteroids, and antibiotics demonstrated that the concentration of substances applied to the RW was two to three orders of magnitude higher in the base compared to the apex. The measurements, however, involved perforating the cochlear bony wall and, in some cases, sampling perilymph. These manipulations can change the flow rate of perilymph and lead to intake of perilymph through the cochlear aqueduct, thereby disguising concentration gradients of the delivered substances. In this study, the suppressive effect of salicylate on cochlear amplification via block of the outer hair cell (OHC) somatic motility was utilized to assess salicylate diffusion along an intact guinea pig cochlea in vivo. Salicylate solution was applied to the RW and threshold elevation of auditory nerve responses was measured at different times and frequencies after application. Resultant concentrations of salicylate along the cochlea were calculated by fitting the experimental data using a mathematical model of the diffusion and clearing of salicylate in a tube of variable diameter combined with a model describing salicylate action on cochlear amplification. Concentrations reach a steady-state at different times for different cochlear locations and it takes longer to reach the steady-state at more apical locations. Even at the steady-state, the predicted concentration at the apex is negligible. Model predictions for the geometry of the longer human cochlea show even higher differences in the steady-state concentrations of the drugs between cochlear base and apex. Our findings confirm conclusions that achieving therapeutic drug concentrations throughout the entire cochlear duct is hardly possible when the drugs are applied to the RW and are distributed via passive diffusion. Assisted methods of drug delivery are needed to reach a more uniform distribution of drugs along the cochlea.",0
"Risk of Distant Metastasis in Parathyroid Carcinoma and Its Effect on Survival: A Retrospective Review from a High-Volume Center. Background: Development of distant metastases (DM) is associated with markedly decreased survival in parathyroid carcinoma (PC). We sought to identify factors associated with development of DM and to quantify the effect that development of DM had on overall survival (OS). Methods: Patients with surgically resected local/regional PC treated or surveilled at a tertiary-referral cancer hospital from 1980 to 2017 were included. We assessed the association between biochemical and clinicopathologic factors (preoperative parathyroid hormone (PTH) levels, sex, race, age, preoperative serum calcium levels, serum calcium levels at 6 months postop, tumor size, and extent of resection) with the development of DM. We also assessed the effect of development of DM on OS. Results: Seventy-five patients with PC were assessed; 17 (22.7%) developed DM at a median follow-up of 77 months. The cumulative incidence of DM in the cohort was 20, 30, and 38% at 5, 10, and 20 years respectively. Tumor size > 3.2 cm based on recursive partitioning analysis was the only significant predictor for development of DM (hazard ratio (HR) = 3.51; 95% confidence interval [CI] 1.04–11.91; p = 0.04). Median OS for the entire cohort was 17 years compared with 40 months for the cohort who developed DM. The HR for death after distant metastasis was 9.6 (95% CI 4.2–22.3; p < 0.0001). Conclusions: Development of distant metastasis during surveillance is associated with decreased OS, including late recurrences. Primary tumor size should be considered in future interval surveillance and development of treatment algorithms.",0
"A cellular complex of BACE1 and γ-secretase sequentially generates Aβ from its full-length precursor. Intramembrane proteolysis of transmembrane substrates by the presenilin-γ-secretase complex is preceded and regulated by shedding of the substrate's ectodomain by a-or β-secretase. We asked whether β-and γ-secretases interact to mediate efcient sequential processing of APP, generating the amyloid β (Aβ) peptides that initiate Alzheimer's disease. We describe a hitherto unrecognized multiprotease complex containing active β-and γ-secretases. BACE1 coimmunoprecipitated and cofractionated with γ-secretase in cultured cells and in mouse and human brain. An endogenous high molecular weight (HMW) complex (∼5 MD) containing β-and γ-secretases and holo-APP was catalytically active in vitro and generated a full array of Aβ peptides, with physiological Aβ42/40 ratios. Te isolated complex responded properly to γ-secretase modulators. Alzheimer's-causing mutations in presenilin altered the Aβ42/40 peptide ratio generated by the HMW β/γ-secretase complex indistinguishably from that observed in whole cells. Tus, Aβ is generated from holo-APP by a BACE1-γ-secretase complex that provides sequential, efcient RIP processing of full-length substrates to fnal products.",0
"Prediction of novel mouse TLR9 agonists using a random forest approach. Background: Toll-like receptor 9 is a key innate immune receptor involved in detecting infectious diseases and cancer. TLR9 activates the innate immune system following the recognition of single-stranded DNA oligonucleotides (ODN) containing unmethylated cytosine-guanine (CpG) motifs. Due to the considerable number of rotatable bonds in ODNs, high-throughput in silico screening for potential TLR9 activity via traditional structure-based virtual screening approaches of CpG ODNs is challenging. In the current study, we present a machine learning based method for predicting novel mouse TLR9 (mTLR9) agonists based on features including count and position of motifs, the distance between the motifs and graphically derived features such as the radius of gyration and moment of Inertia. We employed an in-house experimentally validated dataset of 396 single-stranded synthetic ODNs, to compare the results of five machine learning algorithms. Since the dataset was highly imbalanced, we used an ensemble learning approach based on repeated random down-sampling. Results: Using in-house experimental TLR9 activity data we found that random forest algorithm outperformed other algorithms for our dataset for TLR9 activity prediction. Therefore, we developed a cross-validated ensemble classifier of 20 random forest models. The average Matthews correlation coefficient and balanced accuracy of our ensemble classifier in test samples was 0.61 and 80.0%, respectively, with the maximum balanced accuracy and Matthews correlation coefficient of 87.0% and 0.75, respectively. We confirmed common sequence motifs including 'CC', 'GG','AG', 'CCCG' and 'CGGC' were overrepresented in mTLR9 agonists. Predictions on 6000 randomly generated ODNs were ranked and the top 100 ODNs were synthesized and experimentally tested for activity in a mTLR9 reporter cell assay, with 91 of the 100 selected ODNs showing high activity, confirming the accuracy of the model in predicting mTLR9 activity. Conclusion: We combined repeated random down-sampling with random forest to overcome the class imbalance problem and achieved promising results. Overall, we showed that the random forest algorithm outperformed other machine learning algorithms including support vector machines, shrinkage discriminant analysis, gradient boosting machine and neural networks. Due to its predictive performance and simplicity, the random forest technique is a useful method for prediction of mTLR9 ODN agonists.",0
"Assessment of Accuracy of an Artificial Intelligence Algorithm to Detect Melanoma in Images of Skin Lesions. Importance: A high proportion of suspicious pigmented skin lesions referred for investigation are benign. Techniques to improve the accuracy of melanoma diagnoses throughout the patient pathway are needed to reduce the pressure on secondary care and pathology services. Objective: To determine the accuracy of an artificial intelligence algorithm in identifying melanoma in dermoscopic images of lesions taken with smartphone and digital single-lens reflex (DSLR) cameras. Design, Setting, and Participants: This prospective, multicenter, single-arm, masked diagnostic trial took place in dermatology and plastic surgery clinics in 7 UK hospitals. Dermoscopic images of suspicious and control skin lesions from 514 patients with at least 1 suspicious pigmented skin lesion scheduled for biopsy were captured on 3 different cameras. Data were collected from January 2017 to July 2018. Clinicians and the Deep Ensemble for Recognition of Malignancy, a deterministic artificial intelligence algorithm trained to identify melanoma in dermoscopic images of pigmented skin lesions using deep learning techniques, assessed the likelihood of melanoma. Initial data analysis was conducted in September 2018; further analysis was conducted from February 2019 to August 2019. Interventions: Clinician and algorithmic assessment of melanoma. Main Outcomes and Measures: Area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity of the algorithmic and specialist assessment, determined using histopathology diagnosis as the criterion standard. Results: The study population of 514 patients included 279 women (55.7%) and 484 white patients (96.8%), with a mean (SD) age of 52.1 (18.6) years. A total of 1550 images of skin lesions were included in the analysis (551 [35.6%] biopsied lesions; 999 [64.4%] control lesions); 286 images (18.6%) were used to train the algorithm, and a further 849 (54.8%) images were missing or unsuitable for analysis. Of the biopsied lesions that were assessed by the algorithm and specialists, 125 (22.7%) were diagnosed as melanoma. Of these, 77 (16.7%) were used for the primary analysis. The algorithm achieved an AUROC of 90.1% (95% CI, 86.3%-94.0%) for biopsied lesions and 95.8% (95% CI, 94.1%-97.6%) for all lesions using iPhone 6s images; an AUROC of 85.8% (95% CI, 81.0%-90.7%) for biopsied lesions and 93.8% (95% CI, 91.4%-96.2%) for all lesions using Galaxy S6 images; and an AUROC of 86.9% (95% CI, 80.8%-93.0%) for biopsied lesions and 91.8% (95% CI, 87.5%-96.1%) for all lesions using DSLR camera images. At 100% sensitivity, the algorithm achieved a specificity of 64.8% with iPhone 6s images. Specialists achieved an AUROC of 77.8% (95% CI, 72.5%-81.9%) and a specificity of 69.9%. Conclusions and Relevance: In this study, the algorithm demonstrated an ability to identify melanoma from dermoscopic images of selected lesions with an accuracy similar to that of specialists..",1
"Comparative genome-scale metabolic modeling of metallo-beta-lactamase-producing multidrug-resistant klebsiella pneumoniae clinical isolates. The emergence and spread of metallo-beta-lactamase-producing multidrug-resistant (MDR) Klebsiella pneumoniae is a serious public health threat, which is further complicated by the increased prevalence of colistin resistance. The link between antimicrobial resistance acquired by strains of Klebsiella and their unique metabolic capabilities has not been determined. Here, we reconstruct genome-scale metabolic models for 22 K. pneumoniae strains with various resistance profiles to different antibiotics, including two strains exhibiting colistin resistance isolated from Cairo, Egypt. We use the models to predict growth capabilities on 265 different sole carbon, nitrogen, sulfur, and phosphorus sources for all 22 strains. Alternate nitrogen source utilization of glutamate, arginine, histidine, and ethanolamine among others provided discriminatory power for identifying resistance to amikacin, tetracycline, and gentamicin. Thus, genome-scale model based predictions of growth capabilities on alternative substrates may lead to construction of classification trees that are indicative of antibiotic resistance in Klebsiella isolates.",0
"Identifying facial phenotypes of genetic disorders using deep learning. Syndromic genetic conditions, in aggregate, affect 8% of the population(1). Many syndromes have recognizable facial features(2) that are highly informative to clinical geneticists(3-5). Recent studies show that facial analysis technologies measured up to the capabilities of expert clinicians in syndrome identification(6-9). However, these technologies identified only a few disease phenotypes, limiting their role in clinical settings, where hundreds of diagnoses must be considered. Here we present a facial image analysis framework, DeepGestalt, using computer vision and deep-learning algorithms, that quantifies similarities to hundreds of syndromes. DeepGestalt outperformed clinicians in three initial experiments, two with the goal of distinguishing subjects with a target syndrome from other syndromes, and one of separating different genetic subtypes in Noonan syndrome. On the final experiment reflecting a real clinical setting problem, DeepGestalt achieved 91% top-10 accuracy in identifying the correct syndrome on 502 different images. The model was trained on a dataset of over 17,000 images representing more than 200 syndromes, curated through a community-driven phenotyping platform. DeepGestalt potentially adds considerable value to phenotypic evaluations in clinical genetics, genetic testing, research and precision medicine.",1
"Multi-Institutional Validation of Deep Learning for Pretreatment Identification of Extranodal Extension in Head and Neck Squamous Cell Carcinoma. PURPOSE: Extranodal extension (ENE) is a well-established poor prognosticator and an indication for adjuvant treatment escalation in patients with head and neck squamous cell carcinoma (HNSCC). Identification of ENE on pretreatment imaging represents a diagnostic challenge that limits its clinical utility. We previously developed a deep learning algorithm that identifies ENE on pretreatment computed tomography (CT) imaging in patients with HNSCC. We sought to validate our algorithm performance for patients from a diverse set of institutions and compare its diagnostic ability to that of expert diagnosticians. METHODS: We obtained preoperative, contrast-enhanced CT scans and corresponding pathology results from two external data sets of patients with HNSCC: an external institution and The Cancer Genome Atlas (TCGA) HNSCC imaging data. Lymph nodes were segmented and annotated as ENE-positive or ENE-negative on the basis of pathologic confirmation. Deep learning algorithm performance was evaluated and compared directly to two board-certified neuroradiologists. RESULTS: A total of 200 lymph nodes were examined in the external validation data sets. For lymph nodes from the external institution, the algorithm achieved an area under the receiver operating characteristic curve (AUC) of 0.84 (83.1% accuracy), outperforming radiologists' AUCs of 0.70 and 0.71 (P = .02 and P = .01). Similarly, for lymph nodes from the TCGA, the algorithm achieved an AUC of 0.90 (88.6% accuracy), outperforming radiologist AUCs of 0.60 and 0.82 (P < .0001 and P = .16). Radiologist diagnostic accuracy improved when receiving deep learning assistance. CONCLUSION: Deep learning successfully identified ENE on pretreatment imaging across multiple institutions, exceeding the diagnostic ability of radiologists with specialized head and neck experience. Our findings suggest that deep learning has utility in the identification of ENE in patients with HNSCC and has the potential to be integrated into clinical decision making.",1
"Association Between Surgical Skin Markings in Dermoscopic Images and Diagnostic Performance of a Deep Learning Convolutional Neural Network for Melanoma Recognition. Importance: Deep learning convolutional neural networks (CNNs) have shown a performance at the level of dermatologists in the diagnosis of melanoma. Accordingly, further exploring the potential limitations of CNN technology before broadly applying it is of special interest. Objective: To investigate the association between gentian violet surgical skin markings in dermoscopic images and the diagnostic performance of a CNN approved for use as a medical device in the European market. Design and Setting: A cross-sectional analysis was conducted from August 1, 2018, to November 30, 2018, using a CNN architecture trained with more than 120 000 dermoscopic images of skin neoplasms and corresponding diagnoses. The association of gentian violet skin markings in dermoscopic images with the performance of the CNN was investigated in 3 image sets of 130 melanocytic lesions each (107 benign nevi, 23 melanomas). Exposures: The same lesions were sequentially imaged with and without the application of a gentian violet surgical skin marker and then evaluated by the CNN for their probability of being a melanoma. In addition, the markings were removed by manually cropping the dermoscopic images to focus on the melanocytic lesion. Main Outcomes and Measures: Sensitivity, specificity, and area under the curve (AUC) of the receiver operating characteristic (ROC) curve for the CNN's diagnostic classification in unmarked, marked, and cropped images. Results: In all, 130 melanocytic lesions (107 benign nevi and 23 melanomas) were imaged. In unmarked lesions, the CNN achieved a sensitivity of 95.7% (95% CI, 79%-99.2%) and a specificity of 84.1% (95% CI, 76.0%-89.8%). The ROC AUC was 0.969. In marked lesions, an increase in melanoma probability scores was observed that resulted in a sensitivity of 100% (95% CI, 85.7%-100%) and a significantly reduced specificity of 45.8% (95% CI, 36.7%-55.2%, P < .001). The ROC AUC was 0.922. Cropping images led to the highest sensitivity of 100% (95% CI, 85.7%-100%), specificity of 97.2% (95% CI, 92.1%-99.0%), and ROC AUC of 0.993. Heat maps created by vanilla gradient descent backpropagation indicated that the blue markings were associated with the increased false-positive rate. Conclusions and Relevance: This study's findings suggest that skin markings significantly interfered with the CNN's correct diagnosis of nevi by increasing the melanoma probability scores and consequently the false-positive rate. A predominance of skin markings in melanoma training images may have induced the CNN's association of markings with a melanoma diagnosis. Accordingly, these findings suggest that skin markings should be avoided in dermoscopic images intended for analysis by a CNN. Trial Registration: German Clinical Trial Register (DRKS) Identifier: DRKS00013570.",1
"Reconstruction of the Global Neural Crest Gene Regulatory Network In Vivo. Precise control of developmental processes is encoded in the genome in the form of gene regulatory networks (GRNs). Such multi-factorial systems are difficult to decode in vertebrates owing to their complex gene hierarchies and dynamic molecular interactions. Here we present a genome-wide in vivo reconstruction of the GRN underlying development of the multipotent neural crest (NC) embryonic cell population. By coupling NC-specific epigenomic and transcriptional profiling at population and single-cell levels with genome/epigenome engineering in vivo, we identify multiple regulatory layers governing NC ontogeny, including NC-specific enhancers and super-enhancers, novel trans-factors, and cis-signatures allowing reverse engineering of the NC-GRN at unprecedented resolution. Furthermore, identification and dissection of divergent upstream combinatorial regulatory codes has afforded new insights into opposing gene circuits that define canonical and neural NC fates early during NC ontogeny. Our integrated approach, allowing dissection of cell-type-specific regulatory circuits in vivo, has broad implications for GRN discovery and investigation.",0
"Inhibition of PI3K/Akt/NF-κB signaling with leonurine for ameliorating the progression of osteoarthritis: In vitro and in vivo studies. Osteoarthritis (OA) is characterized as the degeneration and destruction of articular cartilage. In recent decades, leonurine (LN), the main active component in medical and edible dual purpose plant Herba Leonuri, has been shown associated with potent anti-inflammatory effects in several diseases. In the current study, we examined the protective effects of LN in the inhibition of OA development as well as its underlying mechanism both in vitro and in vivo experiments. In vitro, interleukin-1 beta (IL-1β) induced over-production of prostaglandin E2, nitric oxide, inducible nitric oxide synthase, cyclooxygenase-2, interleukin-6 and tumor necrosis factor alpha were all inhibited significantly by the pretreatment of LN at a dose-dependent manner (5, 10, and 20 µM). Moreover, the expression of thrombospondin motifs 5 (ADAMTS5) and metalloproteinase 13 (MMP13) was downregulated by LN. All these changes led to the IL-1β induced degradation of extracellular matrix. Mechanistically, the LN suppressed IL-1β induced activation of the PI3K/Akt/NF-κB signaling pathway cascades. Meanwhile, it was also demonstrated in our molecular docking studies that LN had strong binding abilities to PI3K. In addition, LN was observed exerting protective effects in a surgical induced model of OA. To sum up, this study indicated LN could be applied as a promising therapeutic agent in the treatment of OA.",0
"miR-192/215-5p act as tumor suppressors and link Crohn's disease and colorectal cancer by targeting common metabolic pathways: An integrated informatics analysis and experimental study. MicroRNAs have emerged as key regulators involved in a variety of biological processes. Previous studies have demonstrated that miR-192/215 participated in progression of Crohn's disease and colorectal cancer. However, their concrete relationships and regulation networks in diseases remain unclear. Here, we used bioinformatics methods to expound miR-192/215-5p macrocontrol regulatory networks shared by two diseases. For data mining and figure generation, several miRNA prediction tools, Human miRNA tissue atlas, FunRich, miRcancer, MalaCards, STRING, GEPIA, cBioPortal, GEO databases, Pathvisio, Graphpad Prism 6 software, etc. are extensively applied. miR-192/215-5p were specially distributed in colon tissues and enriched biological pathways were closely associated with human cancers. Emerging role of miR-192/215-5p and their common pathways in Crohn's disease and colorectal cancer was also analyzed. Based on results derived from multiple approaches, we identified the biological functions of miR-192/215-5p as a tumor suppressor and link Crohn's disease and colorectal cancer by targeting triglyceride synthesis and extracellular matrix remodeling pathways.",0
"A deep learning framework for neuroscience. Systems neuroscience seeks explanations for how the brain implements a wide variety of perceptual, cognitive and motor tasks. Conversely, artificial intelligence attempts to design computational systems based on the tasks they will have to solve. In artificial neural networks, the three components specified by design are the objective functions, the learning rules and the architectures. With the growing success of deep learning, which utilizes brain-inspired architectures, these three designed components have increasingly become central to how we model, engineer and optimize complex artificial learning systems. Here we argue that a greater focus on these components would also benefit systems neuroscience. We give examples of how this optimization-based framework can drive theoretical and experimental progress in neuroscience. We contend that this principled perspective on systems neuroscience will help to generate more rapid progress.",0
"Prognostic value of aberrantly expressed methylation gene profiles in lung squamous cell carcinoma: A study based on The Cancer Genome Atlas. Currently, research on genome-scale epigenetic modifications for studying the pathogenesis of lung cancer is lacking. Aberrant DNA methylation, as the most common and important modification in epigenetics, is an important means of regulating genomic function and can be used as a biomarker for the diagnosis and prognosis of lung squamous cell carcinoma (LUSC). In this paper, methylation information and gene expression data from patients with LUSC were extracted from the TCGA database. Univariate and multivariate COX analyses were used to screen abnormally methylated genes related to the prognosis of LUSC. The relationship between key DNA methylation sites and the transcriptional expression of LUSC-related genes was explored. A prognostic risk model constructed by four abnormally methylated genes (VAX1, CH25H, AdCyAP1, and Irx1) was used to predict the prognosis of LUSC patients. Also, the methylation levels of the key gene IRX1 are significantly correlated with the prognosis and correlated with the methylation of the site cg09232937 and cg10530883. This study is based on high-throughput data mining and provides an effective bioinformatics basis for further understanding the pathogenesis and prognosis of LUSC, which has important theoretical significance for follow-up studies on LUSC.",0
Structure and function of a monocarboxylate transporter homolog specific for L-lactate. Monocarboxylate transporters play important roles in certain cancers. We have reported structures of an L-lactate-transporting solute carrier family 16 homolog with bound substrate and inhibitor. The structures show the transporter in the pharmacologically relevant outward-open conformation. Structure–function analysis provides insights into the molecular working mechanisms of ligand binding and L-lactate transport.,0
"Detection of herpesvirus capsids in transmission electron microscopy images using transfer learning. The detailed analysis of secondary envelopment of the Human betaherpesvirus 5/human cytomegalovirus (HCMV) from transmission electron microscopy (TEM) images is an important step towards understanding the mechanisms underlying the formation of infectious virions. As a step towards a software-based quantification of different stages of HCMV virion morphogenesis in TEM, we developed a transfer learning approach based on convolutional neural networks (CNNs) that automatically detects HCMV nucleocapsids in TEM images. In contrast to existing image analysis techniques that require time-consuming manual definition of structural features, our method automatically learns discriminative features from raw images without the need for extensive pre-processing. For this a constantly growing TEM image database of HCMV infected cells was available which is unique regarding image quality and size in the terms of virological EM. From the two investigated types of transfer learning approaches, namely feature extraction and fine-tuning, the latter enabled us to successfully detect HCMV nucleocapsids in TEM images. Our detection method has outperformed some of the existing image analysis methods based on discriminative textural indicators and radial density profiles for virus detection in TEM images. In summary, we could show that the method of transfer learning can be used for an automated detection of viral capsids in TEM images with high specificity using standard computers. This method is highly adaptable and in future could be easily extended to automatically detect and classify virions of other viruses and even distinguish different virion maturation stages.",1
"Building the case for actionable ethics in digital health research supported by artificial intelligence. The digital revolution is disrupting the ways in which health research is conducted, and subsequently, changing healthcare. Direct-to-consumer wellness products and mobile apps, pervasive sensor technologies and access to social network data offer exciting opportunities for researchers to passively observe and/or track patients 'in the wild' and 24/7. The volume of granular personal health data gathered using these technologies is unprecedented, and is increasingly leveraged to inform personalized health promotion and disease treatment interventions. The use of artificial intelligence in the health sector is also increasing. Although rich with potential, the digital health ecosystem presents new ethical challenges for those making decisions about the selection, testing, implementation and evaluation of technologies for use in healthcare. As the 'Wild West' of digital health research unfolds, it is important to recognize who is involved, and identify how each party can and should take responsibility to advance the ethical practices of this work. While not a comprehensive review, we describe the landscape, identify gaps to be addressed, and offer recommendations as to how stakeholders can and should take responsibility to advance socially responsible digital health research.",0
"Importance of medical data preprocessing in predictive modeling and risk factor discovery for the frailty syndrome. BACKGROUND: Increasing life expectancy results in more elderly people struggling with age related diseases and functional conditions. This poses huge challenges towards establishing new approaches for maintaining health at a higher age. An important aspect for age related deterioration of the general patient condition is frailty. The frailty syndrome is associated with a high risk for falls, hospitalization, disability, and finally increased mortality. Using predictive data mining enables the discovery of potential risk factors and can be used as clinical decision support system, which provides the medical doctor with information on the probable clinical patient outcome. This enables the professional to react promptly and to avert likely adverse events in advance. METHODS: Medical data of 474 study participants containing 284 health related parameters, including questionnaire answers, blood parameters and vital parameters from the Toledo Study for Healthy Aging (TSHA) was used. Binary classification models were built in order to distinguish between frail and non-frail study subjects. RESULTS: Using the available TSHA data and the discovered potential predictors, it was possible to design, develop and evaluate a variety of different predictive models for the frailty syndrome. The best performing model was the support vector machine (SVM, 78.31%). Moreover, a methodology was developed, making it possible to explore and to use incomplete medical data and further identify potential predictors and enable interpretability. CONCLUSIONS: This work demonstrates that it is feasible to use incomplete, imbalanced medical data for the development of a predictive model for the frailty syndrome. Moreover, potential predictive factors have been discovered, which were clinically approved by the clinicians. Future work will improve prediction accuracy, especially with regard to separating the group of frail patients into frail and pre-frail ones and analyze the differences among them.",1
"A new method for mining information of co-expression network based on multi-cancers integrated data. Background: Gene co-expression network is a favorable method to reveal the nature of disease. With the development of cancer, the way to build gene co-expression networks based on cancer data has been become a hot spot. However, there are still a limited number of current node measurement methods and node mining strategies for multi-cancers network construction. Methods: In this paper, we introduce a new method for mining information of co-expression network based on multi-cancers integrated data, named PMN. We construct the network by combining the different types of relevant measures (linear and nonlinear rules) for different nodes based on integrated gene expression data of multi-cancers from The Cancer Genome Atlas (TCGA). For mining genes, we combine different properties (local and global characteristics) of the nodes. Results: We uncover more suspicious abnormally expressed genes and shared pathways of different cancers. And we have also found some proven genes and pathways; of course, there are some suspicious factors and molecules that need clinical validation. Conclusions: The results demonstrate that our method is very effective in excavating gene co-expression genes of multi-cancers.",0
"The Splicing Code Goes Deep. The importance of genomic sequence context in generating transcriptome diversity through RNA splicing is independently unmasked by two studies in this issue (Jaganathan et al., 2019; Baeza-Centurion et al., 2019).",0
"Temporally constrained ICA with threshold and its application to fMRI data. BACKGROUND: Although independent component analysis (ICA) has been widely applied to functional magnetic resonance imaging (fMRI) data to reveal spatially independent brain networks, the order indetermination of ICA leads to the problem of target component selection. The temporally constrained independent component analysis (TCICA) is capable of automatically extracting the desired spatially independent components by adding the temporal prior information of the task to the mixing matrix for fMRI data analysis. However, the TCICA method can only extract a single component that tends to be a mix of multiple task-related components when there exist several independent components related to one task. METHODS: In this study, we proposed a TCICA with threshold (TCICA-Thres) method that performed TCICA outside the threshold and performed FastICA inside the threshold to automatically extract all the target components related to one task. The proposed approach was tested using simulated fMRI data and was applied to a real fMRI experiment using 13 subjects. Additionally, the performance of TCICA-Thres was compared with that of FastICA and TCICA. RESULTS: The results from the simulation and the fMRI data demonstrated that TCICA-Thres better extracted the task-related components than TCICA. Moreover, TCICA-Thres outperformed FastICA in robustness to noise, spatial detection power and computational time. CONCLUSIONS: The proposed TCICA-Thres solves the limitations of TCICA and extends the application of TCICA in fMRI data analysis.",1
"Effect of Including Important Clinical Variables on Accuracy of the Lung Allocation Score for Cystic Fibrosis and Chronic Obstructive Pulmonary Disease. Rationale: Clinical variables associated with shortened survival in patients with advanced-stage cystic fibrosis (CF) are not included in the lung allocation score (LAS).Objectives: To identify variables associated with wait-list and post-transplant mortality for CF lung transplant candidates using a novel database and to analyze the impact of including new CF-specific variables in the LAS system.Methods: A deterministic matching algorithm identified patients from the Scientific Registry of Transplant Recipients and the Cystic Fibrosis Foundation Patient Registry. LAS wait-list and post-transplant survival models were recalculated using CF-specific variables. This multicenter, retrospective, population-based study of all lung transplant wait-list candidates aged 12 years or older from January 1, 2011, to December 31, 2014, included 9,043 patients on the lung transplant waiting list and 6,110 lung transplant recipients between 2011 and 2014, comprising 1,020 and 677 with CF, respectively.Measurements and Main Results: Measured outcomes were changes in LAS and lung allocation rank. For CF candidates, any Burkholderia sp. (hazard ratio [HR], 2.8; 95% confidence interval [CI], 1.2-6.6), 29-42 days hospitalized (HR 2.8; CI 1.3-5.9), massive hemoptysis (HR 2.1; CI 1.1-3.9), and relative drop in FEV1 >/=30% over 12 months (HR 1.7; CI 1.0-2.8) increased wait-list mortality risk; pulmonary exacerbation time 15-28 days (1.8; 1.1-2.9) increased post-transplant mortality risk. A relative drop in FEV1 >/=10% in chronic obstructive pulmonary disease (COPD) candidates was associated with increased wait-list mortality risk (HR 2.6; CI 1.2-5.4). Variability in LAS score and rank increased in patients with CF. Priority for transplant increased for COPD candidates. Access did not change for other diagnosis groups.Conclusions: Adding CF-specific variables improved discrimination among wait-listed CF candidates and benefited COPD candidates.",0
"Allyl rhodanine azo dye derivatives: Potential antimicrobials target d-alanyl carrier protein ligase and nucleoside diphosphate kinase. 3-Allyl-5-(4-arylazo)-2-thioxothiazolidine-4-one (HLn) ligands (where n = 1 to 3) were hypothesized to have antimicrobial activities mediated through inhibition of new antimicrobial targets. The ligands (HLn) were synthesized and characterized by infrared (IR) and 1H nuclear magnetic resonance (1H NMR) spectra. The ligands (HLn) were in silico screened to their potential inhibition to models of d-alanyl carrier protein ligase (DltA) (from Bacillus cereus, PDB code 3FCE) and nucleoside diphosphate kinase (NDK) (from Staphylococcus aureus; PDB code 3Q8U). HL3 ligand has the best energy and mode of binding to both NDK and DltA, even though its binding to DltA was stronger than that to NDK. In antimicrobial activity of HL3 ligand, morphological and cytological changes in HL3-treated bacteria agreed with the in silico results. The HL3 ligand showed significant antimicrobial activity against B. cereus, S. aureus, and Fusarium oxysporium. The HL3-treated bacterial cells appeared malformed and incompletely separated. Its cell walls appeared electron-lucent and ruptured. They contained more mesosomes than normal cells. It was found that the HL3 ligand represented as a bactericide against B. cereus and S. aureusby blocking target DltA, and may target NDK.",0
"Incorporating adjustments for variability in control group response rates in network meta-analysis: a case study of biologics for rheumatoid arthritis. BACKGROUND: The importance of adjusting for cross-study heterogeneity in control group response rates when conducting network meta-analyses (NMA) was demonstrated using a case study involving a comparison of biologics for the treatment of moderate-to-severe rheumatoid arthritis. METHODS: Bayesian NMAs were conducted for American College of Rheumatology (ACR) 50 treatment response based upon a set of randomized controlled trials (RCTs) identified by a recently completed systematic review of the literature. In addition to the performance of an unadjusted NMA, a model adjusting for cross-study heterogeneity of control group response rates using meta-regression was fit to the data. Model fit was evaluated, and findings from both analyses were compared with regard to clinical interpretations. RESULTS: ACR 50 response data from a total of 51 RCTs and 16,223 patients were analyzed. Inspection of cross-study variability in control group response rates identified considerable differences between studies. NMA incorporating adjustment for this variability was associated with an average change of 38.1% in the magnitude of the ORs between treatment comparisons, and over 64% of the odds ratio changed by 15% or more. Important changes in the clinical interpretations drawn from treatment comparisons were identified with this improved modeling approach. CONCLUSIONS: In comparing biologics for moderate to severe rheumatoid arthritis, failure to adjust for cross-trial differences in the control arm response rates in NMA can lead to biased estimates of comparative efficacy between treatments.",0
"Artificial intelligence in cancer imaging: Clinical challenges and applications. Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability to receive treatment, and their responses to treatment. Challenges remain in the accurate detection, characterization, and monitoring of cancers despite improved technologies. Radiographic assessment of disease most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI) promises to make great strides in the qualitative interpretation of cancer imaging by expert clinicians, including volumetric delineation of tumors over time, extrapolation of the tumor genotype and biological course from its radiographic phenotype, prediction of clinical outcome, and assessment of the impact of disease and treatment on adjacent organs. AI may automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions on whether or not to administer an intervention, and subsequent observation to a yet to be envisioned paradigm. Here, the authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types (lung, brain, breast, and prostate) to illustrate how common clinical problems are being addressed. Although most studies evaluating AI applications in oncology to date have not been vigorously validated for reproducibility and generalizability, the results do highlight increasingly concerted efforts in pushing AI technology to clinical use and to impact future directions in cancer care.",0
"Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study. BACKGROUND: For virtually every patient with colorectal cancer (CRC), hematoxylin-eosin (HE)-stained tissue slides are available. These images contain quantitative information, which is not routinely used to objectively extract prognostic biomarkers. In the present study, we investigated whether deep convolutional neural networks (CNNs) can extract prognosticators directly from these widely available images. METHODS AND FINDINGS: We hand-delineated single-tissue regions in 86 CRC tissue slides, yielding more than 100,000 HE image patches, and used these to train a CNN by transfer learning, reaching a nine-class accuracy of >94% in an independent data set of 7,180 images from 25 CRC patients. With this tool, we performed automated tissue decomposition of representative multitissue HE images from 862 HE slides in 500 stage I-IV CRC patients in the The Cancer Genome Atlas (TCGA) cohort, a large international multicenter collection of CRC tissue. Based on the output neuron activations in the CNN, we calculated a ""deep stroma score,"" which was an independent prognostic factor for overall survival (OS) in a multivariable Cox proportional hazard model (hazard ratio [HR] with 95% confidence interval [CI]: 1.99 [1.27-3.12], p = 0.0028), while in the same cohort, manual quantification of stromal areas and a gene expression signature of cancer-associated fibroblasts (CAFs) were only prognostic in specific tumor stages. We validated these findings in an independent cohort of 409 stage I-IV CRC patients from the ""Darmkrebs: Chancen der Verhutung durch Screening"" (DACHS) study who were recruited between 2003 and 2007 in multiple institutions in Germany. Again, the score was an independent prognostic factor for OS (HR 1.63 [1.14-2.33], p = 0.008), CRC-specific OS (HR 2.29 [1.5-3.48], p = 0.0004), and relapse-free survival (RFS; HR 1.92 [1.34-2.76], p = 0.0004). A prospective validation is required before this biomarker can be implemented in clinical workflows. CONCLUSIONS: In our retrospective study, we show that a CNN can assess the human tumor microenvironment and predict prognosis directly from histopathological images.",1
"Prediction of recurrent venous thrombosis in all patients with a first venous thrombotic event: The Leiden Thrombosis Recurrence Risk Prediction model (L-TRRiP). BACKGROUND: Recurrent venous thromboembolism (VTE) is common. Current guidelines suggest that patients with unprovoked VTE should continue anticoagulants unless they have a high bleeding risk, whereas all others can stop. Prediction models may refine this dichotomous distinction, but existing models apply only to patients with unprovoked first thrombosis. We aimed to develop a prediction model for all patients with first VTE, either provoked or unprovoked. METHODS AND FINDINGS: Data were used from two population-based cohorts of patients with first VTE from the Netherlands (Multiple Environment and Genetic Assessment of Risk Factors for Venous Thrombosis [MEGA] follow-up study, performed from 1994 to 2009; model derivation; n = 3,750) and from Norway (Tromso study, performed from 1999 to 2016; model validation; n = 663). Four versions of a VTE prediction model were developed: model A (clinical, laboratory, and genetic variables), model B (clinical variables and fewer laboratory markers), model C (clinical and genetic factors), and model D (clinical variables only). The outcome measure was recurrent VTE. To determine the discriminatory power, Harrell's C-statistic was calculated. A prognostic score was assessed for each patient. Kaplan-Meier plots for the observed recurrence risks were created in quintiles of the prognostic scores. For each patient, the 2-year predicted recurrence risk was calculated. Models C and D were validated in the Tromso study. During 19,201 person-years of follow-up (median duration 5.7 years) in the MEGA study, 507 recurrences occurred. Model A had the highest predictive capability, with a C-statistic of 0.73 (95% CI 0.71-0.76). The discriminative performance was somewhat lower in the other models, with C-statistics of 0.72 for model B, 0.70 for model C, and 0.69 for model D. Internal validation showed a minimal degree of optimism bias. Models C and D were externally validated, with C-statistics of 0.64 (95% CI 0.62-0.66) and 0.65 (95% CI 0.63-0.66), respectively. According to model C, in 2,592 patients with provoked first events, 367 (15%) patients had a predicted 2-year risk of >10%, whereas in 1,082 patients whose first event was unprovoked, 484 (45%) had a predicted 2-year risk of <10%. A limitation of both cohorts is that laboratory measurements were missing in a substantial proportion of patients, which therefore were imputed. CONCLUSIONS: The prediction model we propose applies to patients with provoked or unprovoked first VTE-except for patients with (a history of) cancer-allows refined risk stratification, and is easily usable. For optimal individualized treatment, a management study in which bleeding risks are also taken into account is necessary.",1
"Representation learning in intraoperative vital signs for heart failure risk prediction. BACKGROUND: The probability of heart failure during the perioperative period is 2% on average and it is as high as 17% when accompanied by cardiovascular diseases in China. It has been the most significant cause of postoperative death of patients. However, the patient is managed by the flow of information during the operation, but a lot of clinical information can make it difficult for medical staff to identify the information relevant to patient care. There are major practical and technical barriers to understand perioperative complications. METHODS: In this work, we present three machine learning methods to estimate risks of heart failure, which extract intraoperative vital signs monitoring data into different modal representations (statistical learning representation, text learning representation, image learning representation). Firstly, we extracted features of vital signs monitoring data of surgical patients by statistical analysis. Secondly, the vital signs data is converted into text information by Piecewise Approximate Aggregation (PAA) and Symbolic Aggregate Approximation (SAX), then Latent Dirichlet Allocation (LDA) model is used to extract text topics of patients for heart failure prediction. Thirdly, the vital sign monitoring time series data of the surgical patient is converted into a grid image by using the grid representation, and then the convolutional neural network is directly used to identify the grid image for heart failure prediction. We evaluated the proposed methods in the monitoring data of real patients during the perioperative period. RESULTS: In this paper, the results of our experiment demonstrate the Gradient Boosting Decision Tree (GBDT) classifier achieves the best results in the prediction of heart failure by statistical feature representation. The sensitivity, specificity and the area under the curve (AUC) of the best method can reach 83, 85 and 84% respectively. CONCLUSIONS: The experimental results demonstrate that representation learning model of vital signs monitoring data of intraoperative patients can effectively capture the physiological characteristics of postoperative heart failure.",1
"Value of Molecular Classification for Prognostic Assessment of Adrenocortical Carcinoma. Importance: The risk stratification of adrenocortical carcinoma (ACC) based on tumor proliferation index and stage is limited. Adjuvant therapy after surgery is recommended for most patients. Pan-genomic studies have identified distinct molecular groups closely associated with outcome. Objective: To compare the molecular classification for prognostic assessment of ACC with other known prognostic factors. Design, Setting, and Participants: In this retrospective biomarker analysis, ACC tumor samples from 368 patients who had undergone surgical tumor removal were collected from March 1, 2005, to September 30, 2015 (144 in the training cohort and 224 in the validation cohort) at 21 referral centers with a median follow-up of 35 months (interquartile range, 18-74 months). Data were analyzed from March 2016 to March 2018. Exposures: Meta-analysis of pan-genomic studies (transcriptome, methylome, chromosome alteration, and mutational profiles) was performed on the training cohort. Targeted biomarker analysis, including targeted gene expression (BUB1B and PINK1), targeted methylation (PAX5, GSTP1, PYCARD, and PAX6), and targeted next-generation sequencing, was performed on the training and validation cohorts. Main Outcomes and Measures: Disease-free survival. Cox proportional hazards regression and C indexes were used to assess the prognostic value of each model. Results: Of the 368 patients (mean [SD] age, 49 [16] years), 144 were in the training cohort (100 [69.4%] female) and 224 were in the validation cohort (142 [63.4%] female). In the training cohort, pan-genomic measures classified ACC into 3 molecular groups (A1, A2, and A3-B), with 5-year survival of 9% for group A1, 45% for group A2, and 82% for group A3-B (log-rank P <.001). Molecular class was an independent prognostic factor of recurrence in stage I to III ACC after complete surgery (hazard ratio, 55.91; 95% CI, 8.55-365.40; P <.001). The combination of European Network for the Study of Adrenal Tumors (ENSAT) stage, tumor proliferation index, and molecular class provided the most discriminant prognostic model (C index, 0.88). In the validation cohort, the molecular classification, determined by targeted biomarker measures, was confirmed as an independent prognostic factor of recurrence (hazard ratio, 5.96 [95% CI, 1.81-19.58], P =.003 for the targeted classifier combining expression, methylation, and chromosome alterations; and 2.61 [95% CI, 1.31-5.19], P =.006 for the targeted classifier combining methylation, chromosome alterations, and mutational profile). The prognostic value of the molecular markers was limited for patients with stage IV ACC. Conclusions and Relevance: The findings suggest that in localized ACC, targeted classifiers may be used as independent markers of recurrence. The determination of molecular class may improve individual prognostic assessment and thus may spare unnecessary adjuvant treatment.",0
"Computational identification of deleterious synonymous variants in human genomes using a feature-based approach. Background: Although synonymous single nucleotide variants (sSNVs) do not alter the protein sequences, they have been shown to play an important role in human disease. Distinguishing pathogenic sSNVs from neutral ones is challenging because pathogenic sSNVs tend to have low prevalence. Although many methods have been developed for predicting the functional impact of single nucleotide variants, only a few have been specifically designed for identifying pathogenic sSNVs. Results: In this work, we describe a computational model, IDSV (Identification of Deleterious Synonymous Variants), which uses random forest (RF) to detect deleterious sSNVs in human genomes. We systematically investigate a total of 74 multifaceted features across seven categories: splicing, conservation, codon usage, sequence, pre-mRNA folding energy, translation efficiency, and function regions annotation features. Then, to remove redundant and irrelevant features and improve the prediction performance, feature selection is employed using the sequential backward selection method. Based on the optimized 10 features, a RF classifier is developed to identify deleterious sSNVs. The results on benchmark datasets show that IDSV outperforms other state-of-the-art methods in identifying sSNVs that are pathogenic. Conclusions: We have developed an efficient feature-based prediction approach (IDSV) for deleterious sSNVs by using a wide variety of features. Among all the features, a compact and useful feature subset that has an important implication for identifying deleterious sSNVs is identified. Our results indicate that besides splicing and conservation features, a new translation efficiency feature is also an informative feature for identifying deleterious sSNVs. While the function regions annotation and sequence features are weakly informative, they may have the ability to discriminate deleterious sSNVs from benign ones when combined with other features. The data and source code are available on website http://bioinfo.ahu.edu.cn:8080/IDSV.",0
"Algorithm for Resecting Hepatocellular Carcinoma in the Caudate Lobe. OBJECTIVE: To propose an algorithm for resecting hepatocellular carcinoma (HCC) in the caudate lobe. BACKGROUND: Owing to a deep location, resection of HCC originating in the caudate lobe is challenging, but a plausible guideline enabling safe, curable resection remains unknown. METHODS: We developed an algorithm based on sublocation or size of the tumor and liver function to guide the optimal procedure for resecting HCC in the caudate lobe, consisting of 3 portions (Spiegel, process, and caval). Partial resection was prioritized to remove Spiegel or process HCC, while total resection was aimed to remove caval HCC depending on liver function. RESULTS: According to the algorithm, we performed total (n = 43) or partial (n = 158) resections of the caudate lobe for HCC in 174 of 201 patients (compliance rate, 86.6%), with a median blood loss of 400 (10-4530) mL. Postoperative morbidity (Clavien grade >/=III b) and mortality rates were 3.0% and 0%, respectively. After a median follow-up of 2.6 years (range, 0.5-14.3), the 5-year overall and recurrence-free survival rates were 57.3% and 15.3%, respectively. Total and partial resection showed no significant difference in overall survival (71.2% vs 54.0% at 5 yr; P = 0.213), but a significant factor in survival was surgical margin (58.0% vs 45.6%, P = 0.034). The major determinant for survival was vascular invasion (hazard ratio 1.7, 95% CI 1.0-3.1, P = 0.026). CONCLUSIONS: Our algorithm-oriented strategy is appropriate for the resection of HCC originating in the caudate lobe because of the acceptable surgical safety and curability.",0
"Artificial Intelligence for Mammography and Digital Breast Tomosynthesis: Current Concepts and Future Perspectives. Although computer-aided diagnosis (CAD) is widely used in mammography, conventional CAD programs that use prompts to indicate potential cancers on the mammograms have not led to an improvement in diagnostic accuracy. Because of the advances in machine learning, especially with use of deep (multilayered) convolutional neural networks, artificial intelligence has undergone a transformation that has improved the quality of the predictions of the models. Recently, such deep learning algorithms have been applied to mammography and digital breast tomosynthesis (DBT). In this review, the authors explain how deep learning works in the context of mammography and DBT and define the important technical challenges. Subsequently, they discuss the current status and future perspectives of artificial intelligence-based clinical applications for mammography, DBT, and radiomics. Available algorithms are advanced and approach the performance of radiologists-especially for cancer detection and risk prediction at mammography. However, clinical validation is largely lacking, and it is not clear how the power of deep learning should be used to optimize practice. Further development of deep learning models is necessary for DBT, and this requires collection of larger databases. It is expected that deep learning will eventually have an important role in DBT, including the generation of synthetic images.",0
"Asp305Gly mutation improved the activity and stability of the styrene monooxygenase for efficient epoxide production in Pseudomonas putida KT2440. Background: Styrene monooxygenase (SMO) catalyzes the first step of aromatic alkene degradation yielding the corresponding epoxides. Because of its broad spectrum of substrates, the enzyme harbors a great potential for an application in medicine and chemical industries. Results: In this study, we achieved higher enzymatic activity and better stability towards styrene by enlarging the ligand entrance tunnel and improving the hydrophobicity through error-prone PCR and site-saturation mutagenesis. It was found that Asp305 (D305) hindered the entrance of the FAD cofactor according to the model analysis. Therefore, substitution with amino acids possessing shorter side chains, like glycine, opened the entrance tunnel and resulted in up to 2.7 times higher activity compared to the wild-type enzyme. The half-lives of thermal inactivation for the variant D305G at 60 °C was 28.9 h compared to only 3.2 h of the wild type SMO. Moreover, overexpression of SMO in Pseudomonas putida KT2440 with NADH regeneration was carried out in order to improve biotransformation efficiency for epoxide production. A hexadecane/buffer (v/v) biphasic system was applied in order to minimize the inactivation effect of high substrate concentrations on the SMO enzyme. Finally, SMO activities of 190 U/g CDW were measured and a total amount of 20.5 mM (S)-styrene oxide were obtained after 8 h. Conclusions: This study offers an alternative strategy for improved SMO expression and provides an efficient biocatalytic system for epoxide production via engineering the entrance tunnel of the enzyme's active site.",0
"3D regression neural network for the quantification of enlarged perivascular spaces in brain MRI. Enlarged perivascular spaces (EPVS) in the brain are an emerging imaging marker for cerebral small vessel disease, and have been shown to be related to increased risk of various neurological diseases, including stroke and dementia. Automated quantification of EPVS would greatly help to advance research into its etiology and its potential as a risk indicator of disease. We propose a convolutional network regression method to quantify the extent of EPVS in the basal ganglia from 3D brain MRI. We first segment the basal ganglia and subsequently apply a 3D convolutional regression network designed for small object detection within this region of interest. The network takes an image as input, and outputs a quantification score of EPVS. The network has significantly more convolution operations than pooling ones and no final activation, allowing it to span the space of real numbers. We validated our approach using a dataset of 2000 brain MRI scans scored visually. Experiments with varying sizes of training and test sets showed that a good performance can be achieved with a training set of only 200 scans. With a training set of 1000 scans, the intraclass correlation coefficient (ICC) between our scoring method and the expert's visual score was 0.74. Our method outperforms by a large margin - more than 0.10 - four more conventional automated approaches based on intensities, scale-invariant feature transform, and random forest. We show that the network learns the structures of interest and investigate the influence of hyper-parameters on the performance. We also evaluate the reproducibility of our network using a set of 60 subjects scanned twice (scan-rescan reproducibility). On this set our network achieves an ICC of 0.93, while the intrarater agreement reaches 0.80. Furthermore, the automated EPVS scoring correlates similarly to age as visual scoring.",1
"Clinical utility of the 2016 ASE/EACVI recommendations for the evaluation of left ventricular diastolic function in the stratification of post-discharge prognosis in patients with acute heart failure. Aims: Left ventricular diastolic dysfunction (LVDD) has prognostic significance in heart failure (HF). We aimed to assess the impact of LVDD grade stratified by the updated 2016 echocardiographic algorithm (DD2016) on post-discharge outcomes in patients admitted for acute HF and compare with the previous 2009 algorithm (DD2009). Methods and results: The study included 481 patients hospitalized for acute decompensated HF. Comprehensive echocardiography and LVDD evaluation were performed just before hospital discharge. The primary endpoint was a composite of cardiovascular death and readmission for HF. The concordance between DD2016 and DD2009 was moderate (κ = 0.44, P < 0.001); the reclassification rate was 39%. During the follow-up (median: 15 months), 127 (26%) patients experienced the primary endpoint. In the Kaplan-Meier analysis, Grade III in DD2016 showed a lower event-free survival rate than Grades I and II (log rank, P < 0.001 and P = 0.048, respectively) and was independently associated with a higher incidence of the primary endpoint than Grade I [hazard ratio 1.89; 95% confidence interval (CI) 1.17-3.04; P = 0.009]. Grade II or III in DD2016, reflecting elevation of left ventricular (LV) filling pressure, added an incremental predictive value of the primary endpoint to clinical variables irrespective of LV ejection fraction. DD2016 was comparable to DD2009 in predicting the endpoint (net reclassification improvement = 11%; 95% CI -7% to 30%, P = 0.23). Conclusion: Despite simplification of the algorithm for LVDD evaluation, the prognostic value of DD2016 for post-discharge cardiovascular events in HF patients was maintained and not compromised in comparison with DD2009.",0
"Automated Liver Fat Quantification at Nonenhanced Abdominal CT for Population-based Steatosis Assessment. Background Nonalcoholic fatty liver disease and its consequences are a growing public health concern requiring cross-sectional imaging for noninvasive diagnosis and quantification of liver fat. Purpose To investigate a deep learning-based automated liver fat quantification tool at nonenhanced CT for establishing the prevalence of steatosis in a large screening cohort. Materials and Methods In this retrospective study, a fully automated liver segmentation algorithm was applied to noncontrast abdominal CT examinations from consecutive asymptomatic adults by using three-dimensional convolutional neural networks, including a subcohort with follow-up scans. Automated volume-based liver attenuation was analyzed, including conversion to CT fat fraction, and compared with manual measurement in a large subset of scans. Results A total of 11 669 CT scans in 9552 adults (mean age +/- standard deviation, 57.2 years +/- 7.9; 5314 women and 4238 men; median body mass index [BMI], 27.8 kg/m(2)) were evaluated, including 2117 follow-up scans in 1862 adults (mean age, 59.2 years; 971 women and 891 men; mean interval, 5.5 years). Algorithm failure occurred in seven scans. Mean CT liver attenuation was 55 HU +/- 10, corresponding to CT fat fraction of 6.4% (slightly fattier in men than in women [7.4% +/- 6.0 vs 5.8% +/- 5.7%; P < .001]). Mean liver Hounsfield unit varied little by age (<4 HU difference among all age groups) and only weak correlation was seen with BMI (r(2) = 0.14). By category, 47.9% (5584 of 11 669) had negligible or no liver fat (CT fat fraction <5%), 42.4% (4948 of 11 669) had mild steatosis (CT fat fraction of 5%-14%), 8.8% (1025 of 11 669) had moderate steatosis (CT fat fraction of 14%-28%), and 1% (112 of 11 669) had severe steatosis (CT fat fraction >28%). Excellent agreement was seen between automated and manual measurements, with a mean difference of 2.7 HU (median, 3 HU) and r(2) of 0.92. Among the subcohort with longitudinal follow-up, mean change was only -3 HU +/- 9, but 43.3% (806 of 1861) of patients changed steatosis category between first and last scans. Conclusion This fully automated CT-based liver fat quantification tool allows for population-based assessment of hepatic steatosis and nonalcoholic fatty liver disease, with objective data that match well with manual measurement. The prevalence of at least mild steatosis was greater than 50% in this asymptomatic screening cohort. (c) RSNA, 2019.",1
"Landscape of the Plasmodium Interactome Reveals Both Conserved and Species-Specific Functionality. Malaria represents a major global health issue, and the identification of new intervention targets remains an urgent priority. This search is hampered by more than one-third of the genes of malaria-causing Plasmodium parasites being uncharacterized. We report a large-scale protein interaction network in Plasmodium schizonts, generated by combining blue native-polyacrylamide electrophoresis with quantitative mass spectrometry and machine learning. This integrative approach, spanning 3 species, identifies >20,000 putative protein interactions, organized into 600 protein clusters. We validate selected interactions, assigning functions in chromatin regulation to previously unannotated proteins and suggesting a role for an EELM2 domain-containing protein and a putative microrchidia protein as mechanistic links between AP2-domain transcription factors and epigenetic regulation. Our interactome represents a high-confidence map of the native organization of core cellular processes in Plasmodium parasites. The network reveals putative functions for uncharacterized proteins, provides mechanistic and structural insight, and uncovers potential alternative therapeutic targets. More than one-third of Plasmodium genes are uncharacterized functionally. Hillier et al. combine biochemical fractionation, protein correlation profiling, and machine learning to generate a protein interaction network for Plasmodium. This global cellular organization map sheds light on the function of known and uncharacterized proteins and highlights conserved and species-specific functionality.",0
"Drug-induced hypersensitivity syndrome/drug reaction with eosinophilia and systemic symptoms severity score: A useful tool for assessing disease severity and predicting fatal cytomegalovirus disease. BACKGROUND: The prognosis of drug-induced hypersensitivity syndrome (DiHS)/drug reaction with eosinophilia and systemic symptoms (DRESS) is highly unpredictable. Severe complications, either related or unrelated to cytomegalovirus (CMV) reactivation, are a highly probable cause of death. OBJECTIVES: The aim was to establish a scoring system for DiHS/DRESS that can be used to monitor severity, predict prognosis, and stratify the risk of developing CMV disease and complications. METHODS: A retrospective analysis of 55 patients with DiHS/DRESS was performed. A composite score was created using clinical data. DiHS/DRESS patients were also stratified into 3 groups based on the scores to predict the risk of CMV reactivation and complications. RESULTS: This scoring system made it possible to predict CMV disease and complications. Scores >/=4 were associated with the later development of CMV disease and complications, while no patients with scores <4 developed complications. LIMITATIONS: This was a single-institution study with a relatively small patient cohort that lacked a validation cohort. CONCLUSIONS: Our scoring system may be useful for predicting CMV-related complications, and early intervention with anti-CMV agents should be considered in patients with scores >/=4 or with evidence of CMV reactivation.",0
"Pyrazinamide resistance and mutations L19R, R140H, and E144K in Pyrazinamidase of Mycobacterium tuberculosis. Pyrazinamide (PZA) is an important component of first-line antituberculosis drugs activated by Mycobacterium tuberculosis pyrazinamidase (PZase) into its active form pyrazinoic acid. Mutations in the pncA gene have been recognized as the major cause of PZA resistance. We detected some novel mutations, Leucine19Arginine (L19R), Arginine140Histidine (R140H), and Glutamic acid144 Lysine (E144K), in the pncA gene of PZA-resistant isolates in our wet lab PZA drug susceptibility testing and sequencing. As the molecular mechanism of resistance of these variants has not been reported earlier, we have performed multiple analyses to unveil different mechanisms of resistance because of PZase mutations L19R, R140H, and E144K. The mutants and native PZase structures were subjected to comprehensive computational molecular dynamics (MD) simulations at 100 nanoseconds in apo and drug-bound form. Mutants and native PZase binding pocket were compared to observe the consequence of mutations on the binding pocket size. Hydrogen bonding, Gibbs free energy, and natural ligand Fe +2 effect were also analyzed between native and mutants. A significant variation between native and mutant PZase structure activity was observed. The native PZase protein docking score was found to be the maximum, showing strong binding affinity in comparison with mutants. MD simulations explored the effect of the variants on the biological function of PZase. Hydrogen bonding, metal ion Fe +2 deviation, and fluctuation also seemed to be affected because of the mutations L19R, R140H, and E144K. The variants L19R, R140H, and E144K play a significant role in PZA resistance, altering the overall activity of native PZase, including metal ion Fe +2 displacement and free energy. This study offers valuable evidence for better management of drug-resistant tuberculosis.",0
"Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence. Artificial intelligence (AI)-based methods have emerged as powerful tools to transform medical care. Although machine learning classifiers (MLCs) have already demonstrated strong performance in image-based diagnoses, analysis of diverse and massive electronic health record (EHR) data remains challenging. Here, we show that MLCs can query EHRs in a manner similar to the hypothetico-deductive reasoning used by physicians and unearth associations that previous statistical methods have not found. Our model applies an automated natural language processing system using deep learning techniques to extract clinically relevant information from EHRs. In total, 101.6 million data points from 1,362,559 pediatric patient visits presenting to a major referral center were analyzed to train and validate the framework. Our model demonstrates high diagnostic accuracy across multiple organ systems and is comparable to experienced pediatricians in diagnosing common childhood diseases. Our study provides a proof of concept for implementing an AI-based system as a means to aid physicians in tackling large amounts of data, augmenting diagnostic evaluations, and to provide clinical decision support in cases of diagnostic uncertainty or complexity. Although this impact may be most evident in areas where healthcare providers are in relative shortage, the benefits of such an AI system are likely to be universal.",1
"Integrative subspace clustering by common and specific decomposition for applications on cancer subtype identification. Background: Recent high throughput technologies have been applied for collecting heterogeneous biomedical omics datasets. Computational analysis of the multi-omics datasets could potentially reveal deep insights for a given disease. Most existing clustering methods by multi-omics data assume strong consistency among different sources of datasets, and thus may lose efficacy when the consistency is relatively weak. Furthermore, they could not identify the conflicting parts for each view, which might be important in applications such as cancer subtype identification. Methods: In this work, we propose an integrative subspace clustering method (ISC) by common and specific decomposition to identify clustering structures with multi-omics datasets. The main idea of our ISC method is that the original representations for the samples in each view could be reconstructed by the concatenation of a common part and a view-specific part in orthogonal subspaces. The problem can be formulated as a matrix decomposition problem and solved efficiently by our proposed algorithm. Results: The experiments on simulation and text datasets show that our method outperforms other state-of-Art methods. Our method is further evaluated by identifying cancer types using a colorectal dataset. We finally apply our method to cancer subtype identification for five cancers using TCGA datasets, and the survival analysis shows that the subtypes we found are significantly better than other compared methods. Conclusion: We conclude that our ISC model could not only discover the weak common information across views but also identify the view-specific information.",0
"Dynamic edge-based biomarker non-invasively predicts hepatocellular carcinoma with hepatitis B virus infection for individual patients based on blood testing. Hepatitis B virus (HBV)-induced hepatocellular carcinoma (HCC) is a major cause of cancer-related deaths in Asia and Africa. Developing effective and non-invasive biomarkers of HCC for individual patients remains an urgent task for early diagnosis and convenient monitoring. Analyzing the transcriptomic profiles of peripheral blood mononuclear cells from both healthy donors and patients with chronic HBV infection in different states (i.e. HBV carrier, chronic hepatitis B, cirrhosis, and HCC), we identified a set of 19 candidate genes according to our algorithm of dynamic network biomarkers. These genes can both characterize different stages during HCC progression and identify cirrhosis as the critical transition stage before carcinogenesis. The interaction effects (i.e. co-expressions) of candidate genes were used to build an accurate prediction model: the so-called edge-based biomarker. Considering the convenience and robustness of biomarkers in clinical applications, we performed functional analysis, validated candidate genes in other independent samples of our collected cohort, and finally selected COL5A1, HLA-DQB1, MMP2, and CDK4 to build edge panel as prediction models. We demonstrated that the edge panel had great performance in both diagnosis and prognosis in terms of precision and specificity for HCC, especially for patients with alpha-fetoprotein-negative HCC. Our study not only provides a novel edge-based biomarker for non-invasive and effective diagnosis of HBV-associated HCC to each individual patient but also introduces a new way to integrate the interaction terms of individual molecules for clinical diagnosis and prognosis from the network and dynamics perspectives.",0
"Antioxidant potential of ganoderic acid in Notch-1 protein in neuroblastoma. Neuroblastoma is a childhood tumor arising from developing a sympathetic nervous system and causes around 10% of pediatric tumors. Despite advancement in the use of sophisticated techniques in molecular biology, neuroblastoma patient’s survivability rate is very less. Notch pathway is significant in upholding cell maintenance and developmental process of organs. Notch-1 proteins are a ligand-activated transmembrane receptor which decides the fate of the cell. Notch signaling leads to transcription of genes which indulged in numerous diseases including tumor progression. Ganoderic acid, a lanosterol triterpene, isolated from fungus Ganoderma lucidum with a wide range of medicinal values. In the present study, various isoforms of the ganoderic acid and natural inhibitors were docked by molecular docking using Maestro 9 in the Notch-1 signaling pathway. The receptor-based molecular docking exposed the best binding interaction of Notch-1 with ganoderic acid A with GScore (− 8.088), kcal/mol, Lipophilic EvdW (− 1.74), Electro (− 1.18), Glide emodel (− 89.944) with the active participation of Arg 189, Arg 199, Glu 232 residues. On the other hand natural inhibitor, curcumin has GScore (− 7.644), kcal/mol, Lipophilic EvdW (− 2.19), Electro (− 0.73), Glide emodel (− 70.957) with Arg 75 residues involved in docking. The ligand binding affinity of ganoderic acid A in Notch-1 is calculated using MM-GBSA (− 76.782), whereas curcumin has (− 72.815) kcal/mol. The QikProp analyzed the various drug-likeness parameters such as absorption, distribution, metabolism, excretion, and toxicity (ADME/T) and isoforms of ganoderic acid require some modification to fall under Lipinski rule. The ganoderic acid A and curcumin were the best-docked among different compounds and exhibits downregulation in Notch-1 mRNA expression and inhibits proliferation, viability, and ROS activity in IMR-32 cells.",0
"Comparison between two programs for image analysis, machine learning and subsequent classification. In the early 1950s, flow cytometry was developed as the first method for automated quantitative cellular analysis. In the early 1990s, the first equipment for image cytometry (laser scanning cytometry, LSC) became commercially available. As flow cytometry was considered the gold standard, various studies found that the results of flow cytometry and LSC generated comparable results. One of the first programs for image analysis that included morphological parameters was ImageJ, published in 1997. One of the newer programs for image analysis that is not limited to fluorescence images is the free software CellProfiler. In 2008, the same group published a new software, CellProfiler Analyst. One part of CellProfiler Analyst is a supervised machine-learning-based classifier that allows users to conduct imaging-based diagnoses, e.g., cellular diagnosis based on morphology. Another relatively new, free software for image analysis is QuPath. The aim of the present study was to compare two free programs for conducting image analysis, CellProfiler and QuPath, and the subsequent classification based on machine learning. For this study, images of renal tissue were analyzed, and the identified objects were classified. The same images were loaded in both software programs. Advanced statistical analysis was used to compare the two methods. The Bland-Altman assay showed that all of the differences were within the mean ± 1.96 * standard deviation, i.e., the differences are normally distributed, and the software programs are comparable. For the analyzed samples (renal tissue stained with HIF and TUNEL), the use of QuPath was easier because it offers image analysis without a previous processing of the images (e.g., conversion to grayscale, inverted intensities) and an unsupervised machine learning process.",0
"Two-view digital breast tomosynthesis versus digital mammography in a population-based breast cancer screening programme (To-Be): a randomised, controlled trial. Background: Digital breast tomosynthesis is an advancement of mammography, and has the potential to overcome limitations of standard digital mammography. This study aimed to compare first-generation digital breast tomo-synthesis including two-dimensional (2D)synthetic mammograms versus digital mammography in a population-based screening programme. Methods: BreastScreen Norway offers all women aged 50–69 years two-view (craniocaudal and mediolateral oblique)mammographic screening every 2 years and does independent double reading with consensus. We asked all 32 976 women who attended the programme in Bergen in 2016–17, to participate in this randomised, controlled trial with a parallel group design. A study-specific software was developed to allocate women to either digital breast tomosynthesis or digital mammography using a 1:1 simple randomisation method based on participants' unique national identity numbers. The interviewing radiographer did the randomisation by entering the number into the software. Randomisation was done after consent and was therefore concealed from both the women and the radiographer at the time of consent; the algorithm was not disclosed to radiographers during the recruitment period. All data needed for analyses were complete 12 months after the recruitment period ended. The primary outcome measure was screen-detected breast cancer, stratified by screening technique (ie, digital breast tomosynthesis and digital mammography). A log-binomial regression model was used to estimate the efficacy of digital breast tomosynthesis versus digital mammography, defined as the crude risk ratios (RRs)with 95% CIs for screen-detected breast cancer for women screened during the recruitment period. A per-protocol approach was used in the analyses. This trial is registered at ClinicalTrials.gov, number NCT02835625, and is closed to accrual. Findings: Between, Jan 14, 2016, and Dec 31, 2017, 44 266 women were invited to the screening programme in Bergen, and 32 976 (74·5%)attended. After excluding women with breast implants and women who did not consent to participate, 29 453 (89·3%)were eligible for electronic randomisation. 14 734 women were allocated to digital breast tomosynthesis and 14 719 to digital mammography. After randomisation, women with a previous breast cancer were excluded (digital breast tomosynthesis group n=314, digital mammography group n=316), women with metastases from melanoma (digital breast tomosynthesis group n=1), and women who informed the radiographer about breast symptoms after providing consent (digital breast tomosynthesis group n=39, digital mammography group n=34). After exclusions, information from 28 749 women were included in the analyses (digital breast tomosynthesis group n=14 380, digital mammography group n=14 369). The proportion of screen-detected breast cancer among the screened women did not differ between the two groups (95 [0·66%, 0·53–0·79]of 14 380 vs 87 [0·61%, 0·48–0·73]of 14 369; RR 1·09, 95% CI 0·82–1·46; p=0·56). Interpretation: This study indicated that digital breast tomosynthesis including synthetic 2D mammograms was not significantly different from standard digital mammography as a screening tool for the detection of breast cancer in a population-based screening programme. Economic analyses and follow-up studies on interval and consecutive round screen-detected breast cancers are needed to better understand the effect of digital breast tomosynthesis in population-based breast cancer screening. Funding: Cancer Registry of Norway, Department of Radiology at Haukeland University Hospital, University of Oslo, and Research Council of Norway.",0
"High-performance medicine: the convergence of human and artificial intelligence. The use of artificial intelligence, and the deep-learning subtype in particular, has been enabled by the use of labeled big data, along with markedly enhanced computing power and cloud storage, across all sectors. In medicine, this is beginning to have an impact at three levels: for clinicians, predominantly via rapid, accurate image interpretation; for health systems, by improving workflow and the potential for reducing medical errors; and for patients, by enabling them to process their own data to promote health. The current limitations, including bias, privacy and security, and lack of transparency, along with the future directions of these applications will be discussed in this article. Over time, marked improvements in accuracy, productivity, and workflow will likely be actualized, but whether that will be used to improve the patient-doctor relationship or facilitate its erosion remains to be seen.",0
"Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation. BACKGROUND: Optical sensors on wearable devices can detect irregular pulses. The ability of a smartwatch application (app) to identify atrial fibrillation during typical use is unknown. METHODS: Participants without atrial fibrillation (as reported by the participants themselves) used a smartphone (Apple iPhone) app to consent to monitoring. If a smartwatch-based irregular pulse notification algorithm identified possible atrial fibrillation, a telemedicine visit was initiated and an electrocardiography (ECG) patch was mailed to the participant, to be worn for up to 7 days. Surveys were administered 90 days after notification of the irregular pulse and at the end of the study. The main objectives were to estimate the proportion of notified participants with atrial fibrillation shown on an ECG patch and the positive predictive value of irregular pulse intervals with a targeted confidence interval width of 0.10. RESULTS: We recruited 419,297 participants over 8 months. Over a median of 117 days of monitoring, 2161 participants (0.52%) received notifications of irregular pulse. Among the 450 participants who returned ECG patches containing data that could be analyzed - which had been applied, on average, 13 days after notification - atrial fibrillation was present in 34% (97.5% confidence interval [CI], 29 to 39) overall and in 35% (97.5% CI, 27 to 43) of participants 65 years of age or older. Among participants who were notified of an irregular pulse, the positive predictive value was 0.84 (95% CI, 0.76 to 0.92) for observing atrial fibrillation on the ECG simultaneously with a subsequent irregular pulse notification and 0.71 (97.5% CI, 0.69 to 0.74) for observing atrial fibrillation on the ECG simultaneously with a subsequent irregular tachogram. Of 1376 notified participants who returned a 90-day survey, 57% contacted health care providers outside the study. There were no reports of serious app-related adverse events. CONCLUSIONS: The probability of receiving an irregular pulse notification was low. Among participants who received notification of an irregular pulse, 34% had atrial fibrillation on subsequent ECG patch readings and 84% of notifications were concordant with atrial fibrillation. This siteless (no on-site visits were required for the participants), pragmatic study design provides a foundation for large-scale pragmatic studies in which outcomes or adherence can be reliably assessed with user-owned devices. (Funded by Apple; Apple Heart Study ClinicalTrials.gov number, NCT03335800.).",0
"T1 bladder cancer: current considerations for diagnosis and management. Stage T1 bladder cancers invade the lamina propria of the bladder and, despite sharing many of the genetic features of muscle-invasive bladder cancers, are classified as non-muscle-invasive or 'superficial' tumours. Yet, patients with T1 bladder cancer have an overall mortality of 33% and a cancer-specific mortality of 14% at three years after diagnosis, suggesting that these patients have a high risk of progression and, accordingly, require meticulous surgery, endoscopic surveillance and clinical decision-making. We hypothesize that the variability in the outcomes of patients with T1 bladder cancer is a result of both tumour heterogeneity and pathological staging, as well as inconsistencies in risk stratification, endoscopic resection and schedules of delivery of BCG. Owing to limitations in clinical staging, patients with T1 bladder cancer are at risk of both undertreatment with persistent use of BCG despite recurrence, and overtreatment with early cystectomy. Understanding the molecular features of T1 bladder cancers and how they respond to BCG therapy could improve biomarkers for risk stratification to align therapy with biological risk.",0
"Evaluating the performance of convolutional neural networks with direct acyclic graph architectures in automatic segmentation of breast lesion in US images. BACKGROUND: Outlining lesion contours in Ultra Sound (US) breast images is an important step in breast cancer diagnosis. Malignant lesions infiltrate the surrounding tissue, generating irregular contours, with spiculation and angulated margins, whereas benign lesions produce contours with a smooth outline and elliptical shape. In breast imaging, the majority of the existing publications in the literature focus on using Convolutional Neural Networks (CNNs) for segmentation and classification of lesions in mammographic images. In this study our main objective is to assess the ability of CNNs in detecting contour irregularities in breast lesions in US images. METHODS: In this study we compare the performance of two CNNs with Direct Acyclic Graph (DAG) architecture and one CNN with a series architecture for breast lesion segmentation in US images. DAG and series architectures are both feedforward networks. The difference is that a DAG architecture could have more than one path between the first layer and end layer, whereas a series architecture has only one path from the beginning layer to the end layer. The CNN architectures were evaluated with two datasets. RESULTS: With the more complex DAG architecture, the following mean values were obtained for the metrics used to evaluate the segmented contours: global accuracy: 0.956; IOU: 0.876; F measure: 68.77%; Dice coefficient: 0.892. CONCLUSION: The CNN DAG architecture shows the best metric values used for quantitatively evaluating the segmented contours compared with the gold-standard contours. The segmented contours obtained with this architecture also have more details and irregularities, like the gold-standard contours.",1
"Integrated identification and quantification error probabilities for shotgun proteomics. Protein quantification by label-free shotgun proteomics experiments is plagued by a multitude of error sources. Typical pipelines for identifying differential proteins use intermediate filters to control the error rate. However, they often ignore certain error sources and, moreover, regard filtered lists as completely correct in subsequent steps. These two indiscretions can easily lead to a loss of control of the false discovery rate (FDR). We propose a probabilistic graphical model, Triqler, that propagates error information through all steps, employing distributions in favor of point estimates, most notably for missing value imputation. The model outputs posterior probabilities for fold changes between treatment groups, highlighting uncertainty rather than hiding it. We analyzed 3 engineered data sets and achieved FDR control and high sensitivity, even for truly absent proteins. In a bladder cancer clinical data set we discovered 35 proteins at 5% FDR, whereas the original study discovered 1 and MaxQuant/Perseus 4 proteins at this threshold. Compellingly, these 35 proteins showed enrichment for functional annotation terms, whereas the top ranked proteins reported by MaxQuant/ Perseus showed no enrichment. The model executes in minutes and is freely available at https://pypi.org/ project/triqler/.",0
"Detection of familial hypercholesterolaemia: external validation of the FAMCAT clinical case-finding algorithm to identify patients in primary care. BACKGROUND: The vast majority of individuals with familial hypercholesterolaemia in the general population remain unidentified worldwide. Recognising patients most likely to have the condition, to enable targeted specialist assessment and treatment, could prevent major coronary morbidity and mortality. We aimed to evaluate a clinical case-finding algorithm, the familial hypercholesterolaemia case ascertainment tool (FAMCAT), and compare it with currently recommended methods for detection of familial hypercholesterolaemia in primary care. METHODS: In this external validation study, FAMCAT regression equations were applied to a retrospective cohort of patients aged 16 years or older with cholesterol assessed, who were randomly selected from 1500 primary care practices across the UK contributing to the QResearch database. In the main analysis, we assessed the ability of FAMCAT to detect familial hypercholesterolaemia (ie, its discrimination) and compared it with that of other established clinical case-finding approaches recommended internationally (Simon Broome, Dutch Lipid Clinic Network, Make Early Diagnosis to Prevent Early Deaths [MEDPED] and cholesterol concentrations higher than the 99th percentile of the general population in the UK). We assessed discrimination by area under the receiver operating curve (AUROC; ranging from 0.5, indicating pure chance, to 1, indicating perfect discrimination). Using a probability threshold of more than 1 in 500 (prevalence of familial hypercholesterolaemia), we also assessed sensitivity, specificity, positive predictive values, and negative predictive values in the main analysis. FINDINGS: A sample of 750 000 patients who registered in 1500 UK primary care practices that contribute anonymised data to the QResearch database between Jan 1, 1999, and Sept 1, 2017, was randomly selected, of which 747 000 patients were assessed. FAMCAT showed a high degree of discrimination (AUROC 0.832, 95% CI 0.820-0.845), which was higher than that of Simon Broome criteria (0.694, 0.681-0.703), Dutch Lipid Clinic Network criteria (0.724, 0.710-0.738), MEDPED criteria (0.624, 0.609-0.638), and screening cholesterol concentrations higher than the 99th percentile (0.581, 0.570-0.591). Using a 1 in 500 probability threshold, FAMCAT achieved a sensitivity of 84% (1028 predicted vs 1219 observed cases) and specificity of 60% (443 949 predicted vs 745 781 observed non-cases), with a corresponding positive predictive value of 0.84% and a negative predictive value of 99.2%. INTERPRETATION: FAMCAT identifies familial hypercholesterolaemia with greater accuracy than currently recommended approaches and could be considered for clinical case finding of patients with the highest likelihood of having hypercholesterolaemia in primary care. FUNDING: UK National Institute for Health Research School for Primary Care Research.",1
Crystal Structure of the Human Cannabinoid Receptor CB2. The structure of the human cannabinoid receptor CB2 reveals how small molecules affect CB2 differently than CB1 and point to principles that could inform rational and selective drug design.,0
"Tissue differences revealed by gene expression profiles of various cell lines. Mechanisms through which tissues are formed and maintained remain unknown but are fundamental aspects in biology. Tissue-specific gene expression is a valuable tool to study such mechanisms. But in many biomedical studies, cell lines, rather than human body tissues, are used to investigate biological mechanisms Whether or not cell lines maintain their tissue-specific characteristics after they are isolated and cultured outside the human body remains to be explored. In this study, we applied a novel computational method to identify core genes that contribute to the differentiation of cell lines from various tissues. Several advanced computational techniques, such as Monte Carlo feature selection method, incremental feature selection method, and support vector machine (SVM) algorithm, were incorporated in the proposed method, which extensively analyzed the gene expression profiles of cell lines from different tissues. As a result, we extracted a group of functional genes that can indicate the differences of cell lines in different tissues and built an optimal SVM classifier for identifying cell lines in different tissues. In addition, a set of rules for classifying cell lines were also reported, which can give a clearer picture of cell lines in different issues although its performance was not better than the optimal SVM classifier. Finally, we compared such genes with the tissue-specific genes identified by the Genotype-tissue Expression project. Results showed that most expression patterns between tissues remained in the derived cell lines despite some uniqueness that some genes show tissue specificity.",0
"Management of acute radiation dermatitis: A review of the literature and proposal for treatment algorithm. Radiation dermatitis is a common sequela of radiation therapy; up to 95% of patients will develop moderate-to-severe skin reactions. No criterion standard currently exists for the treatment of acute radiation-induced skin toxicity. It is therefore imperative to develop a greater understanding of management options available to allow clinicians to make informed decisions when managing radiation oncology patients. This literature review discusses the topical agents that have been studied for the treatment of acute radiation dermatitis, reviews their mechanisms of action, and presents a treatment algorithm for clinicians managing patients experiencing radiation dermatitis.",0
"Transcriptomic analysis of fetal membranes reveals pathways involved in preterm birth. Background: Preterm birth (PTB), defined as infant delivery before 37 weeks of completed gestation, results from the interaction of both genetic and environmental components and constitutes a complex multifactorial syndrome. Transcriptome analysis of PTB has proven challenging because of the multiple causes of PTB and the numerous maternal and fetal gestational tissues that must interact to facilitate parturition. The transcriptome of the chorioamnion membranes at the site of rupture in PTB and term fetuses may reflect the molecular pathways of preterm labor. Methods: In this work, chorioamnion membranes from severe preterm and term fetuses were analyzed using RNA sequencing. Functional annotations and pathway analysis of differentially expressed genes were performed with the GAGE and GOSeq packages. A subset of differentially expressed genes in PTB was validated in a larger cohort using qRT-PCR and by comparing our results with genes and pathways previously reported in the literature. Results: A total of 270 genes were differentially expressed (DE): 252 were upregulated and 18 were down-regulated in severe preterm births relative to term births. Inflammatory and immunological pathways were upregulated in PTB. Both types of pathways were previously suggested to lead to PTB. Pathways that were not previously reported in PTB, such as the hemopoietic pathway, appeared upregulated in preterm membranes. A group of 18 downregulated genes discriminated between term and severe preterm cases. These genes potentially characterize a severe preterm transcriptome pattern and therefore are candidate genes for understanding the syndrome. Some of the downregulated genes are involved in the nervous system, morphogenesis (WNT1, DLX5, PAPPA2) and ion channel complexes (KCNJ16, KCNB1), making them good candidates as biomarkers of PTB. Conclusions: The identification of this DE gene pattern will help with the development of a multi-gene disease classifier. These markers were generated in an admixed South American population in which PTB has a high incidence. Since the genetic background may differentially impact different populations, it is necessary to include populations such as those from South America and Africa, which are usually excluded from high-throughput approaches. These classifiers should be compared to those in other populations to obtain a global landscape of PTB.",0
"Hippocampal sub-regional differences in the microRNA response to forebrain ischemia. Transient forebrain ischemia, as occurs with cardiac arrest and resuscitation, results in impaired cognitive function secondary to delayed neuronal cell death in hippocampal cornu ammonis-1 (CA1). Comparatively, hippocampal neurons in the adjacent dentate gyrus (DG) survive, suggesting that elucidating the molecular mechanisms underpinning hippocampal sub-regional differences in ischemic tolerance could be central in the development of novel interventions to improve outcome in survivors of forebrain ischemia. MicroRNAs (miRNAs) are non-coding RNAs that modulate the translation of target genes and have been established as an effective therapeutic target for other models of injury. The objective of the present study was to assess and compare post-injury miRNA profiles between CA1 and DG using a rat model of forebrain ischemia. CA1 and DG sub-regions were dissected from rat hippocampi following 10 min of forebrain ischemia at three time points (3 h, 24 h, and 72 h) and at baseline. Pronounced differences between CA1 and DG were observed for several select miRNAs, including miR-181a-5p, a known regulator of cerebral ischemic injury. We complexed fluorescent in situ hybridization with immunohistochemistry to observe cell-type specific and temporal differences in mir-181a-5p expression between CA1 and DG in response to injury. Using established miRNA-mRNA prediction algorithms, we extended our observations in CA1 miRNA dysregulation to identify key functional pathways as potential modulators of CA1 ischemic vulnerability. In summary, our observations support a central role for miRNAs in selective CA1 ischemic vulnerability and suggest that cell-specific miRNA targeting could be a viable clinical approach to preserve CA1 neurons and improve cognitive outcomes for survivors of transient forebrain ischemia.",0
"Analysis of Human Performance Deficiencies Associated with Surgical Adverse Events. Importance: Potentially preventable adverse events remain a formidable cause of patient harm and health care expenditure despite advances in systems-based risk-reduction strategies. Objective: To analyze and describe the incidence of human performance deficiencies (HPDs) during the provision of surgical care to identify opportunities to enhance patient safety. Design, Setting, and Participants: This quality improvement study used a new taxonomy to inform the development and implementation of an HPD classifier tool to categorize HPDs into errors associated with cognitive, technical, and team dynamic functions. The HPD classifier tool was then used to concurrently analyze surgical adverse events in 3 adult hospital affiliates - a level I municipal trauma center, a quaternary care university hospital, and a US Veterans Administration hospital - from January 2, 2018, to June 30, 2018. Surgical trainees presented data describing all adverse events associated with surgical services at weekly hospital-based morbidity and mortality conferences. Adverse events and HPDs were classified in discussion with attending faculty and residents. Data were analyzed from July 9, 2018, to December 23, 2018. Main Outcomes and Measures: The incidence and primary and secondary causes of HPDs were classified using an HPD classifier tool. Results: A total of 188 adverse events were recorded, including 182 adverse events (96.8%) among 5365 patients who underwent surgical operations and 6 adverse events (3.2%) among patients undergoing nonoperative treatment. Among these 188 adverse events, 106 (56.4%) were associated with HPDs. Among these 106 HPD adverse events, a total of 192 HPDs (mean [SD], 1.8 [0.9] HPDs per HPD event) were identified. Human performance deficiencies were categorized as execution (98 HPDs [51.0%]), planning or problem solving (55 HPDs [28.6%]), communication (24 HPDs [12.5%]), teamwork (9 HPDs [4.7%]), and rules violation (6 HPDs [3.1%]). Human performance deficiencies most commonly presented as cognitive errors in execution of care or in case planning or problem solving (99 of 192 HPDs [51.6%]). In contrast, technical execution errors without other associated HPDs were observed in 20 of 192 HPDs (10.4%). Conclusions and Relevance: Human performance deficiencies were identified in more than half of adverse events, most commonly associated with cognitive error in the execution of care. These data provide a framework and impetus for new quality improvement initiatives incorporating cognitive training to mitigate human error in surgery..",0
"Automatically identifying social isolation from clinical narratives for patients with prostate Cancer. BACKGROUND: Social isolation is an important social determinant that impacts health outcomes and mortality among patients. The National Academy of Medicine recently recommended that social isolation be documented in electronic health records (EHR). However, social isolation usually is not recorded or obtained as coded data but rather collected from patient self-report or documented in clinical narratives. This study explores the feasibility and effectiveness of natural language processing (NLP) strategy for identifying patients who are socially isolated from clinical narratives. METHOD: We used data from the Medical University of South Carolina (MUSC) Research Data Warehouse. Patients 18 years-of-age or older who were diagnosed with prostate cancer between January 1, 2014 and May 31, 2017 were eligible for this study. NLP pipelines identifying social isolation were developed via extraction of notes on progress, history and physical, consult, emergency department provider, telephone encounter, discharge summary, plan of care, and radiation oncology. Of 4195 eligible prostate cancer patients, we randomly sampled 3138 patients (75%) as a training dataset. The remaining 1057 patients (25%) were used as a test dataset to evaluate NLP algorithm performance. Standard performance measures for the NLP algorithm, including precision, recall, and F-measure, were assessed by expert manual review using the test dataset. RESULTS: A total of 55,516 clinical notes from 3138 patients were included to develop the lexicon and NLP pipelines for social isolation. Of those, 35 unique patients (1.2%) had social isolation mention(s) in 217 notes. Among 24 terms relevant to social isolation, the most prevalent were ""lack of social support,"" ""lonely,"" ""social isolation,"" ""no friends,"" and ""loneliness"". Among 1057 patients in the test dataset, 17 patients (1.6%) were identified as having social isolation mention(s) in 40 clinical notes. Manual review identified four false positive mentions of social isolation and one false negatives in 154 notes from randomly selected 52 controls. The NLP pipeline demonstrated 90% precision, 97% recall, and 93% F-measure. The major reasons for a false positive included the ambiguities of the experiencer of social isolation, negation, and alternate meaning of words. CONCLUSIONS: Our NLP algorithms demonstrate a highly accurate approach to identify social isolation.",1
"Platelet and red blood cell counts, as well as the concentrations of uric acid, but not homocysteinaemia or oxidative stress, contribute mostly to platelet reactivity in older adults. Purpose. The goal of this study was to estimate the hierarchical contribution of the most commonly recognized cardiovascular risk factors associated with atherogenesis to activation and reactivity of blood platelets in a group of men and women at ages 60-65. Methods. Socioeconomic and anthropometric data were taken from questionnaires. Blood morphology and biochemistry were measured with standard diagnostic methods. Plasma serum homocysteine was measured by immunochemical method. Plasma concentrations of VCAM, ICAM, total antioxidant status, and total oxidant status were estimated with commercial ELISA kits. Markers of oxidative stress of plasma and platelet proteins (concentrations of protein free thiol and amino groups) and lipids (concentrations of lipid peroxides) and generation of superoxide anion by platelets were measured with colorimetric methods. Platelet reactivity was estimated by impedance aggregometry with arachidonate, collagen, and ADP as agonists. Expression of selectin-P and GPIIb/IIIa on blood platelets was tested by flow cytometry. Results. Platelet aggregation associated significantly negatively with HGB and age and significantly positively with PLT, MPV, PCT, PDW, and P-LCR. When platelet reactivity (“cumulative platelet reactivity_aggregation”) was analyzed in a cumulated manner, the negative association with serum concentration of uric acid (Rs = −0 169, p = 0 003) was confirmed. Multivariate analysis revealed that amongst blood morphological parameters, platelet count, plateletcrit, and number of large platelets and uric acid are the most predictive variables for platelet reactivity. Conclusions. The most significant contributors to platelet reactivity in older subjects are platelet morphology, plasma uricaemia, and erythrocyte morphology.",0
"Assessment of Deep Learning Using Nonimaging Information and Sequential Medical Records to Develop a Prediction Model for Nonmelanoma Skin Cancer. Importance: A prediction model for new-onset nonmelanoma skin cancer could enhance prevention measures, but few patient data-driven tools exist for more accurate prediction. Objective: To use machine learning to develop a prediction model for incident nonmelanoma skin cancer based on large-scale, multidimensional, nonimaging medical information. Design, Setting, and Participants: This study used a database comprising 2 million randomly sampled patients from the Taiwan National Health Insurance Research Database from January 1, 1999, to December 31, 2013. A total of 1829 patients with nonmelanoma skin cancer as their first diagnosed cancer and 7665 random controls without cancer were included in the analysis. A convolutional neural network, a deep learning approach, was used to develop a risk prediction model. This risk prediction model used 3-year clinical diagnostic information, medical records, and temporal-sequential information to predict the skin cancer risk of a given patient within the next year. Stepwise feature selection was also performed to investigate important and determining factors of the model. Statistical analysis was performed from November 1, 2016, to October 31, 2018. Main Outcomes and Measures: Sensitivity, specificity, and area under the receiver operating characteristic (AUROC) curve were used to evaluate the performance of the models. Results: A total of 1829 patients (923 women [50.5%] and 906 men [49.5%]; mean [SD] age, 65.3 [15.7] years) with nonmelanoma skin cancer and 7665 random controls without cancer (3951 women [51.5%] and 3714 men [48.4%]; mean [SD] age, 47.5 [17.3] years) were included in the analysis. The 1-year incident nonmelanoma skin cancer risk prediction model using sequential diagnostic information and drug prescription information as a time-incorporated feature matrix could attain an AUROC of 0.89 (95% CI, 0.87-0.91), with a mean (SD) sensitivity of 83.1% (3.5%) and mean (SD) specificity of 82.3% (4.1%). Carcinoma in situ of skin (AUROC, 0.867; -2.80% loss) and other chronic comorbidities (eg, degenerative osteopathy [AUROC, 0.872; -2.32% loss], hypertension [AUROC, 0.879; -1.53% loss], and chronic kidney insufficiency [AUROC, 0.879; -1.52% loss]) served as more discriminative factors for the prediction. Medications such as trazodone, acarbose, systemic antifungal agents, statins, nonsteroidal anti-inflammatory drugs, and thiazide diuretics were the top-ranking discriminative features in the model; each led to more than a 1% decrease of the AUROC when eliminated individually (eg, trazodone AUROC, 0.868; -2.67% reduction; acarbose AUROC, 0.870; -2.50 reduction; and systemic antifungal agents AUROC, 0.875; -1.99 reduction). Conclusions and Relevance: The findings of this study suggest that a risk prediction model may have potential predictive factors for nonmelanoma skin cancer. This model may help health care professionals target high-risk populations for more intensive skin cancer preventive methods.",1
"Bidirectional Control of Autophagy by BECN1 BARA Domain Dynamics. Membrane targeting of the BECN1-containing class III PI 3-kinase (PI3KC3) complexes is pivotal to the regulation of autophagy. The interaction of PI3KC3 complex II and its ubiquitously expressed inhibitor, Rubicon, was mapped to the first β sheet of the BECN1 BARA domain and the UVRAG BARA2 domain by hydrogen-deuterium exchange and cryo-EM. These data suggest that the BARA β sheet 1 unfolds to directly engage the membrane. This mechanism was confirmed using protein engineering, giant unilamellar vesicle assays, and molecular simulations. Using this mechanism, a BECN1 β sheet-1 derived peptide activates both PI3KC3 complexes I and II, while HIV-1 Nef inhibits complex II. These data reveal how BECN1 switches on and off PI3KC3 binding to membranes. The observations explain how PI3KC3 inhibition by Rubicon, activation by autophagy-inducing BECN1 peptides, and inhibition by HIV-1 Nef are mediated by the switchable ability of the BECN1 BARA domain to partially unfold and insert into membranes.",0
"Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. The development of decision support systems for pathology and their deployment in clinical practice have been hindered by the need for large manually annotated datasets. To overcome this problem, we present a multiple instance learning-based deep learning system that uses only the reported diagnoses as labels for training, thereby avoiding expensive and time-consuming pixel-wise manual annotations. We evaluated this framework at scale on a dataset of 44,732 whole slide images from 15,187 patients without any form of data curation. Tests on prostate cancer, basal cell carcinoma and breast cancer metastases to axillary lymph nodes resulted in areas under the curve above 0.98 for all cancer types. Its clinical application would allow pathologists to exclude 65-75% of slides while retaining 100% sensitivity. Our results show that this system has the ability to train accurate classification models at unprecedented scale, laying the foundation for the deployment of computational decision support systems in clinical practice.",1
"Synergistic enhancement of apoptosis by coralyne and paclitaxel in combination on MDA-MB-231 a triple-negative breast cancer cell line. Triple-negative breast cancer (TNBC) is the most outrageous subtype of breast cancer. Emphasizing the urge of new approach in cancer therapy, combinational drug therapy may be proven as an effective strategy. In our previous study, we reported that coralyne (COR) with paclitaxel (PTX) efficiently decreases the proliferation of MDA-MB-231 compared with MCF-7 cell line. Thus, we studied the effect of COR and PTX in combination on apoptosis of MDA-MB-231 cell line. In silico results demonstrated that COR intercalates DNA at a minor groove. In vitro approaches revealed that in combination (COR and PTX) increases the efficacy of apoptosis in MDA-MB-231 cell line by a significant increase in G1/S phase arrest, DNA fragmentation, and change in mitochondria membrane potential. The expression of ATM and ATR a serine/threonine-protein kinase, ataxia telangiectasia and Rad3-related protein were depleted with an increase in time from 24 to 48 hours in concurrent with increased levels of γH2AX indicating that DNA damage routes cells to enter apoptosis. This was confirmed by high levels of caspase-3 and cytochrome c. Also, the decrease in the expression levels of matrix metalloproteinase-9 confirmed the antimetastatic efficacy of COR + PTX. The present study indicates that the synergistic effect of COR and PTX can enhance apoptosis in MDA-MB-231 cell line and may be proven as a potential anticancer therapy against TNBC.",0
"Study on therapeutic action and mechanism of TMZ Combined with RITA against glioblastoma. Background/Aims: Glioblastoma multiforme (GBM) is a malignant and aggressive central nervous system (CNS) tumor with high mortality and low survival rate. Effective treatment of GMB is a challenge worldwide. Temozolomide (TMZ) is a drug used to treat GBM, while the survival period of GBM patients with positive treatment remains less than 15 months. Reactivating p53 and Inducing Tumor Apoptosis (RITA) is a novel potential anti-cancer small molecular drug. Thus, it is essential to discover novel targets or develop effective drugs combination strategy to treat GBM. Methods: The U87 cells and U251 cells (p53 mutated) were treated with DMSO and 1, 5,10, 20 μM RITA, TMZ, RITA+TMA or PFT-α. The cell proliferation was measured using the MTS cell proliferation assay. The cell apoptosis was analyzed by Annexin V-FITC/PI Apoptosis Detection Kit. The key protein expression level was evaluated by WB. Molecular docking and molecular dynamics (MD) simulation methods were applied to simulate the interaction between RITA and ASK1. Results: Herein, we found that combination RITA and TMZ effectively inhibited the proliferation of U87 cells and promoted the apoptosis of U87 cells. Then the mechanism of RITA and TMZ treating GBM were further studied by detecting the expression of the proteins associating with p53 pathway, such as ASK1, Bax, and so on. RITA bound to the amino acids residues in the activation domain of the ASK1, then induced the conformation change of ASK1 receptor, activated ASK1 and caused a series of signal transduction, further resulted in the physiological effects. Conclusion: Taken together, the RITA suppressed the cell proliferation in glioblastoma via targeting ASK1.",0
"Identification of key lncRNAs in the carcinogenesis and progression of colon adenocarcinoma by co-expression network analysis. Colon adenocarcinoma (COAD) is one of the most common cancers, and its carcinogenesis and progression is influenced by multiple long non-coding RNAs (lncRNA), especially through the miRNA sponge effect. In this study, more than 4000 lncRNAs were re-annotated from the microarray datasets through probe sequence mapping to obtain reliable lncRNA expression profiles. As a systems biology method for describing the correlation patterns among genes across microarray samples, weighted gene co-expression network analysis was conducted to identify lncRNA modules associated with the five stepwise stages from normal colonic samples to COAD (n = 94). In the most relevant module (R2 = −0.78, P = 4E-20), four hub lncRNAs were identified (CTD-2396E7.11, PCGF5, RP11-33O4.1, and RP11-164P12.5). Then, these four hub lncRNAs were validated using two other independent datasets including GSE20916 (n = 145) and GSE39582 (n = 552). The results indicated that all hub lncRNAs were significantly negatively correlated with the three-stage colonic carcinogenesis, as well as TNM stages in COAD (one-way analysis of variance P < 0.05). Kaplan-Meier survival curve showed that patients with higher expression of each hub lncRNA had a significantly higher overall survival rate and lower relapse risk (log-rank P < 0.05). In conclusion, through co-expression analysis, we identified and validated four key lncRNAs in association with the carcinogenesis and progression of COAD, and these lncRNAs might have important clinical implications for improving the risk stratification, therapeutic decision and prognosis prediction in COAD patients.",0
"HPOAnnotator: improving large-scale prediction of HPO annotations by low-rank approximation with HPO semantic similarities and multiple PPI networks. Background: As a standardized vocabulary of phenotypic abnormalities associated with human diseases, the Human Phenotype Ontology (HPO) has been widely used by researchers to annotate phenotypes of genes/proteins. For saving the cost and time spent on experiments, many computational approaches have been proposed. They are able to alleviate the problem to some extent, but their performances are still far from satisfactory. Method: For inferring large-scale protein-phenotype associations, we propose HPOAnnotator that incorporates multiple Protein-Protein Interaction (PPI) information and the hierarchical structure of HPO. Specifically, we use a dual graph to regularize Non-negative Matrix Factorization (NMF) in a way that the information from different sources can be seamlessly integrated. In essence, HPOAnnotator solves the sparsity problem of a protein-phenotype association matrix by using a low-rank approximation. Results: By combining the hierarchical structure of HPO and co-annotations of proteins, our model can well capture the HPO semantic similarities. Moreover, graph Laplacian regularizations are imposed in the latent space so as to utilize multiple PPI networks. The performance of HPOAnnotator has been validated under cross-validation and independent test. Experimental results have shown that HPOAnnotator outperforms the competing methods significantly. Conclusions: Through extensive comparisons with the state-of-the-art methods, we conclude that the proposed HPOAnnotator is able to achieve the superior performance as a result of using a low-rank approximation with a graph regularization. It is promising in that our approach can be considered as a starting point to study more efficient matrix factorization-based algorithms.",0
"Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study. BACKGROUND: Whether machine-learning algorithms can diagnose all pigmented skin lesions as accurately as human experts is unclear. The aim of this study was to compare the diagnostic accuracy of state-of-the-art machine-learning algorithms with human readers for all clinically relevant types of benign and malignant pigmented skin lesions. METHODS: For this open, web-based, international, diagnostic study, human readers were asked to diagnose dermatoscopic images selected randomly in 30-image batches from a test set of 1511 images. The diagnoses from human readers were compared with those of 139 algorithms created by 77 machine-learning labs, who participated in the International Skin Imaging Collaboration 2018 challenge and received a training set of 10 015 images in advance. The ground truth of each lesion fell into one of seven predefined disease categories: intraepithelial carcinoma including actinic keratoses and Bowen's disease; basal cell carcinoma; benign keratinocytic lesions including solar lentigo, seborrheic keratosis and lichen planus-like keratosis; dermatofibroma; melanoma; melanocytic nevus; and vascular lesions. The two main outcomes were the differences in the number of correct specific diagnoses per batch between all human readers and the top three algorithms, and between human experts and the top three algorithms. FINDINGS: Between Aug 4, 2018, and Sept 30, 2018, 511 human readers from 63 countries had at least one attempt in the reader study. 283 (55.4%) of 511 human readers were board-certified dermatologists, 118 (23.1%) were dermatology residents, and 83 (16.2%) were general practitioners. When comparing all human readers with all machine-learning algorithms, the algorithms achieved a mean of 2.01 (95% CI 1.97 to 2.04; p<0.0001) more correct diagnoses (17.91 [SD 3.42] vs 19.92 [4.27]). 27 human experts with more than 10 years of experience achieved a mean of 18.78 (SD 3.15) correct answers, compared with 25.43 (1.95) correct answers for the top three machine algorithms (mean difference 6.65, 95% CI 6.06-7.25; p<0.0001). The difference between human experts and the top three algorithms was significantly lower for images in the test set that were collected from sources not included in the training set (human underperformance of 11.4%, 95% CI 9.9-12.9 vs 3.6%, 0.8-6.3; p<0.0001). INTERPRETATION: State-of-the-art machine-learning classifiers outperformed human experts in the diagnosis of pigmented skin lesions and should have a more important role in clinical practice. However, a possible limitation of these algorithms is their decreased performance for out-of-distribution images, which should be addressed in future research. FUNDING: None.",1
"Suite of meshless algorithms for accurate computation of soft tissue deformation for surgical simulation. The ability to predict patient-specific soft tissue deformations is key for computer-integrated surgery systems and the core enabling technology for a new era of personalized medicine. Element-Free Galerkin (EFG) methods are better suited for solving soft tissue deformation problems than the finite element method (FEM) due to their capability of handling large deformation while also eliminating the necessity of creating a complex predefined mesh. Nevertheless, meshless methods based on EFG formulation, exhibit three major limitations: (i) meshless shape functions using higher order basis cannot always be computed for arbitrarily distributed nodes (irregular node placement is crucial for facilitating automated discretization of complex geometries); (ii) imposition of the Essential Boundary Conditions (EBC) is not straightforward; and, (iii) numerical (Gauss) integration in space is not exact as meshless shape functions are not polynomial. This paper presents a suite of Meshless Total Lagrangian Explicit Dynamics (MTLED) algorithms incorporating a Modified Moving Least Squares (MMLS) method for interpolating scattered data both for visualization and for numerical computations of soft tissue deformation, a novel way of imposing EBC for explicit time integration, and an adaptive numerical integration procedure within the Meshless Total Lagrangian Explicit Dynamics algorithm. The appropriateness and effectiveness of the proposed methods is demonstrated using comparisons with the established non-linear procedures from commercial finite element software ABAQUS and experiments with very large deformations. To demonstrate the translational benefits of MTLED we also present a realistic brain-shift computation.",0
"Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging. Artificial intelligence (AI) has transformed key aspects of human life. Machine learning (ML), which is a subset of AI wherein machines autonomously acquire information by extracting patterns from large databases, has been increasingly used within the medical community, and specifically within the domain of cardiovascular diseases. In this review, we present a brief overview of ML methodologies that are used for the construction of inferential and predictive data-driven models. We highlight several domains of ML application such as echocardiography, electrocardiography, and recently developed non-invasive imaging modalities such as coronary artery calcium scoring and coronary computed tomography angiography. We conclude by reviewing the limitations associated with contemporary application of ML algorithms within the cardiovascular disease field.",0
Hit identification of FGFR1 inhibitors using receptor-based virtual screening. Aim. To identify novel FGFR1 inhibitors using the virtual screening approach. Methods. Virtual screening of a small organic compounds library was performed by molecular docking using the Autodock 4.2.6 program package. The compounds activity was determined by in vitro biochemical tests using γ-32P ATP. Results. In vitro experiments demonstrated that 18 compounds belonging to three chemical classes had an inhibitory activity against FGFR1 with IC50 values in the range from 1.8 to 71 μM. Conclusions. Several FGFR1 inhibitors were found using molecular modeling and biochemical testing. These compounds are excellent candidates for further chemical optimization.,0
"Avenanthramide A triggers potent ROS-mediated anti-tumor effects in colorectal cancer by directly targeting DDX3. Colorectal cancer (CRC) is a common malignant gastrointestinal tumor with high mortality worldwide. Drug resistance and cytotoxicity to normal cells are the main causes of chemotherapeutic treatment failure in CRC. Therefore, extracting the bioactive compounds from natural products with anti-carcinogenic activity and minimal side-effects is a promising strategy against CRC. The present study aims to evaluate the anti-carcinogenic properties of avenanthramides (AVNs) extracted from oats bran and clarify the underlying molecular mechanisms. We demonstrated that AVNs treatment suppressed mitochondrial bioenergetic generation, resulting in mitochondrial swelling and increased reactive oxygen species (ROS) production. Further study indicated that AVNs treatment significantly reduced DDX3 expression, an oncogenic RNA helicase highly expressed in human CRC tissues. DDX3 overexpression reversed the ROS-mediated CRC apoptosis induced by AVNs. Of note, we identified Avenanthramide A (AVN A) as the effective ingredient in AVNs extracts. AVN A blocked the ATPase activity of DDX3 and induced its degradation by directly binding to the Arg287 and Arg294 residues in DDX3. In conclusion, these innovative findings highlight that AVNs extracts, in particular its bioactive compound AVN A may crack the current hurdles in the way of CRC treatment.",0
"Discovery of Distinct Immune Phenotypes Using Machine Learning in Pulmonary Arterial Hypertension. RATIONALE: Accumulating evidence implicates inflammation in pulmonary arterial hypertension (PAH) and therapies targeting immunity are under investigation, although it remains unknown if distinct immune phenotypes exist. OBJECTIVE: Identify PAH immune phenotypes based on unsupervised analysis of blood proteomic profiles. METHODS AND RESULTS: In a prospective observational study of group 1 PAH patients evaluated at Stanford University (discovery cohort; n=281) and University of Sheffield (validation cohort; n=104) between 2008 and 2014, we measured a circulating proteomic panel of 48 cytokines, chemokines, and factors using multiplex immunoassay. Unsupervised machine learning (consensus clustering) was applied in both cohorts independently to classify patients into proteomic immune clusters, without guidance from clinical features. To identify central proteins in each cluster, we performed partial correlation network analysis. Clinical characteristics and outcomes were subsequently compared across clusters. Four PAH clusters with distinct proteomic immune profiles were identified in the discovery cohort. Cluster 2 (n=109) had low cytokine levels similar to controls. Other clusters had unique sets of upregulated proteins central to immune networks-cluster 1 (n=58; TRAIL [tumor necrosis factor-related apoptosis-inducing ligand], CCL5 [C-C motif chemokine ligand 5], CCL7, CCL4, MIF [macrophage migration inhibitory factor]), cluster 3 (n=77; IL [interleukin]-12, IL-17, IL-10, IL-7, VEGF [vascular endothelial growth factor]), and cluster 4 (n=37; IL-8, IL-4, PDGF-beta [platelet-derived growth factor beta], IL-6, CCL11). Demographics, PAH clinical subtypes, comorbidities, and medications were similar across clusters. Noninvasive and hemodynamic surrogates of clinical risk identified cluster 1 as high-risk and cluster 3 as low-risk groups. Five-year transplant-free survival rates were unfavorable for cluster 1 (47.6%; 95% CI, 35.4%-64.1%) and favorable for cluster 3 (82.4%; 95% CI, 72.0%-94.3%; across-cluster P<0.001). Findings were replicated in the validation cohort, where machine learning classified 4 immune clusters with comparable proteomic, clinical, and prognostic features. CONCLUSIONS: Blood cytokine profiles distinguish PAH immune phenotypes with differing clinical risk that are independent of World Health Organization group 1 subtypes. These phenotypes could inform mechanistic studies of disease pathobiology and provide a framework to examine patient responses to emerging therapies targeting immunity.",1
"Assessment of Machine Learning vs Standard Prediction Rules for Predicting Hospital Readmissions. Importance: Hospital readmissions are associated with patient harm and expense. Ways to prevent hospital readmissions have focused on identifying patients at greatest risk using prediction scores. Objective: To identify the type of score that best predicts hospital readmissions. Design, Setting, and Participants: This prognostic study included 14 062 consecutive adult hospital patients with 16 649 discharges from a tertiary care center, suburban community hospital, and urban critical access hospital in Maryland from September 1, 2016, through December 31, 2016. Patients not included as eligible discharges by the Centers for Medicare & Medicaid Services or the Chesapeake Regional Information System for Our Patients were excluded. A machine learning rank score, the Baltimore score (B score) developed using a machine learning technique, for each individual hospital using data from the 2 years before September 1, 2016, was compared with standard readmission risk assessment scores to predict 30-day unplanned readmissions. Main Outcomes and Measures: The 30-day readmission rate evaluated using various readmission scores: B score, HOSPITAL score, modified LACE score, and Maxim/RightCare score. Results: Of the 10 732 patients (5605 [52.2%] male; mean [SD] age, 54.56 [22.42] years) deemed to be eligible for the study, 1422 were readmitted. The area under the receiver operating characteristic curve (AUROC) for individual rules was 0.63 (95% CI, 0.61-0.65) for the HOSPITAL score, which was significantly lower than the 0.66 for modified LACE score (95% CI, 0.64-0.68; P < .001). The B score machine learning score was significantly better than all other scores; 48 hours after admission, the AUROC of the B score was 0.72 (95% CI, 0.70-0.73), which increased to 0.78 (95% CI, 0.77-0.79) at discharge (all P < .001). At the hospital using Maxim/RightCare score, the AUROC was 0.63 (95% CI, 0.59-0.69) for HOSPITAL, 0.64 (95% CI, 0.61-0.68) for Maxim/RightCare, and 0.66 (95% CI, 0.62-0.69) for modified LACE score. The B score was 0.72 (95% CI, 0.69-0.75) 48 hours after admission and 0.81 (95% CI, 0.79-0.84) at discharge. In directly comparing the B score with the sensitivity at cutoff values for modified LACE, HOSPITAL, and Maxim/RightCare scores, the B score was able to identify the same number of readmitted patients while flagging 25.5% to 54.9% fewer patients. Conclusions and Relevance: Among 3 hospitals in different settings, an automated machine learning score better predicted readmissions than commonly used readmission scores. More efficiently targeting patients at higher risk of readmission may be the first step toward potentially preventing readmissions.",1
"Clinical risk prediction with random forests for survival, longitudinal, and multivariate (RF-SLAM) data analysis. BACKGROUND: Clinical research and medical practice can be advanced through the prediction of an individual's health state, trajectory, and responses to treatments. However, the majority of current clinical risk prediction models are based on regression approaches or machine learning algorithms that are static, rather than dynamic. To benefit from the increasing emergence of large, heterogeneous data sets, such as electronic health records (EHRs), novel tools to support improved clinical decision making through methods for individual-level risk prediction that can handle multiple variables, their interactions, and time-varying values are necessary. METHODS: We introduce a novel dynamic approach to clinical risk prediction for survival, longitudinal, and multivariate (SLAM) outcomes, called random forest for SLAM data analysis (RF-SLAM). RF-SLAM is a continuous-time, random forest method for survival analysis that combines the strengths of existing statistical and machine learning methods to produce individualized Bayes estimates of piecewise-constant hazard rates. We also present a method-agnostic approach for time-varying evaluation of model performance. RESULTS: We derive and illustrate the method by predicting sudden cardiac arrest (SCA) in the Left Ventricular Structural (LV) Predictors of Sudden Cardiac Death (SCD) Registry. We demonstrate superior performance relative to standard random forest methods for survival data. We illustrate the importance of the number of preceding heart failure hospitalizations as a time-dependent predictor in SCA risk assessment. CONCLUSIONS: RF-SLAM is a novel statistical and machine learning method that improves risk prediction by incorporating time-varying information and accommodating a large number of predictors, their interactions, and missing values. RF-SLAM is designed to easily extend to simultaneous predictions of multiple, possibly competing, events and/or repeated measurements of discrete or continuous variables over time. TRIAL REGISTRATION: LV Structural Predictors of SCD Registry (clinicaltrials.gov, NCT01076660), retrospectively registered 25 February 2010.",1
"Ultra-short term HRV features as surrogates of short term HRV: a case study on mental stress detection in real life. BACKGROUND: This paper suggests a method to assess the extent to which ultra-short Heart Rate Variability (HRV) features (less than 5 min) can be considered as valid surrogates of short HRV features (nominally 5 min). Short term HRV analysis has been widely investigated for mental stress assessment, whereas the validity of ultra-short HRV features remains unclear. Therefore, this study proposes a method to explore the extent to which HRV excerpts can be shortened without losing their ability to automatically detect mental stress. METHODS: ECGs were acquired from 42 healthy subjects during a university examination and resting condition. 23 features were extracted from HRV excerpts of different lengths (i.e., 30 s, 1 min, 2 min, 3 min, and 5 min). Significant differences between rest and stress phases were investigated using non-parametric statistical tests at different time-scales. Features extracted from each ultra-short length were compared with the standard short HRV features, assumed as the benchmark, via Spearman's rank correlation analysis and Bland-Altman plots during rest and stress phases. Using data-driven machine learning approaches, a model aiming to detect mental stress was trained, validated and tested using short HRV features, and assessed on the ultra-short HRV features. RESULTS: Six out of 23 ultra-short HRV features (MeanNN, StdNN, MeanHR, StdHR, HF, and SD2) displayed consistency across all of the excerpt lengths (i.e., from 5 to 1 min) and 3 out of those 6 ultra-short HRV features (MeanNN, StdHR, and HF) achieved good performance (accuracy above 88%) when employed in a well-dimensioned automatic classifier. CONCLUSION: This study concluded that 6 ultra-short HRV features are valid surrogates of short HRV features for mental stress investigation.",1
"Quantifying Drug Combination Synergy along Potency and Efficacy Axes. Two goals motivate treating diseases with drug combinations: reduce off-target toxicity by minimizing doses (synergistic potency) and improve outcomes by escalating effect (synergistic efficacy). Established drug synergy frameworks obscure such distinction, failing to harness the potential of modern chemical libraries. We therefore developed multi-dimensional synergy of combinations (MuSyC), a formalism based on a generalized, multi-dimensional Hill equation, which decouples synergistic potency and efficacy. In mutant-EGFR-driven lung cancer, MuSyC reveals that combining a mutant-EGFR inhibitor with inhibitors of other kinases may result only in synergistic potency, whereas synergistic efficacy can be achieved by co-targeting mutant-EGFR and epigenetic regulation or microtubule polymerization. In mutant-BRAF melanoma, MuSyC determines whether a molecular correlate of BRAFi insensitivity alters a BRAF inhibitor's potency, efficacy, or both. These findings showcase MuSyC's potential to transform the enterprise of drug-combination screens by precisely guiding translation of combinations toward dose reduction, improved efficacy, or both. Meyer et al. developed a framework for measuring drug combination synergy. The framework, termed MuSyC, distinguishes between two types of synergy. The first quantifies the change in the maximal effect with the combination (synergistic efficacy), and the second measures the change in a drug's potency due to the combination (synergistic potency). By decoupling these two synergies conflated in prior methods, MuSyC rationally guides discovery and translation of drug combinations for the improvement of therapeutic efficacy and reduction of off-target toxicities via dose reduction.",0
"Systematic expression analysis of ligand-receptor pairs reveals important cell-to-cell interactions inside glioma. Background: Glioma is the most commonly diagnosed malignant and aggressive brain cancer in adults. Traditional researches mainly explored the expression profile of glioma at cell-population level, but ignored the heterogeneity and interactions of among glioma cells. Methods: Here, we firstly analyzed the single-cell RNA-seq (scRNA-seq) data of 6341 glioma cells using manifold learning and identified neoplastic and healthy cells infiltrating in tumor microenvironment. We systematically revealed cell-to-cell interactions inside gliomas based on corresponding scRNA-seq and TCGA RNA-seq data. Results: A total of 16 significantly correlated autocrine ligand-receptor signal pairs inside neoplastic cells were identified based on the scRNA-seq and TCGA data of glioma. Furthermore, we explored the intercellular communications between cancer stem-like cells (CSCs) and macrophages, and identified 66 ligand-receptor pairs, some of which could significantly affect prognostic outcomes. An efficient machine learning model was constructed to accurately predict the prognosis of glioma patients based on the ligand-receptor interactions. Conclusion: Collectively, our study not only reveals functionally important cell-to-cell interactions inside glioma, but also detects potentially prognostic markers for predicting the survival of glioma patients.",0
"Multiple neurosteroid and cholesterol binding sites in voltage-dependent anion channel-1 determined by photo-affinity labeling. Voltage-dependent anion channel-1 (VDAC1) is a mitochondrial porin that is implicated in cellular metabolism and apoptosis, and modulated by numerous small molecules including lipids. VDAC1 binds sterols, including cholesterol and neurosteroids such as allopregnanolone. Biochemical and computational studies suggest that VDAC1 binds multiple cholesterol molecules, but photolabeling studies have identified only a single cholesterol and neurosteroid binding site at E73. To identify all the binding sites of neurosteroids in VDAC1, we apply photo-affinity labeling using two sterol-based photolabeling reagents with complementary photochemistry: 5α-6-AziP which contains an aliphatic diazirine, and KK200 which contains a trifluoromethyl-phenyldiazirine (TPD) group. 5α-6-AziP and KK200 photolabel multiple residues within an E73 pocket confirming the presence of this site and mapping sterol orientation within this pocket. In addition, KK200 photolabels four other sites consistent with the finding that VDAC1 co-purifies with five cholesterol molecules. Both allopregnanolone and cholesterol competitively prevent photolabeling at E73 and three other sites indicating that these are common sterol binding sites shared by both neurosteroids and cholesterol. Binding at the functionally important residue E73 suggests a possible role for sterols in regulating VDAC1 signaling and interaction with partner proteins.",0
"Alleviation of exhaustion-induced immunosuppression and sepsis by immune checkpoint blockers sequentially administered with antibiotics—analysis of a new mathematical model. Background: Sepsis-associated immune dysregulation, involving hyper-inflammation and immunosuppression, is common in intensive care patients, often leading to multiple organ dysfunction and death. The aim of this study was to identify the main driving force underlying immunosuppression in sepsis, and to suggest new therapeutic avenues for controlling this immune impairment and alleviating excessive pathogen load. Methods: We developed two minimalistic (skeletal) mathematical models of pathogen-associated inflammation, which focus on the dynamics of myeloid, lymphocyte, and pathogen numbers in blood. Both models rely on the assumption that the presence of the pathogen causes a bias in hematopoietic stem cell differentiation toward the myeloid developmental line. Also in one of the models, we assumed that continuous exposure to pathogens induces lymphocyte exhaustion. In addition, we also created therapy models, both by antibiotics and by immunotherapy with PD-1/PD-L1 checkpoint inhibitors. Assuming realistic parameter ranges, we simulated the pathogen-associated inflammation models in silico with or without various antibiotic and immunotherapy schedules. Results: Computer simulations of the two models show that the assumption of lymphocyte exhaustion is a prerequisite for attaining sepsis-associated immunosuppression, and that the ability of the innate and adaptive immune systems to control infections depends on the pathogen’s replication rate. Simulation results further show that combining antibiotics with immune checkpoint blockers can suffice for defeating even an aggressive pathogen within a relatively short period. This is so as long as the drugs are administered soon after diagnosis. In contrast, when applied as monotherapies, antibiotics or immune checkpoint blockers fall short of eliminating aggressive pathogens in reasonable time. Conclusions: Our results suggest that lymphocyte exhaustion crucially drives immunosuppression in sepsis, and that one can efficiently resolve both immunosuppression and pathogenesis by timely coupling of antibiotics with an immune checkpoint blocker, but not by either one of these two treatment modalities alone. Following experimental validation, our model can be adapted to explore the potential of other therapeutic options in this field.",0
"The Extended Supervised Learning Event (ESLE): Assessing Nontechnical Skills in Emergency Medicine Trainees in the Workplace. STUDY OBJECTIVE: The contribution of emergency medicine clinicians' nontechnical skills in providing safe, high-quality care in the emergency department (ED) is well known. In 2015, the UK Royal College of Emergency Medicine introduced explicit validated descriptors of nontechnical skills needed to function effectively in the ED. A new nontechnical skills assessment tool that provided a score for 12 domains of nontechnical skills and detailed narrative feedback, the Extended Supervised Learning Event (ESLE), was introduced and was mandated as part of the Royal College of Emergency Medicine assessment schedule. We aim to evaluate the psychometric reliability of the ESLE in its first year of use. METHODS: ESLEs were mandated for all UK emergency medicine trainees in the final 4 years of a 6-year national training program from August 2015. The completed assessments were uploaded to the Royal College of Emergency Medicine e-portfolio. All assessments recorded in the Royal College of Emergency Medicine e-portfolio database between August 2015 and August 2016 were anonymized and analyzed for psychometric reliability, using generalizability theory. Decision analysis was used to model the effect of altering the number of episodes and assessors on reliability. RESULTS: A total of 1,390 ESLEs were analyzed. The majority (62%) of the variation in nontechnical skills scores was attributable to the trainee's ability. The circumstances of the event (eg, case complexity, workload) accounted for 21% and the stringency or leniency of assessors the remaining 16%. Decision analysis suggests that 3 ESLEs by 2 or more assessors, as currently recommended in the Royal College of Emergency Medicine curriculum, provide an assessment with a reliability coefficient of 0.8. CONCLUSION: Board-certified-equivalent emergency medicine supervisors are able to provide reliable assessments of emergency medicine trainees' nontechnical skills in the workplace by using the ESLE.",0
"Identification of biomarkers correlated with hypertrophic cardiomyopathy with co-expression analysis. Hypertrophic cardiomyopathy (HCM) is reported to be the most common genetic heart disease. To identify key module and candidate biomarkers correlated with clinical prognosis of patients with HCM, we carried out this study with co-expression analysis. To construct a co-expression network of hub genes correlated with HCM, the Weighted Gene Co-expression Network Analysis (WGCNA) was performed. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed by Database for Annotation, Visualization and Integrated Discovery (DAVID). The protein-protein interaction network analysis of central genes was performed to recognize the interactions of central genes. Gene set enrichment analyses were carried out to discover the possible mechanisms involved in the pathways promoted by hub genes. To validate the hub genes, quantitative real-time polymerase chain reaction (RT-PCR) was performed. Based on the results of topological overlap measure based clustering, 2,351 differentially expressed genes (DEGs) were identified. Those genes were included in six different modules. Of these modules, the yellow and the blue modules showed a pivotal correlation with HCM. DEGs were enriched in immune system procedure associated GO terms and KEGG pathways. We identified nine hub genes (TYROBP, STAT3, CSF1R, ITGAM, SYK, ITGB2, LILRB2, LYN, and HCK) affected the immune system significantly. Among the genes we validated with RT-PCR, TYROBP, CSF1R, and SYK showed significant increasing expression levels in model HCM rats. In conclusion, we identified two modules and nine hub genes, which were prominently associated with HCM. We found that immune system may play a crucial role in the HCM. Accordingly, those genes and pathways might become therapeutic targets with clinical usefulness in the future.",0
"Clinical and Genomic Risk to Guide the Use of Adjuvant Therapy for Breast Cancer. BACKGROUND: The use of adjuvant chemotherapy in patients with breast cancer may be guided by clinicopathological factors and a score based on a 21-gene assay to determine the risk of recurrence. Whether the level of clinical risk of breast cancer recurrence adds prognostic information to the recurrence score is not known. METHODS: We performed a prospective trial involving 9427 women with hormone-receptor-positive, human epidermal growth factor receptor 2-negative, axillary node-negative breast cancer, in whom an assay of 21 genes had been performed, and we classified the clinical risk of recurrence of breast cancer as low or high on the basis of the tumor size and histologic grade. The effect of clinical risk was evaluated by calculating hazard ratios for distant recurrence with the use of Cox proportional-hazards models. The initial endocrine therapy was tamoxifen alone in the majority of the premenopausal women who were 50 years of age or younger. RESULTS: The level of clinical risk was prognostic of distant recurrence in women with an intermediate 21-gene recurrence score of 11 to 25 (on a scale of 0 to 100, with higher scores indicating a worse prognosis or a greater potential benefit from chemotherapy) who were randomly assigned to endocrine therapy (hazard ratio for the comparison of high vs. low clinical risk, 2.73; 95% confidence interval [CI], 1.93 to 3.87) or to chemotherapy plus endocrine (chemoendocrine) therapy (hazard ratio, 2.41; 95% CI, 1.66 to 3.48) and in women with a high recurrence score (a score of 26 to 100), all of whom were assigned to chemoendocrine therapy (hazard ratio, 3.17; 95% CI, 1.94 to 5.19). Among women who were 50 years of age or younger who had received endocrine therapy alone, the estimated (+/-SE) rate of distant recurrence at 9 years was less than 5% (</=1.8+/-0.9%) with a low recurrence score (a score of 0 to 10), irrespective of clinical risk, and 4.7+/-1.0% with an intermediate recurrence score and low clinical risk. In this age group, the estimated distant recurrence at 9 years exceeded 10% among women with a high clinical risk and an intermediate recurrence score who received endocrine therapy alone (12.3+/-2.4%) and among those with a high recurrence score who received chemoendocrine therapy (15.2+/-3.3%). CONCLUSIONS: Clinical-risk stratification provided prognostic information that, when added to the 21-gene recurrence score, could be used to identify premenopausal women who could benefit from more effective therapy. (Funded by the National Cancer Institute and others; ClinicalTrials.gov number, NCT00310180.).",0
"Fast Adipogenesis Tracking System (FATS) - A robust, high-throughput, automation-ready adipogenesis quantification technique 10 Technology 1004 Medical Biotechnology. Adipogenesis is essential in in vitro experimentation to assess differentiation capability of stem cells, and therefore, its accurate measurement is important. Quantitative analysis of adipogenic levels, however, is challenging and often susceptible to errors due to non-specific reading or manual estimation by observers. To this end, we developed a novel adipocyte quantification algorithm, named Fast Adipogenesis Tracking System (FATS), based on computer vision libraries. The FATS algorithm is versatile and capable of accurately detecting and quantifying percentage of cells undergoing adipogenic and browning differentiation even under difficult conditions such as the presence of large cell clumps or high cell densities. The algorithm was tested on various cell lines including 3T3-L1 cells, adipose-derived mesenchymal stem cells (ASCs), and induced pluripotent stem cell (iPSC)-derived cells. The FATS algorithm is particularly useful for adipogenic measurement of embryoid bodies derived from pluripotent stem cells and was capable of accurately distinguishing adipogenic cells from false-positive stains. We then demonstrate the effectiveness of the FATS algorithm for screening of nuclear receptor ligands that affect adipogenesis in the high-throughput manner. Together, the FATS offer a universal and automated image-based method to quantify adipocyte differentiation of different cell lines in both standard and high-throughput workflows.",0
"Identification of Chemotype Agonists for Human Resolvin D1 Receptor DRV1 with Pro-Resolving Functions. Resolution of acute inflammation is governed, in part, by specialized pro-resolving mediators, including lipoxins, resolvins, protectins, and maresins. Among them, resolvin D1 (RvD1) exhibits potent pro-resolving functions via activating human resolvin D1 receptor (DRV1/GPR32). RvD1 is a complex molecule that requires challenging organic synthesis, diminishing its potential as a therapeutic. Therefore, we implemented a high-throughput screening of small-molecule libraries and identified several chemotypes that activated recombinant DRV1, represented by NCGC00120943 (C1A), NCGC00135472 (C2A), pMPPF, and pMPPI. These chemotypes also elicited rapid impedance changes in cells overexpressing recombinant DRV1. With human macrophages, they each stimulated phagocytosis of serum-treated zymosan at concentrations comparable with that of RvD1, the endogenous DRV1 ligand. In addition, macrophage phagocytosis of live E. coli was significantly increased by these chemotypes in DRV1-transfected macrophages, compared with mock-transfected cells. Taken together, these chemotypes identified by unbiased screens act as RvD1 mimetics, exhibiting pro-resolving functions via interacting with human DRV1. Chiang et al. identified synthetic molecules that activate human DRV1/GPR32 receptor using high-throughput screening (>48,000 compounds). DRV1/GPR32 and its ligand resolvin D1 display pro-resolving actions in inflammation. These molecules act as resolvin D1 mimetics, offering templates to facilitate therapeutic development targeting DRV1 to promote resolution of inflammation.",0
"Epitope-based immunoinformatics approach on RNA-dependent RNA polymerase (RdRp) protein complex of Nipah virus (NiV). Persistent outbreaks of Nipah virus (NiV) with severe case fatality throw a major challenge on researchers to develop a drug or vaccine to combat the disease. With little knowledge of its molecular mechanisms, we utilized the proteome data of NiV to evaluate the potency of three major proteins (phosphoprotein, polymerase, and nucleocapsid protein) in the RNA-dependent RNA polymerase complex to count as a possible candidate for epitope-based vaccine design. Profound computational analysis was used on the above proteins individually to explore the T-cell immune properties like antigenicity, immunogenicity, binding to major histocompatibility complex class I and class II alleles, conservancy, toxicity, and population coverage. Based on these predictions the peptide ‘ELRSELIGY’ of phosphoprotein and ‘YPLLWSFAM’ of nulceocapsid protein were identified as the best-predicted T-cell epitopes and molecular docking with human leukocyte antigen-C (HLA-C*12:03) molecule was effectuated followed by validation with molecular dynamics simulation. The B-cell epitope predictions suggest that the sequence positions 421 to 471 in phosphoprotein, 606 to 640 in polymerase and 496 to 517 in nucleocapsid protein are the best-predicted regions for B-cell immune response. However, the further experimental circumstance is required to test and validate the efficacy of the subunit peptide for potential candidacy against NiV.",0
"Rome Foundation Working Team Report on Post-Infection Irritable Bowel Syndrome. BACKGROUND & AIMS: The existence of postinfection irritable bowel syndrome (PI-IBS) has been substantiated by epidemiology studies conducted in diverse geographic and clinical settings. However, the available evidence has not been well summarized, and there is little guidance for diagnosis and treatment of PI-IBS. The ROME Foundation has produced a working team report to summarize the available evidence on the pathophysiology of PI-IBS and provide guidance for diagnosis and treatment, based on findings reported in the literature and clinical experience. METHODS: The working team conducted an evidence-based review of publication databases for articles describing the clinical features (diagnosis), pathophysiology (intestinal sensorimotor function, microbiota, immune dysregulation, barrier dysfunction, enteroendocrine pathways, and genetics), and animal models of PI-IBS. We used a Delphi-based consensus system to create guidelines for management of PI-IBS and a developed treatment algorithm based on published findings and experiences of team members. RESULTS: PI-IBS develops in about 10% of patients with infectious enteritis. Risk factors include female sex, younger age, psychological distress during or before acute gastroenteritis, and severity of the acute episode. The pathogenesis of PI-PBS appears to involve changes in the intestinal microbiome as well as epithelial, serotonergic, and immune system factors. However, these mechanisms are incompletely understood. There are no evidence-based, effective pharmacologic strategies for treatment of PI-IBS. We provide a consensus-based treatment algorithm, based on clinical presentation and potential disease mechanisms. CONCLUSIONS: Based on a systematic review of the literature and team experience, we summarize the clinical features, pathophysiology (from animal models and human studies), and progression of PI-IBS. Based on these findings, we present an algorithm for diagnosis and treatment of PI-IBS based on team consensus. We also propose areas for future investigation.",0
"Reproducibility of CT Radiomic Features within the Same Patient: Influence of Radiation Dose and CT Reconstruction Settings. Background Results of recent phantom studies show that variation in CT acquisition parameters and reconstruction techniques may make radiomic features largely nonreproduceable and of limited use for prognostic clinical studies. Purpose To investigate the effect of CT radiation dose and reconstruction settings on the reproducibility of radiomic features, as well as to identify correction factors for mitigating these sources of variability. Materials and Methods This was a secondary analysis of a prospective study of metastatic liver lesions in patients who underwent staging with single-energy dual-source contrast material-enhanced staging CT between September 2011 and April 2012. Technique parameters were altered, resulting in 28 CT data sets per patient that included different dose levels, section thicknesses, kernels, and reconstruction algorithm settings. By using a training data set (n = 76), reproducible intensity, shape, and texture radiomic features (reproducibility threshold, R(2) >/= 0.95) were selected and correction factors were calculated by using a linear model to convert each radiomic feature to its estimated value in a reference technique. By using a test data set (n = 75), the reproducibility of hierarchical clustering based on 106 radiomic features measured with different CT techniques was assessed. Results Data in 78 patients (mean age, 60 years +/- 10; 33 women) with 151 liver lesions were included. The percentage of radiomic features deemed reproducible for any variation of the different technical parameters was 11% (12 of 106). Of all technical parameters, reconstructed section thickness had the largest impact on the reproducibility of radiomic features (12.3% [13 of 106]) if only one technical parameter was changed while all other technical parameters were kept constant. The results of the hierarchical cluster analysis showed improved clustering reproducibility when reproducible radiomic features with dedicated correction factors were used (rho = 0.39-0.71 vs rho = 0.14-0.47). Conclusion Most radiomic features are highly affected by CT acquisition and reconstruction settings, to the point of being nonreproducible. Selecting reproducible radiomic features along with study-specific correction factors offers improved clustering reproducibility. (c) RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Sosna in this issue.",1
"A systematic analysis of genomics-based modeling approaches for prediction of drug response to cytotoxic chemotherapies. Background: The availability and generation of large amounts of genomic data has led to the development of a new paradigm in cancer treatment emphasizing a precision approach at the molecular and genomic level. Statistical modeling techniques aimed at leveraging broad scale in vitro, in vivo, and clinical data for precision drug treatment has become an active area of research. As a rapidly developing discipline at the crossroads of medicine, computer science, and mathematics, techniques ranging from accepted to those on the cutting edge of artificial intelligence have been utilized. Given the diversity and complexity of these techniques a systematic understanding of fundamental modeling principles is essential to contextualize influential factors to better understand results and develop new approaches. Methods: Using data available from the Genomics of Drug Sensitivity in Cancer (GDSC) and the NCI60 we explore principle components regression, linear and non-linear support vector regression, and artificial neural networks in combination with different implementations of correlation based feature selection (CBF) on the prediction of drug response for several cytotoxic chemotherapeutic agents. Results: Our results indicate that the regression method and features used have marginal effects on Spearman correlation between the predicted and measured values as well as prediction error. Detailed analysis of these results reveal that the bulk relationship between tissue of origin and drug response is a major driving factor in model performance. Conclusion: These results display one of the challenges in building predictive models for drug response in pan-cancer models. Mainly, that bulk genotypic traits where the signal to noise ratio is high is the dominant behavior captured in these models. This suggests that improved techniques of feature selection that can discriminate individual cell response from histotype response will yield more successful pan-cancer models.",0
"Quantitative Super-Resolution Microscopy of the Mammalian Glycocalyx. The mammalian glycocalyx is a heavily glycosylated extramembrane compartment found on nearly every cell. Despite its relevance in both health and disease, studies of the glycocalyx remain hampered by a paucity of methods to spatially classify its components. We combine metabolic labeling, bioorthogonal chemistry, and super-resolution localization microscopy to image two constituents of cell-surface glycans, N-acetylgalactosamine (GalNAc) and sialic acid, with 10–20 nm precision in 2D and 3D. This approach enables two measurements: glycocalyx height and the distribution of individual sugars distal from the membrane. These measurements show that the glycocalyx exhibits nanoscale organization on both cell lines and primary human tumor cells. Additionally, we observe enhanced glycocalyx height in response to epithelial-to-mesenchymal transition and to oncogenic KRAS activation. In the latter case, we trace increased height to an effector gene, GALNT7. These data highlight the power of advanced imaging methods to provide molecular and functional insights into glycocalyx biology. Nearly every cell in the human body is encapsulated by an extramembrane glycosylated compartment—the glycocalyx—that is the cell's first point of contact with the extracellular environment. Möckl et al. combine bioorthogonal chemistry with quantitative super-resolution imaging to investigate glycocalyx nanoscale organization and its relationship to oncogenic cellular transformation.",0
"Machine learning algorithms estimating prognosis and guiding therapy in adult congenital heart disease: data from a single tertiary centre including 10 019 patients. AIMS: To assess the utility of machine learning algorithms on estimating prognosis and guiding therapy in a large cohort of patients with adult congenital heart disease (ACHD) or pulmonary hypertension at a single, tertiary centre. METHODS AND RESULTS: We included 10 019 adult patients (age 36.3 +/- 17.3 years) under follow-up at our institution between 2000 and 2018. Clinical and demographic data, ECG parameters, cardiopulmonary exercise testing, and selected laboratory markers where collected and included in deep learning (DL) algorithms. Specific DL-models were built based on raw data to categorize diagnostic group, disease complexity, and New York Heart Association (NYHA) class. In addition, models were developed to estimate need for discussion at multidisciplinary team (MDT) meetings and to gauge prognosis of individual patients. Overall, the DL-algorithms-based on over 44 000 medical records-categorized diagnosis, disease complexity, and NYHA class with an accuracy of 91.1%, 97.0%, and 90.6%, respectively in the test sample. Similarly, patient presentation at MDT-meetings was predicted with a test sample accuracy of 90.2%. During a median follow-up time of 8 years, 785 patients died. The automatically derived disease severity-score derived from clinical information was related to survival on Cox analysis independently of demographic, exercise, laboratory, and ECG parameters. CONCLUSION: We present herewith the utility of machine learning algorithms trained on large datasets to estimate prognosis and potentially to guide therapy in ACHD. Due to the largely automated process involved, these DL-algorithms can easily be scaled to multi-institutional datasets to further improve accuracy and ultimately serve as online based decision-making tools.",1
"Detection and classification of myocardial delayed enhancement patterns on mr images with deep neural networks: A feasibility study. Purpose: To evaluate whether deep neural networks trained on a similar number of images to that required during physician training in the American College of Cardiology Core Cardiovascular Training Statement can acquire the capability to detect and classify myocardial delayed enhancement (MDE) patterns. Materials and Methods: The authors retrospectively evaluated 1995 MDE images for training and validation of a deep neural network. Images were from 200 consecutive patients who underwent cardiovascular MRI and were obtained from the institutional database. Experienced cardiac MR image readers classified the images as showing the following MDE patterns: No pattern, epicardial enhancement, subendocardial enhancement, midwall enhancement, focal enhancement, transmural enhancement, and nondiagnostic. Data were divided into training and validation datasets by using a fourfold cross-validation method. Three untrained deep neural network architectures using the convolutional neural network (CNN) technique were trained with the training dataset images. The detection and classification accuracies of the trained CNNs were calculated with validation data. Results: The 1995 MDE images were classified by human readers as follows: No pattern, 926; epicardial enhancement, 91; subendocardial enhancement, 458; midwall enhancement, 118; focal enhancement, 141; transmural enhancement, 190; and nondiagnostic, 190. GoogLeNet, AlexNet, and ResNet-152 CNNs demonstrated accuracies of 79.5% (1592 of 1995 images), 78.9% (1574 of 1995 images), and 82.1% (1637 of 1995 images), respectively. Conclusion: Deep learning with CNNs using a limited amount of training data, less than that required during physician training, achieved high diagnostic performance in the detection of MDE on MR images.",1
"Development and Validation of a Deep Learning-Based Automated Detection Algorithm for Major Thoracic Diseases on Chest Radiographs. Importance: Interpretation of chest radiographs is a challenging task prone to errors, requiring expert readers. An automated system that can accurately classify chest radiographs may help streamline the clinical workflow. Objectives: To develop a deep learning-based algorithm that can classify normal and abnormal results from chest radiographs with major thoracic diseases including pulmonary malignant neoplasm, active tuberculosis, pneumonia, and pneumothorax and to validate the algorithm's performance using independent data sets. Design, Setting, and Participants: This diagnostic study developed a deep learning-based algorithm using single-center data collected between November 1, 2016, and January 31, 2017. The algorithm was externally validated with multicenter data collected between May 1 and July 31, 2018. A total of 54 221 chest radiographs with normal findings from 47 917 individuals (21 556 men and 26 361 women; mean [SD] age, 51 [16] years) and 35 613 chest radiographs with abnormal findings from 14 102 individuals (8373 men and 5729 women; mean [SD] age, 62 [15] years) were used to develop the algorithm. A total of 486 chest radiographs with normal results and 529 with abnormal results (1 from each participant; 628 men and 387 women; mean [SD] age, 53 [18] years) from 5 institutions were used for external validation. Fifteen physicians, including nonradiology physicians, board-certified radiologists, and thoracic radiologists, participated in observer performance testing. Data were analyzed in August 2018. Exposures: Deep learning-based algorithm. Main Outcomes and Measures: Image-wise classification performances measured by area under the receiver operating characteristic curve; lesion-wise localization performances measured by area under the alternative free-response receiver operating characteristic curve. Results: The algorithm demonstrated a median (range) area under the curve of 0.979 (0.973-1.000) for image-wise classification and 0.972 (0.923-0.985) for lesion-wise localization; the algorithm demonstrated significantly higher performance than all 3 physician groups in both image-wise classification (0.983 vs 0.814-0.932; all P < .005) and lesion-wise localization (0.985 vs 0.781-0.907; all P < .001). Significant improvements in both image-wise classification (0.814-0.932 to 0.904-0.958; all P < .005) and lesion-wise localization (0.781-0.907 to 0.873-0.938; all P < .001) were observed in all 3 physician groups with assistance of the algorithm. Conclusions and Relevance: The algorithm consistently outperformed physicians, including thoracic radiologists, in the discrimination of chest radiographs with major thoracic diseases, demonstrating its potential to improve the quality and efficiency of clinical practice.",1
"PROTEOFORMER 2.0: Further developments in the ribosome profiling-assisted proteogenomic hunt for new proteoforms. PROTEOFORMER is a pipeline that enables the automated processing of data derived from ribosome profiling (RIBO-seq, i.e. The sequencing of ribosome-protected mRNA fragments). As such, genome-wide ribosome occupancies lead to the delineation of data-specific translation product candidates and these can improve the mass spectrometry-based identification. Since its first publication, different upgrades, new features and extensions have been added to the PROTEOFORMER pipeline. Some of the most important upgrades include P-site offset calculation during mapping, comprehensive data preexploration, the introduction of two alternative proteoform calling strategies and extended pipeline output features. These novelties are illustrated by analyzing ribosome profiling data of human HCT116 and Jurkat data. The different proteoform calling strategies are used alongside one another and in the end combined together with reference sequences from UniProt. Matching mass spectrometry data are searched against this extended search space with MaxQuant. Overall, besides annotated proteoforms, this pipeline leads to the identification and validation of different categories of new proteoforms, including translation products of up- and downstream open reading frames, 5' and 3' extended and truncated proteoforms, single amino acid variants, splice variants and translation products of so-called noncoding regions. Further, proof-of-concept is reported for the improvement of spectrum matching by including Prosit, a deep neural network strategy that adds extra fragmentation spectrum intensity features to the analysis. In the light of ribosome profiling-driven proteogenomics, it is shown that this allows validating the spectrum matches of newly identified proteoforms with elevated stringency. These updates and novel conclusions provide new insights and lessons for the ribosome profiling-based proteogenomic research field.",0
"Molecular inhibitory mechanism study on the potent inhibitor brigatinib against four crizotinib-resistant ALK mutations. As a potent and selective drug, brigatinib exhibits high efficacy against wild-type and mutant anaplastic lymphoma kinase (ALK) proteins to treat non–small cell lung cancer. In this work, the mechanisms of brigatinib binding to wild type and four mutant ALKs were investigated to gain insight into the dynamic energetic and structural information with respect to the design of novel inhibitors. Comparison between ALK-brigatinib and ALK-crizotinib suggests that the scaffold of brigatinib is well anchored to the residue Met1199 of hinge region by two hydrogen bonds, and the residue Lys1150 has the strong electrostatic interaction with the dimethylphosphine oxide moiety in brigatinib. These ALK mutations have significant influences on the flexibility of P-loop region and DFG sequences, but do not impair the hydrogen bonds between brigatinib and the residue Met1199 of hinge region. And mutations (L1196M, G1269A, F1174L, and R1275Q) induce diverse conformational changes of brigatinib and the obvious energy variation of residues Glu1167, Arg1209, Asp1270, and Asp1203. Together, the detailed explanation of mechanisms of those mutations with brigatinib further provide several guidelines for the development of more effective ALK inhibitors.",0
"Segmentation and classification in MRI and US fetal imaging: Recent trends and future prospects. Fetal imaging is a burgeoning topic. New advancements in both magnetic resonance imaging and (3D) ultrasound currently allow doctors to diagnose fetal structural abnormalities such as those involved in twin-to-twin transfusion syndrome, gestational diabetes mellitus, pulmonary sequestration and hypoplasia, congenital heart disease, diaphragmatic hernia, ventriculomegaly, etc. Considering the continued breakthroughs in utero image analysis and (3D) reconstruction models, it is now possible to gain more insight into the ongoing development of the fetus. Best prenatal diagnosis performances rely on the conscious preparation of the clinicians in terms of fetal anatomy knowledge. Therefore, fetal imaging will likely span and increase its prevalence in the forthcoming years. This review covers state-of-the-art segmentation and classification methodologies for the whole fetus and, more specifically, the fetal brain, lungs, liver, heart and placenta in magnetic resonance imaging and (3D) ultrasound for the first time. Potential applications of the aforementioned methods into clinical settings are also inspected. Finally, improvements in existing approaches as well as most promising avenues to new areas of research are briefly outlined.",0
"Extracting pathway-level signatures from proteogenomic data in breast cancer using independent component analysis. Recent advances in the multi-omics characterization necessitate knowledge integration across different data types that go beyond individual biomarker discovery. In this study, we apply independent component analysis (ICA) to human breast cancer proteogenomics data to retrieve mechanistic information. We show that as an unsupervised feature extraction method, ICA was able to construct signatures with known biological relevance on both transcriptome and proteome levels. Moreover, proteome and transcriptome signatures can be associated by their respective correlation with patient clinical features, providing an integrated description of phenotype-related biological processes. Our results demonstrate that the application of ICA to proteogenomics data could lead to pathway-level knowledge discovery. Potential extension of this approach to other data and cancer types may contribute to pan-cancer integration of multi-omics information.",0
"Kaempferol attenuates liver fibrosis by inhibiting activin receptor–like kinase 5. Liver fibrosis is a common public health problem. Patients with liver fibrosis are more likely to develop cirrhosis, or hepatocellular carcinoma (HCC) as a more serious consequence. Numerous therapeutic approaches have emerged, but the final clinical outcome remains unsatisfactory. Here, we discovered a flavonoid natural product kaempferol that could dramatically ameliorate liver fibrosis formation. Our data showed that intraperitoneal injection of kaempferol could significantly decrease the necroinflammatory scores and collagen deposition in the liver tissue. In addition, serum alanine aminotransferase (ALT), aspartate aminotransferase (AST), laminin (LN) and hyaluronic acid (HA) levels were significantly down-regulated in kaempferol treatment group compared with those in the control group. Our study also demonstrated that kaempferol markedly inhibited the synthesis of collagen and activation of hepatic stellate cells (HSCs) both in vivo and in vitro. Furthermore, the results of Western blotting revealed that kaempferol could down-regulate Smad2/3 phosphorylation dose-dependently. These bioactivities of kaempferol may result from its targeted binding to the ATP-binding pocket of activin receptor–like kinase 5 (ALK5), as suggested by the molecular docking study and LanthaScreen Eu kinase binding assay. Above all, our data indicate that kaempferol may prove to be a novel agent for the treatment of liver fibrosis or other fibroproliferative diseases.",0
"Estimating the loss of lifetime function using flexible parametric relative survival models. BACKGROUND: Within cancer care, dynamic evaluations of the loss in expectation of life provides useful information to patients as well as physicians. The loss of lifetime function yields the conditional loss in expectation of life given survival up to a specific time point. Due to the inevitable censoring in time-to-event data, loss of lifetime estimation requires extrapolation of both the patient and general population survival function. In this context, the accuracy of different extrapolation approaches has not previously been evaluated. METHODS: The loss of lifetime function was computed by decomposing the all-cause survival function using the relative and general population survival function. To allow extrapolation, the relative survival function was fitted using existing parametric relative survival models. In addition, we introduced a novel mixture cure model suitable for extrapolation. The accuracy of the estimated loss of lifetime function using various extrapolation approaches was assessed in a simulation study and by data from the Danish Cancer Registry where complete follow-up was available. In addition, we illustrated the proposed methodology by analyzing recent data from the Danish Lymphoma Registry. RESULTS: No uniformly superior extrapolation method was found, but flexible parametric mixture cure models and flexible parametric relative survival models seemed to be suitable in various scenarios. CONCLUSION: Using extrapolation to estimate the loss of lifetime function requires careful consideration of the relative survival function outside the available follow-up period. We propose extensive sensitivity analyses when estimating the loss of lifetime function.",0
"Applications of machine learning in drug discovery and development. Drug discovery and development pipelines are long, complex and depend on numerous factors. Machine learning (ML) approaches provide a set of tools that can improve discovery and decision making for well-specified questions with abundant, high-quality data. Opportunities to apply ML occur in all stages of drug discovery. Examples include target validation, identification of prognostic biomarkers and analysis of digital pathology data in clinical trials. Applications have ranged in context and methodology, with some approaches yielding accurate predictions and insights. The challenges of applying ML lie primarily with the lack of interpretability and repeatability of ML-generated results, which may limit their application. In all areas, systematic and comprehensive high-dimensional data still need to be generated. With ongoing efforts to tackle these issues, as well as increasing awareness of the factors needed to validate ML approaches, the application of ML can promote data-driven decision making and has the potential to speed up the process and reduce failure rates in drug discovery and development.",0
"A retrospective study on clinical manifestations of neonates with FXIII-A deficiency. We assessed clinical presentations and the rate of central nervous system (CNS) bleeding in neonates with FXIIID who exhibited bleeding diathesis in the early days of their lives. A total of 27 neonates presented bleeding or abnormal clinical symptoms, diagnosed with FXIII deficiency were evaluated. Factor XIII concentrate was initiated as the first-line of treatment, and prophylactic therapy was given to all patients. Umbilical cord bleeding, delayed detachment of umbilical stunt, seizure, hematoma, and ecchymosis were concurrent complications in 27 (100%), 5 (18.5%), 5 (18.5%), 3 (11.1%), and 1 (3.7%) of the patients, respectively. History of having CNS bleeding was detected in 13 (48.1%) patients. There was no significant association between CNS bleeding and gender, familial history of FXIIID, or other clinical presentations. Also, there was no significant difference in the mean age of the patients who had CNS bleeding (3.4 ± 0.9 days) and without CNS bleeding (2.9 ± 0.7 days). However, a near significant threshold difference between the patients with and without CNS bleeding was found regarding the mean number of suspicious FXIIID death in their family (1.8 ± 0.5 and 0.7 ± 0.1, respectively, P = 0.05). Therefore, a suggested diagnostic algorithm based on prenatal diagnosis could be useful for timely detection of FXIII deficiency in neonates.",0
"ICU staffing feature phenotypes and their relationship with patients' outcomes: an unsupervised machine learning analysis. PURPOSE: To study whether ICU staffing features are associated with improved hospital mortality, ICU length of stay (LOS) and duration of mechanical ventilation (MV) using cluster analysis directed by machine learning. METHODS: The following variables were included in the analysis: average bed to nurse, physiotherapist and physician ratios, presence of 24/7 board-certified intensivists and dedicated pharmacists in the ICU, and nurse and physiotherapist autonomy scores. Clusters were defined using the partition around medoids method. We assessed the association between clusters and hospital mortality using logistic regression and with ICU LOS and MV duration using competing risk regression. RESULTS: Analysis included data from 129,680 patients admitted to 93 ICUs (2014-2015). Three clusters were identified. The features distinguishing between the clusters were: the presence of board-certified intensivists in the ICU 24/7 (present in Cluster 3), dedicated pharmacists (present in Clusters 2 and 3) and the extent of nurse autonomy (which increased from Clusters 1 to 3). The patients in Cluster 3 exhibited the best outcomes, with lower adjusted hospital mortality [odds ratio 0.92 (95% confidence interval (CI), 0.87-0.98)], shorter ICU LOS [subhazard ratio (SHR) for patients surviving to ICU discharge 1.24 (95% CI 1.22-1.26)] and shorter durations of MV [SHR for undergoing extubation 1.61(95% CI 1.54-1.69)]. Cluster 1 had the worst outcomes. CONCLUSION: Patients treated in ICUs combining 24/7 expert intensivist coverage, a dedicated pharmacist and nurses with greater autonomy had the best outcomes. All of these features represent achievable targets that should be considered by policy makers with an interest in promoting equal and optimal ICU care.",1
"Machine learning analysis of DNA methylation profiles distinguishes primary lung squamous cell carcinomas from head and neck metastases. Head and neck squamous cell carcinoma (HNSC) patients are at risk of suffering from both pulmonary metastases or a second squamous cell carcinoma of the lung (LUSC). Differentiating pulmonary metastases from primary lung cancers is of high clinical importance, but not possible in most cases with current diagnostics. To address this, we performed DNA methylation profiling of primary tumors and trained three different machine learning methods to distinguish metastatic HNSC from primary LUSC. We developed an artificial neural network that correctly classified 96.4% of the cases in a validation cohort of 279 patients with HNSC and LUSC as well as normal lung controls, outperforming support vector machines (95.7%) and random forests (87.8%). Prediction accuracies of more than 99% were achieved for 92.1% (neural network), 90% (support vector machine), and 43% (random forest) of these cases by applying thresholds to the resulting probability scores and excluding samples with low confidence. As independent clinical validation of the approach, we analyzed a series of 51 patients with a history of HNSC and a second lung tumor, demonstrating the correct classifications based on clinicopathological properties. In summary, our approach may facilitate the reliable diagnostic differentiation of pulmonary metastases of HNSC from primary LUSC to guide therapeutic decisions.",1
"Evaluation of Machine-Learning Algorithms for Predicting Opioid Overdose Risk Among Medicare Beneficiaries With Opioid Prescriptions. Importance: Current approaches to identifying individuals at high risk for opioid overdose target many patients who are not truly at high risk. Objective: To develop and validate a machine-learning algorithm to predict opioid overdose risk among Medicare beneficiaries with at least 1 opioid prescription. Design, Setting, and Participants: A prognostic study was conducted between September 1, 2017, and December 31, 2018. Participants (n = 560 057) included fee-for-service Medicare beneficiaries without cancer who filled 1 or more opioid prescriptions from January 1, 2011, to December 31, 2015. Beneficiaries were randomly and equally divided into training, testing, and validation samples. Exposures: Potential predictors (n = 268), including sociodemographics, health status, patterns of opioid use, and practitioner-level and regional-level factors, were measured in 3-month windows, starting 3 months before initiating opioids until loss of follow-up or the end of observation. Main Outcomes and Measures: Opioid overdose episodes from inpatient and emergency department claims were identified. Multivariate logistic regression (MLR), least absolute shrinkage and selection operator-type regression (LASSO), random forest (RF), gradient boosting machine (GBM), and deep neural network (DNN) were applied to predict overdose risk in the subsequent 3 months after initiation of treatment with prescription opioids. Prediction performance was assessed using the C statistic and other metrics (eg, sensitivity, specificity, and number needed to evaluate [NNE] to identify one overdose). The Youden index was used to identify the optimized threshold of predicted score that balanced sensitivity and specificity. Results: Beneficiaries in the training (n = 186 686), testing (n = 186 685), and validation (n = 186 686) samples had similar characteristics (mean [SD] age of 68.0 [14.5] years, and approximately 63% were female, 82% were white, 35% had disabilities, 41% were dual eligible, and 0.60% had at least 1 overdose episode). In the validation sample, the DNN (C statistic = 0.91; 95% CI, 0.88-0.93) and GBM (C statistic = 0.90; 95% CI, 0.87-0.94) algorithms outperformed the LASSO (C statistic = 0.84; 95% CI, 0.80-0.89), RF (C statistic = 0.80; 95% CI, 0.75-0.84), and MLR (C statistic = 0.75; 95% CI, 0.69-0.80) methods for predicting opioid overdose. At the optimized sensitivity and specificity, DNN had a sensitivity of 92.3%, specificity of 75.7%, NNE of 542, positive predictive value of 0.18%, and negative predictive value of 99.9%. The DNN classified patients into low-risk (76.2% [142 180] of the cohort), medium-risk (18.6% [34 579] of the cohort), and high-risk (5.2% [9747] of the cohort) subgroups, with only 1 in 10 000 in the low-risk subgroup having an overdose episode. More than 90% of overdose episodes occurred in the high-risk and medium-risk subgroups, although positive predictive values were low, given the rare overdose outcome. Conclusions and Relevance: Machine-learning algorithms appear to perform well for risk prediction and stratification of opioid overdose, especially in identifying low-risk subgroups that have minimal risk of overdose.",1
"Use of natural language processing to improve predictive models for imaging utilization in children presenting to the emergency department. OBJECTIVE: To examine the association between the medical imaging utilization and information related to patients' socioeconomic, demographic and clinical factors during the patients' ED visits; and to develop predictive models using these associated factors including natural language elements to predict the medical imaging utilization at pediatric ED. METHODS: Pediatric patients' data from the 2012-2016 United States National Hospital Ambulatory Medical Care Survey was included to build the models to predict the use of imaging in children presenting to the ED. Multivariable logistic regression models were built with structured variables such as temperature, heart rate, age, and unstructured variables such as reason for visit, free text nursing notes and combined data available at triage. NLP techniques were used to extract information from the unstructured data. RESULTS: Of the 27,665 pediatric ED visits included in the study, 8394 (30.3%) received medical imaging in the ED, including 6922 (25.0%) who had an X-ray and 1367 (4.9%) who had a computed tomography (CT) scan. In the predictive model including only structured variables, the c-statistic was 0.71 (95% CI: 0.70-0.71) for any imaging use, 0.69 (95% CI: 0.68-0.70) for X-ray, and 0.77 (95% CI: 0.76-0.78) for CT. Models including only unstructured information had c-statistics of 0.81 (95% CI: 0.81-0.82) for any imaging use, 0.82 (95% CI: 0.82-0.83) for X-ray, and 0.85 (95% CI: 0.83-0.86) for CT scans. When both structured variables and free text variables were included, the c-statistics reached 0.82 (95% CI: 0.82-0.83) for any imaging use, 0.83 (95% CI: 0.83-0.84) for X-ray, and 0.87 (95% CI: 0.86-0.88) for CT. CONCLUSIONS: Both CT and X-rays are commonly used in the pediatric ED with one third of the visits receiving at least one. Patients' socioeconomic, demographic and clinical factors presented at ED triage period were associated with the medical imaging utilization. Predictive models combining structured and unstructured variables available at triage performed better than models using structured or unstructured variables alone, suggesting the potential for use of NLP in determining resource utilization.",1
"Binomial classification of pediatric elbow fractures using a deep learning multiview approach emulating radiologist decision making. Purpose: To determine the feasibility of using deep learning with a multiview approach, similar to how a human radiologist reviews multiple images, for binomial classification of acute pediatric elbow radiographic abnormalities. Materials and Methods: A total of 21 456 radiographic studies containing 58 817 images of the elbow and associated radiology reports over the course of a 4-year period from January 2014 through December 2017 at a dedicated children’s hospital were retrospectively retrieved. Mean age was 7.2 years, and 43% were female patients. The studies were binomially classified, based on the reports, as either positive or negative for acute or subacute traumatic abnormality. The studies were randomly divided into a training set containing 20 350 studies and a validation set containing the remaining 1106 studies. A multiview approach was used for the model by combining both a convolutional neural network and recurrent neural network to interpret an entire series of three radiographs together. Sensitivity, specificity, positive predictive value, negative predictive value, area under the receiver operating characteristic curve (AUC), and their 95% confidence intervals were calculated. Results: AUC was 0.95, and accuracy was 88% for the model on the studied dataset. Sensitivity for the model was 91% (536 of 590), while the specificity for the model was 84% (434 of 516). Of 241 supracondylar fractures, one was missed. Of 88 lateral condylar fractures, one was missed. Of 77 elbow effusions without fracture, 15 were missed. Of 184 other abnormalities, 37 were missed. Conclusion: Deep learning can effectively classify acute and nonacute pediatric elbow abnormalities on radiographs in the setting of trauma. A recurrent neural network was used to classify an entire radiographic series, arrive at a decision based on all views, and identify fractures in pediatric patients with variable skeletal immaturity.",1
"Convolutional Neural Networks for Radiologic Images: A Radiologist's Guide. Deep learning has rapidly advanced in various fields within the past few years and has recently gained particular attention in the radiology community. This article provides an introduction to deep learning technology and presents the stages that are entailed in the design process of deep learning radiology research. In addition, the article details the results of a survey of the application of deep learning-specifically, the application of convolutional neural networks-to radiologic imaging that was focused on the following five major system organs: chest, breast, brain, musculoskeletal system, and abdomen and pelvis. The survey of the studies is followed by a discussion about current challenges and future trends and their potential implications for radiology. This article may be used as a guide for radiologists planning research in the field of radiologic image analysis using convolutional neural networks.",0
"Synthesis and Characterization of 3-(1-((3,4-Dihydroxyphenethyl)amino)ethylidene)-chroman-2,4-dione as a Potential Antitumor Agent. The newly synthesized coumarin derivative with dopamine, 3-(1-((3,4-dihydroxyphenethyl)amino)ethylidene)-chroman-2,4-dione, was completely structurally characterized by X-ray crystallography. It was shown that several types of hydrogen bonds are present, which additionally stabilize the structure. The compound was tested in vitro against different cell lines, healthy human keratinocyte HaCaT, cervical squamous cell carcinoma SiHa, breast carcinoma MCF7, and hepatocellular carcinoma HepG2. Compared to control, the new derivate showed a stronger effect on both healthy and carcinoma cell lines, with the most prominent effect on the breast carcinoma MCF7 cell line. The molecular docking study, obtained for ten different conformations of the new compound, showed its inhibitory nature against CDKS protein. Lower inhibition constant, relative to one of 4-OH-coumarine, proved stronger and more numerous interactions with CDKS protein. These interactions were carefully examined for both parent molecule and derivative and explained from a structural point of view.",0
"Pharmacotherapeutics and molecular mechanism of phytochemicals in alleviating hormone-responsive breast cancer. Breast cancer (BC) is the leading cause of death among women worldwide devoid of effective treatment. It is therefore important to develop agents that can reverse, reduce, or slow the growth of BC. The use of natural products as chemopreventive agents provides enormous advantages. The aim of the current investigation is to determine the efficacy of the phytochemicals against BC along with the approved drugs to screen the most desirable and effective phytocompound. In the current study, 36 phytochemicals have been evaluated against aromatase to identify the potential candidate drug along with the approved drugs employing the Cdocker module accessible on the Discovery Studio (DS) v4.5 and thereafter analysing the stability of the protein ligand complex using GROningen MAchine for Chemical Simulations v5.0.6 (GROMACS). Additionally, these compounds were assessed for the inhibitory features employing the structure-based pharmacophore (SBP). The Cdocker protocol available with the DS has computed higher dock scores for the phytochemicals complemented by lower binding energies. The top-ranked compounds that have anchored with key residues located at the binding pocket of the protein were subjected to molecular dynamics (MD) simulations employing GROMACS. The resultant findings reveal the stability of the protein backbone and further guide to comprehend on the involvement of key residues Phe134, Val370, and Met374 that mechanistically inhibit BC. Among 36 compounds, curcumin, capsaicin, rosmarinic acid, and 6-shogaol have emerged as promising phytochemicals conferred with the highest Cdocker interaction energy, key residue interactions, stable MD results than reference drugs, and imbibing the key inhibitory features. Taken together, the current study illuminates the use of natural compounds as potential drugs against BC. Additionally, these compounds could also serve as scaffolds in designing and development of new drugs.",0
"Dynamic changes to lipid mediators support transitions among macrophage subtypes during muscle regeneration. Muscle damage elicits a sterile immune response that facilitates complete regeneration. Here, we used mass spectrometry-based lipidomics to map the mediator lipidome during the transition from inflammation to resolution and regeneration in skeletal muscle injury. We observed temporal regulation of glycerophospholipids and production of pro-inflammatory lipid mediators (for example, leukotrienes and prostaglandins) and specialized pro-resolving lipid mediators (for example, resolvins and lipoxins) that were modulated by ibuprofen. These time-dependent profiles were recapitulated in sorted neutrophils and Ly6C(hi) and Ly6C(lo) muscle-infiltrating macrophages, with a distinct pro-resolving signature observed in Ly6C(lo) macrophages. RNA sequencing of macrophages stimulated with resolvin D2 showed similarities to transcriptional changes found during the temporal transition from Ly6C(hi) macrophage to Ly6C(lo) macrophage. In vivo, resolvin D2 increased Ly6C(lo) macrophages and functional improvement of the regenerating muscle. These results reveal dynamic lipid mediator signatures of innate immune cells and provide a proof of concept for their exploitable effector roles in muscle regeneration.",0
"Comparison of Long-term Survival Benefits in Trials of Immune Checkpoint Inhibitor vs Non-Immune Checkpoint Inhibitor Anticancer Agents Using ASCO Value Framework and ESMO Magnitude of Clinical Benefit Scale. Importance: Recently, anticancer agents have generated excitement owing to their capacity to preserve long-term durable survival in some patients who are represented by a tail of the survival curve. However, because traditional measures of clinical benefit may not accurately capture durable survival, amendments to various valuation frameworks have been proposed to capture this benefit. Objectives: To determine how frequently immune checkpoint inhibitor (ICI) anticancer agents vs non-ICI anticancer agents displayed trends of long-term durable survival, as defined by the American Society of Clinical Oncology Value Framework version 2 (ASCO-VF v2) and European Society of Medical Oncology Magnitude of Clinical Benefit Scale version 1.1 (ESMO-MCBS v1.1), as well as to further analyze the degree of agreement between ASCO and ESMO frameworks. Design, Setting, and Participants: In this cohort study, anticancer agents from phase 2 or 3 randomized clinical trials (RCTs) cited for clinical efficacy evidence in drug approval by the US Food and Drug Administration between January 2011 and March 2018 were identified. Data required for the ASCO-VF v2 tail-of-the-curve bonus and the ESMO-MCBS v1.1 immunotherapy-triggered long-term plateau adjustments were extracted from relevant RCTs. Frequency and difference in proportions were calculated to determine how often survival benefits were awarded to anticancer agents overall and to ICI and non-ICI anticancer agents individually. Main Outcomes and Measures: American Society of Clinical Oncology Value Framework v2 tail-of-the-curve bonuses and ESMO-MCBS v1.1 immunotherapy-triggered long-term plateau adjustments. Results: In total, 247 RCTs were identified, and 100 RCTs involving 57164 patients were included, with 14 examining ICI agents (1 ipilimumab, 5 pembrolizumab, 5 nivolumab, 2 atezolizumab, and 1 durvalumab) and 86 examining non-ICI agents (74 targeted therapy, 8 chemotherapy, 3 hormone therapy, and 1 radiopharmaceutical). Randomized clinical trials were awarded ASCO-VF v2 tail-of-the-curve bonuses more often than ESMO-MCBS v1.1 immunotherapy-triggered long-term plateau adjustments (ASCO-VF v2, 45.0% [8 of 14 ICI RCTs and 37 of 86 non-ICI RCTs] vs ESMO-MCBS v1.1, 2.6% [1 of 12 ICI RCTs and 1 of 66 non-ICI RCTs). Randomized clinical trials for ICIs were not more likely to receive an ASCO-VF v2 bonus or ESMO-MCBS v1.1 adjustment than non-ICI RCTs (ASCO-VF: risk difference, 0.14; 95% CI, -0.14 to 0.42; P =.32; ESMO-MCBS: risk difference, 0.07; 95% CI, -0.09 to 0.23; P =.40). Poor agreement was found between the framework algorithms in identifying long-term survival benefits from RCTs (κ = 0.01; 95% CI, -0.23 to 0.22; P =.50). Conclusions and Relevance: The ASCO-VF v2 and ESMO-MCBS v1.1 may require additional refinement to accurately capture the benefit of durable long-term survival, or ICI agents may not preserve long-term survival as conventionally thought.",0
"Believing in dopamine. Midbrain dopamine signals are widely thought to report reward prediction errors that drive learning in the basal ganglia. However, dopamine has also been implicated in various probabilistic computations, such as encoding uncertainty and controlling exploration. Here, we show how these different facets of dopamine signalling can be brought together under a common reinforcement learning framework. The key idea is that multiple sources of uncertainty impinge on reinforcement learning computations: uncertainty about the state of the environment, the parameters of the value function and the optimal action policy. Each of these sources plays a distinct role in the prefrontal cortex–basal ganglia circuit for reinforcement learning and is ultimately reflected in dopamine activity. The view that dopamine plays a central role in the encoding and updating of beliefs brings the classical prediction error theory into alignment with more recent theories of Bayesian reinforcement learning.",0
"Recursive neural networks in hospital bed occupancy forecasting. BACKGROUND: Efficient planning of hospital bed usage is a necessary condition to minimize the hospital costs. In the presented work we deal with the problem of occupancy forecasting in the scale of several months, with a focus on personnel's holiday planning. METHODS: We construct a model based on a set of recursive neural networks, which performs an occupancy prediction using historical admission and release data combined with external factors such as public and school holidays. The model requires no personal information on patients or staff. It is optimized for a 60 days forecast during the summer season (May-September). RESULTS: An average mean absolute percentage error (MAPE) of 6.24% was computed on 8 validation sets. CONCLUSIONS: The proposed machine learning model has shown to be competitive to standard time-series forecasting models and can be recommended for incorporation in medium-size hospitals automatized scheduling and decision making.",1
"PV-LVNet: Direct left ventricle multitype indices estimation from 2D echocardiograms of paired apical views with deep neural networks. Accurate direct estimation of the left ventricle (LV) multitype indices from two-dimensional (2D) echocardiograms of paired apical views, i.e., paired apical four-chamber (A4C) and two-chamber (A2C), is of great significance to clinically evaluate cardiac function. It enables a comprehensive assessment from multiple dimensions and views. Yet it is extremely challenging and has never been attempted, due to significantly varied LV shape and appearance across subjects and along cardiac cycle, the complexity brought by the paired different views, unexploited inter-frame indices relatedness hampering working effect, and low image quality preventing segmentation. We propose a paired-views LV network (PV-LVNet) to automatically and directly estimate LV multitype indices from paired echo apical views. Based on a newly designed Res-circle Net, the PV-LVNet robustly locates LV and automatically crops LV region of interest from A4C and A2C sequence with location module and image resampling, then accurately and consistently estimates 7 different indices of multiple dimensions (1D, 2D & 3D) and views (A2C, A4C, and union of A2C+A4C) with indices module. The experiments show that our method achieves high performance with accuracy up to 2.85mm mean absolute error and internal consistency up to 0.974 Cronbach's alpha for the cardiac indices estimation. All of these indicate that our method enables an efficient, accurate and reliable cardiac function diagnosis in clinical.",1
"Automatic spondylolisthesis grading from MRIs across modalities using faster adversarial recognition network. Grading spondylolisthesis into several stages from MRI images is challenging because detecting critical vertebrae and locating landmarks in images of different characteristics is difficult. We propose Faster Adversarial Recognition (FAR) network to accurately perform spondylolisthesis grading by excellently detecting critical vertebrae without the need of locating the landmarks. The FAR network introduces the adversarial scheme by using a multi-task recognition network as the generator and an adversarial module as the discriminator. The multi-task recognition network (generator) is an integrated network that can reliably perform multi-scale hierarchical feature learning, critical vertebrae detection, detected vertebrae classification, bounding box regression, and spondylolisthesis grading in a hybrid supervised manner. The adversarial module (discriminator) takes the detection results as inputs to supervise the generative network by leveraging the high-order statistics of the distribution of the detected bounding box coordinates. The FAR network is evaluated to be accurate and robust in spondylolisthesis grading (training accuracy: 0.9883 ± 0.0094, testing accuracy: 0.8933 ± 0.0276) for MRI images of different modalities, which can be attributed to the excellent critical vertebrae detection (detection mAP75 for training: 1 ± 0, for testing: 0.9636 ± 0.0180, and IoU (Intersection-over-union) ≥ 0.9/0.8 for most detections with their corresponding ground truth in the training/testing dataset). This accuracy is comparable to that of the physicians and outperforms other state-of-the-art methods. These results indicate the potential of our framework to perform spondylolisthesis grading for clinical diagnosis.",1
"Transmembrane 4 L Six Family Member 5 Senses Arginine for mTORC1 Signaling. The mechanistic target of rapamycin complex (mTORC1) is a signaling hub on the lysosome surface, responding to lysosomal amino acids. Although arginine is metabolically important, the physiological arginine sensor that activates mTOR remains unclear. Here, we show that transmembrane 4 L six family member 5 (TM4SF5) translocates from plasma membrane to lysosome upon arginine sufficiency and senses arginine, culminating in mTORC1/S6K1 activation. TM4SF5 bound active mTOR upon arginine sufficiency and constitutively bound amino acid transporter SLC38A9. TM4SF5 binding to the cytosolic arginine sensor Castor1 decreased upon arginine sufficiency, thus allowing TM4SF5-mediated sensing of metabolic amino acids. TM4SF5 directly bound free L-arginine via its extracellular loop possibly for the efflux, being supported by mutant study and homology and molecular docking modeling. Therefore, we propose that lysosomal TM4SF5 senses and enables arginine efflux for mTORC1/S6K1 activation, and arginine-auxotroph in hepatocellular carcinoma may be targeted by blocking the arginine sensing using anti-TM4SF5 reagents. Lysosomal arginine is involved in mTORC1/S6K1 activation for cell growth. Jung et al. identify TM4SF5 as a membrane-based sensor of physiologic levels of arginine. TM4SF5 forms a complex with mTOR and the amino acid transporter SLC38A9 on lysosomal membranes in an arginine-regulated manner, leading to arginine efflux and mTOR/S6K1 activation.",0
"Spurious interaction as a result of categorization. BACKGROUND: It is common in applied epidemiological and clinical research to convert continuous variables into categorical variables by grouping values into categories. Such categorized variables are then often used as exposure variables in some regression model. There are numerous statistical arguments why this practice should be avoided, and in this paper we present yet another such argument. METHODS: We show that categorization may lead to spurious interaction in multiple regression models. We give precise analytical expressions for when this may happen in the linear regression model with normally distributed exposure variables, and we show by simulations that the analytical results are valid also for other distributions. Further, we give an interpretation of the results in terms of a measurement error problem. RESULTS: We show that, in the case of a linear model with two normally distributed exposure variables, both categorized at the same cut point, a spurious interaction will be induced unless the two variables are categorized at the median or they are uncorrelated. In simulations with exposure variables following other distributions, we confirm this general effect of categorization, but we also show that the effect of the choice of cut point varies over different distributions. CONCLUSION: Categorization of continuous exposure variables leads to a number of problems, among them spurious interaction effects. Hence, this practice should be avoided and other methods should be considered.",0
"Structural bioinformatics insights into the CARD-CARD interaction mediated by the mitochondrial antiviral-signaling protein of black carp. The innate immune system offers the first line of defense against invading microbial pathogens through the recognition of conserved pathogen-associated molecular patterns (PAMPs) by pattern recognition receptors (PRRs). The host innate immune system through PRRs, the sensors for PAMPs, induces the production of cytokines. Among different families of PRRs, the retinoic acid-inducible gene I (RIG-I)-like receptors (RLRs), and its mitochondrial adaptor ie, the mitochondrial antiviral-signaling (MAVS) protein, are crucial for RLR-triggered interferon (IFN) antiviral immunity. Recent studies have shown that the N-terminal caspase recruitment domain (CARD) and transmembrane domain play a pivotal role in oligomerization of black carp MAVS (BcMAVS), crucial for the host innate immune response against viral invasion. In this study, we have used molecular modeling, docking, and molecular dynamics (MD) simulation approaches to shed molecular insights into the oligomerization mechanism of BcMAVSCARD. MD simulation and interaction analysis portrayed that the type-I surface patches of BcMAVS CARD make the major contribution to the interaction. Moreover, the evidence from surface patches and critical residues involved in the said interaction is found to be similar to that of the human counterpart and requires further investigation for legitimacy. Altogether, our study provided crucial information on oligomerization of BcMAVS CARDs and might be helpful for clarifying the innate immune response against pathogens and downstream signaling in fishes.",0
"Hierarchical segmentation using equivalence test (HiSET): Application to DCE image sequences. Dynamical contrast enhanced (DCE) imaging allows non invasive access to tissue micro-vascularization. It appears as a promising tool to build imaging biomarkers for diagnostic, prognosis or anti-angiogenesis treatment monitoring of cancer. However, quantitative analysis of DCE image sequences suffers from low signal to noise ratio (SNR). SNR may be improved by averaging functional information in a large region of interest when it is functionally homogeneous. We propose a novel method for automatic segmentation of DCE image sequences into functionally homogeneous regions, called DCE-HiSET. Using an observation model which depends on one parameter a and is justified a posteriori, DCE-HiSET is a hierarchical clustering algorithm. It uses the p-value of a multiple equivalence test as dissimilarity measure and consists of two steps. The first exploits the spatial neighborhood structure to reduce complexity and takes advantage of the regularity of anatomical features, while the second recovers (spatially) disconnected homogeneous structures at a larger (global) scale. Given a minimal expected homogeneity discrepancy for the multiple equivalence test, both steps stop automatically by controlling the Type I error. This provides an adaptive choice for the number of clusters. Assuming that the DCE image sequence is functionally piecewise constant with signals on each piece sufficiently separated, we prove that DCE-HiSET will retrieve the exact partition with high probability as soon as the number of images in the sequence is large enough. The minimal expected homogeneity discrepancy appears as the tuning parameter controlling the size of the segmentation. DCE-HiSET has been implemented in C++ for 2D and 3D image sequences with competitive speed.",0
"Diagnosing growth in low-grade gliomas with and without longitudinal volume measurements: A retrospective observational study. Background Low-grade gliomas cause significant neurological morbidity by brain invasion. There is no universally accepted objective technique available for detection of enlargement of low-grade gliomas in the clinical setting; subjective evaluation by clinicians using visual comparison of longitudinal radiological studies is the gold standard. The aim of this study is to determine whether a computer-assisted diagnosis (CAD) method helps physicians detect earlier growth of low-grade gliomas. Methods and findings We reviewed 165 patients diagnosed with grade 2 gliomas, seen at the University of Alabama at Birmingham clinics from 1 July 2017 to 14 May 2018. MRI scans were collected during the spring and summer of 2018. Fifty-six gliomas met the inclusion criteria, including 19 oligodendrogliomas, 26 astrocytomas, and 11 mixed gliomas in 30 males and 26 females with a mean age of 48 years and a range of follow-up of 150.2 months (difference between highest and lowest values). None received radiation therapy. We also studied 7 patients with an imaging abnormality without pathological diagnosis, who were clinically stable at the time of retrospective review (14 May 2018). This study compared growth detection by 7 physicians aided by the CAD method with retrospective clinical reports. The tumors of 63 patients (56 + 7) in 627 MRI scans were digitized, including 34 grade 2 gliomas with radiological progression and 22 radiologically stable grade 2 gliomas. The CAD method consisted of tumor segmentation, computing volumes, and pointing to growth by the online abrupt change-of-point method, which considers only past measurements. Independent scientists have evaluated the segmentation method. In 29 of the 34 patients with progression, the median time to growth detection was only 14 months for CAD compared to 44 months for current standard of care radiological evaluation (p < 0.001). Using CAD, accurate detection of tumor enlargement was possible with a median of only 57% change in the tumor volume as compared to a median of 174% change of volume necessary to diagnose tumor growth using standard of care clinical methods (p < 0.001). In the radiologically stable group, CAD facilitated growth detection in 13 out of 22 patients. CAD did not detect growth in the imaging abnormality group. The main limitation of this study was its retrospective design; nevertheless, the results depict the current state of a gold standard in clinical practice that allowed a significant increase in tumor volumes from baseline before detection. Such large increases in tumor volume would not be permitted in a prospective design. The number of glioma patients (n = 56) is a limitation; however, it is equivalent to the number of patients in phase II clinical trials. Conclusions The current practice of visual comparison of longitudinal MRI scans is associated with significant delays in detecting growth of low-grade gliomas. Our findings support the idea that physicians aided by CAD detect growth at significantly smaller volumes than physicians using visual comparison alone. This study does not answer the questions whether to treat or not and which treatment modality is optimal. Nonetheless, early growth detection sets the stage for future clinical studies that address these questions and whether early therapeutic interventions prolong survival and improve quality of life.",0
"A Map of Human Type 1 Diabetes Progression by Imaging Mass Cytometry. Type 1 diabetes (T1D) results from the autoimmune destruction of insulin-producing β cells. A comprehensive picture of the changes during T1D development is lacking due to limited sample availability, inability to sample longitudinally, and the paucity of technologies enabling comprehensive tissue profiling. Here, we analyzed 1,581 islets from 12 human donors, including eight with T1D, using imaging mass cytometry (IMC). IMC enabled simultaneous measurement of 35 biomarkers with single-cell and spatial resolution. We performed pseudotime analysis of islets through T1D progression from snapshot data to reconstruct the evolution of β cell loss and insulitis. Our analyses revealed that β cell destruction is preceded by a β cell marker loss and by recruitment of cytotoxic and helper T cells. The approaches described herein demonstrate the value of IMC for improving our understanding of T1D pathogenesis, and our data lay the foundation for hypothesis generation and follow-on experiments.",0
"Review of Clinical Applications for Virtual Monoenergetic Dual-Energy CT. In this article, the authors discuss the technical background and summarize the current body of literature regarding virtual monoenergetic (VM) images derived from dual-energy CT data, which can be reconstructed between 40 and 200 keV. Substantially improved iodine attenuation at lower kiloelectron volt levels and reduced beam-hardening artifacts at higher kiloelectron volt levels have been demonstrated from all major manufacturers of dual-energy CT units. Improved contrast attenuation with VM imaging at lower kiloelectron volt levels enables better delineation and diagnostic accuracy in the detection of various vascular or oncologic abnormalities. Low-kiloelectron-volt VM imaging may be useful for salvaging CT studies with suboptimal contrast material delivery or providing additional information on the arterial vasculature obtained from venous phase acquisitions. For patients with renal impairment, substantial reductions in the use of iodinated contrast material can be achieved by using lower-energy VM imaging. The authors recommend routine reconstruction of VM images at 50 keV when using dual-energy CT to exploit the increased contrast properties. For reduction of beam-hardening artifacts, VM imaging at 120 keV is useful for the initial assessment.",0
"Deep Learning-based Method for Fully Automatic Quantification of Left Ventricle Function from Cine MR Images: A Multivendor, Multicenter Study. Purpose To develop a deep learning-based method for fully automated quantification of left ventricular (LV) function from short-axis cine MR images and to evaluate its performance in a multivendor and multicenter setting. Materials and Methods This retrospective study included cine MRI data sets obtained from three major MRI vendors in four medical centers from 2008 to 2016. Three convolutional neural networks (CNNs) with the U-NET architecture were trained on data sets of increasing variability: (a) a single-vendor, single-center, homogeneous cohort of 100 patients (CNN1); (b) a single-vendor, multicenter, heterogeneous cohort of 200 patients (CNN2); and (c) a multivendor, multicenter, heterogeneous cohort of 400 patients (CNN3). All CNNs were tested on an independent multivendor, multicenter data set of 196 patients. CNN performance was evaluated with respect to the manual annotations from three experienced observers in terms of (a) LV detection accuracy, (b) LV segmentation accuracy, and (c) LV functional parameter accuracy. Automatic and manual results were compared with the paired Wilcoxon test, Pearson correlation, and Bland-Altman analysis. Results CNN3 achieved the highest performance on the independent testing data set. The average perpendicular distance compared with manual analysis was 1.1 mm +/- 0.3 for CNN3, compared with 1.5 mm +/- 1.0 for CNN1 (P < .05) and 1.3 mm +/- 0.6 for CNN2 (P < .05). The LV function parameters derived from CNN3 showed a high correlation (r(2) >/= 0.98) and agreement with those obtained by experts for data sets from different vendors and centers. Conclusion A deep learning-based method trained on a data set with high variability can achieve fully automated and accurate cine MRI analysis on multivendor, multicenter cine MRI data. (c) RSNA, 2018 See also the editorial by Colletti in this issue.",1
"Predicting Splicing from Primary Sequence with Deep Learning. The splicing of pre-mRNAs into mature transcripts is remarkable for its precision, but the mechanisms by which the cellular machinery achieves such specificity are incompletely understood. Here, we describe a deep neural network that accurately predicts splice junctions from an arbitrary pre-mRNA transcript sequence, enabling precise prediction of noncoding genetic variants that cause cryptic splicing. Synonymous and intronic mutations with predicted splice-altering consequence validate at a high rate on RNA-seq and are strongly deleterious in the human population. De novo mutations with predicted splice-altering consequence are significantly enriched in patients with autism and intellectual disability compared to healthy controls and validate against RNA-seq in 21 out of 28 of these patients. We estimate that 9%-11% of pathogenic mutations in patients with rare genetic disorders are caused by this previously underappreciated class of disease variation.",0
"AliClu - Temporal sequence alignment for clustering longitudinal clinical data. BACKGROUND: Patient stratification is a critical task in clinical decision making since it can allow physicians to choose treatments in a personalized way. Given the increasing availability of electronic medical records (EMRs) with longitudinal data, one crucial problem is how to efficiently cluster the patients based on the temporal information from medical appointments. In this work, we propose applying the Temporal Needleman-Wunsch (TNW) algorithm to align discrete sequences with the transition time information between symbols. These symbols may correspond to a patient's current therapy, their overall health status, or any other discrete state. The transition time information represents the duration of each of those states. The obtained TNW pairwise scores are then used to perform hierarchical clustering. To find the best number of clusters and assess their stability, a resampling technique is applied. RESULTS: We propose the AliClu, a novel tool for clustering temporal clinical data based on the TNW algorithm coupled with clustering validity assessments through bootstrapping. The AliClu was applied for the analysis of the rheumatoid arthritis EMRs obtained from the Portuguese database of rheumatologic patient visits (Reuma.pt). In particular, the AliClu was used for the analysis of therapy switches, which were coded as letters corresponding to biologic drugs and included their durations before each change occurred. The obtained optimized clusters allow one to stratify the patients based on their temporal therapy profiles and to support the identification of common features for those groups. CONCLUSIONS: The AliClu is a promising computational strategy to analyse longitudinal patient data by providing validated clusters and by unravelling the patterns that exist in clinical outcomes. Patient stratification is performed in an automatic or semi-automatic way, allowing one to tune the alignment, clustering, and validation parameters. The AliClu is freely available at https://github.com/sysbiomed/AliClu.",0
"Circuit mechanisms for the maintenance and manipulation of information in working memory. Recently it has been proposed that information in working memory (WM) may not always be stored in persistent neuronal activity but can be maintained in 'activity-silent' hidden states, such as synaptic efficacies endowed with short-term synaptic plasticity. To test this idea computationally, we investigated recurrent neural network models trained to perform several WM-dependent tasks, in which WM representation emerges from learning and is not a priori assumed to depend on self-sustained persistent activity. We found that short-term synaptic plasticity can support the short-term maintenance of information, provided that the memory delay period is sufficiently short. However, in tasks that require actively manipulating information, persistent activity naturally emerges from learning, and the amount of persistent activity scales with the degree of manipulation required. These results shed insight into the current debate on WM encoding and suggest that persistent activity can vary markedly between short-term memory tasks with different cognitive demands.",1
"A comparison between two semantic deep learning frameworks for the autosomal dominant polycystic kidney disease segmentation based on magnetic resonance images. BACKGROUND: The automatic segmentation of kidneys in medical images is not a trivial task when the subjects undergoing the medical examination are affected by Autosomal Dominant Polycystic Kidney Disease (ADPKD). Several works dealing with the segmentation of Computed Tomography images from pathological subjects were proposed, showing high invasiveness of the examination or requiring interaction by the user for performing the segmentation of the images. In this work, we propose a fully-automated approach for the segmentation of Magnetic Resonance images, both reducing the invasiveness of the acquisition device and not requiring any interaction by the users for the segmentation of the images. METHODS: Two different approaches are proposed based on Deep Learning architectures using Convolutional Neural Networks (CNN) for the semantic segmentation of images, without needing to extract any hand-crafted features. In details, the first approach performs the automatic segmentation of images without any procedure for pre-processing the input. Conversely, the second approach performs a two-steps classification strategy: a first CNN automatically detects Regions Of Interest (ROIs); a subsequent classifier performs the semantic segmentation on the ROIs previously extracted. RESULTS: Results show that even though the detection of ROIs shows an overall high number of false positives, the subsequent semantic segmentation on the extracted ROIs allows achieving high performance in terms of mean Accuracy. However, the segmentation of the entire images input to the network remains the most accurate and reliable approach showing better performance than the previous approach. CONCLUSION: The obtained results show that both the investigated approaches are reliable for the semantic segmentation of polycystic kidneys since both the strategies reach an Accuracy higher than 85%. Also, both the investigated methodologies show performances comparable and consistent with other approaches found in literature working on images from different sources, reducing both the invasiveness of the analyses and the interaction needed by the users for performing the segmentation task.",1
"Identification of Vertebral Fractures by Convolutional Neural Networks to Predict Nonvertebral and Hip Fractures: A Registry-based Cohort Study of Dual X-ray Absorptiometry. Background Detection of vertebral fractures (VFs) aids in management of osteoporosis and targeting of fracture prevention therapies. Purpose To determine whether convolutional neural networks (CNNs) can be trained to identify VFs at VF assessment (VFA) performed with dual-energy x-ray absorptiometry and if VFs identified by CNNs confer a similar prognosis compared with the expert reader reference standard. Materials and Methods In this retrospective study, 12 742 routine clinical VFA images obtained from February 2010 to December 2017 and reported as VF present or absent were used for CNN training and testing. All reporting physicians were diagnostic imaging specialists with at least 10 years of experience. Randomly selected training and validation sets were used to produce a CNN ensemble that calculates VF probability. A test set (30%; 3822 images) was used to assess CNN agreement with the human expert reader reference standard and CNN prediction of incident non-VFs. Statistical analyses included area under the receiver operating characteristic curve, two-tailed Student t tests, prevalence- and bias-adjusted kappa value, Kaplan-Meier curves, and Cox proportional hazard models. Results This study included 12 742 patients (mean age, 76 years +/- 7; 12 013 women). The CNN ensemble demonstrated an area under the receiver operating characteristic curve of 0.94 (95% confidence interval [CI]: 0.93, 0.95) for VF detection that corresponded to sensitivity of 87.4% (534 of 611), specificity of 88.4% (2838 of 3211), and prevalence- and bias-adjusted kappa value of 0.77. On the basis of incident fracture data available for 2813 patients (mean follow up, 3.7 years), hazard ratios adjusted for baseline fracture probability were 1.7 (95% CI: 1.3, 2.2) for CNN versus 1.8 (95% CI: 1.3, 2.3) for expert reader-detected VFs for incident non-VF and 2.3 (95% CI: 1.5, 3.5) versus 2.4 (95% CI: 1.5, 3.7) for incident hip fracture. Conclusion Convolutional neural networks can identify vertebral fractures on vertebral fracture assessment images with high accuracy, and these convolutional neural network-identified vertebral fractures predict clinical fracture outcomes. (c) RSNA, 2019 Online supplemental material is available for this article.",1
"DoubletFinder: Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors. Single-cell RNA sequencing (scRNA-seq) data are commonly affected by technical artifacts known as “doublets,” which limit cell throughput and lead to spurious biological conclusions. Here, we present a computational doublet detection tool—DoubletFinder—that identifies doublets using only gene expression data. DoubletFinder predicts doublets according to each real cell's proximity in gene expression space to artificial doublets created by averaging the transcriptional profile of randomly chosen cell pairs. We first use scRNA-seq datasets where the identity of doublets is known to show that DoubletFinder identifies doublets formed from transcriptionally distinct cells. When these doublets are removed, the identification of differentially expressed genes is enhanced. Second, we provide a method for estimating DoubletFinder input parameters, allowing its application across scRNA-seq datasets with diverse distributions of cell types. Lastly, we present “best practices” for DoubletFinder applications and illustrate that DoubletFinder is insensitive to an experimentally validated kidney cell type with “hybrid” expression features.",0
"CT Angiography of the Aorta: Contrast Timing by Using a Fixed versus a Patient-specific Trigger Delay. Background Optimal timing of the CT scan relative to the contrast media bolus remains a challenging task given the shorter scan durations of modern CT scanners, as well as interpatient variability. Purpose To compare contrast opacification in CT angiography of the aorta between a cohort with fixed trigger delay and a cohort with patient-specific individualized trigger delay for contrast media timing with bolus tracking. Materials and Methods In this prospective study (January-August 2018), CT angiography of the thoracoabdominal aorta with bolus tracking was performed in two different study cohorts: one with a fixed trigger delay of 4 seconds (fixed cohort) and one with a patient-specific trigger delay (individualized cohort). All CT and contrast media protocol parameters were kept identical among cohorts. Objective image quality was evaluated by one reader; two readers assessed subjective image quality. Student t test was used to test for differences in mean attenuation; the Wilcoxon-Mann-Whitney test was used to test for differences in noise, contrast-to-noise ratio, and subjective image quality. Results The fixed cohort had 108 study participants (16 women; mean age +/- standard deviation, 72 years +/- 10); the individualized cohort had 108 participants (16 women; mean age, 72 years +/- 12). The trigger delay in the individualized cohort ranged from 6.4-11.3 seconds (mean, 9.2 seconds). There was higher overall attenuation in the individualized cohort than in the fixed cohort (486 HU +/- 92 for individualized vs 438 HU +/- 99 for fixed; P < .001), with increasing differences from the aortic arch (8 HU) to the iliac arteries (95 HU). The regression model indicated uniform attenuation in the individualized cohort and decreasing attenuation in the fixed cohort (decrease of 87 HU by the iliac arteries; P < .001). There was no difference between cohorts for image noise (20 vs 19; P = .41), but contrast-to-noise ratio (21 vs 19; P = .04) and subjective image quality were higher in the individualized cohort than in the fixed cohort (excellent or good image quality, 100% vs 67%; P < .001). Conclusion Compared with a fixed delay time after bolus tracking, a patient-specific individualized trigger delay improves image quality and provides uniform contrast attenuation for CT angiography of the aorta. (c)RSNA, 2019.",0
"Development of MAP4 Kinase Inhibitors as Motor Neuron-Protecting Agents. Disease-causing mutations in many neurodegenerative disorders lead to proteinopathies that trigger endoplasmic reticulum (ER) stress. However, few therapeutic options exist for patients with these diseases. Using an in vitro screening platform to identify compounds that protect human motor neurons from ER stress-mediated degeneration, we discovered that compounds targeting the mitogen-activated protein kinase kinase kinase kinase (MAP4K) family are neuroprotective. The kinase inhibitor URMC-099 (compound 1) stood out as a promising lead compound for further optimization. We coupled structure-based compound design with functional activity testing in neurons subjected to ER stress to develop a series of analogs with improved MAP4K inhibition and concomitant increases in potency and efficacy. Further structural modifications were performed to enhance the pharmacokinetic profiles of the compound 1 derivatives. Prostetin/12k emerged as an exceptionally potent, metabolically stable, and blood-brain barrier-penetrant compound that is well suited for future testing in animal models of neurodegeneration.",0
"Prevalence and Predictability of Low-Yield Inpatient Laboratory Diagnostic Tests. Importance: Laboratory testing is an important target for high-value care initiatives, constituting the highest volume of medical procedures. Prior studies have found that up to half of all inpatient laboratory tests may be medically unnecessary, but a systematic method to identify these unnecessary tests in individual cases is lacking. Objective: To systematically identify low-yield inpatient laboratory testing through personalized predictions. Design, Setting, and Participants: In this retrospective diagnostic study with multivariable prediction models, 116637 inpatients treated at Stanford University Hospital from January 1, 2008, to December 31, 2017, a total of 60929 inpatients treated at University of Michigan from January 1, 2015, to December 31, 2018, and 13940 inpatients treated at the University of California, San Francisco from January 1 to December 31, 2018, were assessed. Main Outcomes and Measures: Diagnostic accuracy measures, including sensitivity, specificity, negative predictive values (NPVs), positive predictive values (PPVs), and area under the receiver operating characteristic curve (AUROC), of machine learning models when predicting whether inpatient laboratory tests yield a normal result as defined by local laboratory reference ranges. Results: In the recent data sets (July 1, 2014, to June 30, 2017) from Stanford University Hospital (including 22664 female inpatients with a mean [SD] age of 58.8 [19.0] years and 22016 male inpatients with a mean [SD] age of 59.0 [18.1] years), among the top 20 highest-volume tests, 792397 were repeats of orders within 24 hours, including tests that are physiologically unlikely to yield new information that quickly (eg, white blood cell differential, glycated hemoglobin, and serum albumin level). The best-performing machine learning models predicted normal results with an AUROC of 0.90 or greater for 12 stand-alone laboratory tests (eg, sodium AUROC, 0.92 [95% CI, 0.91-0.93]; sensitivity, 98%; specificity, 35%; PPV, 66%; NPV, 93%; lactate dehydrogenase AUROC, 0.93 [95% CI, 0.93-0.94]; sensitivity, 96%; specificity, 65%; PPV, 71%; NPV, 95%; and troponin I AUROC, 0.92 [95% CI, 0.91-0.93]; sensitivity, 88%; specificity, 79%; PPV, 67%; NPV, 93%) and 10 common laboratory test components (eg, hemoglobin AUROC, 0.94 [95% CI, 0.92-0.95]; sensitivity, 99%; specificity, 17%; PPV, 90%; NPV, 81%; creatinine AUROC, 0.96 [95% CI, 0.96-0.97]; sensitivity, 93%; specificity, 83%; PPV, 79%; NPV, 94%; and urea nitrogen AUROC, 0.95 [95% CI, 0.94, 0.96]; sensitivity, 87%; specificity, 89%; PPV, 77%; NPV 94%). Conclusions and Relevance: The findings suggest that low-yield diagnostic testing is common and can be systematically identified through data-driven methods and patient context-aware predictions. Implementing machine learning models appear to be able to quantify the level of uncertainty and expected information gained from diagnostic tests explicitly, with the potential to encourage useful testing and discourage low-value testing that incurs direct costs and indirect harms..",1
"Iterative fully convolutional neural networks for automatic vertebra segmentation and identification. Precise segmentation and anatomical identification of the vertebrae provides the basis for automatic analysis of the spine, such as detection of vertebral compression fractures or other abnormalities. Most dedicated spine CT and MR scans as well as scans of the chest, abdomen or neck cover only part of the spine. Segmentation and identification should therefore not rely on the visibility of certain vertebrae or a certain number of vertebrae. We propose an iterative instance segmentation approach that uses a fully convolutional neural network to segment and label vertebrae one after the other, independently of the number of visible vertebrae. This instance-by-instance segmentation is enabled by combining the network with a memory component that retains information about already segmented vertebrae. The network iteratively analyzes image patches, using information from both image and memory to search for the next vertebra. To efficiently traverse the image, we include the prior knowledge that the vertebrae are always located next to each other, which is used to follow the vertebral column. The network concurrently performs multiple tasks, which are segmentation of a vertebra, regression of its anatomical label and prediction whether the vertebra is completely visible in the image, which allows to exclude incompletely visible vertebrae from further analyses. The predicted anatomical labels of the individual vertebrae are additionally refined with a maximum likelihood approach, choosing the overall most likely labeling if all detected vertebrae are taken into account. This method was evaluated with five diverse datasets, including multiple modalities (CT and MR), various fields of view and coverages of different sections of the spine, and a particularly challenging set of low-dose chest CT scans. For vertebra segmentation, the average Dice score was 94.9+/-2.1% with an average absolute symmetric surface distance of 0.2+/-10.1mm. The anatomical identification had an accuracy of 93%, corresponding to a single case with mislabeled vertebrae. Vertebrae were classified as completely or incompletely visible with an accuracy of 97%. The proposed iterative segmentation method compares favorably with state-of-the-art methods and is fast, flexible and generalizable.",1
"Can personalized use of NSAIDs be a reality in the clinic?. The use of NSAIDs in rheumatology could be improved by an appropriate risk scoring system that accounts for adverse events such as bleeding and thrombosis. Such a risk score has now been developed using data from the PRECISION trial, but is this score ready to be applied in clinical practice?",0
"A hybrid neural network model for predicting kidney disease in hypertension patients based on electronic health records. BACKGROUND: Disease prediction based on Electronic Health Records (EHR) has become one hot research topic in biomedical community. Existing work mainly focuses on the prediction of one target disease, and little work is proposed for multiple associated diseases prediction. Meanwhile, a piece of EHR usually contains two main information: the textual description and physical indicators. However, existing work largely adopts statistical models with discrete features from numerical physical indicators in EHR, and fails to make full use of textual description information. METHODS: In this paper, we study the problem of kidney disease prediction in hypertension patients by using neural network model. Specifically, we first model the prediction problem as a binary classification task. Then we propose a hybrid neural network which incorporates Bidirectional Long Short-Term Memory (BiLSTM) and Autoencoder networks to fully capture the information in EHR. RESULTS: We construct a dataset based on a large number of raw EHR data. The dataset consists of totally 35,332 records from hypertension patients. Experimental results show that the proposed neural model achieves 89.7% accuracy for the task. CONCLUSIONS: A hybrid neural network model was presented. Based on the constructed dataset, the comparison results of different models demonstrated the effectiveness of the proposed neural model. The proposed model outperformed traditional statistical models with discrete features and neural baseline systems.",1
"Adversarial training with cycle consistency for unsupervised super-resolution in endomicroscopy. In recent years, endomicroscopy has become increasingly used for diagnostic purposes and interventional guidance. It can provide intraoperative aids for real-time tissue characterization and can help to perform visual investigations aimed for example to discover epithelial cancers. Due to physical constraints on the acquisition process, endomicroscopy images, still today have a low number of informative pixels which hampers their quality. Post-processing techniques, such as Super-Resolution (SR), are a potential solution to increase the quality of these images. SR techniques are often supervised, requiring aligned pairs of low-resolution (LR) and high-resolution (HR) images patches to train a model. However, in our domain, the lack of HR images hinders the collection of such pairs and makes supervised training unsuitable. For this reason, we propose an unsupervised SR framework based on an adversarial deep neural network with a physically-inspired cycle consistency, designed to impose some acquisition properties on the super-resolved images. Our framework can exploit HR images, regardless of the domain where they are coming from, to transfer the quality of the HR images to the initial LR images. This property can be particularly useful in all situations where pairs of LR/HR are not available during the training. Our quantitative analysis, validated using a database of 238 endomicroscopy video sequences from 143 patients, shows the ability of the pipeline to produce convincing super-resolved images. A Mean Opinion Score (MOS) study also confirms this quantitative image quality assessment.",1
"Identification of verapamil binding sites within human Kv1.5 channel using mutagenesis and docking simulation. Background/Aims: The phenylalkylamine class of L-type Ca2+ channel antagonist verapamil prolongs the effective refractory period (ERP) of human atrium, which appears to contribute to the efficacy of verapamil in preventing reentrant-based atrial arrhythmias including atrial fibrillation. This study was designed to investigate the molecular and electrophysiological mechanism underlying the action of verapamil on human Kv1.5 (hKv1.5) channel that determines action potential duration and ERP in human atrium. Methods: Site-directed mutagenesis created 10 single-point mutations within pore region of hKv1.5 channel. Whole-cell patch-clamp method investigated the effect of verapamil on wild-type and mutant hKv1.5 channels heterologously expressed in Chinese hamster ovary cells. Docking simulation was conducted using open-state homology model of hKv1.5 channel pore. Results: Verapamil preferentially blocked hKv1.5 channel in its open state with IC50 of 2.4±0.6 mM (n = 6). The blocking effect of verapamil was significantly attenuated in T479A, T480A, I502A, V505A, I508A, L510A, V512A and V516A mutants, compared with wild-type hKv1.5 channel. Computer docking simulation predicted that verapamil is positioned within central cavity of channel pore and has contact with Thr479, Thr480, Val505, Ile508, Ala509, Val512, Pro513 and Val516. Conclusion: Verapamil acts as an open-channel blocker of hKv1.5 channel, presumably due to direct binding to specific amino acids within pore region of hKv1.5 channel, such as Thr479, Thr480, Val505, Ile508, Val512 and Val516. This blocking effect of verapamil on hKv1.5 channel appears to contribute at least partly to prolongation of atrial ERP and resultant antiarrhythmic action on atrial fibrillation in humans.",0
"Catestatin regulates vesicular quanta through modulation of cholinergic and peptidergic (PACAPergic) stimulation in PC12 cells. We have previously shown that the chromogranin A (CgA)-derived peptide catestatin (CST: hCgA352–372) inhibits nicotine-induced secretion of catecholamines from the adrenal medulla and chromaffin cells. In the present study, we seek to determine whether CST regulates dense core (DC) vesicle (DCV) quanta (catecholamine and chromogranin/secretogranin proteins) during acute (0.5-h treatment) or chronic (24-h treatment) cholinergic (nicotine) or peptidergic (PACAP, pituitary adenylyl cyclase activating polypeptide) stimulation of PC12 cells. In acute experiments, we found that both nicotine (60 μM) and PACAP (0.1 μM) decreased intracellular norepinephrine (NE) content and increased 3H‐NE secretion, with both effects markedly inhibited by co-treatment with CST (2 μM). In chronic experiments, we found that nicotine and PACAP both reduced DCV and DC diameters and that this effect was likewise prevented by CST. Nicotine or CST alone increased expression of CgA protein and together elicited an additional increase in CgA protein, implying that nicotine and CST utilize separate signaling pathways to activate CgA expression. In contrast, PACAP increased expression of CgB and SgII proteins, with a further potentiation by CST. CST augmented the expression of tyrosine hydroxylase (TH) but did not increase intracellular NE levels, presumably due to its inability to cause post-translational activation of TH through serine phosphorylation. Co-treatment of CST with nicotine or PACAP increased quantal size, plausibly due to increased synthesis of CgA, CgB and SgII by CST. We conclude that CST regulates DCV quanta by acutely inhibiting catecholamine secretion and chronically increasing expression of CgA after nicotinic stimulation and CgB and SgII after PACAPergic stimulation.",0
"Prediction of LC-MS/MS properties of peptides from sequence by deep learning. Deep learning models for prediction of three key LC-MS/MS properties from peptide sequences were developed. The LC-MS/MS properties or behaviors are indexed retention times (iRT), MS1 or survey scan charge state distributions, and sequence ion intensities of HCD spectra. A common core deep supervised learning architecture, bidirectional long-short term memory (LSTM) recurrent neural networks was used to construct the three prediction models. Two featurization schemes were proposed and demonstrated to allow for efficient encoding of modifications. The iRT and charge state distribution models were trained with on order of 105 data points each. An HCD sequence ion prediction model was trained with 2 × 106 experimental spectra. The iRT prediction model and HCD sequence ion prediction model provide improved accuracies over the start-of-the-art models available in literature. The MS1 charge state distribution prediction model offers excellent performance. The prediction models can be used to enhance peptide identification and quantification in data-dependent acquisition and data-independent acquisition (DIA) experiments as well as to assist MRM (multiple reaction monitoring) and PRM (parallel reaction monitoring) experiment design.",0
"A novel resveratrol derivative induces mitotic arrest, centrosome fragmentation and cancer cell death by inhibiting γ-tubulin. Background: Resveratrol and its natural stilbene-containing derivatives have been extensively investigated as potential chemotherapeutic agents. The synthetic manipulation of the stilbene scaffold has led to the generation of new analogues with improved anticancer activity and better bioavailability. In the present study we investigated the anticancer activity of a novel trimethoxystilbene derivative (3,4,4′-trimethoxylstilbene), where two methoxyl groups are adjacent on the benzene ring (ortho configuration), and compared its activity to 3,5,4′-trimethoxylstilbene, whose methoxyl groups are in meta configuration. Results: We provide evidence that the presence of the two methoxyl groups in ortho configuration renders 3,4,4′-trimethoxystilbene more efficient than the meta isomer in inhibiting cell proliferation and producing apoptotic death in colorectal cancer cells. Confocal microscopy of α- and γ-tubulin staining shows that the novel compound strongly depolymerizes the mitotic spindle and produces fragmentation of the pericentrosomal material. Computer assisted docking studies indicate that both molecules potentially interact with γ-tubulin, and that 3,4,4′-trimethoxystilbene is likely to establish stronger interactions with the protein. Conclusions: These findings demonstrate the ortho configuration confers higher specificity for γ-tubulin with respect to α-tubulin on 3,4,4′ trimethoxystilbene, allowing it to be defined as a new γ-tubulin inhibitor. A strong interaction with γ-tubulin might be a defining feature of molecules with high anticancer activity, as shown for the 3,4,4′ isomer.",0
"Potent and specific MTH1 inhibitors targeting gastric cancer. Human mutT homolog 1(MTH1), the oxidized dNTP pool sanitizer enzyme, has been reported to be highly expressed in various malignant tumors. However, the oncogenic role of MTH1 in gastric cancer remains to be determined. In the current study, we found that MTH1 was overexpressed in human gastric cancer tissues and cells. Using an in vitro MTH1 inhibitor screening system, the compounds available in our laboratory were screened and the small molecules containing 5-cyano-6-phenylpyrimidine structure were firstly found to show potently and specifically inhibitory effect on MTH1, especially compound MI-743 with IC50 = 91.44 ± 1.45 nM. Both molecular docking and target engagement experiments proved that MI-743 can directly bind to MTH1. Moreover, MI-743 could not only inhibit cell proliferation in up to 16 cancer cell lines, especially gastric cancer cells HGC-27 and MGC-803, but also significantly induce MTH1-related 8-oxo-dG accumulation and DNA damage. Furthermore, the growth of xenograft tumours derived by injection of MGC-803 cells in nude mice was also significantly inhibited by MI-743 treatment. Importantly, MTH1 knockdown by siRNA in those two gastric cancer cells exhibited the similar findings. Our findings indicate that MTH1 is highly expressed in human gastric cancer tissues and cell lines. Small molecule MI-743 with 5-cyano-6-phenylpyrimidine structure may serve as a novel lead compound targeting the overexpressed MTH1 for gastric cancer treatment.",0
"Principles for Integrative Structural Biology Studies. Integrative structure determination is a powerful approach to modeling the structures of biological systems based on data produced by multiple experimental and theoretical methods, with implications for our understanding of cellular biology and drug discovery. This Primer introduces the theory and methods of integrative approaches, emphasizing the kinds of data that can be effectively included in developing models and using the nuclear pore complex as an example to illustrate the practice and challenges involved. These guidelines are intended to aid the researcher in understanding and applying integrative structural methods to systems of their interest and thus take advantage of this rapidly evolving field.",0
"Machine Learning and Natural Language Processing for Geolocation-Centric Monitoring and Characterization of Opioid-Related Social Media Chatter. Importance: Automatic curation of consumer-generated, opioid-related social media big data may enable real-time monitoring of the opioid epidemic in the United States. Objective: To develop and validate an automatic text-processing pipeline for geospatial and temporal analysis of opioid-mentioning social media chatter. Design, Setting, and Participants: This cross-sectional, population-based study was conducted from December 1, 2017, to August 31, 2019, and used more than 3 years of publicly available social media posts on Twitter, dated from January 1, 2012, to October 31, 2015, that were geolocated in Pennsylvania. Opioid-mentioning tweets were extracted using prescription and illicit opioid names, including street names and misspellings. Social media posts (tweets) (n = 9006) were manually categorized into 4 classes, and training and evaluation of several machine learning algorithms were performed. Temporal and geospatial patterns were analyzed with the best-performing classifier on unlabeled data. Main Outcomes and Measures: Pearson and Spearman correlations of county- and substate-level abuse-indicating tweet rates with opioid overdose death rates from the Centers for Disease Control and Prevention WONDER database and with 4 metrics from the National Survey on Drug Use and Health for 3 years were calculated. Classifier performances were measured through microaveraged F1 scores (harmonic mean of precision and recall) or accuracies and 95% CIs. Results: A total of 9006 social media posts were annotated, of which 1748 (19.4%) were related to abuse, 2001 (22.2%) were related to information, 4830 (53.6%) were unrelated, and 427 (4.7%) were not in the English language. Yearly rates of abuse-indicating social media post showed statistically significant correlation with county-level opioid-related overdose death rates (n = 75) for 3 years (Pearson r = 0.451, P <.001; Spearman r = 0.331, P =.004). Abuse-indicating tweet rates showed consistent correlations with 4 NSDUH metrics (n = 13) associated with nonmedical prescription opioid use (Pearson r = 0.683, P =.01; Spearman r = 0.346, P =.25), illicit drug use (Pearson r = 0.850, P <.001; Spearman r = 0.341, P =.25), illicit drug dependence (Pearson r = 0.937, P <.001; Spearman r = 0.495, P =.09), and illicit drug dependence or abuse (Pearson r = 0.935, P <.001; Spearman r = 0.401, P =.17) over the same 3-year period, although the tests lacked power to demonstrate statistical significance. A classification approach involving an ensemble of classifiers produced the best performance in accuracy or microaveraged F1 score (0.726; 95% CI, 0.708-0.743). Conclusions and Relevance: The correlations obtained in this study suggest that a social media-based approach reliant on supervised machine learning may be suitable for geolocation-centric monitoring of the US opioid epidemic in near real time.",1
"Stem cells and extracellular vesicles: Biological regulators of physiology and disease. Many different subpopulations of subcellular extracellular vesicles (EVs) have been described. EVs are released from all cell types and have been shown to regulate normal physiological homeostasis, as well as pathological states by influencing cell proliferation, differentiation, organ homing, injury and recovery, as well as disease progression. In this review, we focus on the bidirectional actions of vesicles from normal and diseased cells on normal or leukemic target cells; and on the leukemic microenvironment as a whole. EVs from human bone marrow mesenchymal stem cells (MSC) can have a healing effect, reversing the malignant phenotype in prostate and colorectal cancer, as well as mitigating radiation damage to marrow. The role of EVs in leukemia and their bimodal cross talk with the encompassing microenvironment remains to be fully characterized. This may provide insight for clinical advances via the application of EVs as potential therapy and the employment of statistical and machine learning models to capture the pleiotropic effects EVs endow to a dynamic microenvironment, possibly allowing for precise therapeutic intervention.",0
"Comparison of somatic variant detection algorithms using Ion Torrent targeted deep sequencing data. Background: The application of next-generation sequencing in cancer has revealed the genomic landscape of many tumour types and is nowadays routinely used in research and clinical settings. Multiple algorithms have been developed to detect somatic variation from sequencing data using either paired tumour-blood or tumour-only samples. Most of these methods have been developed and evaluated for the identification of somatic variation using Illumina sequencing datasets of moderate coverage. However, a comprehensive evaluation of somatic variant detection algorithms on Ion Torrent targeted deep sequencing data has not been performed. Methods: We have applied three somatic detection algorithms, Torrent Variant Caller, MuTect2 and VarScan2, on a large cohort of ovarian cancer patients comprising of 208 paired tumour-blood samples and 253 tumour-only samples sequenced deeply on Ion Torrent Proton platform across 330 amplicons. Subsequently, the concordance and performance of the three somatic variant callers were assessed. Results: We have observed low concordance across the algorithms with only 0.5% of SNV and 0.02% of INDEL calls in common across all three methods. The intersection of all methods showed better performance when assessed using correlation with known mutational signatures, overlap with COSMIC variation and by examining the variant characteristics. The Torrent Variant Caller also performed well with the advantage of not eliminating a high number of variants that could lead to high type II error. Conclusions: Our results suggest that caution should be taken when applying state-of-the-art somatic variant algorithms to Ion Torrent targeted deep sequencing data. Better quality control procedures and strategies that combine results from multiple methods should ensure that higher accuracy is achieved. This is essential to ensure that results from bioinformatics pipelines using Ion Torrent deep sequencing can be robustly applied in cancer research and in the clinic.",0
"Comparison of methods for early-readmission prediction in a high-dimensional heterogeneous covariates and time-to-event outcome framework. BACKGROUND: Choosing the most performing method in terms of outcome prediction or variables selection is a recurring problem in prognosis studies, leading to many publications on methods comparison. But some aspects have received little attention. First, most comparison studies treat prediction performance and variable selection aspects separately. Second, methods are either compared within a binary outcome setting (where we want to predict whether the readmission will occur within an arbitrarily chosen delay or not) or within a survival analysis setting (where the outcomes are directly the censored times), but not both. In this paper, we propose a comparison methodology to weight up those different settings both in terms of prediction and variables selection, while incorporating advanced machine learning strategies. METHODS: Using a high-dimensional case study on a sickle-cell disease (SCD) cohort, we compare 8 statistical methods. In the binary outcome setting, we consider logistic regression (LR), support vector machine (SVM), random forest (RF), gradient boosting (GB) and neural network (NN); while on the survival analysis setting, we consider the Cox Proportional Hazards (PH), the CURE and the C-mix models. We also propose a method using Gaussian Processes to extract meaningfull structured covariates from longitudinal data. RESULTS: Among all assessed statistical methods, the survival analysis ones obtain the best results. In particular the C-mix model yields the better performances in both the two considered settings (AUC =0.94 in the binary outcome setting), as well as interesting interpretation aspects. There is some consistency in selected covariates across methods within a setting, but not much across the two settings. CONCLUSIONS: It appears that learning withing the survival analysis setting first (so using all the temporal information), and then going back to a binary prediction using the survival estimates gives significantly better prediction performances than the ones obtained by models trained ""directly"" within the binary outcome setting.",1
"High-order coordination of cortical spiking activity modulates perceptual accuracy. The accurate relay of electrical signals within cortical networks is key to perception and cognitive function. Theoretically, it has long been proposed that temporal coordination of neuronal spiking activity controls signal transmission and behavior. However, whether and how temporally precise neuronal coordination in population activity influences perception are unknown. Here, we recorded populations of neurons in early and mid-level visual cortex (areas V1 and V4) simultaneously to discover that the precise temporal coordination between the spiking activity of three or more cells carries information about visual perception in the absence of firing rate modulation. The accuracy of perceptual responses correlated with high-order spiking coordination within V4, but not V1, and with feedforward coordination between V1 and V4. These results indicate that while visual stimuli are encoded in the discharge rates of neurons, perceptual accuracy is related to temporally precise spiking coordination within and between cortical networks.",0
"State of the Art in Abdominal CT: The Limits of Iterative Reconstruction Algorithms. The development and widespread adoption of iterative reconstruction (IR) algorithms for CT have greatly facilitated the contemporary practice of radiation dose reduction during abdominal CT examinations. IR mitigates the increased image noise typically associated with reduced radiation dose levels, thereby maintaining subjective image quality and diagnostic confidence for a variety of clinical tasks. Mounting evidence, however, points to important limitations of this method involving radiologists' ability to perform low-contrast diagnostic tasks, such as the detection of liver metastases or pancreatic masses. Radiologists need to be aware that use of IR can result in a decline of spatial resolution for low-contrast structures and degradation of low-contrast detectability when radiation dose reductions exceed approximately 25%. This article will review the principles of IR algorithm technology, describe the various commercial implementations of IR in CT, and review published studies that have evaluated the ability of IR to preserve diagnostic performance for low-contrast diagnostic tasks. In addition, future developments in CT noise reduction techniques and methods to rigorously evaluate their diagnostic performance will be discussed.",0
"Blood group alters platelet binding kinetics to von Willebrand factor and consequently platelet function. Blood type O is associated with a lower risk of myocardial infarction. Platelets play a critical role in myocardial infarction. It is not known whether the expression of blood group antigens on platelet proteins alters platelet function; we hypothesized that platelet function would be different between donors with blood type O and those with non-O. To address this hypothesis, we perfused blood from healthy type O donors (n = 33) or non-O donors (n = 54) over pooled plasma derived von Willebrand factor (VWF) protein and purified blood type-specific VWF at arterial shear and measured platelet translocation dynamics. We demonstrate for the first time that type O platelets travel farther at greater speeds before forming stable bonds with VWF. To further characterize these findings, we used a novel analytical model of platelet interaction. Modeling revealed that the kinetics for GPIb/VWF binding rate are significantly lower for type Ocompared with non-Oplatelets. Our results demonstrate that platelets from type O donors interact less with VWF at arterial shear than non-O platelets. Our results suggest a potential mechanism for the reduced risk of myocardial infarction associated with blood type O.",0
"Identification of the A293 (AVE1231) binding site in the cardiac two-pore-domain potassium channel TASK-1: A common low affinity antiarrhythmic drug binding site. Background/Aims: The two-pore-domain potassium channel TASK-1 regulates atrial action potential duration. Due to the atrium-specific expression of TASK-1 in the human heart and the functional upregulation of TASK-1 currents in atrial fibrillation (AF), TASK-1 represents a promising target for the treatment of AF. Therefore, detailed knowledge of the molecular determinants of TASK-1 inhibition may help to identify new drugs for the future therapy of AF. In the current study, the molecular determinants of TASK-1 inhibition by the potent and antiarrhythmic compound A293 (AVE1231) were studied in detail. Methods: Alanine-scanning mutagenesis together with two-electrode voltage-clamp recordings were combined with in silico docking experiments. Results: Here, we have identified Q126 located in the M2 segment together with L239 and N240 of the M4 segment as amino acids essential for the A293-mediated inhibition of TASK-1. These data indicate a binding site which is different to that of A1899 for which also residues of the pore signature sequence and the late M4 segments are essential. Using in silico docking experiments, we propose a binding site at the lower end of the cytosolic pore, located at the entry to lateral side fenestrations of TASK-1. Strikingly, TASK-1 inhibition by the low affinity antiarrhythmic TASK-1 blockers propafenone, amiodarone and carvedilol was also strongly diminished by mutations at this novel binding site. Conclusion: We have identified the A293 binding site in the central cavity of TASK-1 and propose that this site might represent a conserved site of action for many low affinity antiarrhythmic TASK-1 blockers.",0
"Regional times to equilibria and their impact on semi-quantification of [18F]AV-1451 uptake. The semi-quantitative estimate standardised uptake value ratios (SUVR) correlate well with specific binding of the tracer expressed as distribution volume ratios (DVR) for the tau positron emission tomography tracer [18F]AV-1451 uptake and are therefore widely used as proxy for tracer binding. With regard to tracer kinetic modelling, there exists a time point when SUVR deviates minimally from DVR, occurring when the specific binding reaches a transient equilibrium. Here, we have investigated whether the time to equilibrium affects the agreement between SUVR and DVR across different brain regions. We show that the time required to reach equilibrium differs across brain regions, resulting in region-specific biases. However, even though the 80–100 min post-injection time window did not show the smallest bias numerically, the disagreement between SUVR and DVR varied least between regions during this time. In conclusion, our findings suggest a regional component to the bias of SUVR related to the time to transient equilibrium of the specific binding. [18F]AV-1451 uptake should consequently be interpreted with some caution when compared across brain regions using this method of quantification. The commonly used time window 80–100 min post-injection shows the most consistent bias across regions and is recommended for semi-quantification of [18F]AV-1451.",0
"Automated CT and MRI liver segmentation and biometry using a generalized convolutional neural network. Purpose: To assess feasibility of training a convolutional neural network (CNN) to automate liver segmentation across different imaging modalities and techniques used in clinical practice and to apply this technique to enable automation of liver biometry. Materials and Methods: A two-dimensional U-Net CNN was trained for liver segmentation in two stages by using 330 abdominal MRI and CT examinations. First, the neural network was trained with unenhanced multiecho spoiled gradient-echo images from 300 MRI examinations to yield multiple signal weightings. Then, transfer learning was used to generalize the CNN with additional images from 30 contrast material–enhanced MRI and CT examinations. Performance of the CNN was assessed by using a distinct multiinstitutional dataset curated from multiple sources (498 subjects). Segmentation accuracy was evaluated by computing Dice scores. These segmentations were used to compute liver volume from CT and T1-weighted MRI examinations and to estimate hepatic proton density fat fraction (PDFF) from multiecho T2*-weighted MRI examinations. Quantitative volumetry and PDFF estimates were compared between automated and manual segmentation by using Pearson correlation and Bland-Altman statistics. Results: Dice scores were 0.94 ± 0.06 for CT (n = 230), 0.95 ± 0.03 (n = 100) for T1-weighted MRI, and 0.92 ± 0.05 for T2*weighted MRI (n = 168). Liver volume measured with manual and automated segmentation agreed closely for CT (95% limits of agreement: −298 mL, 180 mL) and T1-weighted MRI (95% limits of agreement: −358 mL, 180 mL). Hepatic PDFF measured by the two segmentations also agreed closely (95% limits of agreement: −0.62%, 0.80%). Conclusion: By using a transfer-learning strategy, this study has demonstrated the feasibility of a CNN to be generalized to perform liver segmentation across different imaging techniques and modalities. With further refinement and validation, CNNs may have broad applicability for multimodal liver volumetry and hepatic tissue characterization.",1
"Predicting drug response of tumors from integrated genomic profiles by deep neural networks. Background: The study of high-throughput genomic profiles from a pharmacogenomics viewpoint has provided unprecedented insights into the oncogenic features modulating drug response. A recent study screened for the response of a thousand human cancer cell lines to a wide collection of anti-cancer drugs and illuminated the link between cellular genotypes and vulnerability. However, due to essential differences between cell lines and tumors, to date the translation into predicting drug response in tumors remains challenging. Recently, advances in deep learning have revolutionized bioinformatics and introduced new techniques to the integration of genomic data. Its application on pharmacogenomics may fill the gap between genomics and drug response and improve the prediction of drug response in tumors. Results: We proposed a deep learning model to predict drug response (DeepDR) based on mutation and expression profiles of a cancer cell or a tumor. The model contains three deep neural networks (DNNs), i) a mutation encoder pre-trained using a large pan-cancer dataset (The Cancer Genome Atlas; TCGA) to abstract core representations of high-dimension mutation data, ii) a pre-trained expression encoder, and iii) a drug response predictor network integrating the first two subnetworks. Given a pair of mutation and expression profiles, the model predicts IC50 values of 265 drugs. We trained and tested the model on a dataset of 622 cancer cell lines and achieved an overall prediction performance of mean squared error at 1.96 (log-scale IC50 values). The performance was superior in prediction error or stability than two classical methods (linear regression and support vector machine) and four analog DNN models of DeepDR, including DNNs built without TCGA pre-training, partly replaced by principal components, and built on individual types of input data. We then applied the model to predict drug response of 9059 tumors of 33 cancer types. Using per-cancer and pan-cancer settings, the model predicted both known, including EGFR inhibitors in non-small cell lung cancer and tamoxifen in ER+ breast cancer, and novel drug targets, such as vinorelbine for TTN-mutated tumors. The comprehensive analysis further revealed the molecular mechanisms underlying the resistance to a chemotherapeutic drug docetaxel in a pan-cancer setting and the anti-cancer potential of a novel agent, CX-5461, in treating gliomas and hematopoietic malignancies. Conclusions: Here we present, as far as we know, the first DNN model to translate pharmacogenomics features identified from in vitro drug screening to predict the response of tumors. The results covered both well-studied and novel mechanisms of drug resistance and drug targets. Our model and findings improve the prediction of drug response and the identification of novel therapeutic options.",0
"Breast cancer histopathology image classification through assembling multiple compact CNNs. BACKGROUND: Breast cancer causes hundreds of thousands of deaths each year worldwide. The early stage diagnosis and treatment can significantly reduce the mortality rate. However, the traditional manual diagnosis needs intense workload, and diagnostic errors are prone to happen with the prolonged work of pathologists. Automatic histopathology image recognition plays a key role in speeding up diagnosis and improving the quality of diagnosis. METHODS: In this work, we propose a breast cancer histopathology image classification by assembling multiple compact Convolutional Neural Networks (CNNs). First, a hybrid CNN architecture is designed, which contains a global model branch and a local model branch. By local voting and two-branch information merging, our hybrid model obtains stronger representation ability. Second, by embedding the proposed Squeeze-Excitation-Pruning (SEP) block into our hybrid model, the channel importance can be learned and the redundant channels are thus removed. The proposed channel pruning scheme can decrease the risk of overfitting and produce higher accuracy with the same model size. At last, with different data partition and composition, we build multiple models and assemble them together to further enhance the model generalization ability. RESULTS: Experimental results show that in public BreaKHis dataset, our proposed hybrid model achieves comparable performance with the state-of-the-art. By adopting the multi-model assembling scheme, our method outperforms the state-of-the-art in both patient level and image level accuracy for BACH dataset. CONCLUSIONS: We propose a novel compact breast cancer histopathology image classification scheme by assembling multiple compact hybrid CNNs. The proposed scheme achieves promising results for the breast cancer image classification task. Our method can be used in breast cancer auxiliary diagnostic scenario, and it can reduce the workload of pathologists as well as improve the quality of diagnosis.",1
"Machine learning based automated dynamic quantification of left heart chamber volumes. Aims: Studies have demonstrated the ability of a new automated algorithm for volumetric analysis of 3D echocardiographic (3DE) datasets to provide accurate and reproducible measurements of left ventricular and left atrial (LV, LA) volumes at end-systole and end-diastole. Recently, this methodology was expanded using a machine learning (ML) approach to automatically measure chamber volumes throughout the cardiac cycle, resulting in LV and LA volume-time curves. We aimed to validate ejection and filling parameters obtained from these curves by comparing them to independent well-validated reference techniques. Methods and results: We studied 20 patients referred for cardiac magnetic resonance (CMR) examinations, who underwent 3DE imaging the same day. Volume-time curves were obtained for both LV and LA chambers using the ML algorithm (Philips HeartModel), and independently conventional 3DE volumetric analysis (TomTec), and CMR images (slice-by-slice, frame-by-frame manual tracing). Automatically derived LV and LA volumes and ejection/filling parameters were compared against both reference techniques. Minor manual correction of the automatically detected LV and LA borders was needed in 4/20 and 5/20 cases, respectively. Time required to generate volume-time curves was 35 ± 17 s using ML algorithm, 3.6 ± 0.9 min using conventional 3DE analysis, and 96 ± 14 min using CMR. Volume-time curves obtained by all three techniques were similar in shape and magnitude. In both comparisons, ejection/filling parameters showed no significant inter-technique differences. Bland-Altman analysis confirmed small biases, despite wide limits of agreement. Conclusion: The automated ML algorithm can quickly measure dynamic LV and LA volumes and accurately analyse ejection/filling parameters. Incorporation of this algorithm into the clinical workflow may increase the utilization of 3DE imaging.",1
"A graph-cut approach for pulmonary artery-vein segmentation in noncontrast CT images. Lung vessel segmentation has been widely explored by the biomedical image processing community; however, the differentiation of arterial from venous irrigation is still a challenge. Pulmonary artery–vein (AV) segmentation using computed tomography (CT) is growing in importance owing to its undeniable utility in multiple cardiopulmonary pathological states, especially those implying vascular remodelling, allowing the study of both flow systems separately. We present a new framework to approach the separation of tree-like structures using local information and a specifically designed graph-cut methodology that ensures connectivity as well as the spatial and directional consistency of the derived subtrees. This framework has been applied to the pulmonary AV classification using a random forest (RF) pre-classifier to exploit the local anatomical differences of arteries and veins. The evaluation of the system was performed using 192 bronchopulmonary segment phantoms, 48 anthropomorphic pulmonary CT phantoms, and 26 lungs from noncontrast CT images with precise voxel-based reference standards obtained by manually labelling the vessel trees. The experiments reveal a relevant improvement in the accuracy (∼ 20%) of the vessel particle classification with the proposed framework with respect to using only the pre-classification based on local information applied to the whole area of the lung under study. The results demonstrated the accurate differentiation between arteries and veins in both clinical and synthetic cases, specifically when the image quality can guarantee a good airway segmentation, which opens a huge range of possibilities in the clinical study of cardiopulmonary diseases.",0
"Deep learning for automated segmentation of liver lesions at ct in patients with colorectal cancer liver metastases. Purpose: To evaluate the performance, agreement, and efficiency of a fully convolutional network (FCN) for liver lesion detection and segmentation at CT examinations in patients with colorectal liver metastases (CLMs). Materials and Methods: This retrospective study evaluated an automated method using an FCN that was trained, validated, and tested with 115, 15, and 26 contrast material–enhanced CT examinations containing 261, 22, and 105 lesions, respectively. Manual detection and segmentation by a radiologist was the reference standard. Performance of fully automated and user-corrected segmentations was compared with that of manual segmentations. The interuser agreement and interaction time of manual and user-corrected segmentations were assessed. Analyses included sensitivity and positive predictive value of detection, segmentation accuracy, Cohen k, BlandAltman analyses, and analysis of variance. Results: In the test cohort, for lesion size smaller than 10 mm (n = 30), 10–20 mm (n = 35), and larger than 20 mm (n = 40), the detection sensitivity of the automated method was 10%, 71%, and 85%; positive predictive value was 25%, 83%, and 94%; Dice similarity coefficient was 0.14, 0.53, and 0.68; maximum symmetric surface distance was 5.2, 6.0, and 10.4 mm; and average symmetric surface distance was 2.7, 1.7, and 2.8 mm, respectively. For manual and user-corrected segmentation, k values were 0.42 (95% confidence interval: 0.24, 0.63) and 0.52 (95% confidence interval: 0.36, 0.72); normalized interreader agreement for lesion volume was −0.10 ± 0.07 (95% confidence interval) and −0.10 ± 0.08; and mean interaction time was 7.7 minutes ± 2.4 (standard deviation) and 4.8 minutes ± 2.1 (P <.001), respectively. Conclusion: Automated detection and segmentation of CLM by using deep learning with convolutional neural networks, when manually corrected, improved efficiency but did not substantially change agreement on volumetric measurements.",1
"REST and Neural Gene Network Dysregulation in iPSC Models of Alzheimer's Disease. Meyer et al. derive neural progenitors, neurons, and cerebral organoids from sporadic Alzheimer's disease (SAD) and APOE4 gene-edited iPSCs. SAD and APOE4 expression alter the neural transcriptome and differentiation in part through loss of function of the transcriptional repressor REST. Thus, neural gene network dysregulation may lead to Alzheimer's disease.",0
"Fostering a healthy ai ecosystem for radiology: Conclusions of the 2018 rsna summit on ai in radiology. The 2018 RSNA Summit on AI in Radiology brought together a diverse group of stakeholders to identify and prioritize areas of need related to artificial intelligence in radiology. This article presents the proceedings of the summit with emphasis on RSNA’s role in leading, organizing, and catalyzing change during this important time in radiology.",0
"A classification framework for exploiting sparse multi-variate temporal features with application to adverse drug event detection in medical records. BACKGROUND: Adverse drug events (ADEs) as well as other preventable adverse events in the hospital setting incur a yearly monetary cost of approximately $3.5 billion, in the United States alone. Therefore, it is of paramount importance to reduce the impact and prevalence of ADEs within the healthcare sector, not only since it will result in reducing human suffering, but also as a means to substantially reduce economical strains on the healthcare system. One approach to mitigate this problem is to employ predictive models. While existing methods have been focusing on the exploitation of static features, limited attention has been given to temporal features. METHODS: In this paper, we present a novel classification framework for detecting ADEs in complex Electronic health records (EHRs) by exploiting the temporality and sparsity of the underlying features. The proposed framework consists of three phases for transforming sparse and multi-variate time series features into a single-valued feature representation, which can then be used by any classifier. Moreover, we propose and evaluate three different strategies for leveraging feature sparsity by incorporating it into the new representation. RESULTS: A large-scale evaluation on 15 ADE datasets extracted from a real-world EHR system shows that the proposed framework achieves significantly improved predictive performance compared to state-of-the-art. Moreover, our framework can reveal features that are clinically consistent with medical findings on ADE detection. CONCLUSIONS: Our study and experimental findings demonstrate that temporal multi-variate features of variable length and with high sparsity can be effectively utilized to predict ADEs from EHRs. Two key advantages of our framework are that it is method agnostic, i.e., versatile, and of low computational cost, i.e., fast; hence providing an important building block for future exploitation within the domain of machine learning from EHRs.",0
"Personal clinical history predicts antibiotic resistance of urinary tract infections. Antibiotic resistance is prevalent among the bacterial pathogens causing urinary tract infections. However, antimicrobial treatment is often prescribed 'empirically', in the absence of antibiotic susceptibility testing, risking mismatched and therefore ineffective treatment. Here, linking a 10-year longitudinal data set of over 700,000 community-acquired urinary tract infections with over 5,000,000 individually resolved records of antibiotic purchases, we identify strong associations of antibiotic resistance with the demographics, records of past urine cultures and history of drug purchases of the patients. When combined together, these associations allow for machine-learning-based personalized drug-specific predictions of antibiotic resistance, thereby enabling drug-prescribing algorithms that match an antibiotic treatment recommendation to the expected resistance of each sample. Applying these algorithms retrospectively, over a 1-year test period, we find that they greatly reduce the risk of mismatched treatment compared with the current standard of care. The clinical application of such algorithms may help improve the effectiveness of antimicrobial treatments.",1
"A Deep Learning Model to Triage Screening Mammograms: A Simulation Study. Background Recent deep learning (DL) approaches have shown promise in improving sensitivity but have not addressed limitations in radiologist specificity or efficiency. Purpose To develop a DL model to triage a portion of mammograms as cancer free, improving performance and workflow efficiency. Materials and Methods In this retrospective study, 223 109 consecutive screening mammograms performed in 66 661 women from January 2009 to December 2016 were collected with cancer outcomes obtained through linkage to a regional tumor registry. This cohort was split by patient into 212 272, 25 999, and 26 540 mammograms from 56 831, 7021, and 7176 patients for training, validation, and testing, respectively. A DL model was developed to triage mammograms as cancer free and evaluated on the test set. A DL-triage workflow was simulated in which radiologists skipped mammograms triaged as cancer free (interpreting them as negative for cancer) and read mammograms not triaged as cancer free by using the original interpreting radiologists' assessments. Sensitivities, specificities, and percentage of mammograms read were calculated, with and without the DL-triage-simulated workflow. Statistics were computed across 5000 bootstrap samples to assess confidence intervals (CIs). Specificities were compared by using a two-tailed t test (P < .05) and sensitivities were compared by using a one-sided t test with a noninferiority margin of 5% (P < .05). Results The test set included 7176 women (mean age, 57.8 years +/- 10.9 [standard deviation]). When reading all mammograms, radiologists obtained a sensitivity and specificity of 90.6% (173 of 191; 95% CI: 86.6%, 94.7%) and 93.5% (24 625 of 26 349; 95% CI: 93.3%, 93.9%). In the DL-simulated workflow, the radiologists obtained a sensitivity and specificity of 90.1% (172 of 191; 95% CI: 86.0%, 94.3%) and 94.2% (24 814 of 26 349; 95% CI: 94.0%, 94.6%) while reading 80.7% (21 420 of 26 540) of the mammograms. The simulated workflow improved specificity (P = .002) and obtained a noninferior sensitivity with a margin of 5% (P < .001). Conclusion This deep learning model has the potential to reduce radiologist workload and significantly improve specificity without harming sensitivity. (c) RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Kontos and Conant in this issue.",1
"Quantification of Uncertainty in Peptide-MHC Binding Prediction Improves High-Affinity Peptide Selection for Therapeutic Design. The computational identification of peptides that can bind the major histocompatibility complex (MHC) with high affinity is an essential step in developing personal immunotherapies and vaccines. We introduce PUFFIN, a deep residual network-based computational approach that quantifies uncertainty in peptide-MHC affinity prediction that arises from observational noise and the lack of relevant training examples. With PUFFIN's uncertainty metrics, we define binding likelihood, the probability a peptide binds to a given MHC allele at a specified affinity threshold. Compared to affinity point estimates, we find that binding likelihood correlates better with the observed affinity and reduces false positives in high-affinity peptide design. When applied to examine an existing peptide vaccine, PUFFIN identifies an alternative vaccine formulation with higher binding likelihood. PUFFIN is freely available for download at http://github.com/gifford-lab/PUFFIN. Machine-learning models that predict the binding affinity of a peptide-MHC pair are essential in peptide-based therapeutic design, but state-of-the-art methods provide point estimates of affinity that do not consider measurement noise and model uncertainty. We introduce PUFFIN, a method that quantifies the prediction uncertainty and prioritizes peptides with “binding likelihood” to achieve improved accuracy in high-affinity peptide selection for therapeutic design.",0
"Facilitating accurate health provider directories using natural language processing. BACKGROUND: Accurate information in provider directories are vital in health care including health information exchange, health benefits exchange, quality reporting, and in the reimbursement and delivery of care. Maintaining provider directory data and keeping it up to date is challenging. The objective of this study is to determine the feasibility of using natural language processing (NLP) techniques to combine disparate resources and acquire accurate information on health providers. METHODS: Publically available state licensure lists in Connecticut were obtained along with National Plan and Provider Enumeration System (NPPES) public use files. Connecticut licensure lists textual information of each health professional who is licensed to practice within the state. A NLP-based system was developed based on healthcare provider taxonomy code, location, name and address information to identify textual data within the state and federal records. Qualitative and quantitative evaluation were performed, and the recall and precision were calculated. RESULTS: We identified nurse midwives, nurse practitioners, and dentists in the State of Connecticut. The recall and precision were 0.95 and 0.93 respectively. Using the system, we were able to accurately acquire 6849 of the 7177 records of health provider directory information. CONCLUSIONS: The authors demonstrated that the NLP- based approach was effective at acquiring health provider information. Furthermore, the NLP-based system can always be applied to update information further reducing processing burdens as data changes.",1
"Predicting the early risk of chronic kidney disease in patients with diabetes using real-world data. Diagnostic procedures, therapeutic recommendations, and medical risk stratifications are based on dedicated, strictly controlled clinical trials. However, a plethora of real-world medical data exists, whereupon the increase in data volume comes at the expense of completeness, uniformity, and control. Here, a case-by-case comparison shows that the predictive power of our real world data-based model for diabetes-related chronic kidney disease outperforms published algorithms, which were derived from clinical study data.",1
"Exploring sand fly salivary proteins to design multiepitope subunit vaccine to fight against visceral leishmaniasis. Visceral leishmaniasis (VL) is caused by the parasites of Leishmania donovani complex, leads to the death of 20 000 to 40 000 people from 56 affected countries, worldwide. Till date, there is not a single available vaccine candidate to prevent the VL infection, and treatment only relies upon expensive and toxic chemotherapeutic options. Consequently, immunoinformatics approach was applied to design a multiepitope-based subunit vaccine to enhance the humoral as well as cell-mediated immunity. Constructed vaccine candidate was further subjected to evaluation on allergenicity and antigenicity and physiochemical parameters. Later on, disulfide engineering was performed to increase the stability of vaccine construct. Also, molecular docking and molecular dynamics simulation study were performed to check the binding affinity and stability of toll-like receptor-4 to vaccine construct complex. Finally, codon optimization and in silico cloning were performed to ensure the expression of proposed vaccine construct in a microbial expression system.",0
"Quercetin relieved diabetic neuropathic pain by inhibiting upregulated P2X4 receptor in dorsal root ganglia. The upregulation of nociceptive ion channels expressed in dorsal root ganglia (DRG) contributes to the development and retaining of diabetic pain symptoms. The flavonoid quercetin (3,3′,4′,5,7-pentahydroxyflavone) is a component extracted from various fruits and vegetables and exerts anti-inflammatory, analgesic, anticarcinogenic, antiulcer, and antihypertensive effects. However, the exact mechanism underlying quercetin's analgesic action remains poorly understood. The aim of this study was to investigate the effects of quercetin on diabetic neuropathic pain related to the P2X4 receptor in the DRG of type 2 diabetic rat model. Our data showed that both mechanical withdrawal threshold and thermal withdrawal latency in diabetic rats treated with quercetin were higher compared with those in untreated diabetic rats. The expression levels of P2X4 messenger RNA and protein in the DRG of diabetic rats were increased compared with the control rats, while quercetin treatment significantly inhibited such enhanced P2X4 expression in diabetic rats. The satellite glial cells (SGCs) enwrap the neuronal soma in the DRG. Quercetin treatment also lowered the elevated coexpression of P2X4 and glial fibrillary acidic protein (a marker of SGCs) and decreased the upregulation of phosphorylated p38 mitogen-activated protein kinase (p38MAPK) in the DRG of diabetic rats. Quercetin significantly reduced the P2X4 agonist adenosine triphosphate-activated currents in HEK293 cells transfected with P2X4 receptors. Thus, our data demonstrate that quercetin may decrease the upregulation of the P2X4 receptor in DRG SGCs, and consequently inhibit P2X4 receptor-mediated p38MAPK activation to relieve the mechanical and thermal hyperalgesia in diabetic rats.",0
"Opioid overdose detection using smartphones. Early detection and rapid intervention can prevent death from opioid overdose. At high doses, opioids (particularly fentanyl) can cause rapid cessation of breathing (apnea), hypoxemic/hypercarbic respiratory failure, and death, the physiologic sequence by which people commonly succumb from unintentional opioid overdose. We present algorithms that run on smartphones and unobtrusively detect opioid overdose events and their precursors. Our proof-of- concept contactless system converts the phone into a short-range active sonar using frequency shifts to identify respiratory depression, apnea, and gross motor movements associated with acute opioid toxicity. We develop algorithms and perform testing in two environments: (i) an approved supervised injection facility (SIF), where people self-inject illicit opioids, and (ii) the operating room (OR), where we simulate rapid, opioid-induced overdose events using routine induction of general anesthesia. In the SIF (n = 209), our system identified postinjection, opioid-induced central apnea with 96% sensitivity and 98% specificity and identified respiratory depression with 87% sensitivity and 89% specificity. These two key events commonly precede fatal opioid overdose. In the OR, our algorithm identified 19 of 20 simulated overdose events. Given the reliable reversibility of acute opioid toxicity, smartphone-enabled overdose detection coupled with the ability to alert naloxone-equipped friends and family or emergency medical services (EMS) could hold potential as a low-barrier, harm reduction intervention.",1
"Optimal dose of selective serotonin reuptake inhibitors, venlafaxine, and mirtazapine in major depression: a systematic review and dose-response meta-analysis. Background: Depression is the single largest contributor to non-fatal health loss worldwide. Second-generation antidepressants are the first-line option for pharmacological management of depression. Optimising their use is crucial in reducing the burden of depression; however, debate about their dose dependency and their optimal target dose is ongoing. We have aimed to summarise the currently available best evidence to inform this clinical question. Methods: We did a systematic review and dose-response meta-analysis of double-blind, randomised controlled trials that examined fixed doses of five selective serotonin reuptake inhibitors (SSRIs; citalopram, escitalopram, fluoxetine, paroxetine, and sertraline), venlafaxine, or mirtazapine in the acute treatment of adults (aged 18 years or older) with major depression, identified from the Cochrane Central Register of Controlled Trials, CINAHL, Embase, LILACS, MEDLINE, PsycINFO, AMED, PSYNDEX, websites of drug licensing agencies and pharmaceutical companies, and trial registries. We imposed no language restrictions, and the search was updated until Jan 8, 2016. Doses of SSRIs were converted to fluoxetine equivalents. Trials of antidepressants for patients with depression and a serious concomitant physical illness were excluded. The main outcomes were efficacy (treatment response defined as 50% or greater reduction in depression severity), tolerability (dropouts due to adverse effects), and acceptability (dropouts for any reasons), all after a median of 8 weeks of treatment (range 4–12 weeks). We used a random-effects, dose-response meta-analysis model with flexible splines for SSRIs, venlafaxine, and mirtazapine. Findings: 28 554 records were identified through our search (24 524 published and 4030 unpublished records). 561 published and 121 unpublished full-text records were assessed for eligibility, and 77 studies were included (19 364 participants; mean age 42·5 years, SD 11·0; 7156 [60·9%] of 11 749 reported were women). For SSRIs (99 treatment groups), the dose-efficacy curve showed a gradual increase up to doses between 20 mg and 40 mg fluoxetine equivalents, and a flat to decreasing trend through the higher licensed doses up to 80 mg fluoxetine equivalents. Dropouts due to adverse effects increased steeply through the examined range. The relationship between the dose and dropouts for any reason indicated optimal acceptability for the SSRIs in the lower licensed range between 20 mg and 40 mg fluoxetine equivalents. Venlafaxine (16 treatment groups) had an initially increasing dose-efficacy relationship up to around 75–150 mg, followed by a more modest increase, whereas for mirtazapine (11 treatment groups) efficacy increased up to a dose of about 30 mg and then decreased. Both venlafaxine and mirtazapine showed optimal acceptability in the lower range of their licensed dose. These results were robust to several sensitivity analyses. Interpretation: For the most commonly used second-generation antidepressants, the lower range of the licensed dose achieves the optimal balance between efficacy, tolerability, and acceptability in the acute treatment of major depression. Funding: Japan Society for the Promotion of Science, Swiss National Science Foundation, and National Institute for Health Research.",0
"Association between childhood anhedonia and alterations in large-scale resting-state networks and task-evoked activation. Importance: Anhedonia can present in children and predict detrimental clinical outcomes. Objective: To map anhedonia in children onto changes in intrinsic large-scale connectivity and task-evoked activation and to probe the specificity of these changes in anhedonia against other clinical phenotypes (low mood, anxiety, and attention-deficit/hyperactivity disorder ADHD). Design, Setting, and Participants: Functional magnetic resonance imaging (fMRI) data were from the first annual release of the Adolescent Brain Cognitive Development study, collected between September 2016 and September 2017 and analyzed between April and September 2018. Cross-sectional data of children aged 9 to 10 years from unreferred, community samples during rest (n = 2878) and during reward anticipation (n = 2874) and working memory (n = 2745) were analyzed. Main Outcomes and Measures: Alterations in fMRI data during rest, reward anticipation, and working memory were examined, using both frequentist and Bayesian approaches. Functional MRI connectivity within large-scale networks, between networks, and between networks and subcortical regions were examined during rest. Functional MRI activation were examined during reward anticipation and working memory using the monetary incentive delayed and N-back tasks, respectively. Results: Among 2878 children with adequate-quality resting-state fMRI data (mean SD age, 10.03 0.62 years; 1400 girls 48.6%), children with anhedonia (261 9.1%), compared with those without anhedonia (2617 90.9%), showed hypoconnectivity among various large-scale networks and subcortical regions, including between the arousal-related cingulo-opercular network and reward-related ventral striatum area (mean SD with anhedonia, 0.08 0.10 vs without anhedonia, 0.10 0.10; t2,876 = 3.33; P <.001; qfalse discovery rate = 0.03; lnBayes factor10 = 2.85). Such hypoconnectivity did not manifest among children with low mood (277 of 2878 9.62%), anxiety (109 of 2878 3.79%), or ADHD (459 of 2878 15.95%), suggesting specificity. Similarly, among 2874 children (mean SD age, 10.03 0.62 years; 1414 girls 49.2%) with high-quality task-evoked fMRI data, children with anhedonia (248 of 2874 8.63%) demonstrated hypoactivation during reward anticipation in various areas, including the dorsal striatum and areas of the cingulo-opercular network. This hypoactivity was not found among children with low mood (268 of 2874 9.32%), anxiety (90 of 2874 3.13%), or ADHD (473 of 2874 16.46%). Moreover, we also found context- and phenotype-specific double dissociations; while children with anhedonia showed altered activation during reward anticipation (but not working memory), those with ADHD showed altered activation during working memory (but not reward anticipation). Conclusions and Relevance: Using the Adolescent Brain Cognitive Development study data set, phenotype-specific alterations were found in intrinsic large-scale connectivity and task-evoked activation in children with anhedonia. The hypoconnectivity at rest and hypoactivation during reward anticipation complementarily map anhedonia onto aberrations in neural-cognitive processes: lack of intrinsic reward-arousal integration during rest and diminishment of extrinsic reward-arousal activity during reward anticipation. These findings help delineate the pathophysiological underpinnings of anhedonia in children.",0
"Phloretin and phloridzin improve insulin sensitivity and enhance glucose uptake by subverting PPARγ/Cdk5 interaction in differentiated adipocytes. Activators of peroxisome proliferator-activated receptor-γ (PPARγ agonists) are therapeutically promising candidates against insulin resistance and hyperglycemia. Synthetic PPARγ agonists are known to effectively enhance insulin sensitivity, but these are also associated with adverse side-effects and rising cost of treatment. Therefore, natural PPARγ targeting ligands are desirable alternatives for the management of insulin resistance associated with type 2 diabetes. Phloretin (PT) and Phloridzin (PZ) are predominant apple phenolics, which are recognized for their various pharmacological functions. The present study assessed the potential of PT and PZ in enhancing insulin sensitivity and glucose uptake by inhibiting Cdk5 activation and corresponding PPARγ phosphorylation in differentiated 3T3L1 cells. In silico docking and subsequent validation using 3T3L1 cells revealed that PT and PZ not only block the ser273 site of PPARγ but also inhibit the activation of Cdk5 itself, thereby, indicating their potent PPARγ regulatory attributes. Corroborating this, application of PT and PZ significantly enhanced the accumulation of cellular triglycerides as well as expression of insulin-sensitizing genes in adipocytes ultimately resulting in improved glucose uptake. Taken together, the present study reports that PT and PZ inhibit Cdk5 activation, which could be directly influencing the apparent PPARγ inhibition at ser273, ultimately resulting in improved insulin sensitivity and glucose uptake.",0
"Vitellaria paradoxa nutshells from seven sub-Saharan countries as potential herbal medicines for treating diabetes based on chemical compositions, HPLC fingerprints and bioactivity evaluation. The aim of the study was to determine the feasibility of the Vitellaria paradoxa nutshell as a new medicinal resource for treating diabetes. A total of forty-one compounds were identified by HPLC-DAD-Q-TOF-MS and phytochemical methods in V. paradoxa nutshell methanol extract. Based on HPLC fingerprints, four characteristic constituents were quantified and the origin of twenty-eight V. paradoxa nutshells from seven sub-Saharan countries was compared, which were classified into three groups with chemometric method. Twenty-eight samples contained high total phenolic content, and exhibited moderate-higher antioxidant activity and strong α-glucosidase inhibitory activity. Furthermore, all fractions and isolated compounds were evaluated for their antioxidant and α-glucosidase inhibitory activities, and α-glucosidase inhibitory action mechanism of four characteristic constituents including protocatechuic acid, 3, 5, 7-trihydroxycoumarin, (2R, 3R)-(+)-taxifolin and quercetin was investigated via molecular docking method, which were all stabilized by hydrogen bonds with α-glucosidase. The study provided an effective approach to waste utilization of V. paradoxa nutshell, which would help to resolve waste environmental pollution and provide a basis for developing potential herbal resource for treating diabetes.",0
"Predicting overall survival of patients with hepatocellular carcinoma using a three-category method based on DNA methylation and machine learning. Hepatocellular carcinoma (HCC) is closely associated with abnormal DNA methylation. In this study, we analyzed 450K methylation chip data from 377 HCC samples and 50 adjacent normal samples in the TCGA database. We screened 47,099 differentially methylated sites using Cox regression as well as SVM-RFE and FW-SVM algorithms, and constructed a model using three risk categories to predict the overall survival based on 134 methylation sites. The model showed a 10-fold cross-validation score of 0.95 and satisfactory predictive power, and correctly classified 26 of 33 samples in testing set obtained by stratified sampling from high, intermediate and low risk groups.",0
"Computer aided quantification of intratumoral stroma yields an independent prognosticator in rectal cancer. Purpose: Tumor-stroma ratio (TSR) serves as an independent prognostic factor in colorectal cancer and other solid malignancies. The recent introduction of digital pathology in routine tissue diagnostics holds opportunities for automated TSR analysis. We investigated the potential of computer-aided quantification of intratumoral stroma in rectal cancer whole-slide images. Methods: Histological slides from 129 rectal adenocarcinoma patients were analyzed by two experts who selected a suitable stroma hot-spot and visually assessed TSR. A semi-automatic method based on deep learning was trained to segment all relevant tissue types in rectal cancer histology and subsequently applied to the hot-spots provided by the experts. Patients were assigned to a ‘stroma-high’ or ‘stroma-low’ group by both TSR methods (visual and automated). This allowed for prognostic comparison between the two methods in terms of disease-specific and disease-free survival times. Results: With stroma-low as baseline, automated TSR was found to be prognostic independent of age, gender, pT-stage, lymph node status, tumor grade, and whether adjuvant therapy was given, both for disease-specific survival (hazard ratio = 2.48 (95% confidence interval 1.29–4.78)) and for disease-free survival (hazard ratio = 2.05 (95% confidence interval 1.11–3.78)). Visually assessed TSR did not serve as an independent prognostic factor in multivariate analysis. Conclusions: This work shows that TSR is an independent prognosticator in rectal cancer when assessed automatically in user-provided stroma hot-spots. The deep learning-based technology presented here may be a significant aid to pathologists in routine diagnostics.",0
Author Correction: End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. An amendment to this paper has been published and can be accessed via a link at the top of the paper.,0
"Challenges in unsupervised clustering of single-cell RNA-seq data. Single-cell RNA sequencing (scRNA-seq) allows researchers to collect large catalogues detailing the transcriptomes of individual cells. Unsupervised clustering is of central importance for the analysis of these data, as it is used to identify putative cell types. However, there are many challenges involved. We discuss why clustering is a challenging problem from a computational point of view and what aspects of the data make it challenging. We also consider the difficulties related to the biological interpretation and annotation of the identified clusters.",0
"Automated organ-level classification of free-text pathology reports to support a radiology follow-up tracking engine. Purpose: To evaluate the performance of machine learning algorithms on organ-level classification of semistructured pathology reports, to incorporate surgical pathology monitoring into an automated imaging recommendation follow-up engine. Materials and Methods: This retrospective study included 2013 pathology reports from patients who underwent abdominal imaging at a large tertiary care center between 2012 and 2018. The reports were labeled by two annotators as relevant to four abdominal organs: Liver, kidneys, pancreas and/or adrenal glands, or none. Automated classification methods were compared: Simple string matching, random forests, extreme gradient boosting, support vector machines, and two neural network architectures—convolutional neural networks and long short-term memory networks. Three methods from the literature were used to provide interpretability and qualitative validation of the learned network features. Results: The neural networks performed well on the four-organ classification task (F1 score: 96.3% for convolutional neural network and 96.7% for long short-term memory vs 89.9% for support vector machines, 93.9% for extreme gradient boosting, 82.8% for random forests, and 75.2% for simple string matching). Multiple methods were used to visualize the decision-making process of the network, verifying that the networks used similar heuristics to a human annotator. The neural networks were able to classify, with a high degree of accuracy, pathology reports written in unseen formats, suggesting the networks had learned a generalizable encoding of the salient features. Conclusion: Neural network-based approaches achieve high performance on organ-level pathology report classification, suggesting that it is feasible to use them within automated tracking systems.",1
"Delirium detection in older acute medical inpatients: a multicentre prospective comparative diagnostic test accuracy study of the 4AT and the confusion assessment method. BACKGROUND: Delirium affects > 15% of hospitalised patients but is grossly underdetected, contributing to poor care. The 4 'A's Test (4AT, www.the4AT.com ) is a short delirium assessment tool designed for routine use without special training. The primary objective was to assess the accuracy of the 4AT for delirium detection. The secondary objective was to compare the 4AT with another commonly used delirium assessment tool, the Confusion Assessment Method (CAM). METHODS: This was a prospective diagnostic test accuracy study set in emergency departments or acute medical wards involving acute medical patients aged >/= 70. All those without acutely life-threatening illness or coma were eligible. Patients underwent (1) reference standard delirium assessment based on DSM-IV criteria and (2) were randomised to either the index test (4AT, scores 0-12; prespecified score of > 3 considered positive) or the comparator (CAM; scored positive or negative), in a random order, using computer-generated pseudo-random numbers, stratified by study site, with block allocation. Reference standard and 4AT or CAM assessments were performed by pairs of independent raters blinded to the results of the other assessment. RESULTS: Eight hundred forty-three individuals were randomised: 21 withdrew, 3 lost contact, 32 indeterminate diagnosis, 2 missing outcome, and 785 were included in the analysis. Mean age was 81.4 (SD 6.4) years. 12.1% (95/785) had delirium by reference standard assessment, 14.3% (56/392) by 4AT, and 4.7% (18/384) by CAM. The 4AT had an area under the receiver operating characteristic curve of 0.90 (95% CI 0.84-0.96). The 4AT had a sensitivity of 76% (95% CI 61-87%) and a specificity of 94% (95% CI 92-97%). The CAM had a sensitivity of 40% (95% CI 26-57%) and a specificity of 100% (95% CI 98-100%). CONCLUSIONS: The 4AT is a short, pragmatic tool which can help improving detection rates of delirium in routine clinical care. TRIAL REGISTRATION: International standard randomised controlled trial number (ISRCTN) 53388093 . Date applied 30/05/2014; date assigned 02/06/2014.",0
"Assessment of Convolutional Neural Networks for Automated Classification of Chest Radiographs. Purpose To assess the ability of convolutional neural networks (CNNs) to enable high-performance automated binary classification of chest radiographs. Materials and Methods In a retrospective study, 216 431 frontal chest radiographs obtained between 1998 and 2012 were procured, along with associated text reports and a prospective label from the attending radiologist. This data set was used to train CNNs to classify chest radiographs as normal or abnormal before evaluation on a held-out set of 533 images hand-labeled by expert radiologists. The effects of development set size, training set size, initialization strategy, and network architecture on end performance were assessed by using standard binary classification metrics; detailed error analysis, including visualization of CNN activations, was also performed. Results Average area under the receiver operating characteristic curve (AUC) was 0.96 for a CNN trained with 200 000 images. This AUC value was greater than that observed when the same model was trained with 2000 images (AUC = 0.84, P < .005) but was not significantly different from that observed when the model was trained with 20 000 images (AUC = 0.95, P > .05). Averaging the CNN output score with the binary prospective label yielded the best-performing classifier, with an AUC of 0.98 (P < .005). Analysis of specific radiographs revealed that the model was heavily influenced by clinically relevant spatial regions but did not reliably generalize beyond thoracic disease. Conclusion CNNs trained with a modestly sized collection of prospectively labeled chest radiographs achieved high diagnostic performance in the classification of chest radiographs as normal or abnormal; this function may be useful for automated prioritization of abnormal chest radiographs. (c) RSNA, 2018 Online supplemental material is available for this article. See also the editorial by van Ginneken in this issue.",1
"Evaluating a fully automated pulmonary nodule detection approach and its impact on radiologist performance. Purpose: To compare sensitivity in the detection of lung nodules between the deep learning (DL) model and radiologists using various patient population and scanning parameters and to assess whether the radiologists’ detection performance could be enhanced when using the DL model for assistance. Materials and Methods: A total of 12 754 thin-section chest CT scans from January 2012 to June 2017 were retrospectively collected for DL model training, validation, and testing. Pulmonary nodules from these scans were categorized into four types: Solid, subsolid, calcified, and pleural. The testing dataset was divided into three cohorts based on radiation dose, patient age, and CT manufacturer. Detection performance of the DL model was analyzed by using a free-response receiver operating characteristic curve. Sensitivities of the DL model and radiologists were compared by using exploratory data analysis. False-positive detection rates of the DL model were compared within each cohort. Detection performance of the same radiologist with and without the DL model were compared by using nodule-level sensitivity and patient-level localization receiver operating characteristic curves. Results: The DL model showed elevated overall sensitivity compared with manual review of pulmonary nodules. No significant dependence regarding radiation dose, patient age range, or CT manufacturer was observed. The sensitivity of the junior radiologist was significantly dependent on patient age. When radiologists used the DL model for assistance, their performance improved and reading time was reduced. Conclusion: DL shows promise to enhance the identification of pulmonary nodules and benefit nodule management.",1
"Frequency-splitting dynamic MRI reconstruction using multi-scale 3D convolutional sparse coding and automatic parameter selection. In this paper, we propose a novel image reconstruction algorithm using multi-scale 3D convolutional sparse coding and a spectral decomposition technique for highly undersampled dynamic Magnetic Resonance Imaging (MRI) data. The proposed method recovers high-frequency information using a shared 3D convolution-based dictionary built progressively during the reconstruction process in an unsupervised manner, while low-frequency information is recovered using a total variation-based energy minimization method that leverages temporal coherence in dynamic MRI. Additionally, the proposed 3D dictionary is built across three different scales to more efficiently adapt to various feature sizes, and elastic net regularization is employed to promote a better approximation to the sparse input data. We also propose an automatic parameter selection technique based on a genetic algorithm to find optimal parameters for our numerical solver which is a variant of the alternating direction method of multipliers (ADMM). We demonstrate the performance of our method by comparing it with state-of-the-art methods on 15 single-coil cardiac, 7 single-coil DCE, and a multi-coil brain MRI datasets at different sampling rates (12.5%, 25% and 50%). The results show that our method significantly outperforms the other state-of-the-art methods in reconstruction quality with a comparable running time and is resilient to noise.",0
"Heterogeneous information network based clustering for precision traditional Chinese medicine. BACKGROUND: Traditional Chinese medicine (TCM) is a highly important complement to modern medicine and is widely practiced in China and in many other countries. The work of Chinese medicine is subject to the two factors of the inheritance and development of clinical experience of famous Chinese medicine practitioners and the difficulty in improving the service capacity of basic Chinese medicine practitioners. Heterogeneous information networks (HINs) are a kind of graphical model for integrating and modeling real-world information. Through HINs, we can integrate and model the large-scale heterogeneous TCM data into structured graph data and use this as a basis for analysis. METHODS: Mining categorizations from TCM data is an important task for precision medicine. In this paper, we propose a novel structured learning model to solve the problem of formula regularity, a pivotal task in prescription optimization. We integrate clustering with ranking in a heterogeneous information network. RESULTS: The results from experiments on the Pharmacopoeia of the People's Republic of China (ChP) demonstrate the effectiveness and accuracy of the proposed model for discovering useful categorizations of formulas. CONCLUSIONS: We use heterogeneous information networks to model TCM data and propose a TCM-HIN. Combining the heterogeneous graph with the probability graph, we proposed the TCM-Clus algorithm, which combines clustering with ranking and classifies traditional Chinese medicine prescriptions. The results of the categorizations can help Chinese medicine practitioners to make clinical decision.",0
"NNAlign-MA; MHC peptidome deconvolution for accurate MHC binding motif characterization and improved t-cell epitope predictions. The set of peptides presented on a cell's surface by MHC molecules is known as the immunopeptidome. Current mass spectrometry technologies allow for identification of large peptidomes, and studies have proven these data to be a rich source of information for learning the rules of MHC-mediated antigen presentation. Immunopeptidomes are usually poly-specific, containing multiple sequence motifs matching the MHC molecules expressed in the system under investigation. Motif deconvolution -the process of associating each ligand to its presenting MHC molecule(s)- is therefore a critical and challenging step in the analysis of MS-eluted MHC ligand data. Here, we describe NNAlign-MA, a computational method designed to address this challenge and fully benefit from large, poly-specific data sets of MS-eluted ligands. NNAlign-MA simultaneously performs the tasks of (1) clustering peptides into individual specificities; (2) automatic annotation of each cluster to an MHC molecule; and (3) training of a prediction model covering all MHCs present in the training set. NNAlign-MA was benchmarked on large and diverse data sets, covering class I and class II data. In all cases, the method was demonstrated to outperform state-ofthe- art methods, effectively expanding the coverage of alleles for which accurate predictions can be made, resulting in improved identification of both eluted ligands and T-cell epitopes. Given its high flexibility and ease of use, we expect NNAlign-MA to serve as an effective tool to increase our understanding of the rules of MHC antigen presentation and guide the development of novel T-cellbased therapeutics.",0
"Caries Detection with Near-Infrared Transillumination Using Deep Learning. Dental caries is the most prevalent chronic condition worldwide. Early detection can significantly improve treatment outcomes and reduce the need for invasive procedures. Recently, near-infrared transillumination (TI) imaging has been shown to be effective for the detection of early stage lesions. In this work, we present a deep learning model for the automated detection and localization of dental lesions in TI images. Our method is based on a convolutional neural network (CNN) trained on a semantic segmentation task. We use various strategies to mitigate issues related to training data scarcity, class imbalance, and overfitting. With only 185 training samples, our model achieved an overall mean intersection-over-union (IOU) score of 72.7% on a 5-class segmentation task and specifically an IOU score of 49.5% and 49.0% for proximal and occlusal carious lesions, respectively. In addition, we constructed a simplified task, in which regions of interest were evaluated for the binary presence or absence of carious lesions. For this task, our model achieved an area under the receiver operating characteristic curve of 83.6% and 85.6% for occlusal and proximal lesions, respectively. Our work demonstrates that a deep learning approach for the analysis of dental images holds promise for increasing the speed and accuracy of caries detection, supporting the diagnoses of dental practitioners, and improving patient outcomes.",0
"Attention-Based Deep Neural Networks for Detection of Cancerous and Precancerous Esophagus Tissue on Histopathological Slides. Importance: Deep learning-based methods, such as the sliding window approach for cropped-image classification and heuristic aggregation for whole-slide inference, for analyzing histological patterns in high-resolution microscopy images have shown promising results. These approaches, however, require a laborious annotation process and are fragmented. Objective: To evaluate a novel deep learning method that uses tissue-level annotations for high-resolution histological image analysis for Barrett esophagus (BE) and esophageal adenocarcinoma detection. Design, Setting, and Participants: This diagnostic study collected deidentified high-resolution histological images (N = 379) for training a new model composed of a convolutional neural network and a grid-based attention network. Histological images of patients who underwent endoscopic esophagus and gastroesophageal junction mucosal biopsy between January 1, 2016, and December 31, 2018, at Dartmouth-Hitchcock Medical Center (Lebanon, New Hampshire) were collected. Main Outcomes and Measures: The model was evaluated on an independent testing set of 123 histological images with 4 classes: normal, BE-no-dysplasia, BE-with-dysplasia, and adenocarcinoma. Performance of this model was measured and compared with that of the current state-of-the-art sliding window approach using the following standard machine learning metrics: accuracy, recall, precision, and F1 score. Results: Of the independent testing set of 123 histological images, 30 (24.4%) were in the BE-no-dysplasia class, 14 (11.4%) in the BE-with-dysplasia class, 21 (17.1%) in the adenocarcinoma class, and 58 (47.2%) in the normal class. Classification accuracies of the proposed model were 0.85 (95% CI, 0.81-0.90) for the BE-no-dysplasia class, 0.89 (95% CI, 0.84-0.92) for the BE-with-dysplasia class, and 0.88 (95% CI, 0.84-0.92) for the adenocarcinoma class. The proposed model achieved a mean accuracy of 0.83 (95% CI, 0.80-0.86) and marginally outperformed the sliding window approach on the same testing set. The F1 scores of the attention-based model were at least 8% higher for each class compared with the sliding window approach: 0.68 (95% CI, 0.61-0.75) vs 0.61 (95% CI, 0.53-0.68) for the normal class, 0.72 (95% CI, 0.63-0.80) vs 0.58 (95% CI, 0.45-0.69) for the BE-no-dysplasia class, 0.30 (95% CI, 0.11-0.48) vs 0.22 (95% CI, 0.11-0.33) for the BE-with-dysplasia class, and 0.67 (95% CI, 0.54-0.77) vs 0.58 (95% CI, 0.44-0.70) for the adenocarcinoma class. However, this outperformance was not statistically significant. Conclusions and Relevance: Results of this study suggest that the proposed attention-based deep neural network framework for BE and esophageal adenocarcinoma detection is important because it is based solely on tissue-level annotations, unlike existing methods that are based on regions of interest. This new model is expected to open avenues for applying deep learning to digital pathology.",1
"Pharmacoinformatics and molecular docking reveal potential drug candidates against Schizophrenia to target TAAR6. Schizophrenia (SZ) is a complex disabling disorder that leads to the mental disability and afflicts 1% of the world's total population and placed in top ten medical disorders. In current work, bioinformatics analyses were carried out on Trace amine (TA)-associated receptor 6 (TAAR6) to recognize the potential drugs and compounds against SZ. Comparative modeling and threading-based approaches were utilized for the structure prediction of TAAR6. Fifty-nine predicted structures were evaluated by various model assessment techniques and final model having only eight amino acids in the outlier region and 98.5% overall quality factor was chosen for further pharmacoinformatics and molecular docking analyses. From an extensive literature review, 11 Food and Drug Administration (FDA) approved drugs were analyzed by computational techniques and Aripiprazole was found as the most effective drug against SZ by targeting TAAR6. Here, we report five novel molecules which exhibited the highest binding affinity, effective drug properties, and interestingly, observed better results than the approved selected drugs against SZ by targeting TAAR6. The docking analyses revealed that Arg-92, Trp-98, Gln-191, Thr-192, Ala-290, Cys-291, Tyr-293, and Glu-294 residues were observed as critical interacting residues in receptor-ligand interactions. Absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties, Lipinski rule of five, highest binding affinity coupled with virtual screening (VS), and pharmacophore modeling approach illustrated that aripiprazole (−8.6 kcal/mol) and TAAR6_0094 (−9.3 kcal/mol) are potential inhibitors for targeting TAAR6. It is suggested that schizophrenic patients have to use Aripiprazole for the medication of SZ by targeting TAAR6 and develop effective therapies by utilizing scrutinized novel compound.",0
"Whole-Virome Analysis Sheds Light on Viral Dark Matter in Inflammatory Bowel Disease. The human gut virome is thought to significantly impact the microbiome and human health. However, most virome analyses have been performed on a limited fraction of known viruses. Using whole-virome analysis on a published keystone inflammatory bowel disease (IBD) cohort and an in-house ulcerative colitis dataset, we shed light on the composition of the human gut virome in IBD beyond this identifiable minority. We observe IBD-specific changes to the virome and increased numbers of temperate phage sequences in individuals with Crohn's disease. Unlike prior database-dependent methods, no changes in viral richness were observed. Among IBD subjects, the changes in virome composition reflected alterations in bacterial composition. Furthermore, incorporating both bacteriome and virome composition offered greater classification power between health and disease. This approach to analyzing whole virome across cohorts highlights significant IBD signals, which may be crucial for developing future biomarkers and therapeutics.",0
"Transcriptomics-Based Screening Identifies Pharmacological Inhibition of Hsp90 as a Means to Defer Aging. Aging strongly influences human morbidity and mortality. Thus, aging-preventive compounds could greatly improve our health and lifespan. Here we screened for such compounds, known as geroprotectors, employing the power of transcriptomics to predict biological age. Using age-stratified human tissue transcriptomes and machine learning, we generated age classifiers and applied these to transcriptomic changes induced by 1,309 different compounds in human cells, ranking these compounds by their ability to induce a “youthful” transcriptional state. Testing the top candidates in C. elegans, we identified two Hsp90 inhibitors, monorden and tanespimycin, which extended the animals’ lifespan and improved their health. Hsp90 inhibition induces expression of heat shock proteins known to improve protein homeostasis. Consistently, monorden treatment improved the survival of C. elegans under proteotoxic stress, and its benefits depended on the cytosolic unfolded protein response-inducing transcription factor HSF-1. Taken together, our method represents an innovative geroprotector screening approach and was able to identify a class that acts by improving protein homeostasis.",0
"Development and Evaluation of a Machine Learning Model for the Early Identification of Patients at Risk for Sepsis. STUDY OBJECTIVE: The Third International Consensus Definitions (Sepsis-3) Task Force recommended the use of the quick Sequential [Sepsis-related] Organ Failure Assessment (qSOFA) score to screen patients for sepsis outside of the ICU. However, subsequent studies raise concerns about the sensitivity of qSOFA as a screening tool. We aim to use machine learning to develop a new sepsis screening tool, the Risk of Sepsis (RoS) score, and compare it with a slate of benchmark sepsis-screening tools, including the Systemic Inflammatory Response Syndrome, Sequential Organ Failure Assessment (SOFA), qSOFA, Modified Early Warning Score, and National Early Warning Score. METHODS: We used retrospective electronic health record data from adult patients who presented to 49 urban community hospital emergency departments during a 22-month period (N=2,759,529). We used the Rhee clinical surveillance criteria as our standard definition of sepsis and as the primary target for developing our model. The data were randomly split into training and test cohorts to derive and then evaluate the model. A feature selection process was carried out in 3 stages: first, we reviewed existing models for sepsis screening; second, we consulted with local subject matter experts; and third, we used a supervised machine learning called gradient boosting. Key metrics of performance included alert rate, area under the receiver operating characteristic curve, sensitivity, specificity, and precision. Performance was assessed at 1, 3, 6, 12, and 24 hours after an index time. RESULTS: The RoS score was the most discriminant screening tool at all time thresholds (area under the receiver operating characteristic curve 0.93 to 0.97). Compared with the next most discriminant benchmark (Sequential Organ Failure Assessment), RoS was significantly more sensitive (67.7% versus 49.2% at 1 hour and 84.6% versus 80.4% at 24 hours) and precise (27.6% versus 12.2% at 1 hour and 28.8% versus 11.4% at 24 hours). The sensitivity of qSOFA was relatively low (3.7% at 1 hour and 23.5% at 24 hours). CONCLUSION: In this retrospective study, RoS was more timely and discriminant than benchmark screening tools, including those recommend by the Sepsis-3 Task Force. Further study is needed to validate the RoS score at independent sites.",1
"Clinical Implications of Transcriptomic Changes After Neoadjuvant Chemotherapy in Patients with Triple-Negative Breast Cancer. Background: Pathological response to neoadjuvant chemotherapy (NAC) is critical in prognosis and selection of systemic treatments for patients with triple-negative breast cancer (TNBC). The aim of this study is to identify gene expression-based markers to predict response to NAC. Patients and Methods: A survey of 43 publicly available gene expression datasets was performed. We identified a cohort of TNBC patients treated with NAC (n = 708). Gene expression data from different studies were renormalized, and the differences between pretreatment (pre-NAC), on-treatment (post-C1), and surgical (Sx) specimens were evaluated. Euclidean statistical distances were calculated to estimate changes in gene expression patterns induced by NAC. Hierarchical clustering and pathway enrichment analyses were used to characterize relationships between differentially expressed genes and affected gene pathways. Machine learning was employed to refine a gene expression signature with the potential to predict response to NAC. Results: Forty nine genes consistently affected by NAC were involved in enhanced regulation of wound response, chemokine release, cell division, and decreased programmed cell death in residual invasive disease. The statistical distances between pre-NAC and post-C1 significantly predicted pathological complete response [area under the curve (AUC) = 0.75; p = 0.003; 95% confidence interval (CI) 0.58–0.92]. Finally, the expression of CCND1, a cyclin that forms complexes with CDK4/6 to promote the cell cycle, was the most informative feature in pre-NAC biopsies to predict response to NAC. Conclusions: The results of this study reveal significant transcriptomic changes induced by NAC and suggest that chemotherapy-induced gene expression changes observed early in therapy may be good predictors of response to NAC.",1
"Radiomics model to predict early progression of nonmetastatic nasopharyngeal carcinoma after intensity modulation radiation therapy: A multicenter study. Purpose: To examine the prognostic value of a machine learning model trained with pretreatment MRI radiomic features in the assessment of patients with nonmetastatic nasopharyngeal carcinoma (NPC) who are at risk for 3-year disease progression after intensitymodulated radiation therapy and to explain the radiomics features in the model. Materials and Methods: A total of 277 patients with nonmetastatic NPC admitted between March 2008 and December 2014 at two imaging centers were retrospectively reviewed. Patients were allocated to a discovery or validation cohort based on where they underwent MRI (discovery cohort, n = 217; validation cohort, n = 60). A total of 525 radiomics features extracted from contrast material– enhanced T1- or T2-weighted MRI studies and five clinical features were subjected to radiomic machine learning modeling to predict 3-year disease progression. Feature selection was performed by analyzing robustness to resampling, reproducibility between observers, and redundancy. Features for the final model were selected with Kaplan-Meier analysis and the log-rank test. A support vector machine was used as the classifier for the model. To interpret the pattern learned from the model, Shapley additive explanations (SHAP) was applied. Results: The final model yielded an area under the receiver operating characteristic curve of 0.80 in both the discovery (95% bootstrap confidence interval: 0.80, 0.81) and independent validation (95% bootstrap confidence interval: 0.73, 0.89) cohorts. Analysis with SHAP revealed that tumor shape sphericity, first-order mean absolute deviation, T stage, and overall stage were important factors in 3-year disease progression. Conclusion: These results add to the growing evidence of the role of radiomics in the assessment of NPC. By using explanatory techniques, such as SHAP, the complex interaction of features learned by the model may be understood.",1
"Algorithmic approach to management of acute ocular chemical injuries-I's and E's of Management. Ocular chemical injuries are associated with significant morbidity leading to vision loss and ocular surface damage. Appropriate management and intervention in the acute stage dictates the final outcome as well as the prognosis for visual rehabilitative procedures in chronic stage. Classifying the parameters to be addressed in the acute stage and providing an algorithmic approach for managing the alterations in each of them will facilitate the primary goal of ensuring epithelialization of the ocular surface. Broadly categorizing them into the I's and E's (inciting agent, inflammation, epithelial defect, ischemia, exposure and intraocular pressure) and treating each will directly and/or indirectly influence re-epithelialization of the ocular surface, which in turn will reduce or prevent the various detrimental sequelae of ocular chemical injury.",0
Author Correction: Do no harm: a roadmap for responsible machine learning for health care. An amendment to this paper has been published and can be accessed via a link at the top of the paper.,0
"Clustering analysis of microRNA and mRNA expression data from TCGA using maximum edge-weighted matching algorithms. Background: microRNA (miRNA) is a short RNA (∼ 22 nt) that regulates gene expression at the posttranscriptional level. Aberration of miRNA expressions could affect their targeting mRNAs involved in cancer-related signaling pathways. We conduct clustering analysis of miRNA and mRNA using expression data from the Cancer Genome Atlas (TCGA). We combine the Hungarian algorithm and blossom algorithm in graph theory. Data analysis is done using programming language R and Python. Methods: We first quantify edge-weights of the miRNA-mRNA pairs by combining their expression correlation coefficient in tumor (T-CC) and correlation coefficient in normal (N-CC). We thereby introduce a bipartite graph partition procedure to identify cluster candidates. Specifically, we propose six weight formulas to quantify the change of miRNA-mRNA expression T-CC relative to N-CC, and apply the traditional hierarchical clustering to subjectively evaluate the different weight formulas of miRNA-mRNA pairs. Among these six different weight formulas, we choose the optimal one, which we define as the integrated mean value weights, to represent the connections between miRNA and mRNAs. Then the Hungarian algorithm and the blossom algorithm are employed on the miRNA-mRNA bipartite graph to passively determine the clusters. The combination of Hungarian and the blossom algorithms is dubbed maximum weighted merger method (MWMM). Results: MWMM identifies clusters of different sizes that meet the mathematical criterion that internal connections inside a cluster are relatively denser than external connections outside the cluster and biological criterion that the intra-cluster Gene Ontology (GO) term similarities are larger than the inter-cluster GO term similarities. MWMM is developed using breast invasive carcinoma (BRCA) as training data set, but can also applies to other cancer type data sets. MWMM shows advantage in GO term similarity in most cancer types, when compared to other algorithms. Conclusions: miRNAs and mRNAs that are likely to be affected by common underlying causal factors in cancer can be clustered by MWMM approach and potentially be used as candidate biomarkers for different cancer types and provide clues for targets of precision medicine in cancer treatment.",0
"Evidence that recurrent circuits are critical to the ventral stream's execution of core object recognition behavior. Non-recurrent deep convolutional neural networks (CNNs) are currently the best at modeling core object recognition, a behavior that is supported by the densely recurrent primate ventral stream, culminating in the inferior temporal (IT) cortex. If recurrence is critical to this behavior, then primates should outperform feedforward-only deep CNNs for images that require additional recurrent processing beyond the feedforward IT response. Here we first used behavioral methods to discover hundreds of these 'challenge' images. Second, using large-scale electrophysiology, we observed that behaviorally sufficient object identity solutions emerged ~30 ms later in the IT cortex for challenge images compared with primate performance-matched 'control' images. Third, these behaviorally critical late-phase IT response patterns were poorly predicted by feedforward deep CNN activations. Notably, very-deep CNNs and shallower recurrent CNNs better predicted these late IT responses, suggesting that there is a functional equivalence between additional nonlinear transformations and recurrence. Beyond arguing that recurrent circuits are critical for rapid object identification, our results provide strong constraints for future recurrent model development.",1
"Privacy-protecting estimation of adjusted risk ratios using modified Poisson regression in multi-center studies. BACKGROUND: Multi-center studies can generate robust and generalizable evidence, but privacy considerations and legal restrictions often make it challenging or impossible to pool individual-level data across data-contributing sites. With binary outcomes, privacy-protecting distributed algorithms to conduct logistic regression analyses have been developed. However, the risk ratio often provides a more transparent interpretation of the exposure-outcome association than the odds ratio. Modified Poisson regression has been proposed to directly estimate adjusted risk ratios and produce confidence intervals with the correct nominal coverage when individual-level data are available. There are currently no distributed regression algorithms to estimate adjusted risk ratios while avoiding pooling of individual-level data in multi-center studies. METHODS: By leveraging the Newton-Raphson procedure, we adapted the modified Poisson regression method to estimate multivariable-adjusted risk ratios using only summary-level information in multi-center studies. We developed and tested the proposed method using both simulated and real-world data examples. We compared its results with the results from the corresponding pooled individual-level data analysis. RESULTS: Our proposed method produced the same adjusted risk ratio estimates and standard errors as the corresponding pooled individual-level data analysis without pooling individual-level data across data-contributing sites. CONCLUSIONS: We developed and validated a distributed modified Poisson regression algorithm for valid and privacy-protecting estimation of adjusted risk ratios and confidence intervals in multi-center studies. This method allows computation of a more interpretable measure of association for binary outcomes, along with valid construction of confidence intervals, without sharing of individual-level data.",0
"A White-Box Machine Learning Approach for Revealing Antibiotic Mechanisms of Action. Current machine learning techniques enable robust association of biological signals with measured phenotypes, but these approaches are incapable of identifying causal relationships. Here, we develop an integrated ""white-box"" biochemical screening, network modeling, and machine learning approach for revealing causal mechanisms and apply this approach to understanding antibiotic efficacy. We counter-screen diverse metabolites against bactericidal antibiotics in Escherichia coli and simulate their corresponding metabolic states using a genome-scale metabolic network model. Regression of the measured screening data on model simulations reveals that purine biosynthesis participates in antibiotic lethality, which we validate experimentally. We show that antibiotic-induced adenine limitation increases ATP demand, which elevates central carbon metabolism activity and oxygen consumption, enhancing the killing effects of antibiotics. This work demonstrates how prospective network modeling can couple with machine learning to identify complex causal mechanisms underlying drug efficacy.",0
"Quercetin modifies 5′CpG promoter methylation and reactivates various tumor suppressor genes by modulating epigenetic marks in human cervical cancer cells. The central role of epigenomic alterations in carcinogenesis has been widely acknowledged, particularly the impact of DNA methylation on gene expression across all stages of carcinogenesis is considered vital for both diagnostic and therapeutic strategies. Dietary phytochemicals hold great promise as safe anticancer agents and effective epigenetic modulators. This study was designed to investigate the potential of a phytochemical, quercetin as a modulator of the epigenetic pathways for anticancer strategies. Biochemical activity of DNA methyltransferases (DNMTs), histone deacetylases (HDACs), histone methyltransferases (HMTs), and global genomic DNA methylation was quantitated by an enzyme-linked immunosorbent assay based assay in quercetin-treated HeLa cells. Molecular docking studies were performed to predict the interaction of quercetin with DNMTs and HDACs. Quantitative methylation array was used to assess quercetin-mediated alterations in the promoter methylation of selected tumor suppressor genes (TSGs). Quercetin induced modulation of chromatin modifiers including DNMTs, HDACs, histone acetyltransferases (HAT) and HMTs, and TSGs were assessed by quantitative reverse transcription PCR (qRT-PCR). It was found that quercetin modulates the expression of various chromatin modifiers and decreases the activity of DNMTs, HDACs, and HMTs in a dose-dependent manner. Molecular docking results suggest that quercetin could function as a competitive inhibitor by interacting with residues in the catalytic cavity of several DNMTs and HDACs. Quercetin downregulated global DNA methylation levels in a dose- and time-dependent manner. The tested TSGs showed steep dose-dependent decline in promoter methylation with the restoration of their expression. Our study provides an understanding of the quercetin's mechanism of action and will aid in its development as a candidate for epigenetic-based anticancer therapy.",0
"Pre and post-hoc diagnosis and interpretation of malignancy from breast DCE-MRI. We propose a new method for breast cancer screening from DCE-MRI based on a post-hoc approach that is trained using weakly annotated data (i.e., labels are available only at the image level without any lesion delineation). Our proposed post-hoc method automatically diagnosis the whole volume and, for positive cases, it localizes the malignant lesions that led to such diagnosis. Conversely, traditional approaches follow a pre-hoc approach that initially localises suspicious areas that are subsequently classified to establish the breast malignancy – this approach is trained using strongly annotated data (i.e., it needs a delineation and classification of all lesions in an image). We also aim to establish the advantages and disadvantages of both approaches when applied to breast screening from DCE-MRI. Relying on experiments on a breast DCE-MRI dataset that contains scans of 117 patients, our results show that the post-hoc method is more accurate for diagnosing the whole volume per patient, achieving an AUC of 0.91, while the pre-hoc method achieves an AUC of 0.81. However, the performance for localising the malignant lesions remains challenging for the post-hoc method due to the weakly labelled dataset employed during training.",1
"An integrative bioinformatics pipeline to demonstrate the alteration of the interaction between the ALDH2*2 allele with NAD+ and Disulfiram. Alcohol use disorder (AUD) is a multifactorial psychiatric behavior disorder. Disulfiram is the first approved drug by the Food and Drug Administration for alcohol-dependent patients, which targets the ALDH2 enzyme. Several genes are known to be involved in alcohol metabolism; mutations in any of these genes are known to be associated with AUD. The E504K mutation in the ALDH2 of the precursor protein or the E487K of the mature protein (E504K/E487K; ALDH2*2 allele) is carried by approximately 8% of the world population. In this study, we aimed to test the known inactive allele ALDH2*2, to validate the use of our extensive computational pipeline (in silico tools, molecular modeling, and molecular docking) for testing the interaction between the ALDH2*2 allele, NAD+, and Disulfiram. In silico predictions showed that the E504K variant of ALDH2 to be pathogenic and destabilizing with the maximum number of prediction in silico tools. Consequently, we studied the effect of this mutation mainly on the interaction between NAD+-E504K and Disulfiram-E504K complexes using molecular docking technique, and molecular dynamics (MD) analysis. From the molecular docking analysis with NAD+, we observed that the interaction affinity of the NAD+ decreases with the impact of E504K variant. On the other hand, the drug Disulfiram showed similar interaction in both the native and mutant ALDH2 proteins. Further, the comprehensive MD analysis predicted that the E504K destabilizes the protein and influences the NAD+ and Disulfiram interactions. Our findings reveal that the interaction of NAD+ to the protein is disturbed by the E504K/E487K variant whereas the drug Disulfiram has a similar effect as both native ALDH2 and ALDH2 bearing E504K/E487K variant. This study provides a platform to understand the effect of E504K/E487K on the molecular interaction with NAD+ and Disulfiram.",0
"Chronic Obstructive Pulmonary Disease: Thoracic CT Texture Analysis and Machine Learning to Predict Pulmonary Ventilation. Background Fixed airflow limitation and ventilation heterogeneity are common in chronic obstructive pulmonary disease (COPD). Conventional noncontrast CT provides airway and parenchymal measurements but cannot be used to directly determine lung function. Purpose To develop, train, and test a CT texture analysis and machine-learning algorithm to predict lung ventilation heterogeneity in participants with COPD. Materials and Methods In this prospective study (ClinicalTrials.gov: NCT02723474; conducted from January 2010 to February 2017), participants were randomized to optimization (n = 1), training (n = 67), and testing (n = 27) data sets. Hyperpolarized (HP) helium 3 ((3)He) MRI ventilation maps were co-registered with thoracic CT to provide ground truth labels, and 87 quantitative imaging features were extracted and normalized to lung averages to generate 174 features. The volume-of-interest dimension and the training data sampling method were optimized to maximize the area under the receiver operating characteristic curve (AUC). Forward feature selection was performed to reduce the number of features; logistic regression, linear support vector machine, and quadratic support vector machine classifiers were trained through fivefold cross validation. The highest-performing classification model was applied to the test data set. Pearson coefficients were used to determine the relationships between the model, MRI, and pulmonary function measurements. Results The quadratic support vector machine performed best in training and was applied to the test data set. Model-predicted ventilation maps had an accuracy of 88% (95% confidence interval [CI]: 88%, 88%) and an AUC of 0.82 (95% CI: 0.82, 0.83) when the HP (3)He MRI ventilation maps were used as the reference standard. Model-predicted ventilation defect percentage (VDP) was correlated with VDP at HP (3)He MRI (r = 0.90, P < .001). Both model-predicted and HP (3)He MRI VDP were correlated with forced expiratory volume in 1 second (FEV1) (model: r = -0.65, P < .001; MRI: r = -0.70, P < .001), ratio of FEV1 to forced vital capacity (model: r = -0.73, P < .001; MRI: r = -0.75, P < .001), diffusing capacity (model: r = -0.69, P < .001; MRI: r = -0.65, P < .001), and quality-of-life score (model: r = 0.59, P = .001; MRI: r = 0.65, P < .001). Conclusion Model-predicted ventilation maps generated by using CT textures and machine learning were correlated with MRI ventilation maps (r = 0.90, P < .001). (c) RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Fain in this issue.",1
"Precision de novo peptide sequencing using mirror proteases of ac-lysarginase and trypsin for large-scale proteomics. De novo peptide sequencing for large-scale proteomics remains challenging because of the lack of full coverage of ion series in tandem mass spectra. We developed a mirror protease of trypsin, acetylated LysargiNase (Ac-LysargiNase), with superior activity and stability. The mirror spectrum pairs derived from the Ac-LysargiNase and trypsin treated samples can generate full b and y ion series, which provide mutual complementarity of each other, and allow us to develop a novel algorithm, pNovoM, for de novo sequencing. Using pNovoM to sequence peptides of purified proteins, the accuracy of the sequence was close to 100%. More importantly, from a large-scale yeast proteome sample digested with trypsin and Ac-LysargiNase individually, 48% of all tandem mass spectra formed mirror spectrum pairs, 97% of which contained full coverage of ion series, resulting in precision de novo sequencing of full-length peptides by pNovoM. This enabled pNovoM to successfully sequence 21,249 peptides from 3,753 proteins and interpreted 44-152% more spectra than pNovo and PEAKS at a 5% FDR at the spectrum level. Moreover, the mirror protease strategy had an obvious advantage in sequencing long peptides. We believe that the combination of mirror protease strategy and pNovoM will be an effective approach for precision de novo sequencing on both single proteins and proteome samples.",0
"S-9. PEV-induced hp1a propagation does not correlate with the expression of the genes located near the euheterochromatin breakpoint. Position effect variegation (PEV) is a disturbance of the expression of euchromatic genes transferred into the heterochromatin vicinity caused by the changes in its chromatin organization (heterochromatinization). Little is known about the molecular mechanisms of interactions between gene transcription machinery and the large-scale chromatin structures like heterochromatin, and the chromosomal rearrangement In(2)A4 provide a convenient model to study PEV. The aim of our work was to track the changes in chromatin organization of euchromatin in the vicinity of In(2)A4 new eu-heterochromatin borders and analyze the possible correlations between chromatin changes and the functional organization of the affected regions. Methods: We’ve performed analysis of genome-wide HP1a distribution in In(2)A4/ In(2)A4 homozygous flies and in the control wild type flies by ChIP-Seq with qPCR verification and bioinformatic analysis of the received data. Results: In(2)A4 rearrangement is an inversion in the left arm of chromosome 2 with a breakpoint in the satellite block in the 2L pericentromeric heterochromatin. This results in two new eu-heterochromatin boundaries – one near the main block of 2L heterochromatin and another one near the separated small heterochromatin block. ChIP-Seq data on HP1a distribution shows an enrichment for HP1a in the euchromatin regions near the new eu-heterochromatin borders. HP1a spreads up to 200 kb from the main pericentromeric block and up to 50 kb from the small block. No apparent correlation between HP1a enrichment and genes expression levels (studied in [1]) or gene amenability to PEV were detected. The unusual enrichment in HP1a immediately near the small separated heterochromatin block was observed. Conclusions: In In(2)A4, HP1a propagates at a distance of up to 200 kb from the breakpoints and there is no apparent correlation between HP1a enrichment and expression levels of genes in the affected region as well as no correlation between HP1a binding and sensitivity of any particular gene to heterochroma-tin repression. It seems that HP1a propagation occurs independently of local chromatin organization defined by regulatory elements.",0
"Virulence of Pseudomonas aeruginosa exposed to carvacrol: alterations of the Quorum sensing at enzymatic and gene levels. The main goal of this study was to evaluate the inhibition of Pseudomonas aeruginosa virulence factors and Quorum Sensing during exposure to carvacrol. P. aeruginosa (ATCC 10154) was exposed to carvacrol determining changes in biofilm development, motility, acyl-homoserine lactones (AHL) synthesis and relative expression of lasI/lasR. Docking analysis was used to determinate interactions between carvacrol with LasI and LasR proteins. P. aeruginosa produced 60% lower AHLs when exposed to carvacrol (1.9 mM) compared to control, without affecting cellular viability, indicating a reduction on the LasI synthase activity. AHL-C12, C6, and C4 were detected and related to biofilm development, motility, and pyocyanin production, respectively. The presence of carvacrol reduced the expression of lasR, without affecting lasI gen. Moreover, computational docking showed interactions of carvacrol with amino acids in the active site pocket of LasI (−5.6 kcal mol−1) and within the binding pocket of LasR (−6.7 kcal mol−1) of P. aeruginosa. These results demonstrated that virulence of P. aeruginosa was reduced by carvacrol, by inhibiting LasI activity with the concomitant reduction on the expression of lasR, biofilm and swarming motility. This study provides relevant information about the effect of carvacrol against quorum sensing to inhibit virulence factors of P. aeruginosa at enzymatic and gene levels. These findings can contribute to the development of natural anti-QS products, which can affect pathogenesis.",0
"MultiPLIER: A Transfer Learning Framework for Transcriptomics Reveals Systemic Features of Rare Disease. Most gene expression datasets generated by individual researchers are too small to fully benefit from unsupervised machine-learning methods. In the case of rare diseases, there may be too few cases available, even when multiple studies are combined. To address this challenge, we utilize transfer learning to extract coordinated expression patterns and use learned patterns to analyze small rare disease datasets. We trained a pathway-level information extractor (PLIER) model on a large public data compendium comprising multiple experiments, tissues, and biological conditions and then transferred the model to small datasets in an approach we call MultiPLIER. Models constructed from the public data compendium included features that aligned well to known biological factors and were more comprehensive than those constructed from individual datasets or conditions. When transferred to rare disease datasets, the models describe biological processes related to disease severity more effectively than models trained only on a given dataset. Building models of gene expression with machine-learning techniques can reveal key regulatory processes that go awry in disease. However, certain datasets are too small to support detailed models. We find that models can be trained on a large, public compendia of gene expression data and then transferred to datasets of interest, including rare disease datasets, to reveal consistent disease-associated patterns across datasets and tissues.",0
"Organ Changes Associated with Provider-Assessed Responses in Patients with Chronic Graft-versus-Host Disease. Assessments of overall improvement and worsening of chronic graft-versus-host disease (GVHD) manifestations by the algorithm recommended by National Institutes of Health (NIH) response criteria do not align closely with those reported by providers, particularly when patients have mixed responses with improvement in some manifestations but worsening in others. To elucidate the changes that influence provider assessment of response, we used logistic regression to generate an overall change index based on specific manifestations of chronic GVHD measured at baseline and 6 months later. We hypothesized that this overall change index would correlate strongly with overall improvement as determined by providers. The analysis included 488 patients from 2 prospective observational studies who were randomly assigned in a 3:2 ratio to discovery and replication cohorts. Changes in bilirubin and scores of the lower gastrointestinal tract, mouth, joint/fascia, lung, and skin were correlated with provider-assessed improvement, suggesting that the main NIH response measures capture relevant information. Conversely, changes in the eye, esophagus, and upper gastrointestinal tract did not correlate with provider-assessed response, suggesting that these scales could be modified or dropped from the NIH response assessment. The area under the receiver operator characteristic curve in the replication cohort was 0.72, indicating that the scoring algorithm for overall change based on NIH response measures is not well calibrated with provider-assessed response.",0
"Robust CTCF-Based Chromatin Architecture Underpins Epigenetic Changes in the Heart Failure Stress-Gene Response. BACKGROUND: The human genome folds in 3 dimensions to form thousands of chromatin loops inside the nucleus, encasing genes and cis-regulatory elements for accurate gene expression control. Physical tethers of loops are anchored by the DNA-binding protein CTCF and the cohesin ring complex. Because heart failure is characterized by hallmark gene expression changes, it was recently reported that substantial CTCF-related chromatin reorganization underpins the myocardial stress-gene response, paralleled by chromatin domain boundary changes observed in CTCF knockout. METHODS: We undertook an independent and orthogonal analysis of chromatin organization with mouse pressure-overload model of myocardial stress (transverse aortic constriction) and cardiomyocyte-specific knockout of Ctcf. We also downloaded published data sets of similar cardiac mouse models and subjected them to independent reanalysis. RESULTS: We found that the cardiomyocyte chromatin architecture remains broadly stable in transverse aortic constriction hearts, whereas Ctcf knockout resulted in approximately 99% abolition of global chromatin loops. Disease gene expression changes correlated instead with differential histone H3K27-acetylation enrichment at their respective proximal and distal interacting genomic enhancers confined within these static chromatin structures. Moreover, coregulated genes were mapped out as interconnected gene sets on the basis of their multigene 3D interactions. CONCLUSIONS: This work reveals a more stable genome-wide chromatin framework than previously described. Myocardial stress-gene transcription responds instead through H3K27-acetylation enhancer enrichment dynamics and gene networks of coregulation. Robust and intact CTCF looping is required for the induction of a rapid and accurate stress response.",0
"Artificial Intelligence Algorithms to Assess Hormonal Status from Tissue Microarrays in Patients with Breast Cancer. Importance: Immunohistochemistry (IHC) is the most widely used assay for identification of molecular biomarkers. However, IHC is time consuming and costly, depends on tissue-handling protocols, and relies on pathologists' subjective interpretation. Image analysis by machine learning is gaining ground for various applications in pathology but has not been proposed to replace chemical-based assays for molecular detection. Objective: To assess the prediction feasibility of molecular expression of biomarkers in cancer tissues, relying only on tissue architecture as seen in digitized hematoxylin-eosin (H&E)-stained specimens. Design, Setting, and Participants: This single-institution retrospective diagnostic study assessed the breast cancer tissue microarrays library of patients from Vancouver General Hospital, British Columbia, Canada. The study and analysis were conducted from July 1, 2015, through July 1, 2018. A machine learning method, termed morphological-based molecular profiling (MBMP), was developed. Logistic regression was used to explore correlations between histomorphology and biomarker expression, and a deep convolutional neural network was used to predict the biomarker expression in examined tissues. Main Outcomes and Measures: Positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristics curve measures of MBMP for assessment of molecular biomarkers. Results: The database consisted of 20600 digitized, publicly available H&E-stained sections of 5356 patients with breast cancer from 2 cohorts. The median age at diagnosis was 61 years for cohort 1 (412 patients) and 62 years for cohort 2 (4944 patients), and the median follow-up was 12.0 years and 12.4 years, respectively. Tissue histomorphology was significantly correlated with the molecular expression of all 19 biomarkers assayed, including estrogen receptor (ER), progesterone receptor (PR), and ERBB2 (formerly HER2). Expression of ER was predicted for 105 of 207 validation patients in cohort 1 (50.7%) and 1059 of 2046 validation patients in cohort 2 (51.8%), with PPVs of 97% and 98%, respectively, NPVs of 68% and 76%, respectively, and accuracy of 91% and 92%, respectively, which were noninferior to traditional IHC (PPV, 91%-98%; NPV, 51%-78%; and accuracy, 81%-90%). Diagnostic accuracy improved given more data. Morphological analysis of patients with ER-negative/PR-positive status by IHC revealed resemblance to patients with ER-positive status (Bhattacharyya distance, 0.03) and not those with ER-negative/PR-negative status (Bhattacharyya distance, 0.25). This suggests a false-negative IHC finding and warrants antihormonal therapy for these patients. Conclusions and Relevance: For at least half of the patients in this study, MBMP appeared to predict biomarker expression with noninferiority to IHC. Results suggest that prediction accuracy is likely to improve as data used for training expand. Morphological-based molecular profiling could be used as a general approach for mass-scale molecular profiling based on digitized H&E-stained images, allowing quick, accurate, and inexpensive methods for simultaneous profiling of multiple biomarkers in cancer tissues..",1
"Development of a Mouse Pain Scale Using Sub-second Behavioral Mapping and Statistical Modeling. Rodents are the main model systems for pain research, but determining their pain state is challenging. To develop an objective method to assess pain sensation in mice, we adopt high-speed videography to capture sub-second behavioral features following hind paw stimulation with both noxious and innocuous stimuli and identify several differentiating parameters indicating the affective and reflexive aspects of nociception. Using statistical modeling and machine learning, we integrate these parameters into a single index and create a “mouse pain scale,” which allows us to assess pain sensation in a graded manner for each withdrawal. We demonstrate the utility of this method by determining sensations triggered by three different von Frey hairs and optogenetic activation of two different nociceptor populations. Our behavior-based “pain scale” approach will help improve the rigor and reproducibility of using withdrawal reflex assays to assess pain sensation in mice. Abdus-Saboor et al. develop a behavior-centered “mouse pain scale” using high-speed videography, statistical modeling, and machine learning. With this method, they assess the sensation induced by noxious, innocuous, and optogenetic stimuli. This method will improve the reliability of using the mouse hind paw withdrawal to measure pain.",0
"The gut microbiome signatures discriminate healthy from pulmonary tuberculosis patients. Cross talk occurs between the human gut and the lung through a gut-lung axis involving the gut microbiota. However, the signatures of the human gut microbiota after active Mycobacterium tuberculosis infection have not been fully understood. Here, we investigated changes in the gut microbiota in tuberculosis (TB) patients by shotgun sequencing the gut microbiomes of 31 healthy controls and 46 patients. We observed a dramatic changes in gut microbiota in tuberculosis patients as reflected by significant decreases in species number and microbial diversity. The gut microbiota of TB patients were mostly featured by the striking decrease of short-chain fatty acids (SCFAs)-producingbacteria as well as associated metabolic pathways. A classification model based on the abundance of three species, Haemophilus parainfluenzae, Roseburia inulinivorans, and Roseburia hominis, performed well for discriminating between healthy and diseased patients. Additionally, the healthy and diseased states can be distinguished by SNPs in the species of B. vulgatus. We present a comprehensive profile of changes in the microbiota in clinical TB patients. Our findings will shed light on the design of future diagnoses and treatments for M. tuberculosis infections.",0
Author Correction: Reporting guidelines for clinical trials evaluating artificial intelligence interventions are needed. An amendment to this paper has been published and can be accessed via a link at the top of the paper.,0
"P53-mediated PI3K/AKT/mTOR pathway played a role in PtoXDPT-induced EMT inhibition in liver cancer cell lines. Epithelial-mesenchymal transition (EMT) involves metastasis and drug resistance; thus, a new EMT reversing agent is required. It has shown that wild-type p53 can reverse EMT back to epithelial characteristics, and iron chelator acting as a p53 inducer has been demonstrated. Moreover, recent study revealed that etoposide could also inhibit EMT. Therefore, combination of etoposide with iron chelator might achieve better inhibition of EMT. To this end, we prepared di-2-pyridineketone hydrazone dithiocarbamate S-propionate podophyllotoxin ester (PtoxDpt) that combined the podophyllotoxin (Ptox) structural unit (etoposide) with the dithiocarbamate unit (iron chelator) through the hybridization strategy. The resulting PtoxDpt inherited characteristics from parent structural units, acting as both the p53 inducer and topoisomerase II inhibitor. In addition, the PtoxDpt exhibited significant inhibition in migration and invasion, which correlated with downregulation of matrix metalloproteinase (MMP). More importantly, PtoxDpt could inhibit EMT in the absence or presence of TGF-β1, concomitant to the ROS production, and the additional evidence revealed that PtoxDpt downregulated AKT/mTOR through upregulation of p53, indicating that PtoxDpt induced EMT inhibition through the p53/PI3K/AKT/mTOR pathway.",0
"Natural language processing of radiology reports for identification of skeletal site-specific fractures. BACKGROUND: Osteoporosis has become an important public health issue. Most of the population, particularly elderly people, are at some degree of risk of osteoporosis-related fractures. Accurate identification and surveillance of patient populations with fractures has a significant impact on reduction of cost of care by preventing future fractures and its corresponding complications. METHODS: In this study, we developed a rule-based natural language processing (NLP) algorithm for identification of twenty skeletal site-specific fractures from radiology reports. The rule-based NLP algorithm was based on regular expressions developed using MedTagger, an NLP tool of the Apache Unstructured Information Management Architecture (UIMA) pipeline to facilitate information extraction from clinical narratives. Radiology notes were retrieved from the Mayo Clinic electronic health records data warehouse. We developed rules for identifying each fracture type according to physicians' knowledge and experience, and refined these rules via verification with physicians. This study was approved by the institutional review board (IRB) for human subject research. RESULTS: We validated the NLP algorithm using the radiology reports of a community-based cohort at Mayo Clinic with the gold standard constructed by medical experts. The micro-averaged results of sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1-score of the proposed NLP algorithm are 0.930, 1.0, 1.0, 0.941, 0.961, respectively. The F1-score is 1.0 for 8 fractures, and above 0.9 for a total of 17 out of 20 fractures (85%). CONCLUSIONS: The results verified the effectiveness of the proposed rule-based NLP algorithm in automatic identification of osteoporosis-related skeletal site-specific fractures from radiology reports. The NLP algorithm could be utilized to accurately identify the patients with fractures and those who are also at high risk of future fractures due to osteoporosis. Appropriate care interventions to those patients, not only the most at-risk patients but also those with emerging risk, would significantly reduce future fractures.",1
"Cell mitosis event analysis in phase contrast microscopy images using deep learning. In this paper, we solve the problem of mitosis event localization and its stage localization in time-lapse phase-contrast microscopy images. Our method contains three steps: first, we formulate a Low-Rank Matrix Recovery (LRMR) model to find salient regions from microscopy images and extract candidate patch sequences, which potentially contain mitosis events; second, we classify each candidate patch sequence by our proposed Hierarchical Convolution Neural Network (HCNN) with visual appearance and motion cues; third, for the detected mitosis sequences, we further segment them into four temporal stages by our proposed Two-stream Bidirectional Long-Short Term Memory (TS-BLSTM). In the experiments, we validate our system (LRMR, HCNN, and TS-BLSTM) and evaluate the mitosis event localization and stage localization performance. The proposed method outperforms state-of-the-arts by achieving 99.2% precision and 98.0% recall for mitosis event localization and 0.62 frame error on average for mitosis stage localization in five challenging image sequences.",1
"Enhanced Recovery After Surgery (ERAS) Pathway in Esophagectomy: Is a Reasonable Prediction of Hospital Stay Possible?. OBJECTIVE: To assess whether perioperative variables or deviation from enhanced recovery after surgery (ERAS) items could be associated with delayed discharge after esophagectomy, and to convert them into a scoring system to predict it. SUMMARY BACKGROUND DATA: ERAS perioperative pathways have been recently applied to esophageal resections. However, low adherence to ERAS items and high rates of protocol deviations are often reported. METHODS: All patients who underwent esophagectomy between April 2012 and March 2017 were managed with a standardized perioperative pathway according to ERAS principles. The target length of stay was set at eighth postoperative day (POD). All significant variables at bivariate analysis were entered into a logistic regression to produce a predictive score. An initial validation of the score accuracy was carried out on a separate patient sample. RESULTS: Two hundred eighty-six patients were included in the study. Multivariate regression analysis showed that American Society of Anesthesiology score >/= 3, surgery duration > 255 min, ""nonhybrid"" esophagectomy, and failure to mobilize patients within 24 h from surgery were associated with delayed discharge. The logistic regression model was statistically significant (P < 0.001) and correctly classified 81.9% of cases. The sensitivity was 96.6%, and the specificity was 17.6%. The prediction score applied to 23 patients correctly identified 100% of those discharged after eighth POD. CONCLUSIONS: The results of this study seem to be clinically meaningful and in line with those from other studies. The initial validation revealed good predictive properties.",0
"Automated Design of Pluripotent Stem Cell Self-Organization. Human pluripotent stem cells (hPSCs) have the intrinsic ability to self-organize into complex multicellular organoids that recapitulate many aspects of tissue development. However, robustly directing morphogenesis of hPSC-derived organoids requires novel approaches to accurately control self-directed pattern formation. Here, we combined genetic engineering with computational modeling, machine learning, and mathematical pattern optimization to create a data-driven approach to control hPSC self-organization by knock down of genes previously shown to affect stem cell colony organization, CDH1 and ROCK1. Computational replication of the in vitro system in silico using an extended cellular Potts model enabled machine learning-driven optimization of parameters that yielded emergence of desired patterns. Furthermore, in vitro the predicted experimental parameters quantitatively recapitulated the in silico patterns. These results demonstrate that morphogenic dynamics can be accurately predicted through model-driven exploration of hPSC behaviors via machine learning, thereby enabling spatial control of multicellular patterning to engineer human organoids and tissues. A record of this paper's Transparent Peer Review process is included in the Supplemental Information.",0
"Efficient Golgi Forward Trafficking Requires GOLPH3-Driven, PI4P-Dependent Membrane Curvature. Vesicle budding for Golgi-to-plasma membrane trafficking is a key step in secretion. Proteins that induce curvature of the Golgi membrane are predicted to be required, by analogy to vesicle budding from other membranes. Here, we demonstrate that GOLPH3, upon binding to the phosphoinositide PI4P, induces curvature of synthetic membranes in vitro and the Golgi in cells. Moreover, efficient Golgi-to-plasma membrane trafficking critically depends on the ability of GOLPH3 to curve the Golgi membrane. Interestingly, uncoupling of GOLPH3 from its binding partner MYO18A results in extensive curvature of Golgi membranes, producing dramatic tubulation of the Golgi, but does not support forward trafficking. Thus, forward trafficking from the Golgi to the plasma membrane requires the ability of GOLPH3 both to induce Golgi membrane curvature and to recruit MYO18A. These data provide fundamental insight into the mechanism of Golgi trafficking and into the function of the unique Golgi secretory oncoproteins GOLPH3 and MYO18A.",0
"Activation of the PP2A catalytic subunit by ivabradine attenuates the development of diabetic cardiomyopathy. Hyperglycemia-induced apoptosis plays a critical role in the pathogenesis of diabetic cardiomyopathy (DCM). Our previous study demonstrated that ivabradine, a selective If current antagonist, significantly attenuated myocardial apoptosis in diabetic mice, but the underlying mechanisms remained unknown. This study investigated the underlying mechanisms by which ivabradine exerts anti-apoptotic effects in experimental DCM. Pretreatment with ivabradine, but not ZD7288 (an established If current blocker), profoundly inhibited high glucose-induced apoptosis via inactivation of nuclear factor (NF)-κB signaling in neonatal rat cardiomyocytes. The effect was abolished by transfection of an siRNA targeting protein phosphatase 2A catalytic subunit (PP2Ac). In streptozotocin-induced diabetic mice, ivabradine treatment significantly inhibited left ventricular hyperpolarization-activated cyclic nucleotide-gated channel 2 (HCN2) and HCN4 (major components of the If current), activated PP2Ac, and attenuated NF-κB signaling activation and apoptosis, in line with improved histological abnormalities, fibrosis, and cardiac dysfunction without affecting hyperglycemia. These effects were not observed in diabetic mice with virus-mediated knockdown of HCN2 or HCN4 after myocardial injection, but were alleviated by knockdown of PP2Acα. Molecular docking and phosphatase activity assay confirmed direct binding of ivabradine to, and activation of, PP2Ac. In conclusion, ivabradine may directly activate PP2Ac, leading to inhibition of NF-κB signaling activation, myocardial apoptosis, and fibrosis, and eventually improving cardiac function in experimental DCM. Taken together, the present findings suggest that ivabradine may be a promising drug for treatment of DCM.",0
"Identification of key gene modules and pathways of human glioma through coexpression network. Glioma causes great harm to people worldwide. Systemic coexpression analysis of this disease could be beneficial for the identification and development of new prognostic and predictive markers in the clinical management of glioma. In this study, we extracted data sets from the Gene Expression Omnibus data set by using “glioma” as the keyword. Then, a coexpression module was constructed with the help of Weighted Gene Coexpression Network Analysis software. Besides, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on the genes in these modules. As a result, the critical modules and target genes were identified. Eight coexpression modules were constructed using the 4,000 genes with a high expression value of the total 141 glioma samples. The result of the analysis of the interaction among these modules showed that there was a high scale independence degree among them. The GO and KEGG enrichment analyses showed that there was a significant difference in the enriched terms and degree among these eight modules, and module 5 was identified as the most important module. Besides, the pathways it was enriched in, hsa04510: Focal adhesion and hsa04610: Complement and coagulation cascades, were determined as the most important pathways. In summary, module 5 and the pathways it was enriched in, hsa04510: Focal adhesion and has 04610: Complement and coagulation cascades, have the potential to serve as biomarkers for patients with glioma.",0
"Detection of Brain Activation in Unresponsive Patients with Acute Brain Injury. BACKGROUND: Brain activation in response to spoken motor commands can be detected by electroencephalography (EEG) in clinically unresponsive patients. The prevalence and prognostic importance of a dissociation between commanded motor behavior and brain activation in the first few days after brain injury are not well understood. METHODS: We studied a prospective, consecutive series of patients in a single intensive care unit who had acute brain injury from a variety of causes and who were unresponsive to spoken commands, including some patients with the ability to localize painful stimuli or to fixate on or track visual stimuli. Machine learning was applied to EEG recordings to detect brain activation in response to commands that patients move their hands. The functional outcome at 12 months was determined with the Glasgow Outcome Scale-Extended (GOS-E; levels range from 1 to 8, with higher levels indicating better outcomes). RESULTS: A total of 16 of 104 unresponsive patients (15%) had brain activation detected by EEG at a median of 4 days after injury. The condition in 8 of these 16 patients (50%) and in 23 of 88 patients (26%) without brain activation improved such that they were able to follow commands before discharge. At 12 months, 7 of 16 patients (44%) with brain activation and 12 of 84 patients (14%) without brain activation had a GOS-E level of 4 or higher, denoting the ability to function independently for 8 hours (odds ratio, 4.6; 95% confidence interval, 1.2 to 17.1). CONCLUSIONS: A dissociation between the absence of behavioral responses to motor commands and the evidence of brain activation in response to these commands in EEG recordings was found in 15% of patients in a consecutive series of patients with acute brain injury. (Supported by the Dana Foundation and the James S. McDonnell Foundation.).",0
"FA-97, a New Synthetic Caffeic Acid Phenethyl Ester Derivative, Protects against Oxidative Stress-Mediated Neuronal Cell Apoptosis and Scopolamine-Induced Cognitive Impairment by Activating Nrf2/HO-1 Signaling. Alzheimer's disease (AD) is an age-related neurodegenerative disorder with cognitive deficits, which is becoming markedly more common in the world. Currently, the exact cause of AD is still unclear, and no curative therapy is available for preventing or mitigating the disease progression. Caffeic acid phenethyl ester (CAPE), a natural phenolic compound derived from honeybee hive propolis, has been reported as a potential therapeutic agent against AD, while its application is limited due to the low water solubility and poor bioavailability. Here, caffeic acid phenethyl ester 4-O-glucoside (FA-97) is synthesized. We validate that FA-97 attenuates H2O2-induced apoptosis in SH-SY5Y and PC12 cells and suppresses H2O2-induced oxidative stress by inhibiting the ROS level, malondialdehyde (MDA) level, and protein carbonylation level, as well as induces cellular glutathione (GSH) and superoxide dismutase (SOD). Mechanistically, FA-97 promotes the nuclear translocation and transcriptional activity of Nrf2 associated with the upregulated expression of HO-1 and NQO-1. The prime importance of Nrf2 activation in the neuroprotective and antioxidant effects of FA-97 is verified by Nrf2 siRNA transfection. In addition, FA-97 prevents scopolamine- (SCOP-) induced learning and memory impairments in vivo via reducing neuronal apoptosis and protecting against cholinergic system dysfunction in the hippocampus and cortex. Moreover, the increased MDA level and low total antioxidant capacity in SCOP-treated mouse brains are reversed by FA-97, with the increased expression of HO-1, NQO-1, and nuclear Nrf2. In conclusion, FA-97 protects against oxidative stress-mediated neuronal cell apoptosis and SCOP-induced cognitive impairment by activating Nrf2/HO-1 signaling, which might be developed as a therapeutic drug for AD.",0
"A Tail-Based Mechanism Drives Nucleosome Demethylation by the LSD2/NPAC Multimeric Complex. Through biophysical, biochemical, and structural studies, including cryo-EM, Marabelli et al. describe how NPAC promotes LSD2 productive interaction with the nucleosome in a rapid and flexible manner. Their findings provide a molecular mechanism for LSD2 activity in the context of H3K4me2 demethylation during Pol II transcriptional elongation.",0
"Quantifying in situ adaptive immune cell cognate interactions in humans. Two-photon excitation microscopy (TPEM) has revolutionized the understanding of adaptive immunity. However, TPEM usually requires animal models and is not amenable to the study of human disease. The recognition of antigen by T cells requires cell contact and is associated with changes in T cell shape. We postulated that by capturing these features in fixed tissue samples, we could quantify in situ adaptive immunity. Therefore, we used a deep convolutional neural network to identify fundamental distance and cell-shape features associated with cognate help (cell-distance mapping (CDM)). In mice, CDM was comparable to TPEM in discriminating cognate T cell-dendritic cell (DC) interactions from non-cognate T cell-DC interactions. In human lupus nephritis, CDM confirmed that myeloid DCs present antigen to CD4(+) T cells and identified plasmacytoid DCs as an important antigen-presenting cell. These data reveal a new approach with which to study human in situ adaptive immunity broadly applicable to autoimmunity, infection, and cancer.",1
"Evaluating the performance of automated sphygmomanometers using a patient simulator. BACKGROUND AND OBJECTIVE: Automated sphygmomanometers use the oscillometric method to measure blood pressure, which is based on an algorithm that relates the amplitude of the oscillometric waveform pulses and the pressure inside the cuff. Validation uses empirical information from clinical trials conducted by each manufacturer. Consequently, measurement algorithms are not harmonized, being based on distinct arterial waveforms, according to each group of volunteers of the clinical test. In the present study, a patient simulator was used to generate standardized, consistent oscillometric waveform pulses to test the algorithms used in six sphygmomanometers. MATERIALS AND METHODS: Six different upper arm and wrist-based automated sphygmomanometers were tested using a patient simulator comprising four different blood pressure levels, Psys/dia (mmHg): 80/50; 120/80; 150/100; 200/150. The devices were also submitted to conformity assessment. The variance of repeatable measurements was also analyzed. RESULTS: All tested automated sphygmomanometers complied with metrological requirements, presenting results within the range of ±2 mmHg for static calibration. Systematic discrepancies, greater than 20 mmHg, were observed between sphygmomanometers' results from upper arm and wrist-based models. Differences reaching 12.8 mmHg in diastolic pressure results were observed among upper arm devices. CONCLUSION: These results may have a clinical impact and indicate the need for a standardized algorithm, with a harmonized approach for validation. Moreover, the algorithm of the wrist-based devices is being affected by the use of the brachial artery waveform as reference for its validation, which also reveals that the current approach needs standardization, especially regarding the use of patient simulators.24299305.",0
"A diversity of interneurons and Hebbian plasticity facilitate rapid compressible learning in the hippocampus. The hippocampus is able to rapidly learn incoming information, even if that information is only observed once. Furthermore, this information can be replayed in a compressed format in either forward or reverse modes during sharp wave-ripples (SPW-Rs). We leveraged state-of-the-art techniques in training recurrent spiking networks to demonstrate how primarily interneuron networks can achieve the following: (1) generate internal theta sequences to bind externally elicited spikes in the presence of inhibition from the medial septum; (2) compress learned spike sequences in the form of a SPW-R when septal inhibition is removed; (3) generate and refine high-frequency assemblies during SPW-R-mediated compression; and (4) regulate the inter-SPW interval timing between SPW-Rs in ripple clusters. From the fast timescale of neurons to the slow timescale of behaviors, interneuron networks serve as the scaffolding for one-shot learning by replaying, reversing, refining, and regulating spike sequences.",0
"Recovery of 3D rib motion from dynamic chest radiography and CT data using local contrast normalization and articular motion model. Dynamic chest radiography (2D x-ray video) is a low-dose and cost-effective functional imaging method with high temporal resolution. While the analysis of rib-cage motion has been shown to be effective for evaluating respiratory function, it has been limited to 2D. We aim at 3D rib-motion analysis for high temporal resolution while keeping the radiation dose at a level comparable to conventional examination. To achieve this, we developed a method for automatically recovering 3D rib motion based on 2D-3D registration of x-ray video and single-time-phase computed tomography. We introduce the following two novel components into the conventional intensity-based 2D-3D registration pipeline: (1) a rib-motion model based on a uniaxial joint to constrain the search space and (2) local contrast normalization (LCN) as a pre-process of x-ray video to improve the cost function of the optimization parameters, which is often called the landscape. The effects of each component on the registration results were quantitatively evaluated through experiments using simulated images and real patients' x-ray videos obtained in a clinical setting. The rotation-angle error of the rib and the mean projection contour distance (mPCD) were used as the error metrics. The simulation experiments indicate that the proposed uniaxial joint model improved registration accuracy. By searching the rotation axis along with the rotation angle of the ribs, the rotation-angle error and mPCD significantly decreased from 2.246+/-1.839 degrees and 1.148+/-0.743 mm to 1.495+/-0.993 degrees and 0.742+/-0.281 mm, compared to simply applying De Troyer's model. The real-image experiments with eight patients demonstrated that LCN improved the cost function space; thus, robustness in optimization resulting in an average mPCD of 1.255+/-0.615 mm. We demonstrated that an anatomical-knowledge based constraint and an intensity normalization, LCN, significantly improved robustness and accuracy in rib-motion reconstruction using chest x-ray video.",0
Histone Octamer Structure Is Altered Early in ISW2 ATP-Dependent Nucleosome Remodeling. Nucleosomes are the fundamental building blocks of chromatin that regulate DNA access and are composed of histone octamers. ATP-dependent chromatin remodelers like ISW2 regulate chromatin access by translationally moving nucleosomes to different DNA regions. We find that histone octamers are more pliable than previously assumed and distorted by ISW2 early in remodeling before DNA enters nucleosomes and the ATPase motor moves processively on nucleosomal DNA. Uncoupling the ATPase activity of ISW2 from nucleosome movement with deletion of the SANT domain from the C terminus of the Isw2 catalytic subunit traps remodeling intermediates in which the histone octamer structure is changed. We find restricting histone movement by chemical crosslinking also traps remodeling intermediates resembling those seen early in ISW2 remodeling with loss of the SANT domain. Other evidence shows histone octamers are intrinsically prone to changing their conformation and can be distorted merely by H3-H4 tetramer disulfide crosslinking. Hada et al. show that ISW2 catalyzes DNA movement through nucleosomes with a short stretch of DNA persistently exiting the nucleosome before DNA enters nucleosomes. A consequence of asynchronous DNA movement is distortion of the histone octamer structure in which the rotational phasing of DNA is maintained on the octamer surface.,0
"Ceramide regulates interaction of Hsd17b4 with Pex5 and function of peroxisomes. The sphingolipid ceramide regulates beta-oxidation of medium and long chain fatty acids in mitochondria. It is not known whether it also regulates oxidation of very long chain fatty acids (VLCFAs) in peroxisomes. Using affinity chromatography, co-immunoprecipitation, and proximity ligation assays we discovered that ceramide interacts with Hsd17b4, an enzyme critical for peroxisomal VLCFA oxidation and docosahexaenoic acid (DHA) generation. Immunocytochemistry showed that Hsd17b4 is distributed to ceramide-enriched mitochondria-associated membranes (CEMAMs). Molecular docking and in vitro mutagenesis experiments showed that ceramide binds to the sterol carrier protein 2-like domain in Hsd17b4 adjacent to peroxisome targeting signal 1 (PTS1), the C-terminal signal for interaction with peroxisomal biogenesis factor 5 (Pex5), a peroxin mediating transport of Hsd17b4 into peroxisomes. Inhibition of ceramide biosynthesis induced translocation of Hsd17b4 from CEMAMs to peroxisomes, interaction of Hsd17b4 with Pex5, and upregulation of DHA. This data indicates a novel role of ceramide as a molecular switch regulating interaction of Hsd17b4 with Pex5 and peroxisomal function.",0
"Prediction of cognitive impairment via deep learning trained with multi-center neuropsychological test data. BACKGROUND: Neuropsychological tests (NPTs) are important tools for informing diagnoses of cognitive impairment (CI). However, interpreting NPTs requires specialists and is thus time-consuming. To streamline the application of NPTs in clinical settings, we developed and evaluated the accuracy of a machine learning algorithm using multi-center NPT data. METHODS: Multi-center data were obtained from 14,926 formal neuropsychological assessments (Seoul Neuropsychological Screening Battery), which were classified into normal cognition (NC), mild cognitive impairment (MCI) and Alzheimer's disease dementia (ADD). We trained a machine learning model with artificial neural network algorithm using TensorFlow (https://www.tensorflow.org) to distinguish cognitive state with the 46-variable data and measured prediction accuracies from 10 randomly selected datasets. The features of the NPT were listed in order of their contribution to the outcome using Recursive Feature Elimination. RESULTS: The ten times mean accuracies of identifying CI (MCI and ADD) achieved by 96.66 ± 0.52% of the balanced dataset and 97.23 ± 0.32% of the clinic-based dataset, and the accuracies for predicting cognitive states (NC, MCI or ADD) were 95.49 ± 0.53 and 96.34 ± 1.03%. The sensitivity to the detection CI and MCI in the balanced dataset were 96.0 and 96.0%, and the specificity were 96.8 and 97.4%, respectively. The 'time orientation' and '3-word recall' score of MMSE were highly ranked features in predicting CI and cognitive state. The twelve features reduced from 46 variable of NPTs with age and education had contributed to more than 90% accuracy in predicting cognitive impairment. CONCLUSIONS: The machine learning algorithm for NPTs has suggested potential use as a reference in differentiating cognitive impairment in the clinical setting.",1
"A Study of High-Grade Serous Ovarian Cancer Origins Implicates the SOX18 Transcription Factor in Tumor Development. Lawrenson et al. profile gene expression and active chromatin in ∼200 ovarian and fallopian epithelial isolates and implement machine learning to demonstrate that most high-grade serous ovarian cancers (HGSOCs) derive from fallopian tube epithelial cells, but a subset may originate from ovarian epithelia. SOX18 induces mesenchymal features to drive early neoplasia in fallopian tube precursors.",0
"Symptom-based stratification of patients with primary Sjögren's syndrome: multi-dimensional characterisation of international observational cohorts and reanalyses of randomised clinical trials. Background: Heterogeneity is a major obstacle to developing effective treatments for patients with primary Sjögren's syndrome. We aimed to develop a robust method for stratification, exploiting heterogeneity in patient-reported symptoms, and to relate these differences to pathobiology and therapeutic response. Methods: We did hierarchical cluster analysis using five common symptoms associated with primary Sjögren's syndrome (pain, fatigue, dryness, anxiety, and depression), followed by multinomial logistic regression to identify subgroups in the UK Primary Sjögren's Syndrome Registry (UKPSSR). We assessed clinical and biological differences between these subgroups, including transcriptional differences in peripheral blood. Patients from two independent validation cohorts in Norway and France were used to confirm patient stratification. Data from two phase 3 clinical trials were similarly stratified to assess the differences between subgroups in treatment response to hydroxychloroquine and rituximab. Findings: In the UKPSSR cohort (n=608), we identified four subgroups: Low symptom burden (LSB), high symptom burden (HSB), dryness dominant with fatigue (DDF), and pain dominant with fatigue (PDF). Significant differences in peripheral blood lymphocyte counts, anti-SSA and anti-SSB antibody positivity, as well as serum IgG, κ-free light chain, β2-microglobulin, and CXCL13 concentrations were observed between these subgroups, along with differentially expressed transcriptomic modules in peripheral blood. Similar findings were observed in the independent validation cohorts (n=396). Reanalysis of trial data stratifying patients into these subgroups suggested a treatment effect with hydroxychloroquine in the HSB subgroup and with rituximab in the DDF subgroup compared with placebo. Interpretation: Stratification on the basis of patient-reported symptoms of patients with primary Sjögren's syndrome revealed distinct pathobiological endotypes with distinct responses to immunomodulatory treatments. Our data have important implications for clinical management, trial design, and therapeutic development. Similar stratification approaches might be useful for patients with other chronic immune-mediated diseases. Funding: UK Medical Research Council, British Sjogren's Syndrome Association, French Ministry of Health, Arthritis Research UK, Foundation for Research in Rheumatology. Video Abstract: [Figure presented]",0
"Genetic Variations rs859, rs4646, and rs372883 in the 3′-Untranslated Regions of Genes Are Associated with a Risk of IgA Nephropathy. Background: Previous studies indicate that genetic factors play an important role in the pathogenesis of IgA nephropathy (IgAN). To evaluate the association between single nucleotide polymorphisms (SNPs) in the 3′-untranslated region (3′-UTR) of genes and IgAN risk, we performed a case-control study in a Chinese Han population. Materials: Twelve SNPs were selected and genotyped in 384 IgAN patients and 357 healthy controls. Odds ratio (OR) and 95% confidence intervals (CI) were calculated by logistic regression adjusted for age and gender. Multifactor dimensionality reduction (MDR) was used to analyze the interaction of SNP-SNP with IgAN risk. Results: Our study demonstrated that IL-16 rs859 (OR = 0.75, p = 0.040) and CYP19A1 rs4646 (OR = 2.58, p = 0.017) polymorphism were related to the risk of IgAN. In stratified analyses by gender, CYP19A1 rs4646 (OR = 2.96, p = 0.015) and BACH1 rs372883 (OR = 1.81, p = 0.038) polymorphisms conferred susceptibility to IgAN in males. Besides, rs372883 reduced IgAN risk in females (OR = 0.44, p = 0.042). We also found rs859 polymorphism was correlated with grade I-II (OR = 0.42, p = 0.028) in subgroup analysis of Lee's classification. Additionally, we found rs4646 polymorphism was correlated with serum creatinine (p = 0.035). Conclusion: Our results suggested that the IL-16 rs859, CYP19A1 rs4646, and BACH1 rs372883 polymorphisms have potential roles in the genetic susceptibility to IgAN in Chinese Han population.",0
"Unraveling the Molecular Mechanism of Action of Empagliflozin in Heart Failure With Reduced Ejection Fraction With or Without Diabetes. The mechanism of action of empagliflozin in heart failure with reduced ejection fraction (HFrEF) was deciphered using deep learning in silico analyses together with in vivo validation. The most robust mechanism of action involved the sodium-hydrogen exchanger (NHE)-1 co-transporter with 94.7% accuracy, which was similar for diabetics and nondiabetics. Notably, direct NHE1 blockade by empagliflozin ameliorated cardiomyocyte cell death by restoring expression of X-linked inhibitor of apoptosis (XIAP) and baculoviral IAP repeat-containing protein 5 (BIRC5). These results were independent of diabetes mellitus comorbidity, suggesting that empagliflozin may emerge as a new treatment in HFrEF.",0
"Inferring Regulatory Programs Governing Region Specificity of Neuroepithelial Stem Cells during Early Hindbrain and Spinal Cord Development. Neuroepithelial stem cells (NSC) from different anatomical regions of the embryonic neural tube's rostrocaudal axis can differentiate into diverse central nervous system tissues, but the transcriptional regulatory networks governing these processes are incompletely understood. Here, we measure region-specific NSC gene expression along the rostrocaudal axis in a human pluripotent stem cell model of early central nervous system development over a 72-h time course, spanning the hindbrain to cervical spinal cord. We introduce Escarole, a probabilistic clustering algorithm for non-stationary time series, and combine it with prior-based regulatory network inference to identify genes that are regulated dynamically and predict their upstream regulators. We identify known regulators of patterning and neural development, including the HOX genes, and predict a direct regulatory connection between the transcription factor POU3F2 and target gene STMN2. We demonstrate that POU3F2 is required for expression of STMN2, suggesting that this regulatory connection is important for region specificity of NSCs.",0
"Mechanistic elucidation of amphetamine metabolism by tyramine oxidase from human gut microbiota using molecular dynamics simulations. The human gut harbors diverse bacterial species in the gut, which play an important role in the metabolism of food and host health. Recent studies have also revealed their role in altering the pharmacological properties and efficacy of oral drugs through promiscuous metabolism. However, the atomistic details of the enzyme-drug interactions of gut bacterial enzymes which can potentially carry out the metabolism of drug molecules are still scarce. A well-known example is the FDA drug amphetamine (a central nervous system stimulant), which has been predicted to undergo promiscuous metabolism by gut bacteria. Therefore, to understand the atomistic details and energy landscape of the gut microbial enzyme-mediated metabolism of this drug, molecular dynamics studies were performed. It was observed that amphetamine binds to tyramine oxidase from the Escherichia coli strain present in the human gut microbiota at the binding site harboring polar and nonpolar amino acids. The stability analysis of amphetamine at the binding site showed that the binding is stable and the free energy for the binding of amphetamine was found to be ~ −51.71 kJ/mol. The insights provided by this study on promiscuous metabolism of amphetamine by a gut enzyme will be very useful to improve the efficacy of the drug.",0
"A novel gene selection algorithm for cancer classification using microarray datasets. Background: Microarray datasets are an important medical diagnostic tool as they represent the states of a cell at the molecular level. Available microarray datasets for classifying cancer types generally have a fairly small sample size compared to the large number of genes involved. This fact is known as a curse of dimensionality, which is a challenging problem. Gene selection is a promising approach that addresses this problem and plays an important role in the development of efficient cancer classification due to the fact that only a small number of genes are related to the classification problem. Gene selection addresses many problems in microarray datasets such as reducing the number of irrelevant and noisy genes, and selecting the most related genes to improve the classification results. Methods: An innovative Gene Selection Programming (GSP) method is proposed to select relevant genes for effective and efficient cancer classification. GSP is based on Gene Expression Programming (GEP) method with a new defined population initialization algorithm, a new fitness function definition, and improved mutation and recombination operators. Support Vector Machine (SVM) with a linear kernel serves as a classifier of the GSP. Results: Experimental results on ten microarray cancer datasets demonstrate that Gene Selection Programming (GSP) is effective and efficient in eliminating irrelevant and redundant genes/features from microarray datasets. The comprehensive evaluations and comparisons with other methods show that GSP gives a better compromise in terms of all three evaluation criteria, i.e., classification accuracy, number of selected genes, and computational cost. The gene set selected by GSP has shown its superior performances in cancer classification compared to those selected by the up-to-date representative gene selection methods. Conclusion: Gene subset selected by GSP can achieve a higher classification accuracy with less processing time.",0
"Current approaches to identify sections within clinical narratives from electronic health records: a systematic review. BACKGROUND: The identification of sections in narrative content of Electronic Health Records (EHR) has demonstrated to improve the performance of clinical extraction tasks; however, there is not yet a shared understanding of the concept and its existing methods. The objective is to report the results of a systematic review concerning approaches aimed at identifying sections in narrative content of EHR, using both automatic or semi-automatic methods. METHODS: This review includes articles from the databases: SCOPUS, Web of Science and PubMed (from January 1994 to September 2018). The selection of studies was done using predefined eligibility criteria and applying the PRISMA recommendations. Search criteria were elaborated by using an iterative and collaborative keyword enrichment. RESULTS: Following the eligibility criteria, 39 studies were selected for analysis. The section identification approaches proposed by these studies vary greatly depending on the kind of narrative, the type of section, and the application. We observed that 57% of them proposed formal methods for identifying sections and 43% adapted a previously created method. Seventy-eight percent were intended for English texts and 41% for discharge summaries. Studies that are able to identify explicit (with headings) and implicit sections correspond to 46%. Regarding the level of granularity, 54% of the studies are able to identify sections, but not subsections. From the technical point of view, the methods can be classified into rule-based methods (59%), machine learning methods (22%) and a combination of both (19%). Hybrid methods showed better results than those relying on pure machine learning approaches, but lower than rule-based methods; however, their scope was more ambitious than the latter ones. Despite all the promising performance results, very few studies reported tests under a formal setup. Almost all the studies relied on custom dictionaries; however, they used them in conjunction with a controlled terminology, most commonly the UMLSⓇ metathesaurus. CONCLUSIONS: Identification of sections in EHR narratives is gaining popularity for improving clinical extraction projects. This study enabled the community working on clinical NLP to gain a formal analysis of this task, including the most successful ways to perform it.",0
"Network plasticity involved in the spread of neural activity within the rhinal cortices as revealed by voltage-sensitive dye imaging in mouse brain slices. The rhinal cortices, such as the perirhinal cortex (PC) and the entorhinal cortex (EC), are located within the bidirectional pathway between the neocortex and the hippocampus. Physiological studies indicate that the perirhinal transmission of neocortical inputs to the EC occurs at an extremely low probability, though many anatomical studies indicated strong connections exist in the pathway. Our previous study in rat brain slices indicated that an increase in excitability in deep layers of the PC/EC border initiated the neural activity transfer from the PC to the EC. In the present study, we hypothesized that such changes in network dynamics are not incidental observations but rather due to the plastic features of the perirhinal network, which links with the EC. To confirm this idea, we analyzed the network properties of neural transmission throughout the rhinal cortices and the plastic behavior of the network by performing a single-photon wide-field optical recording technique with a voltage-sensitive dye (VSD) in mouse brain slices of the PC, the EC, and the hippocampus. The low concentration of 4-aminopyridine (4-AP; 40 μM) enhanced neural activity in the PC, which eventually propagated to the EC via the deep layers of the PC/EC border. Interestingly, washout of 4-AP was unable to reverse entorhinal activation to the previous state. This change in the network property persisted for more than 1 h. This observation was not limited to the application of 4-AP. Burst stimulation to neurons in the perirhinal deep layers also induced the same change of network property. These results indicate the long-lasting modification of physiological connection between the PC and the EC, suggesting the existence of plasticity in the perirhinal-entorhinal network.",0
"MiR-204 regulates type 1 IP3R to control vascular smooth muscle cell contractility and blood pressure. MiR-204 is expressed in vascular smooth muscle cells (VSMC). However, its role in VSMC contraction is not known. We determined if miR-204 controls VSMC contractility and blood pressure through regulation of sarcoplasmic reticulum (SR) calcium (Ca2+) release. Systolic blood pressure (SBP) and vasoreactivity to VSMC contractile agonists (phenylephrine (PE), thromboxane analogue (U46619), endothelin-1 (ET-1), angiotensin-II (Ang II) and norepinephrine (NE) were compared in aortas and mesenteric resistance arteries (MRA) from miR-204−/− mice and wildtype mice (WT). There was no difference in basal systolic blood pressure (SBP) between the two genotypes; however, hypertensive response to Ang II was significantly greater in miR-204−/− mice compared to WT mice. Aortas and MRA of miR-204−/− mice had heightened contractility to all VSMC agonists. In silico algorithms predicted the type 1 Inositol 1, 4, 5-trisphosphate receptor (IP3R1) as a target of miR-204. Aortas and MRA of miR-204−/− mice had higher expression of IP3R1 compared to WT mice. Difference in agonist-induced vasoconstriction between miR-204−/− and WT mice was abolished with pharmacologic inhibition of IP3R1. Furthermore, Ang II-induced aortic IP3R1 was greater in miR-204−/− mice compared to WT mice. In addition, difference in aortic vasoconstriction to VSMC agonists between miR-204−/− and WT mice persisted after Ang II infusion. Inhibition of miR-204 in VSMC in vitro increased IP3R1, and boosted SR Ca2+ release in response to PE, while overexpression of miR-204 downregulated IP3R1. Finally, a sequence-specific nucleotide blocker that targets the miR-204-IP3R1 interaction rescued miR-204-induced downregulation of IP3R1. We conclude that miR-204 controls VSMC contractility and blood pressure through IP3R1-dependent regulation of SR calcium release.",0
"An attention-based deep learning model for clinical named entity recognition of Chinese electronic medical records. BACKGROUND: Clinical named entity recognition (CNER) is important for medical information mining and establishment of high-quality knowledge map. Due to the different text features from natural language and a large number of professional and uncommon clinical terms in Chinese electronic medical records (EMRs), there are still many difficulties in clinical named entity recognition of Chinese EMRs. It is of great importance to eliminate semantic interference and improve the ability of autonomous learning of internal features of the model under the small training corpus. METHODS: From the perspective of deep learning, we integrated the attention mechanism into neural network, and proposed an improved clinical named entity recognition method for Chinese electronic medical records called BiLSTM-Att-CRF, which could capture more useful information of the context and avoid the problem of missing information caused by long-distance factors. In addition, medical dictionaries and part-of-speech (POS) features were also introduced to improve the performance of the model. RESULTS: Based on China Conference on Knowledge Graph and Semantic Computing (CCKS) 2017 and 2018 Chinese EMRs corpus, our BiLSTM-Att-CRF model finally achieved better performance than other widely-used models without additional features(F1-measure of 85.4% in CCKS 2018, F1-measure of 90.29% in CCKS 2017), and achieved the best performance with POS and dictionary features (F1-measure of 86.11% in CCKS 2018, F1-measure of 90.48% in CCKS 2017). In particular, the BiLSTM-Att-CRF model had significant effect on the improvement of Recall. CONCLUSIONS: Our work preliminarily confirmed the validity of attention mechanism in discovering key information and mining text features, which might provide useful ideas for future research in clinical named entity recognition of Chinese electronic medical records. In the future, we will explore the deeper application of attention mechanism in neural network.",1
"Disease quantification on PET/CT images without explicit object delineation. PURPOSE: The derivation of quantitative information from images in a clinically practical way continues to face a major hurdle because of image segmentation challenges. This paper presents a novel approach, called automatic anatomy recognition-disease quantification (AAR-DQ), for disease quantification (DQ) on positron emission tomography/computed tomography (PET/CT) images. This approach explores how to decouple DQ methods from explicit dependence on object (e.g., organ) delineation through the use of only object recognition results from our recently developed automatic anatomy recognition (AAR) method to quantify disease burden. METHOD: The AAR-DQ process starts off with the AAR approach for modeling anatomy and automatically recognizing objects on low-dose CT images of PET/CT acquisitions. It incorporates novel aspects of model building that relate to finding an optimal disease map for each organ. The parameters of the disease map are estimated from a set of training image data sets including normal subjects and patients with metastatic cancer. The result of recognition for an object on a patient image is the location of a fuzzy model for the object which is optimally adjusted for the image. The model is used as a fuzzy mask on the PET image for estimating a fuzzy disease map for the specific patient and subsequently for quantifying disease based on this map. This process handles blur arising in PET images from partial volume effect entirely through accurate fuzzy mapping to account for heterogeneity and gradation of disease content at the voxel level without explicitly performing correction for the partial volume effect. Disease quantification is performed from the fuzzy disease map in terms of total lesion glycolysis (TLG) and standardized uptake value (SUV) statistics. We also demonstrate that the method of disease quantification is applicable even when the ""object"" of interest is recognized manually with a simple and quick action such as interactively specifying a 3D box ROI. Depending on the degree of automaticity for object and lesion recognition on PET/CT, DQ can be performed at the object level either semi-automatically (DQ-MO) or automatically (DQ-AO), or at the lesion level either semi-automatically (DQ-ML) or automatically. RESULTS: We utilized 67 data sets in total: 16 normal data sets used for model building, and 20 phantom data sets plus 31 patient data sets (with various types of metastatic cancer) used for testing the three methods DQ-AO, DQ-MO, and DQ-ML. The parameters of the disease map were estimated using the leave-one-out strategy. The organs of focus were left and right lungs and liver, and the disease quantities measured were TLG, SUVMean, and SUVMax. On phantom data sets, overall error for the three parameters were approximately 6%, 3%, and 0%, respectively, with TLG error varying from 2% for large ""lesions"" (37mm diameter) to 37% for small ""lesions"" (10mm diameter). On patient data sets, for non-conspicuous lesions, those overall errors were approximately 19%, 14% and 0%; for conspicuous lesions, these overall errors were approximately 9%, 7%, 0%, respectively, with errors in estimation being generally smaller for liver than for lungs, although without statistical significance. CONCLUSIONS: Accurate disease quantification on PET/CT images without performing explicit delineation of lesions is feasible following object recognition. Method DQ-MO generally yields more accurate results than DQ-AO although the difference is statistically not significant. Compared to current methods from the literature, almost all of which focus only on lesion-level DQ and not organ-level DQ, our results were comparable for large lesions and were superior for smaller lesions, with less demand on training data and computational resources. DQ-AO and even DQ-MO seem to have the potential for quantifying disease burden body-wide routinely via the AAR-DQ approach.",1
"Sensitivity and specificity of an algorithm based on medico-administrative data to identify hospitalized patients with major bleeding presenting to an emergency department. BACKGROUND: Validation studies on an ICD-10-based algorithm to identify major bleeding events are scarce, and mostly focused on positive predictive values. OBJECTIVE: To evaluate the sensitivity and specificity of an ICD-10-based algorithm in adult patients referred to hospital. METHODS: This was a cross-sectional, retrospective analysis. Among all hospital stays of adult patients referred to Rennes University Hospital, France, through the emergency ward in 2014, we identified major bleeding events according to an index test based on a list of ICD-10 diagnoses. As a reference, a two-step process was applied: firstly, a computerized request for electronic health records from the emergency ward, using several hemorrhage-related diagnostic codes and specific emergency therapies so as to discard stays with a very low probability of bleeding; secondly, a chart review of selected records was conducted by a medical expert blinded to the index test results and each hospital stay was classified into one of two exclusive categories: major bleeding or no major bleeding, according to pre-specified criteria. RESULTS: Out of 16,012 hospital stays, the reference identified 736 major bleeding events and left 15,276 stays considered as without the target condition. The index test identified 637 bleeding events: 293 intracranial hemorrhages, 197 gastrointestinal hemorrhages and 147 other bleeding events. Overall, sensitivity was 65% (95%CI, 62 to 69), and specificity was 99.0%. We observed differential sensitivity and specificity across bleeding types, with the highest values for intracranial hemorrhage. Positive predictive values ranged from 59% for ""other"" bleeding events, to 71% (95%CI, 65 to 78) for gastrointestinal hemorrhage, and 96% for intracranial hemorrhage. CONCLUSIONS: Low sensitivity and differential measures of accuracy across bleeding types support the need for specific data collection and medical validation rather than using an ICD-10-based algorithm for assessing the incidence of major bleeding.",0
"Understanding the molecular interaction of human argonaute-2 and miR-20a complex: A molecular dynamics approach. Argonaute-2 (AGO2), a member of the Argonaute family, is the only member possessing catalytic and RNA silencing activity. In here, a molecular dynamics (MDs) simulation was performed using the crystal structure of human AGO2 protein complex with miR-20a. miR-20a is involved with various kind of biological process like heart and lung development, oncogenic process, etc. In precise, MD simulation was carried out with AGO2 protein complex with wild type, two mutant sites and four mutant sites in guided microRNA (miRNA). It has been noted that root-mean-square deviation (RMSD) of atomic positions of nucleic acid for wild type and two mutant sites guided miRNA has the same pattern of fluctuations, which stabilizes around 0.27 nm after 2 ns. Cα atom of AGO2 protein in the complex shows that this complex with wild type and two mutant site mutation duplex has a stable RMSD value after 20 ns, ranging between 0.14 and 0.21 nm. From the root-mean-square fluctuation (RMSF), we observed an increased pattern of fluctuations for the atoms of four mutant complex of AGO2-miR-20a complex. This increased RMSF of non-mutated nucleic acids is contributed by U-A bond breaking at the site of the nucleotide of U2 of guided miRNA, as observed from the duplex structure taken at different time steps of the simulation. Superimposed structure of the miRNA-mRNA duplex for the three complexes depicts that the three miRNA-mRNA duplexes are stable during the simulation. Current work demonstrates the possible correlations between the conformational changes of this AGO2-miR-20a duplex structure and the interactions of different atoms.",0
"Applications of FLIKA, a Python-based image processing and analysis platform, for studying local events of cellular calcium signaling. The patterning of cytosolic Ca2+ signals underlies their ubiquitous ability to specifically regulate numerous cellular processes. Advances in fluorescence microscopy have made it possible to image these signals with unprecedented temporal and spatial resolution. However, this is a double-edged sword, as the resulting enormous data sets necessitate development of software to automate image processing and analysis. Here, we describe Flika, an open source, graphical user interface program written in the Python environment that contains a suite of built-in image processing tools to enable intuitive visualization of image data and analysis. We illustrate the utility and power of Flika by three applications for studying cellular Ca2+ signaling: a script for measuring single-cell global Ca2+ signals; a plugin for the detection, localization and analysis of subcellular Ca2+ puffs; and a script that implements a novel approach for fluctuation analysis of transient, local Ca2+ fluorescence signals. This article is part of a Special Issue entitled: ECS Meeting edited by Claus Heizmann, Joachim Krebs and Jacques Haiech.",0
"Bioinformatics Analysis of the Core Genes Related to Lupus Nephritis Through a Network and Pathway-Based Approach. In this study, we explored the genes genetically associated with lupus nephritis (LN), and their function by bioinformatics analysis. We collected genes potentially associated with LN from National Center for Biotechnology Information Center (NCBI-Gene) and Online Mendelian Inheritance in Man (OMIM) databases. The major bioinformatics analysis linked with genes was then revealed by weighted gene co-expression network analysis (WGCNA), crosstalk analysis, functional analysis, and Pivot algorithm. Two hundred twenty-three LN-related genes were obtained by intersecting NCBI-Gene and OMIM databases. Two thousand five hundred sixty-eight LN-related proteins and 23 modules were excavated by String protein interaction network and WGCNA co-expression analysis, respectively. Pivot algorithm included no coding RNA, transcription factor and drug indicated the high-count correlation-associated modules related to cancer, kidney pathophysiological changes, and kidney injury, respectively. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analysis based on 23 modules revealed LN-related genes mainly involved in immune response. Moreover, 19 genes that came from intersection of LN, arthritis, pleurisy, and myocarditis have close relationship with immune diseases and immune processes. Our results from this research may have important implications for understanding the genes underlying LN. Also, the framework proposed in this work can be used to research pathological molecular network and genes related to LN.",0
"Modeling RNA-Binding Protein Specificity In Vivo by Precisely Registering Protein-RNA Crosslink Sites. Feng et al. described an algorithm called mCross to accurately define RNA-binding protein specificity by precisely registering protein-RNA crosslink sites using CLIP data. This method was applied to >100 RBPs and identified a noncanoncial binding motif of SRSF1, which implicates the protein in modulating phase separation.",0
"Fast read alignment with incorporation of known genomic variants. BACKGROUND: Many genetic variants have been reported from sequencing projects due to decreasing experimental costs. Compared to the current typical paradigm, read mapping incorporating existing variants can improve the performance of subsequent analysis. This method is supposed to map sequencing reads efficiently to a graphical index with a reference genome and known variation to increase alignment quality and variant calling accuracy. However, storing and indexing various types of variation require costly RAM space. METHODS: Aligning reads to a graph model-based index including the whole set of variants is ultimately an NP-hard problem in theory. Here, we propose a variation-aware read alignment algorithm (VARA), which generates the alignment between read and multiple genomic sequences simultaneously utilizing the schema of the Landau-Vishkin algorithm. VARA dynamically extracts regional variants to construct a pseudo tree-based structure on-the-fly for seed extension without loading the whole genome variation into memory space. RESULTS: We developed the novel high-throughput sequencing read aligner deBGA-VARA by integrating VARA into deBGA. The deBGA-VARA is benchmarked both on simulated reads and the NA12878 sequencing dataset. The experimental results demonstrate that read alignment incorporating genetic variation knowledge can achieve high sensitivity and accuracy. CONCLUSIONS: Due to its efficiency, VARA provides a promising solution for further improvement of variant calling while maintaining small memory footprints. The deBGA-VARA is available at: https://github.com/hitbc/deBGA-VARA.",0
"Implementation and validation of a three-dimensional cardiac motion estimation network. Purpose: To describe an unsupervised three-dimensional cardiac motion estimation network (CarMEN) for deformable motion estimation from two-dimensional cine MR images. Materials and Methods: A function was implemented using CarMEN, a convolutional neural network that takes two three-dimensional input volumes and outputs a motion field. A smoothness constraint was imposed on the field by regularizing the Frobenius norm of its Jacobian matrix. CarMEN was trained and tested with data from 150 cardiac patients who underwent MRI examinations and was validated on synthetic (n = 100) and pediatric (n = 33) datasets. CarMEN was compared to five state-of-the-art nonrigid body registration methods by using several performance metrics, including Dice similarity coefficient (DSC) and end-point error. Results: On the synthetic dataset, CarMEN achieved a median DSC of 0.85, which was higher than all five methods (minimum– maximum median [or MMM], 0.67–0.84; P,.001), and a median end-point error of 1.7, which was lower than (MMM, 2.1–2.7; P,.001) or similar to (MMM, 1.6–1.7; P..05) all other techniques. On the real datasets, CarMEN achieved a median DSC of 0.73 for Automated Cardiac Diagnosis Challenge data, which was higher than (MMM, 0.33; P,.0001) or similar to (MMM, 0.72–0.75; P..05) all other methods, and a median DSC of 0.77 for pediatric data, which was higher than (MMM, 0.71–0.76; P,.0001) or similar to (MMM, 0.77–0.78; P..05) all other methods. All P values were derived from pairwise testing. For all other metrics, CarMEN achieved better accuracy on all datasets than all other techniques except for one, which had the worst motion estimation accuracy. Conclusion: The proposed deep learning–based approach for three-dimensional cardiac motion estimation allowed the derivation of a motion model that balances motion characterization and image registration accuracy and achieved motion estimation accuracy comparable to or better than that of several state-of-the-art image registration algorithms.",1
"Identification of synthetic lethality based on a functional network by using machine learning algorithms. Synthetic lethality is the synthesis of mutations leading to cell death. Tumor-specific synthetic lethality has been targeted in research to improve cancer therapy. With the advances of techniques in molecular biology, such as RNAi and CRISPR/Cas9 gene editing, efforts have been made to systematically identify synthetic lethal interactions, especially for frequently mutated genes in cancers. However, elucidating the mechanism of synthetic lethality remains a challenge because of the complexity of its influencing conditions. In this study, we proposed a new computational method to identify critical functional features that can accurately predict synthetic lethal interactions. This method incorporates several machine learning algorithms and encodes protein-coding genes by an enrichment system derived from gene ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways to represent their functional features. We built a random forest-based prediction engine by using 2120 selected features and obtained a Matthews correlation coefficient of 0.532. We examined the top 15 features and found that most of them have potential roles in synthetic lethality according to previous studies. These results demonstrate the ability of our proposed method to predict synthetic lethal interactions and provide a basis for further characterization of these particular genetic combinations.",0
"An allosteric inhibitory site conserved in the ectodomain of P2X receptor channels. P2X receptors constitute a gene family of cation channels gated by extracellular ATP. They mediate fast ionotropic purinergic signaling in neurons and non-excitable cell types in vertebrates. The highly calcium-permeable P2X4 subtype has been shown to play a significant role in cardiovascular physiology, inflammatory responses and neuro-immune communication. We previously reported the discovery of a P2X4-selective antagonist, the small organic compound BX430, with submicromolar potency for human P2X4 receptors and marked species-dependence (Ase et al., 2015). The present study investigates the molecular basis of P2X4 inhibition by the non-competitive blocker BX430 using a structural and functional approach relying on mutagenesis and electrophysiology. We provide evidence for the critical contribution of a single hydrophobic residue located in the ectodomain of P2X4 channel subunits, Ile312 in human P2X4, which determines blockade by BX430. We also show that the nature of this extracellular residue in various vertebrate P2X4 orthologs underlies their specific sensitivity or resistance to the inhibitory effects of BX430. Taking advantage of high-resolution crystallographic data available on zebrafish P2X4, we used molecular dynamics simulation to model the docking of BX430 on an allosteric binding site around Ile315 (zebrafish numbering) in the ectodomain of P2X4. We also observed that the only substitution I312D (human numbering) that renders P2X4 silent by itself has also a profound silencing effect on all other P2X subtypes tested when introduced at homologous positions. The generic impact of this aspartate mutation on P2X function indicates that the pre-TM2 subregion involved is conserved functionally and defines a novel allosteric inhibitory site present in all P2X receptor channels. This conserved structure-channel activity relationship might be exploited for the rational design of potent P2X subtype-selective antagonists of therapeutic value.",0
"Aspulvinone O, a natural inhibitor of GOT1 suppresses pancreatic ductal adenocarcinoma cells growth by interfering glutamine metabolism. Background: Distinctive from their normal counterparts, cancer cells exhibit unique metabolic dependencies on glutamine to fuel anabolic processes. Specifically, pancreatic ductal adenocarcinoma (PDAC) cells rely on an unconventional metabolic pathway catalyzed by aspartate transaminase 1 (GOT1) to rewire glutamine metabolism and support nicotinamide adenine dinucleotide phosphate (NADPH) production. Thus, the important role of GOT1 in energy metabolism and Reactive Oxygen Species (ROS) balance demonstrates that targeting GOT1 may serve as an important therapeutic target in PDAC. Methods: To assay the binding affinity between Aspulvinone O (AO) and GOT1 proteins, the virtual docking, microscale thermophoresis (MST), cellular thermal shift assay (CETSA) and drug affinity responsive target stability (DARTS) methods were employed. GOT1 was silenced in several PDAC cell lines. The level of OCR and ECR were assayed by seahorse. To evaluate the in vivo anti-tumor efficacy of AO, the xenograft model was built in CB17/scid mouse. Results: Screening of an in-house natural compound library identified the AO as a novel inhibitor of GOT1 and repressed glutamine metabolism, which sensitizes PDAC cells to oxidative stress and suppresses cell proliferation. Virtual docking analysis suggested that AO could bind to the active site of GOT1 and form obvious hydrophobic interaction with Trp141 together with hydrogen bonds with Thr110 and Ser256. Further in vitro validation, including MST, CETSA and DARTS, further demonstrated the specific combining capacity of AO. We also show that the selective inhibition of GOT1 by AO significantly reduces proliferation of PDAC in vitro and in vivo. Conclusions: Taken together, our findings identify AO as a potent bioactive inhibitor of GOT1 and a novel anti-tumour agent for PDAC therapy.",0
"TAS-120 overcomes resistance to atp-competitive FGFR inhibitors in patients with FGFR2 fusion–positive intrahepatic cholangiocarcinoma. ATP-competitive fibroblast growth factor receptor (FGFR) kinase inhibitors, including BGJ398 and Debio 1347, show antitumor activity in patients with intrahepatic cholangiocarcinoma (ICC) harboring activating FGFR2 gene fusions. Unfortunately, acquired resistance develops and is often associated with the emergence of secondary FGFR2 kinase domain mutations. Here, we report that the irreversible pan-FGFR inhibitor TAS-120 demonstrated efficacy in 4 patients with FGFR2 fusion–positive ICC who developed resistance to BGJ398 or Debio 1347. Examination of serial biopsies, circulating tumor DNA (ctDNA), and patient-derived ICC cells revealed that TAS-120 was active against multiple FGFR2 mutations conferring resistance to BGJ398 or Debio 1347. Functional assessment and modeling the clonal outgrowth of individual resistance mutations from polyclonal cell pools mirrored the resistance profiles observed clinically for each inhibitor. Our findings suggest that strategic sequencing of FGFR inhibitors, guided by serial biopsy and ctDNA analysis, may prolong the duration of benefit from FGFR inhibition in patients with FGFR2 fusion–positive ICC. SIGNIFICANCE: ATP-competitive FGFR inhibitors (BGJ398, Debio 1347) show efficacy in FGFR2-altered ICC; however, acquired FGFR2 kinase domain mutations cause drug resistance and tumor progression. We demonstrate that the irreversible FGFR inhibitor TAS-120 provides clinical benefit in patients with resistance to BGJ398 or Debio 1347 and overcomes several FGFR2 mutations in ICC models.",0
"Implementation of genotype-guided dosing of warfarin with point-of-care genetic testing in three UK clinics: a matched cohort study. BACKGROUND: Warfarin is a widely used oral anticoagulant. Determining the correct dose required to maintain the international normalised ratio (INR) within a therapeutic range can be challenging. In a previous trial, we showed that a dosing algorithm incorporating point-of-care genotyping information ('POCT-GGD' approach) led to improved anticoagulation control. To determine whether this approach could translate into clinical practice, we undertook an implementation project using a matched cohort design. METHODS: At three clinics (implementation group; n = 119), initial doses were calculated using the POCT-GGD approach; at another three matched clinics (control group; n = 93), patients were dosed according to the clinic's routine practice. We also utilised data on 640 patients obtained from routinely collected data at comparable clinics. Primary outcome was percentage time in target INR range. Patients and staff from the implementation group also provided questionnaire feedback on POCT-GGD. RESULTS: Mean percentage time in INR target range was 55.25% in the control group and 62.74% in the implementation group; therefore, 7.49% (95% CI 3.41-11.57%) higher in the implementation group (p = 0.0004). Overall, patients and staff viewed POCT-GGD positively, suggesting minor adjustments to allow smooth implementation into practice. CONCLUSIONS: In the first demonstration of the implementation of genotype-guided dosing, we show that warfarin dosing determined using an algorithm incorporating genetic and clinical factors can be implemented smoothly into clinic, to ensure target INR range is reached sooner and maintained. The findings are like our previous randomised controlled trial, providing an alternative method for improving the risk-benefit of warfarin use in daily practice.",0
"Accuracy of oscillometric blood pressure algorithms in healthy adults and in adults with cardiovascular risk factors. Background Fixed-ratio and slope-based algorithms are used to derive oscillometric blood pressure (BP). However, a paucity of published data exists assessing the accuracy of these methods. Our objective was to determine the accuracy of fixed-ratio and slope-based algorithms in healthy adults and in adults with cardiovascular risk factors. Patients and methods Overall, 85 healthy adults (age≥18 years) and 85 adults with cardiovascular risk factors were studied. Three oscillometric and four two-observer mercury-based auscultation measurements were performed in each, according to International Standards Organization 2013 methodology. Two fixed-ratio algorithms and one slope-based algorithm were applied to process oscillometric waveform envelopes and derive oscillometric BP. Paired and unpaired t-tests were used to compare mean oscillometric BP within and between each group, respectively. Results For healthy adults, mean age was 50.3±17.8 years, mean arm circumference was 30.4±3.8 cm, and 62% were female. In the cardiovascular risk group, mean age was 63.8±12.4 years, mean arm circumference was 31.9±4.2 cm, and 62% were female. For systolic BP, the fixed-ratio algorithms produced the lowest mean error and narrowest SD. For diastolic BP, mean errors were similar for all three algorithms, but the fixed-ratio algorithms had higher precision. The comparison of healthy adults and those with cardiovascular risk factor showed high variability for systolic and diastolic BP (SD: 8.113.9 mmHg). Conclusion In both healthy adults and in those with cardiovascular risk factors, the fixed-ratio technique performed better than the slope-based algorithm. High between-group variability indicates that subject-specific algorithms may be needed.",0
"Prediction of comorbid diseases using weighted geometric embedding of human interactome. Background: Comorbidity is the phenomenon of two or more diseases occurring simultaneously not by random chance and presents great challenges to accurate diagnosis and treatment. As an effort toward better understanding the genetic causes of comorbidity, in this work, we have developed a computational method to predict comorbid diseases. Two diseases sharing common genes tend to increase their comorbidity. Previous work shows that after mapping the associated genes onto the human interactome the distance between the two disease modules (subgraphs) is correlated with comorbidity. Methods: To fully incorporate structural characteristics of interactome as features into prediction of comorbidity, our method embeds the human interactome into a high dimensional geometric space with weights assigned to the network edges and uses the projection onto different dimension to ""fingerprint"" disease modules. A supervised machine learning classifier is then trained to discriminate comorbid diseases versus non-comorbid diseases. Results: In cross-validation using a benchmark dataset of more than 10,000 disease pairs, we report that our model achieves remarkable performance of ROC score = 0.90 for comorbidity threshold at relative risk RR = 0 and 0.76 for comorbidity threshold at RR = 1, and significantly outperforms the previous method and the interactome generated by annotated data. To further incorporate prior knowledge pathways association with diseases, we weight the protein-protein interaction network edges according to their frequency of occurring in those pathways in such a way that edges with higher frequency will more likely be selected in the minimum spanning tree for geometric embedding. Such weighted embedding is shown to lead to further improvement of comorbid disease prediction. Conclusion: The work demonstrates that embedding the two-dimension planar graph of human interactome into a high dimensional geometric space allows for characterizing and capturing disease modules (subgraphs formed by the disease associated genes) from multiple perspectives, and hence provides enriched features for a supervised classifier to discriminate comorbid disease pairs from non-comorbid disease pairs more accurately than based on simply the module separation.",0
"The global burden of non-typhoidal salmonella invasive disease: a systematic analysis for the Global Burden of Disease Study 2017. Background: Non-typhoidal salmonella invasive disease is a major cause of global morbidity and mortality. Malnourished children, those with recent malaria or sickle-cell anaemia, and adults with HIV infection are at particularly high risk of disease. We sought to estimate the burden of disease attributable to non-typhoidal salmonella invasive disease for the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017. Methods: We did a systematic review of scientific databases and grey literature, and estimated non-typhoidal salmonella invasive disease incidence and mortality for the years 1990 to 2017, by age, sex, and geographical location using DisMod-MR, a Bayesian meta-regression tool. We estimated case fatality by age, HIV status, and sociodemographic development. We also calculated the HIV-attributable fraction and estimated health gap metrics, including disability-adjusted life-years (DALYs). Findings: We estimated that 535 000 (95% uncertainty interval 409 000–705 000) cases of non-typhoidal salmonella invasive disease occurred in 2017, with the highest incidence in sub-Saharan Africa (34·5 [26·6–45·0] cases per 100 000 person-years) and in children younger than 5 years (34·3 [23·2–54·7] cases per 100 000 person-years). 77 500 (46 400–123 000) deaths were estimated in 2017, of which 18 400 (12 000–27 700) were attributable to HIV. The remaining 59 100 (33 300–98 100) deaths not attributable to HIV accounted for 4·26 million (2·38–7·38) DALYs in 2017. Mean all-age case fatality was 14·5% (9·2–21·1), with higher estimates among children younger than 5 years (13·5% [8·4–19·8]) and elderly people (51·2% [30·2–72·9] among those aged ≥70 years), people with HIV infection (41·8% [30·0–54·0]), and in areas of low sociodemographic development (eg, 15·8% [10·0–22·9] in sub-Saharan Africa). Interpretation: We present the first global estimates of non-typhoidal salmonella invasive disease that have been produced as part of GBD 2017. Given the high disease burden, particularly in children, elderly people, and people with HIV infection, investigating the sources and transmission pathways of non-typhoidal salmonella invasive disease is crucial to implement effective preventive and control measures. Funding: Bill & Melinda Gates Foundation.",0
"Generalised coherent point drift for group-wise multi-dimensional analysis of diffusion brain MRI data. A probabilistic framework for registering generalised point sets comprising multiple voxel-wise data features such as positions, orientations and scalar-valued quantities, is proposed. It is employed for the analysis of magnetic resonance diffusion tensor image (DTI)-derived quantities, such as fractional anisotropy (FA) and fibre orientation, across multiple subjects. A hybrid Student's t-Watson-Gaussian mixture model-based non-rigid registration framework is formulated for the joint registration and clustering of voxel-wise DTI-derived data, acquired from multiple subjects. The proposed approach jointly estimates the non-rigid transformations necessary to register an unbiased mean template (represented as a 7-dimensional hybrid point set comprising spatial positions, fibre orientations and FA values) to white matter regions of interest (ROIs), and approximates the joint distribution of voxel spatial positions, their associated principal diffusion axes, and FA. Specific white matter ROIs, namely, the corpus callosum and cingulum, are analysed across healthy control (HC) subjects (K = 20 samples) and patients diagnosed with mild cognitive impairment (MCI) (K = 20 samples) or Alzheimer's disease (AD) (K = 20 samples) using the proposed framework, facilitating inter-group comparisons of FA and fibre orientations. Group-wise analyses of the latter is not afforded by conventional approaches such as tract-based spatial statistics (TBSS) and voxel-based morphometry (VBM).",0
"Noise reduction in diffusion MRI using non-local self-similar information in joint x-q space. Diffusion MRI affords valuable insights into white matter microstructures, but suffers from low signal-to-noise ratio (SNR), especially at high diffusion weighting (i.e., b-value). To avoid time-intensive repeated acquisition, post-processing algorithms are often used to reduce noise. Among existing methods, non-local means (NLM) has been shown to be particularly effective. However, most NLM algorithms for diffusion MRI focus on patch matching in the spatial domain (i.e., x-space) and disregard the fact that the data live in a combined 6D space covering both spatial domain and diffusion wavevector domain (i.e., q-space). This drawback leads to inaccurate patch matching in curved white matter structures and hence the inability to effectively use recurrent information for noise reduction. The goal of this paper is to overcome this limitation by extending NLM to the joint x-q space. Specifically, we define for each point in the x-q space a spherical patch from which we extract rotation-invariant features for patch matching. The ability to perform patch matching across q-samples allows patches from differentially orientated structures to be used for effective noise removal. Extensive experiments on synthetic, repeated-acquisition, and HCP data demonstrate that our method outperforms state-of-the-art methods, both qualitatively and quantitatively.",0
"exRNA Atlas Analysis Reveals Distinct Extracellular RNA Cargo Types and Their Carriers Present across Human Biofluids. To develop a map of cell-cell communication mediated by extracellular RNA (exRNA), the NIH Extracellular RNA Communication Consortium created the exRNA Atlas resource (https://exrna-atlas.org). The Atlas version 4P1 hosts 5,309 exRNA-seq and exRNA qPCR profiles from 19 studies and a suite of analysis and visualization tools. To analyze variation between profiles, we apply computational deconvolution. The analysis leads to a model with six exRNA cargo types (CT1, CT2, CT3A, CT3B, CT3C, CT4), each detectable in multiple biofluids (serum, plasma, CSF, saliva, urine). Five of the cargo types associate with known vesicular and non-vesicular (lipoprotein and ribonucleoprotein) exRNA carriers. To validate utility of this model, we re-analyze an exercise response study by deconvolution to identify physiologically relevant response pathways that were not detected previously. To enable wide application of this model, as part of the exRNA Atlas resource, we provide tools for deconvolution and analysis of user-provided case-control studies. An extracellular RNA atlas from five human biofluids (serum, plasma, cerebrospinal fluid, saliva, and urine) reveals six extracellular RNA cargo types, including both vesicular and non-vesicular carriers.",0
"A Deep Learning Mammography-based Model for Improved Breast Cancer Risk Prediction. Background Mammographic density improves the accuracy of breast cancer risk models. However, the use of breast density is limited by subjective assessment, variation across radiologists, and restricted data. A mammography-based deep learning (DL) model may provide more accurate risk prediction. Purpose To develop a mammography-based DL breast cancer risk model that is more accurate than established clinical breast cancer risk models. Materials and Methods This retrospective study included 88 994 consecutive screening mammograms in 39 571 women between January 1, 2009, and December 31, 2012. For each patient, all examinations were assigned to either training, validation, or test sets, resulting in 71 689, 8554, and 8751 examinations, respectively. Cancer outcomes were obtained through linkage to a regional tumor registry. By using risk factor information from patient questionnaires and electronic medical records review, three models were developed to assess breast cancer risk within 5 years: a risk-factor-based logistic regression model (RF-LR) that used traditional risk factors, a DL model (image-only DL) that used mammograms alone, and a hybrid DL model that used both traditional risk factors and mammograms. Comparisons were made to an established breast cancer risk model that included breast density (Tyrer-Cuzick model, version 8 [TC]). Model performance was compared by using areas under the receiver operating characteristic curve (AUCs) with DeLong test (P < .05). Results The test set included 3937 women, aged 56.20 years +/- 10.04. Hybrid DL and image-only DL showed AUCs of 0.70 (95% confidence interval [CI]: 0.66, 0.75) and 0.68 (95% CI: 0.64, 0.73), respectively. RF-LR and TC showed AUCs of 0.67 (95% CI: 0.62, 0.72) and 0.62 (95% CI: 0.57, 0.66), respectively. Hybrid DL showed a significantly higher AUC (0.70) than TC (0.62; P < .001) and RF-LR (0.67; P = .01). Conclusion Deep learning models that use full-field mammograms yield substantially improved risk discrimination compared with the Tyrer-Cuzick (version 8) model. (c) RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Sitek and Wolfe in this issue.",1
"Artificial neural network in total survival predicting of multiple myeloma patients. Introduction At the present stage, the prediction of total survival (TS) in multiple myeloma (MM) is usually carried out by the ISS staging system (2005). In real clinical practice, the parameters can significantly differ from the expected, while some patients overcome it, and some do not reach it.The more accurate prediction of the patients TS will optimize the therapeutic tactics choice and take into account the patient`s individual characteristics. The latter reflects the personalized medicine principles, which are the basis of modern trends in therapeutic science. The aim of this work was a study of artificial neural network (ANN), in order to predict TS in patients with MM, because the ANN has the properties of a universal classifier, clusterization and can be used for regression analysis. Methods There were examined 135 patients MM I-III stage with known TS data. At the time of diagnosis, gender, age, Charlson comorbidity index were taken into account, and biochemical parameters such as total protein, albumin, ß2-microglobulin, creatinine, glomerular filtration rate by MDRD, urea, uric acid, lactate dehydrogenase, alanine aminotransferase, aspartate aminotransferase, total bilirubin, indirect bilirubin, glucose were studied. It was necessary to develop the ANN for TS predicting on available clinical data. The ANN based on simple perceptrons was chosen for the study, implemented in the language of artificial intelligence Python. As input, the two Excel spreadsheets were used, storing the initial data on the clinical performance of patients and data on the clinical performance of patients with known TS. As an output document for the information system, an Excel spreadsheet was also used, in which, as a result of the ANN work, the prognostic value of the patient’s TS was determined. The information system is implemented in the form of a doctor automated place, with the ability to transfer information to the hospital information system. Results The two modes of operation were implemented in the system: training and forecasting. In the training mode, the results of clinical data and TS were fed to the input of the neural network and neuron weights were adjusted. ANN training was conducted on all known patients (135 people) and was repeated 100 thousand times to more accurately adjust the significance of clinical parameters affecting TS. In the forecast mode, clinical data results were fed to the input of the neural network and forecasts were formed. The ANN experimental studies are showing the results with artificial neural network (Table 1). Conclusions The experiment showed more accurate TS prediction using ANN, compared to the currently adopted ISS system. In addition, the ANN provides for the study of existing relationships on ready-made models, does not require assumptions of the main distribution of the population, and is able to work with incomplete and fuzzy data. The use of intellectual information technologies opens up new opportunities in the study of dynamic problems in the field of medicine.",0
"Biochemical, machine learning and molecular approaches for the differential diagnosis of Mucopolysaccharidoses. This study was aimed to construct classification and regression tree (CART) model of glycosaminoglycans (GAGs) for the differential diagnosis of Mucopolysaccharidoses (MPS). Two-dimensional electrophoresis and liquid chromatography–tandem mass spectrometry (LC–MS/MS) were used for the qualitative and quantitative analysis of GAGs. Specific enzyme assays and targeted gene sequencing were performed to confirm the diagnosis. Machine learning tools were used to develop CART model based on GAG profile. Qualitative and quantitative CART models showed 96.3% and 98.3% accuracy, respectively, in the differential diagnosis of MPS. The thresholds of different GAGs diagnostic of specific MPS types were established. In 60 MPS positive cases, 46 different mutations were identified in six specific genes. Among 31 different mutations identified in IDUA, nine were nonsense mutations and two were gross deletions while the remaining were missense mutations. In IDS gene, four missense, two frameshift, and one deletion were identified. In NAGLU gene, c.1693C > T and c.1914_1914insT were the most common mutations. Two ARSB, one case each of SGSH and GALNS mutations were observed. LC–MS/MS-based GAG pattern showed higher accuracy in the differential diagnosis of MPS. The mutation spectrum of MPS, specifically in IDUA and IDS genes, is highly heterogeneous among the cases studied.",0
"Self-Organized Synchronous Calcium Transients in a Cultured Human Neural Network Derived from Cerebral Organoids. Cerebral activity is derived from the assembly of activated cells, but it is currently difficult to study human cerebral neuronal network activities. Here, Sakaguchi et al. report self-organized and complex human neural network activity using organoid technology and drug-inducible dynamic changes of the activity that will be useful for future research on human brain function and neuropsychiatric disorders.",0
"Mitochondrial homeostasis in aml and gasping for response in resistance to bcl2 blockade. Understanding resistance to BCL2 inhibition is a critical scientific and clinical challenge. In this issue of Cancer Discovery, two laboratories use unbiased approaches of large loss-of-function CRISPR/Cas 9 screens to discover targetable liabilities in cell signaling and metabolism to acute myeloid leukemia resistant to BCL2 inhibition.",0
"Digital pathology and artificial intelligence. In modern clinical practice, digital pathology has a crucial role and is increasingly a technological requirement in the scientific laboratory environment. The advent of whole-slide imaging, availability of faster networks, and cheaper storage solutions has made it easier for pathologists to manage digital slide images and share them for clinical use. In parallel, unprecedented advances in machine learning have enabled the synergy of artificial intelligence and digital pathology, which offers image-based diagnosis possibilities that were once limited only to radiology and cardiology. Integration of digital slides into the pathology workflow, advanced algorithms, and computer-aided diagnostic techniques extend the frontiers of the pathologist's view beyond a microscopic slide and enable true utilisation and integration of knowledge that is beyond human limits and boundaries, and we believe there is clear potential for artificial intelligence breakthroughs in the pathology setting. In this Review, we discuss advancements in digital slide-based image diagnosis for cancer along with some challenges and opportunities for artificial intelligence in digital pathology.",0
"Heterogeneous effects of alveolar recruitment in acute respiratory distress syndrome: a machine learning reanalysis of the Alveolar Recruitment for Acute Respiratory Distress Syndrome Trial. BACKGROUND: Despite a robust physiological rationale, recruitment manoeuvres with PEEP titration were associated with harm in the Alveolar Recruitment for Acute Respiratory Distress Syndrome Trial (ART). We sought to investigate the potential heterogeneity in treatment effects in patients enrolled in the ART, using a machine learning approach. METHODS: The primary outcome was hospital mortality. Patients were clustered using baseline clinical and physiological data using the k-means for mixed large data method. The heterogeneity in treatment effect between clusters was investigated using Bayesian methods. We further investigated whether baseline driving pressure could modulate the association between treatment arm, cluster, and mortality. RESULTS: Data from all 1010 patients enrolled in the ART were analysed. Partitioning suggested that three clusters were present in the ART population. The largest cluster (Cluster 1) was characterised by patients with pneumonia and requiring vasopressor support. Recruitment manoeuvres with PEEP titration were associated with higher mortality in Cluster 1 (probability of harm of >98%), but this association was absent in Clusters 2 and 3 (probability of harm of 45% and 68%, respectively). Higher baseline driving pressure was associated with a progressive reduction in the association between alveolar recruitment with PEEP titration and mortality. CONCLUSIONS: Recruitment manoeuvre with PEEP titration may be harmful in acute respiratory distress syndrome (ARDS) patients with pneumonia or requiring vasopressor support. Driving pressure appears to modulate the association between the ART study intervention, aetiology of ARDS, and mortality. This machine learning approach may help tailor future RCTs. CLINICAL TRIAL REGISTRATION: NCT01374022.",1
"Association between Parapapillary Choroidal Vessel Density Measured with Optical Coherence Tomography Angiography and Future Visual Field Progression in Patients with Glaucoma. Importance: Investigating the vascular risk factors of glaucoma progression is important to individualize treatment; however, few studies have investigated these factors because the available methods have proven insufficient to evaluate the vascular features of patients with glaucoma. Recently, the advent of optical coherence tomography angiography (OCT-A) allowed both qualitative and quantitative microvascular data to be obtained, to in turn evaluate the perfusion status of different retinal layers. Objective: To determine whether baseline parapapillary choroidal vessel density (VD) as measured by OCT-A was associated with future glaucoma progression. Design, Setting, and Participants: A prospective, observational, comparative study was conducted at Seoul St Mary's Hospital of The Catholic University of Korea from March 1, 2016, to December 31, 2018, for 108 glaucomatous eyes in which the retinal nerve fiber layer thickness and mean deviation were measured by at least 5 serial OCT and visual field (VF) examinations. The participants underwent OCT-A at baseline. Vessel density was measured using the en face image of the choroidal map of OCT-A within the β-zone parapapillary atrophy region. Main Outcomes and Measures: Parapapillary choroidal VD, retinal nerve fiber layer thinning rate, mean deviation rate, and progression of glaucoma as measured by OCT and VF. Results: Among 108 patients (74 women and 34 men; mean [SD] age, 59.2 [13.1] years), 38 (35.2%) showed progression of glaucoma as measured by OCT and 34 (31.5%) showed progression of glaucoma as measured by VF at the last follow-up. The mean (SD) follow-up duration was 2.6 [2.3] years. The presence of disc hemorrhage (odds ratio, 5.57; 95% CI, 3.18-8.29; P =.001), baseline mean deviation (odds ratio, 0.83; 95% CI, 0.71-0.97; P =.02), and parapapillary choroidal VD (odds ratio, 1.18; 95% CI, 1.09-1.28; P =.01) were associated with progression of glaucoma as measured by VF, but not with progression of glaucoma as measured by OCT. Baseline parapapillary choroidal VD (β, 1.08; 95% CI, 1.02-1.13; P <.001) was associated with progression of glaucoma as measured by VF using Cox proportional hazards regression analysis. Conclusions and Relevance: These data suggest that lower parapapillary choroidal VD within the β-zone parapapillary atrophy at baseline among individuals with glaucoma could play some role in the risk of progression of glaucoma as measured by VF. The findings suggest that patients with glaucoma with lower parapapillary choroidal VD within the β-zone parapapillary atrophy at baseline warrant careful monitoring for progression of glaucoma as measured by VF.",0
"Preoperative Radiomic Approach to Evaluate Tumor-Infiltrating CD8+ T Cells in Hepatocellular Carcinoma Patients Using Contrast-Enhanced Computed Tomography. Background: To help identify potential hepatocellular carcinoma (HCC) candidates for immunotherapies, we aimed to develop and validate a radiomics-based biomarker (Rad score) to predict the infiltration of tumor-infiltrating CD8+ T cells in HCC patients, and to evaluate the correlation of Rad score with tumor immune characteristics. Methods: Overall, 142 HCC patients (n = 100 and n = 42 in the training and validation sets, respectively) were subjected to radiomic feature extraction. Imaging features and immunochemistry data of patients in the training set were subjected to elastic-net regularized regression analysis to predict the level of CD8+ T cell infiltration. Results: A Rad score for CD8+ T-cell infiltration, which contained seven variables, was developed and was validated in the validation set (area under the curve [AUC]: training set 0.751, 95% confidence interval [CI] 0.656–0.846; validation set 0.705, 95% CI 0.547–0.863). The decision curve indicated the clinical usefulness of the Rad score. A higher Rad score correlated with superior overall and disease-free survival outcomes (p = 0.012 and 0.0088, respectively). Using the pathological slides, we found that the Rad score positively correlated with the percentage of tumor-infiltrating lymphocytes (TILs; Spearman rho = 0.51, p < 0.0001). Moreover, the Rad score could also discriminate inflamed tumors from immune-desert and immune-excluded tumors (Kruskal–Wallis, p < 0.0001), and higher Rad scores could be found in patients with positive programmed cell death ligand 1 expression in tumor/immune cells, as well as those with positive programmed cell death protein 1 expression. Conclusion: The newly developed Rad score was a powerful predictor of CD8+ T-cell infiltration, which could be useful in identifying potential HCC patients who can benefit from immunotherapies when validated in large-scale prospective cohorts.",1
"Expectation-induced modulation of metastable activity underlies faster coding of sensory stimuli. Sensory stimuli can be recognized more rapidly when they are expected. This phenomenon depends on expectation affecting the cortical processing of sensory information. However, the mechanisms responsible for the effects of expectation on sensory circuits remain elusive. In the present study, we report a novel computational mechanism underlying the expectation-dependent acceleration of coding observed in the gustatory cortex of alert rats. We use a recurrent spiking network model with a clustered architecture capturing essential features of cortical activity, such as its intrinsically generated metastable dynamics. Relying on network theory and computer simulations, we propose that expectation exerts its function by modulating the intrinsically generated dynamics preceding taste delivery. Our model's predictions were confirmed in the experimental data, demonstrating how the modulation of ongoing activity can shape sensory coding. Altogether, these results provide a biologically plausible theory of expectation and ascribe an alternative functional role to intrinsically generated, metastable activity.",0
"Improving low-dose pediatric abdominal CT by using convolutional neural networks. Purpose: To evaluate the efficacy of convolutional neural networks (CNNs) to improve the image quality of low-dose pediatric abdominal CT images. Materials and Methods: Images from 11 pediatric abdominal CT examinations acquired between June and July 2018 were reconstructed with filtered back projection (FBP) and an iterative reconstruction (IR) algorithm. A residual CNN was trained using the FBP image as the input and the difference between FBP and IR as the target such that the network was able to predict the residual image and simulate the IR. CNN-based postprocessing was applied to 20 low-dose pediatric image datasets acquired between December 2016 and December 2017 on a scanner limited to reconstructing FBP images. The FBP and CNN images were evaluated based on objective image noise and subjective image review by two pediatric radiologists. For each of five features, readers rated images on a five-point Likert scale and also indicated their preferred series. Readers also indicated their “overall preference” for CNN versus FBP. Preference and Likert scores were analyzed for individual and combined readers. Interreader agreement was assessed. Results: The CT number remained unchanged between FBP and CNN images. Image noise was reduced by 31% for CNN images (P <.001). CNN was preferred for overall image quality for individual and combined readers. For combined Likert scores, at least one of the two score types (Likert or binary preference) indicated a significant favoring of CNN over FBP for low contrast, image noise, artifacts, and high contrast, whereas the reverse was true for spatial resolution. Conclusion: FBP images can be improved in image space by a well-trained CNN, which may afford a reduction in dose or improvement in image quality on scanners limited to FBP reconstruction.",1
"Detection of Hemodynamically Significant Coronary Stenosis: CT Myocardial Perfusion versus Machine Learning CT Fractional Flow Reserve. Background Direct intraindividual comparison of dynamic CT myocardial perfusion imaging (MPI) and machine learning (ML)-based CT fractional flow reserve (FFR) has not been explored for diagnosing hemodynamically significant coronary artery disease. Purpose To investigate the diagnostic performance of dynamic CT MPI and ML-based CT FFR for functional assessment of coronary stenosis. Materials and Methods Between January 2, 2017, and October 17, 2018, consecutive participants with stable angina were prospectively enrolled. All participants underwent dynamic CT MPI coronary CT angiography and invasive conventional coronary angiography (CCA) FFR within 2 weeks. Receiver operating characteristic (ROC) curve analysis was used to assess diagnostic performance. Results Eighty-six participants (mean age, 67 years +/- 12 [standard deviation]; 67 men) with 157 target vessels were included for final analysis. The mean radiation doses for dynamic CT MPI and coronary CT angiography were 3.6 mSv +/- 1.1 and 2.7 mSv +/- 0.8, respectively. Myocardial blood flow (MBF) was lower in ischemic segments compared with nonischemic segments and reference segments (defined as the territory of vessels without stenosis) (75 mL/100 mL/min +/- 20 vs 148 mL/100 mL/min +/- 22 and 169 mL/100 mL/min +/- 34, respectively, both P < .001). Similarly, CT FFR was also lower for hemodynamically significant lesions than for hemodynamically nonsignificant lesions (0.68 +/- 0.1 vs 0.83 +/- 0.1, respectively, P < .001). MBF had the largest area under the ROC curve (AUC) (using 99 mL/100 mL/min as a cutoff) among all parameters, outperforming ML-based CT FFR (AUC = 0.97 vs 0.85, P < .001). The vessel-based specificity and diagnostic accuracy of MBF were higher than those of ML-based CT FFR (93% vs 68%, P < .001 and 94% vs 78%, respectively, P = .04) whereas the sensitivity of both methods was similar (96% vs 88%, respectively, P = .11). Conclusion Dynamic CT myocardial perfusion imaging was able to help accurately evaluate the hemodynamic significance of coronary stenosis using a reduced amount of radiation. In addition, the myocardial blood flow derived from dynamic CT myocardial perfusion imaging outperformed machine learning-based CT fractional flow reserve for identifying lesions causing ischemia. (c) RSNA, 2019 Online supplemental material is available for this article.See also the editorial by Loewe in this issue.",1
"Supervised learning for bone shape and cortical thickness estimation from CT images for finite element analysis. Knowledge about the thickness of the cortical bone is of high interest for fracture risk assessment. Most finite element model solutions overlook this information because of the coarse resolution of the CT images. To circumvent this limitation, a three-steps approach is proposed. 1) Two initial surface meshes approximating the outer and inner cortical surfaces are generated via a shape regression based on morphometric features and statistical shape model parameters. 2) The meshes are then corrected locally using a supervised learning model build from image features extracted from pairs of QCT (0.3-1 mm resolution) and HRpQCT images (82 microm resolution). As the resulting meshes better follow the cortical surfaces, the cortical thickness can be estimated at sub-voxel precision. 3) The meshes are finally regularized by a Gaussian process model featuring a two-kernel model, which seamlessly enables smoothness and shape-awareness priors during regularization. The resulting meshes yield high-quality mesh element properties, suitable for construction of tetrahedral meshes and finite element simulations. This pipeline was applied to 36 pairs of proximal femurs (17 males, 19 females, 76+/-12 years) scanned under QCT and HRpQCT modalities. On a set of leave-one-out experiments, we quantified accuracy (root mean square error = 0.36+/-0.29 mm) and robustness (Hausdorff distance = 3.90+/-1.57 mm) of the outer surface meshes. The error in the estimated cortical thickness (0.05+/-0.40 mm), and the tetrahedral mesh quality (aspect ratio = 1.4+/-0.02) are also reported. The proposed pipeline produces finite element meshes with patient-specific bone shape and sub-voxel cortical thickness directly from CT scans. It also ensures that the nodes and elements numbering remains consistent and independent of the morphology, which is a distinct advantage in population studies.",1
"Identifying peer experts in online health forums. BACKGROUND: Online health forums have become increasingly popular over the past several years. They provide members with a platform to network with peers and share information, experiential advice, and support. Among the members of health forums, we define ""peer experts"" as a set of lay users who have gained expertise on the particular health topic through personal experience, and who demonstrate credibility in responding to questions from other members. This paper aims to motivate the need to identify peer experts in health forums and study their characteristics. METHODS: We analyze profiles and activity of members of a popular online health forum and characterize the interaction behavior of peer experts. We study the temporal patterns of comments posted by lay users and peer experts to uncover how peer expertise is developed. We further train a supervised classifier to identify peer experts based on their activity level, textual features, and temporal progression of posts. RESULT: A support vector machine classifier with radial basis function kernel was found to be the most suitable model among those studied. Features capturing the key semantic word classes and higher mean user activity were found to be most significant features. CONCLUSION: We define a new class of members of health forums called peer experts, and present preliminary, yet promising, approaches to distinguish peer experts from novice users. Identifying such peer expertise could potentially help improve the perceived reliability and trustworthiness of information in community health forums.",1
"Computational and artificial neural network based study of functional SNPs of human LEPR protein associated with reproductive function. Genetic polymorphisms are mostly associated with inherited diseases, detecting and analyzing the biological significance of functional single-nucleotide polymorphisms (SNPs) using wet laboratory experiments is an arduous task hence the computational analysis of putative SNPs is essential before conducting a study on a large population. SNP in the leptin receptor (LEPR) could result in the retention of intracellular signalling due to the structural and functional instability of the receptor causing abnormal reproductive function in human. In this first comprehensive computational analysis of LEPR gene mutation, we have identified and analyzed the functional consequence and structural significance of the SNPs in LEPR using recently developed several computational algorithms. Thirteen deleterious mutations such as W13C, S93G, I232R, Q307H, Y354C, E497A, Q571H, R612H, K656N, T690A, T699M V741M, and L760R were identified in the LEPR gene coding region. Backpropagation algorithm has been developed to forestall the deleterious nature of SNP and to validate the outcome of the tested computational tools. From ConSurf prediction three SNPs (Q571H, R612H, and T699M) were highly conserved on LEPR protein and the most deleterious variant R612H had one hydrogen bond abolished and severely reduced protein stability. Molecular docking suggested that the mutant (R612H) LEPR had lowest binding energy than native LEPR with the ligand molecule. Thus the energetically destructive changeover of ARG to HIS in R612H could possibly affect the LEPR protein structural stability and functional constancy due to interruption in the amino acid interactions and could result in reproductive disorders in human and increases the complication in obstetric and pregnancy outcome.",0
"Rationale and Application of the Protocol S Anti-Vascular Endothelial Growth Factor Algorithm for Proliferative Diabetic Retinopathy. PURPOSE: To present the rationale, guidelines, and results of ranibizumab treatment for proliferative diabetic retinopathy (PDR) in Diabetic Retinopathy Clinical Research Network (DRCR.net) Protocol S. DESIGN: Post hoc analyses from a randomized clinical trial. PARTICIPANTS: Three hundred five participants (394 study eyes) having PDR without prior panretinal photocoagulation (PRP). METHODS: Intravitreous ranibizumab (0.5 mg) versus PRP for PDR. Ranbizumab-assigned eyes (n = 191) received monthly injections for 6 months unless resolution was achieved after 4 injections. After 6 months, injections could be deferred if neovascularization was stable over 3 consecutive visits (sustained stability). If neovascularization worsened, monthly treatment resumed. Panretinal photocoagulation could be initiated for failure or futility criteria. MAIN OUTCOME MEASURES: Neovascularization status through 2 years. RESULTS: At 1 month, 19% (35 of 188) of ranibizumab-assigned eyes showed complete neovascularization resolution and an additional 60% (113) showed improvement. At 6 months, 52% (80 of 153) showed neovascularization resolution, 3% (4) were improved, 37% (56) were stable, and 8% (13) had worsened since the last visit. Among eyes with versus without resolved neovascularization at 6 months, the median (interquartile range) number of injections between 6 months and 2 years was 4 (1-7; n = 73) versus 7 (4-11; n = 67; P < 0.001). Injections were deferred in 68 of 73 eyes (93%) meeting sustained stability at least once during the study; 62% (42 of 68) resumed injections within 16 weeks after deferral. At 2 years, 43% (66 of 154) showed neovascularization resolution, 5% (7) showed improvement, 23% (36) were stable, and 27% (42) had worsened since the last visit. Only 3 eyes met criteria for failure or futility through 2 years. CONCLUSIONS: The DRCR.net treatment algorithm for PDR can provide excellent clinical outcomes through 2 years for patients initiating anti-vascular endothelial growth factor (VEGF) therapy for PDR. When choosing between anti-VEGF and PRP as first-line therapy for PDR, treatment decisions should be guided by consideration of the relative advantages of each therapeutic method and anticipated patient compliance with follow-up and treatment recommendations.",0
"Childhood Asthma: Advances Using Machine Learning and Mechanistic Studies. A paradigm shift brought by the recognition that childhood asthma is an aggregated diagnosis that comprises several different endotypes underpinned by different pathophysiology, coupled with advances in understanding potentially important causal mechanisms, offers a real opportunity for a step change to reduce the burden of the disease on individual children, families, and society. Data-driven methodologies facilitate the discovery of ""hidden"" structures within ""big healthcare data"" to help generate new hypotheses. These findings can be translated into clinical practice by linking discovered ""phenotypes"" to specific mechanisms and clinical presentations. Epidemiological studies have provided important clues about mechanistic avenues that should be pursued to identify interventions to prevent the development or alter the natural history of asthma-related diseases. Findings from cohort studies followed by mechanistic studies in humans and in neonatal mouse models provided evidence that environments such as traditional farming may offer protection by modulating innate immune responses and that impaired innate immunity may increase susceptibility. The key question of which component of these exposures can be translated into interventions requires confirmation. Increasing mechanistic evidence is demonstrating that shaping the microbiome in early life may modulate immune function to confer protection. Iterative dialogue and continuous interaction between experts with different but complementary skill sets, including data scientists who generate information about the hidden structures within ""big data"" assets, and medical professionals, epidemiologists, basic scientists, and geneticists who provide critical clinical and mechanistic insights about the mechanisms underpinning the architecture of the heterogeneity, are keys to delivering mechanism-based stratified treatments and prevention.",0
"Developing a portable natural language processing based phenotyping system. BACKGROUND: This paper presents a portable phenotyping system that is capable of integrating both rule-based and statistical machine learning based approaches. METHODS: Our system utilizes UMLS to extract clinically relevant features from the unstructured text and then facilitates portability across different institutions and data systems by incorporating OHDSI's OMOP Common Data Model (CDM) to standardize necessary data elements. Our system can also store the key components of rule-based systems (e.g., regular expression matches) in the format of OMOP CDM, thus enabling the reuse, adaptation and extension of many existing rule-based clinical NLP systems. We experimented with our system on the corpus from i2b2's Obesity Challenge as a pilot study. RESULTS: Our system facilitates portable phenotyping of obesity and its 15 comorbidities based on the unstructured patient discharge summaries, while achieving a performance that often ranked among the top 10 of the challenge participants. CONCLUSION: Our system of standardization enables a consistent application of numerous rule-based and machine learning based classification techniques downstream across disparate datasets which may originate across different institutions and data systems.",1
"Novel Machine Learning Approach to Identify Preoperative Risk Factors Associated With Super-Utilization of Medicare Expenditure Following Surgery. Importance: Typically defined as the top 5% of health care users, super-utilizers are responsible for an estimated 40% to 55% of all health care costs. Little is known about which factors may be associated with increased risk of long-term postoperative super-utilization. Objective: To identify clusters of patients with distinct constellations of clinical and comorbid patterns who may be associated with an elevated risk of super-utilization in the year following elective surgery. Design, Setting, and Participants: A retrospective longitudinal cohort study of 1049160 patients who underwent abdominal aortic aneurysm repair, coronary artery bypass graft, colectomy, total hip arthroplasty, total knee arthroplasty, or lung resection were identified from the 100% Medicare inpatient and outpatient Standard Analytic Files at all inpatient facilities performing 1 or more of the evaluated surgical procedures from 2013 to 2015. Data from 2012 to 2016 were used to evaluate expenditures in the year preceding and following surgery. Using a machine learning approach known as Logic Forest, comorbidities and interactions of comorbidities that put patients at an increased chance of becoming a super-utilizer were identified. All comorbidities, as defined by the Charlson (range, 0-24) and Elixhauser (range, 0-29) comorbidity indices, were used in the analysis. Higher scores indicated higher comorbidity burden. Data analysis was completed on November 16, 2018. Main Outcome and Measures: Super-utilization of health care in the year following surgery. Results: In total, 1049160 patients met inclusion criteria and were included in the analytic cohort. Their median (interquartile range) age was 73 (69-78) years, and approximately 40% were male. Super-utilizers comprised 4.8% of the overall cohort (n = 79746) yet incurred 31.7% of the expenditures. Although the difference in overall expenditures per person between super-utilizers ($4049) and low users ($2148) was relatively modest prior to surgery, the difference in expenditures between super-utilizers ($79698) vs low users ($2977) was marked in the year following surgery. Risk factors associated with super-utilization of health care included hemiplegia/paraplegia (odds ratio, 5.2; 95% CI, 4.4-6.2), weight loss (odds ratio, 3.5; 95% CI, 2.9-4.2), and congestive heart failure with chronic kidney disease stages I to IV (odds ratio, 3.4; 95% CI, 3.0-3.9). Conclusions and Relevance: Super-utilizers comprised only a small fraction of the surgical population yet were responsible for a disproportionate amount of Medicare expenditure. Certain subpopulations were associated with super-utilization of health care following surgical intervention despite having lower overall use in the preoperative period.",1
"Deep learning-based CT image reconstruction: Initial evaluation targeting hypovascular hepatic metastases. Purpose: To evaluate the effect of a deep learning-based reconstruction (DLR) method on the conspicuity of hypovascular hepatic metastases on abdominal CT images. Materials and Methods: This retrospective study with institutional review board approval included 58 patients with hypovascular hepatic metastases. A radiologist recorded the standard deviation of attenuation in the paraspinal muscle as the image noise and the contrast-to-noise ratio (CNR). CNR was calculated as region of interest ([ROI]L − ROIT)/N, here ROIL is the mean liver parenchyma attenuation, ROIT, the mean tumor attenuation, and N, the noise. Two other radiologists graded the conspicuity of the liver lesion on a five-point scale where 1 is unidentifiable and 5 is detected without diagnostic compromise. Only the smallest liver lesion in each patient, classified as smaller or larger than 10 mm, was evaluated. The difference between hybrid iterative reconstruction (IR) and DLR images was determined by using a two-sided Wilcoxon signed-rank test. Results: The image noise was significantly lower, and the CNR was significantly higher on DLR images than hybrid IR images (median image noise: 19.2 vs 12.8 HU, P, 001; median CNR: Tumors, 10 mm: 1.9 vs 2.5; tumors. 10 mm: 1.7 vs 2.2, both P, 001). The scores for liver lesions were significantly higher for DLR images than hybrid IR images (P, 01 for both in tumors smaller or larger than 10 mm). Conclusion: DLR improved the quality of abdominal CT images for the evaluation of hypovascular hepatic metastases.",1
"Low plasma lysophosphatidylcholines are associated with impaired mitochondrial oxidative capacity in adults in the Baltimore Longitudinal Study of Aging. The decrease in skeletal muscle mitochondrial oxidative capacity with age adversely affects muscle strength and physical performance. Factors that are associated with this decrease have not been well characterized. Low plasma lysophosphatidylcholines (LPC), a major class of systemic bioactive lipids, are predictive of aging phenotypes such as cognitive impairment and decline of gait speed in older adults. Therefore, we tested the hypothesis that low plasma LPC are associated with impaired skeletal muscle mitochondrial oxidative capacity. Skeletal muscle mitochondrial oxidative capacity was measured using in vivo phosphorus magnetic resonance spectroscopy (31P-MRS) in 385 participants (256 women, 129 men), aged 24–97 years (mean 72.5) in the Baltimore Longitudinal Study of Aging. Postexercise recovery rate of phosphocreatine (PCr), kPCr, was used as a biomarker of mitochondrial oxidative capacity. Plasma LPC were measured using liquid chromatography–tandem mass spectrometry. Adults in the highest quartile of kPCr had higher plasma LPC 16:0 (p = 0.04), 16:1 (p = 0.004), 17:0 (p = 0.01), 18:1 (p = 0.0002), 18:2 (p = 0.002), and 20:3 (p = 0.0007), but not 18:0 (p = 0.07), 20:4 (p = 0.09) compared with those in the lower three quartiles in multivariable linear regression models adjusting for age, sex, and height. Multiple machine-learning algorithms showed an area under the receiver operating characteristic curve of 0.638 (95% confidence interval, 0.554, 0.723) comparing six LPC in adults in the lower three quartiles of kPCr with the highest quartile. Low plasma LPC are associated with impaired mitochondrial oxidative capacity in adults.",0
"Improved cell composition deconvolution method of bulk gene expression profiles to quantify subsets of immune cells. Background: To facilitate the investigation of the pathogenic roles played by various immune cells in complex tissues such as tumors, a few computational methods for deconvoluting bulk gene expression profiles to predict cell composition have been created. However, available methods were usually developed along with a set of reference gene expression profiles consisting of imbalanced replicates across different cell types. Therefore, the objective of this study was to create a new deconvolution method equipped with a new set of reference gene expression profiles that incorporate more microarray replicates of the immune cells that have been frequently implicated in the poor prognosis of cancers, such as T helper cells, regulatory T cells and macrophage M1/M2 cells. Methods: Our deconvolution method was developed by choosing ϵ-support vector regression (ϵ-SVR) as the core algorithm assigned with a loss function subject to the L1-norm penalty. To construct the reference gene expression signature matrix for regression, a subset of differentially expressed genes were chosen from 148 microarray-based gene expression profiles for 9 types of immune cells by using ANOVA and minimizing condition number. Agreement analyses including mean absolute percentage errors and Bland-Altman plots were carried out to compare the performances of our method and CIBERSORT. Results: In silico cell mixtures, simulated bulk tissues, and real human samples with known immune-cell fractions were used as the test datasets for benchmarking. Our method outperformed CIBERSORT in the benchmarks using in silico breast tissue-immune cell mixtures in the proportions of 30:70 and 50:50, and in the benchmark using 164 human PBMC samples. Our results suggest that the performance of our method was at least comparable to that of a state-of-the-art tool, CIBERSORT. Conclusions: We developed a new cell composition deconvolution method and the implementation was entirely based on the publicly available R and Python packages. In addition, we compiled a new set of reference gene expression profiles, which might allow for a more robust prediction of the immune cell fractions from the expression profiles of cell mixtures. The source code of our method could be downloaded from https://github.com/holiday01/deconvolution-to-estimate-immune-cell-subsets.",0
"The triple variable index combines information generated over time from common monitoring variables to identify patients expressing distinct patterns of intraoperative physiology. BACKGROUND: Mean arterial pressure (MAP), bispectral index (BIS), and minimum alveolar concentration (MAC) represent valuable, yet dynamic intraoperative monitoring variables. They provide information related to poor outcomes when considered together, however their collective behavior across time has not been characterized. METHODS: We have developed the Triple Variable Index (TVI), a composite variable representing the sum of z-scores from MAP, BIS, and MAC values that occur together during surgery. We generated a TVI expression profile, defined as the sequential TVI values expressed across time, for each surgery where concurrent MAP, BIS, and MAC monitoring occurred in an adult patient (≥18 years) at the University of Pittsburgh Medical Center between January and July 2014 (n = 5296). Patterns of TVI expression were identified using k-means clustering and compared across numerous patient, procedure, and outcome characteristics. TVI and the triple low state were compared as prediction models for 30-day postoperative mortality. RESULTS: The median frequency MAP, BIS, and MAC were recorded was one measurement every 3, 5, and 5 min. Three expression patterns were identified: elevated, mixed, and depressed. The elevated pattern displayed the highest average MAP, BIS, and MAC values (86.5 mmHg, 45.3, and 0.98, respectively), while the depressed pattern displayed the lowest values (76.6 mmHg, 38.0, 0.66). Patterns (elevated, mixed, depressed) were distinct across the following characteristics: average patient age (52, 53, 54 years), American Society of Anesthesiologists Physical Status 4 (6.7, 16.1, 27.3%) and 5 (0.1, 0.6, 1.6%) categories, cardiac (2.2, 6.5, 16.1%) and emergent (5.8, 10.5, 12.8%) surgery, cardiopulmonary bypass use (0.3, 2.6, 9.8%), intraoperative medication administration including etomidate (3.0, 7.3, 12.6%), hydromorphone (47.6, 26.3, 25.2%), ketamine (11.2, 4.6, 3.0%), dexmedetomidine (18.4, 16.6, 13.6%), phenylephrine (74.0, 74.8, 83.0), epinephrine (2.0, 6.0, 18.0%), norepinephrine (2.4, 7.5, 21.2%), vasopressin (3.4, 7.6, 21.0%), succinylcholine (74.0, 69.0, 61.9%), intraoperative hypotension (28.8, 33.0, 52.3%) and the triple low state (9.4, 30.3, 80.0%) exposure, and 30-day postoperative mortality (0.8, 2.7, 5.6%). TVI was a better predictor of patients that died or survived in the 30 days following surgery compared to cumulative triple low state exposure (AUC 0.68 versus 0.62, p < 0.05). CONCLUSIONS: Surgeries that share similar patterns of TVI expression display distinct patient, procedure, and outcome characteristics.",0
"Contrasting Roles of Transcription Factors Spineless and EcR in the Highly Dynamic Chromatin Landscape of Butterfly Wing Metamorphosis. Development requires highly coordinated changes in chromatin accessibility in order for proper gene regulation to occur. Here, we identify factors associated with major, discrete changes in chromatin accessibility during butterfly wing metamorphosis. By combining mRNA sequencing (mRNA-seq), assay for transposase-accessible chromatin using sequencing (ATAC-seq), and machine learning analysis of motifs, we show that distinct sets of transcription factors are predictive of chromatin opening at different developmental stages. Our data suggest an important role for nuclear hormone receptors early in metamorphosis, whereas PAS-domain transcription factors are strongly associated with later chromatin opening. Chromatin immunoprecipitation sequencing (ChIP-seq) validation of select candidate factors showed spineless binding to be a major predictor of opening chromatin. Surprisingly, binding of ecdysone receptor (EcR), a candidate accessibility factor in Drosophila, was not predictive of opening but instead marked persistent sites. This work characterizes the chromatin dynamics of insect wing metamorphosis, identifies candidate chromatin remodeling factors in insects, and presents a genome assembly of the model butterfly Junonia coenia.",0
"Automated segmentation of cardiomyocyte Z-disks from high-throughput scanning electron microscopy data. BACKGROUND: With the advent of new high-throughput electron microscopy techniques such as serial block-face scanning electron microscopy (SBF-SEM) and focused ion-beam scanning electron microscopy (FIB-SEM) biomedical scientists can study sub-cellular structural mechanisms of heart disease at high resolution and high volume. Among several key components that determine healthy contractile function in cardiomyocytes are Z-disks or Z-lines, which are located at the lateral borders of the sarcomere, the fundamental unit of striated muscle. Z-disks play the important role of anchoring contractile proteins within the cell that make the heartbeat. Changes to their organization can affect the force with which the cardiomyocyte contracts and may also affect signaling pathways that regulate cardiomyocyte health and function. Compared to other components in the cell, such as mitochondria, Z-disks appear as very thin linear structures in microscopy data with limited difference in contrast to the remaining components of the cell. METHODS: In this paper, we propose to generate a 3D model of Z-disks within single adult cardiac cells from an automated segmentation of a large serial-block-face scanning electron microscopy (SBF-SEM) dataset. The proposed fully automated segmentation scheme is comprised of three main modules including ""pre-processing"", ""segmentation"" and ""refinement"". We represent a simple, yet effective model to perform segmentation and refinement steps. Contrast stretching, and Gaussian kernels are used to pre-process the dataset, and well-known ""Sobel operators"" are used in the segmentation module. RESULTS: We have validated our model by comparing segmentation results with ground-truth annotated Z-disks in terms of pixel-wise accuracy. The results show that our model correctly detects Z-disks with 90.56% accuracy. We also compare and contrast the accuracy of the proposed algorithm in segmenting a FIB-SEM dataset against the accuracy of segmentations from a machine learning program called Ilastik and discuss the advantages and disadvantages that these two approaches have. CONCLUSIONS: Our validation results demonstrate the robustness and reliability of our algorithm and model both in terms of validation metrics and in terms of a comparison with a 3D visualisation of Z-disks obtained using immunofluorescence based confocal imaging.",1
"A Prospective Study Identifying Predictive Factors of Cardiac Decompensation After Transjugular Intrahepatic Portosystemic Shunt: The Toulouse Algorithm. BACKGROUND AND AIMS: Transjugular intrahepatic portosystemic shunt (TIPS) is now a standard for the treatment of portal hypertension-related complications. After the TIPS procedure, incidence and risk factors of cardiac decompensation are poorly known. The main objectives were to measure the incidence of the onset of cardiac decompensation after TIPS and identify the predictive factors. APPROACH AND RESULTS: All patients with cirrhosis treated with TIPS between May 2011 and June 2016 were considered for inclusion. They received a cardiac assessment by standard biological parameters, transthoracic echocardiography, and right heart catheterization. Patients were followed for 1 year after TIPS insertion. The main endpoint was the incidence of cardiac decompensation requiring hospitalization. One hundred seventy-four patients were treated by TIPS during the period. One hundred patients who underwent a complete cardiac evaluation were included. A cardiac decompensation occurred in 20% of the patients. The parameters associated with the occurrence of severe cardiac decompensation were a prolonged QT interval corrected (462 vs. 443 ms; P = 0.05), an elevated pre-TIPS brain natriuretic peptide (BNP) or N-terminal pro-brain natriuretic peptide (NT-proBNP) level, an elevated E/A ratio (1.5 vs. 1.0; P = 0.001) and E/e' ratio (11 vs. 7; P < 0.001), and a left atrial dilatation (40 vs. 29 mL/m(2) ; P = 0.011). The presence of aortic stenosis was also associated with cardiac decompensation. A level of BNP <40 pg/mL and NT-proBNP <125 pg/mL allowed identifying patients without risk of cardiac decompensation. Additionally, absence of diastolic dysfunction criteria at echocardiography ruled out the risk of further cardiac decompensation. CONCLUSIONS: Hospitalization for cardiac decompensation is observed in 20% of patients in the year after TIPS insertion. Combining BNP or NT-proBNP levels and echocardiographic parameters should help improve patient selection.",0
"Learning to detect chest radiographs containing pulmonary lesions using visual attention networks. Machine learning approaches hold great potential for the automated detection of lung nodules on chest radiographs, but training algorithms requires very large amounts of manually annotated radiographs, which are difficult to obtain. The increasing availability of PACS (Picture Archiving and Communication System), is laying the technological foundations needed to make available large volumes of clinical data and images from hospital archives. Binary labels indicating whether a radiograph contains a pulmonary lesion can be extracted at scale, using natural language processing algorithms. In this study, we propose two novel neural networks for the detection of chest radiographs containing pulmonary lesions. Both architectures make use of a large number of weakly-labelled images combined with a smaller number of manually annotated x-rays. The annotated lesions are used during training to deliver a type of visual attention feedback informing the networks about their lesion localisation performance. The first architecture extracts saliency maps from high-level convolutional layers and compares the inferred position of a lesion against the true position when this information is available; a localisation error is then back-propagated along with the softmax classification error. The second approach consists of a recurrent attention model that learns to observe a short sequence of smaller image portions through reinforcement learning; the reward function penalises the exploration of areas, within an image, that are unlikely to contain nodules. Using a repository of over 430,000 historical chest radiographs, we present and discuss the proposed methods over related architectures that use either weakly-labelled or annotated images only.",1
"Beyond the hype of big data and artificial intelligence: building foundations for knowledge and wisdom. Big data, coupled with the use of advanced analytical approaches, such as artificial intelligence (AI), have the potential to improve medical outcomes and population health. Data that are routinely generated from, for example, electronic medical records and smart devices have become progressively easier and cheaper to collect, process, and analyze. In recent decades, this has prompted a substantial increase in biomedical research efforts outside traditional clinical trial settings. Despite the apparent enthusiasm of researchers, funders, and the media, evidence is scarce for successful implementation of products, algorithms, and services arising that make a real difference to clinical care. This article collection provides concrete examples of how ""big data"" can be used to advance healthcare and discusses some of the limitations and challenges encountered with this type of research. It primarily focuses on real-world data, such as electronic medical records and genomic medicine, considers new developments in AI and digital health, and discusses ethical considerations and issues related to data sharing. Overall, we remain positive that big data studies and associated new technologies will continue to guide novel, exciting research that will ultimately improve healthcare and medicine-but we are also realistic that concerns remain about privacy, equity, security, and benefit to all.",0
"Performance of a Deep-Learning Algorithm vs Manual Grading for Detecting Diabetic Retinopathy in India. Importance: More than 60 million people in India have diabetes and are at risk for diabetic retinopathy (DR), a vision-threatening disease. Automated interpretation of retinal fundus photographs can help support and scale a robust screening program to detect DR. Objective: To prospectively validate the performance of an automated DR system across 2 sites in India. Design, Setting, and Participants: This prospective observational study was conducted at 2 eye care centers in India (Aravind Eye Hospital and Sankara Nethralaya) and included 3049 patients with diabetes. Data collection and patient enrollment took place between April 2016 and July 2016 at Aravind and May 2016 and April 2017 at Sankara Nethralaya. The model was trained and fixed in March 2016. Interventions: Automated DR grading system compared with manual grading by 1 trained grader and 1 retina specialist from each site. Adjudication by a panel of 3 retinal specialists served as the reference standard in the cases of disagreement. Main Outcomes and Measures: Sensitivity and specificity for moderate or worse DR or referable diabetic macula edema. Results: Of 3049 patients, 1091 (35.8%) were women and the mean (SD) age for patients at Aravind and Sankara Nethralaya was 56.6 (9.0) years and 56.0 (10.0) years, respectively. For moderate or worse DR, the sensitivity and specificity for manual grading by individual nonadjudicator graders ranged from 73.4% to 89.8% and from 83.5% to 98.7%, respectively. The automated DR system's performance was equal to or exceeded manual grading, with an 88.9% sensitivity (95% CI, 85.8-91.5), 92.2% specificity (95% CI, 90.3-93.8), and an area under the curve of 0.963 on the data set from Aravind Eye Hospital and 92.1% sensitivity (95% CI, 90.1-93.8), 95.2% specificity (95% CI, 94.2-96.1), and an area under the curve of 0.980 on the data set from Sankara Nethralaya. Conclusions and Relevance: This study shows that the automated DR system generalizes to this population of Indian patients in a prospective setting and demonstrates the feasibility of using an automated DR grading system to expand screening programs.",1
"Secondary metabolite as therapeutic agent from endophytic fungi Alternaria longipes strain VITN14G of mangrove plant Avicennia officinalis. Endophytic fungi, especially from mangrove plants, are rich source of secondary metabolites, which plays a major role in various pharmacological actions preferably in cancer and bacterial infections. To perceive its role in antidiabetic activity we isolated and tested the metabolites derived from a novel strain Alternaria longipes strain VITN14G obtained from mangrove plant Avicennia officinalis. The crude extract was analyzed for antidiabetic activity and subjected to column chromatography. The isolated fractions were screened in vitro for α-glucosidase and α-amylase inhibitory activities. The cytotoxicity of the isolated fractions was studied on L929 cell lines. Following which, the screened fraction 2 was allowed for structure elucidation using gas chromatography-mass spectrometry, one-dimensional, two-dimensional nuclear magnetic resonance spectroscopy, ultraviolet, and Fourier-transform infrared analysis. The binding energies of the isolated fraction 2 with glycolytic enzymes were calculated by molecular docking studies using AutoDock Vina. The isolated fraction 2 identified as 2,4,6-triphenylaniline, showed no significant difference in α-amylase inhibition rates and a significant difference of 10% in α-glucosidase inhibition rates than that of the standard drug acarbose. Further, the cytotoxicity assay of the isolated fraction 2 resulted in a cell viability of 73.96%. Supportingly, in silico studies showed 2,4,6-triphenylaniline to produce a stronger binding affinity toward the glycolytic enzyme targets. The compound 2,4,6-triphenylaniline isolated from A. longipes strain VITN14G exhibited satisfactory antidiabetic activity for type 2 diabetes in vitro, which will further be confirmed by in vivo studies. Successful outcome of the study will result in a natural substitute for existing synthetic antidiabetic drugs.",0
"Nucleoid Size Scaling and Intracellular Organization of Translation across Bacteria. The scaling of organelles with cell size is thought to be exclusive to eukaryotes. Here, we demonstrate that similar scaling relationships hold for the bacterial nucleoid. Despite the absence of a nuclear membrane, nucleoid size strongly correlates with cell size, independent of changes in DNA amount and across various nutrient conditions. This correlation is observed in diverse bacteria, revealing a near-constant ratio between nucleoid and cell size for a given species. As in eukaryotes, the nucleocytoplasmic ratio in bacteria varies greatly among species. This spectrum of nucleocytoplasmic ratios is independent of genome size, and instead it appears linked to the average population cell size. Bacteria with different nucleocytoplasmic ratios have a cytoplasm with different biophysical properties, impacting ribosome mobility and localization. Together, our findings identify new organizational principles and biophysical features of bacterial cells, implicating the nucleocytoplasmic ratio and cell size as determinants of the intracellular organization of translation. Different bacterial species have different characteristic nucleocytoplasmic ratios, impacting the biophysical properties of the cytosol and the spatial distribution of translation machinery.",0
"IFN-γ enhances cell-mediated cytotoxicity against keratinocytes via JAK2/STAT1 in lichen planus. Lichen planus (LP) is a chronic debilitating inflammatory disease of unknown etiology affecting the skin, nails, and mucosa with no current FDA-approved treatments. It is histologically characterized by dense infiltration of T cells and epidermal keratinocyte apoptosis. Using global transcriptomic profiling of patient skin samples, we demonstrate that LP is characterized by a type II interferon (IFN) inflammatory response. The type II IFN, IFN-γ, is demonstrated to prime keratinocytes and increase their susceptibility to CD8+ T cell-mediated cytotoxic responses through MHC class I induction in a coculture model. We show that this process is dependent on Janus kinase 2 (JAK2) and signal transducer and activator of transcription 1 (STAT1), but not JAK1 or STAT2 signaling. Last, using drug prediction algorithms, we identify JAK inhibitors as promising therapeutic agents in LP and demonstrate that the JAK1/2 inhibitor baricitinib fully protects keratinocytes against cell-mediated cytotoxic responses in vitro. In summary, this work elucidates the role and mechanisms of IFN-γ in LP pathogenesis and provides evidence for the therapeutic use of JAK inhibitors to limit cell-mediated cytotoxicity in patients with LP.",0
"Learning to detect lymphocytes in immunohistochemistry with deep learning. The immune system is of critical importance in the development of cancer. The evasion of destruction by the immune system is one of the emerging hallmarks of cancer. We have built a dataset of 171,166 manually annotated CD3(+) and CD8(+) cells, which we used to train deep learning algorithms for automatic detection of lymphocytes in histopathology images to better quantify immune response. Moreover, we investigate the effectiveness of four deep learning based methods when different subcompartments of the whole-slide image are considered: normal tissue areas, areas with immune cell clusters, and areas containing artifacts. We have compared the proposed methods in breast, colon and prostate cancer tissue slides collected from nine different medical centers. Finally, we report the results of an observer study on lymphocyte quantification, which involved four pathologists from different medical centers, and compare their performance with the automatic detection. The results give insights on the applicability of the proposed methods for clinical use. U-Net obtained the highest performance with an F1-score of 0.78 and the highest agreement with manual evaluation (kappa=0.72), whereas the average pathologists agreement with reference standard was kappa=0.64. The test set and the automatic evaluation procedure are publicly available at lyon19.grand-challenge.org.",1
"In silico identification of natural product inhibitors for γ-secretase activating protein, a therapeutic target for Alzheimer's disease. Alzheimer's disease (AD) is clinically characterized by the aggregation of neurotoxic amyloid-β (Aβ) peptides in the brain. γ-Secretase catalyzes the reaction of Aβ formation. Inhibition of γ-secretase activating protein (GSAP) reduces Aβ production without disrupting other molecular functions and serves as a promising therapeutic target for lowering Aβ and curing AD. Till date, no proven drug is available for curing AD because of the nonexistence of crystal/NMR structure of GSAP. Thus in the present study, for the first time, we adopted in silico method to predict the 3D structure of GSAP via comparative modeling and studied the architecture and function of GSAP through simulation studies. Docking studies with 4153 phytochemicals revealed that GSAP having a better binding affinity with macaflavanone C, (E)-1-[2,4-dihydroxy-3-(3-methylbut-2-enyl)phenyl]-3-(2,2-dimethyl-8-hydroxy-2H-benzopyran-6-yl)prop-2-en-1-one, and monachosorin B as compared with the standard drug, imatinib. Further, the molecular dynamics analysis suggested that only two phytochemicals, namely, macaflavanone C and (E)-1-[2,4-dihydroxy-3-(3-methylbut-2-enyl)phenyl]-3-(2,2-dimethyl-8-hydroxy-2H-benzopyran-6-yl)prop-2-en-1-one) significantly disrupt the original property of GSAP and also cleared the absorption, distribution, metabolism, and excretion test. These natural compounds may be utilized in future for curing AD after further investigations.",0
"MCLPMDA: A novel method for miRNA-disease association prediction based on matrix completion and label propagation. MiRNAs are a class of small non-coding RNAs that are involved in the development and progression of various complex diseases. Great efforts have been made to discover potential associations between miRNAs and diseases recently. As experimental methods are in general expensive and time-consuming, a large number of computational models have been developed to effectively predict reliable disease-related miRNAs. However, the inherent noise and incompleteness in the existing biological datasets have inevitably limited the prediction accuracy of current computational models. To solve this issue, in this paper, we propose a novel method for miRNA-disease association prediction based on matrix completion and label propagation. Specifically, our method first reconstructs a new miRNA/disease similarity matrix by matrix completion algorithm based on known experimentally verified miRNA-disease associations and then utilizes the label propagation algorithm to reliably predict disease-related miRNAs. As a result, MCLPMDA achieved comparable performance under different evaluation metrics and was capable of discovering greater number of true miRNA-disease associations. Moreover, case study conducted on Breast Neoplasms further confirmed the prediction reliability of the proposed method. Taken together, the experimental results clearly demonstrated that MCLPMDA can serve as an effective and reliable tool for miRNA-disease association prediction.",0
"Deep Conservation of cis-Element Variants Regulating Plant Hormonal Responses. Phytohormones regulate many aspects of plant life by activating transcription factors (TFs) that bind sequence-specific response elements (REs) in regulatory regions of target genes. Despite their short length, REs are degenerate, with a core of just 3 to 4 bp. This degeneracy is paradoxical, as it reduces specificity and REs are extremely common in the genome. To study whether RE degeneracy might serve a biological function, we developed an algorithm for the detection of regulatory sequence conservation and applied it to phytohormone REs in 45 angiosperms. Surprisingly, we found that specific RE variants are highly conserved in core hormone response genes. Experimental evidence showed that specific variants act to regulate the magnitude and spatial profile of hormonal response in Arabidopsis (Arabidopsis thaliana) and tomato (Solanum lycopersicum). Our results suggest that hormone-regulated TFs bind a spectrum of REs, each coding for a distinct transcriptional response profile. Our approach has implications for precise genome editing and for rational promoter design.",0
"Incorporating repeated measurements into prediction models in the critical care setting: a framework, systematic review and meta-analysis. BACKGROUND: The incorporation of repeated measurements into multivariable prediction research may greatly enhance predictive performance. However, the methodological possibilities vary widely and a structured overview of the possible and utilized approaches lacks. Therefore, we [1] propose a structured framework for these approaches, [2] determine what methods are currently used to incorporate repeated measurements in prediction research in the critical care setting and, where possible, [3] assess the added discriminative value of incorporating repeated measurements. METHODS: The proposed framework consists of three domains: the observation window (static or dynamic), the processing of the raw data (raw data modelling, feature extraction and reduction) and the type of modelling. A systematic review was performed to identify studies which incorporate repeated measurements to predict (e.g. mortality) in the critical care setting. The within-study difference in c-statistics between models with versus without repeated measurements were obtained and pooled in a meta-analysis. RESULTS: From the 2618 studies found, 29 studies incorporated multiple repeated measurements. The annual number of studies with repeated measurements increased from 2.8/year (2000-2005) to 16.0/year (2016-2018). The majority of studies that incorporated repeated measurements for prediction research used a dynamic observation window, and extracted features directly from the data. Differences in c statistics ranged from - 0.048 to 0.217 in favour of models that utilize repeated measurements. CONCLUSIONS: Repeated measurements are increasingly common to predict events in the critical care domain, but their incorporation is lagging. A framework of possible approaches could aid researchers to optimize future prediction models.",0
"Validation of the Welch Allyn Home blood pressure monitor with professional SureBP algorithm with a special feature of accuracy during involuntary (tremor) patient movement. Background Current blood pressure (BP) measurement guidelines recommend certain patient requirements, especially keeping still for 5 min. Some patients cannot comply. My colleagues and I have reported accurate performance of the Welch Allyn SureBP algorithm for BP estimates during voluntary patient motion. No validation studies for involuntary patient movement (tremor) BP readings have been reported. This paper reports the validation of the Welch Allyn Home BP monitor, the 1700 Series, which contains that same SureBP algorithm, and the results of tremor testing as well. This device has multiple clinical advantages. Patients and methods Eighty-five patients (49 females) were studied using the ANSI/AAMI/ISO 81060-2, 2013 requirements. Three sizes of cuffs were included. The tremor experiments used a simulator programmed to frequency and amplitude of oscillometric impulses typically seen in patients with diseases causing tremors. This is the first protocol developed for this clinical scenario. The device uses an inflation-based algorithm, reducing discomfort and cycle times. Results The mean±SD for the device minus manual readings per ISO Criterion 1 were-2.93±6.64 mmHg for systolic BP and-2.453±5.48 mmHg for diastolic BP. The tremor testing was performed at low, normal, and high BP simulations. The device recorded a BP value for every cycle tested. The errors (device minus manual BP estimates) were quite low. Conclusion The Welch Allyn Home BP monitor is accurate in the presence of involuntary patient motion (tremor). Clinicians can have a high level of confidence in the use of a self-measurement device, which operates using the same algorithm as contained in the 'professional grade' family of devices.",0
"Utilizing Machine Learning Methods for Preoperative Prediction of Postsurgical Mortality and Intensive Care Unit Admission. MINI: We compared the performance of machine learning models against the traditionally derived Combined Assessment of Risk Encountered in Surgery (CARES) model and the American Society of Anaesthesiologists-Physical Status (ASA-PS) in the prediction of 30-day postsurgical mortality and need for intensive care unit (ICU) stay >24 hours. Machine learning can be used to improve surgical risk prediction compared to traditional risk calculators. AUPRC should be used to evaluate model predictive performance instead of AUROC when the dataset is imbalanced.This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc-nd/4.0 OBJECTIVE:: To compare the performance of machine learning models against the traditionally derived Combined Assessment of Risk Encountered in Surgery (CARES) model and the American Society of Anaesthesiologists-Physical Status (ASA-PS) in the prediction of 30-day postsurgical mortality and need for intensive care unit (ICU) stay >24 hours. BACKGROUND: Prediction of surgical risk preoperatively is important for clinical shared decision-making and planning of health resources such as ICU beds. The current growth of electronic medical records coupled with machine learning presents an opportunity to improve the performance of established risk models. METHODS: All patients aged 18 years and above who underwent noncardiac and nonneurological surgery at Singapore General Hospital (SGH) between 1 January 2012 and 31 October 2016 were included. Patient demographics, comorbidities, preoperative laboratory results, and surgery details were obtained from their electronic medical records. Seventy percent of the observations were randomly selected for training, leaving 30% for testing. Baseline models were CARES and ASA-PS. Candidate models were trained using random forest, adaptive boosting, gradient boosting, and support vector machine. Models were evaluated on area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC). RESULTS: A total of 90,785 patients were included, of whom 539 (0.6%) died within 30 days and 1264 (1.4%) required ICU admission >24 hours postoperatively. Baseline models achieved high AUROCs despite poor sensitivities by predicting all negative in a predominantly negative dataset. Gradient boosting was the best performing model with AUPRCs of 0.23 and 0.38 for mortality and ICU admission outcomes respectively. CONCLUSIONS: Machine learning can be used to improve surgical risk prediction compared to traditional risk calculators. AUPRC should be used to evaluate model predictive performance instead of AUROC when the dataset is imbalanced.This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc-nd/4.0.",0
"Calibration: The Achilles heel of predictive analytics. Background: The assessment of calibration performance of risk prediction models based on regression or more flexible machine learning algorithms receives little attention. Main text: Herein, we argue that this needs to change immediately because poorly calibrated algorithms can be misleading and potentially harmful for clinical decision-making. We summarize how to avoid poor calibration at algorithm development and how to assess calibration at algorithm validation, emphasizing balance between model complexity and the available sample size. At external validation, calibration curves require sufficiently large samples. Algorithm updating should be considered for appropriate support of clinical practice. Conclusion: Efforts are required to avoid poor calibration when developing prediction models, to evaluate calibration when validating models, and to update models when indicated. The ultimate aim is to optimize the utility of predictive analytics for shared decision-making and patient counseling.",0
"Precise Temporal Regulation of Molecular Diffusion within Dendritic Spines by Actin Polymers during Structural Plasticity. The biochemical transduction of excitatory synaptic signals occurs in the cytoplasm within dendritic spines. The associated reaction kinetics are shaped by the mobility of the signaling molecules; however, accurate monitoring of diffusional events within the femtoliter-sized spine structures has not yet been demonstrated. Here, we applied two-photon fluorescence correlation spectroscopy and raster image correlation spectroscopy to monitor protein dynamics within spines, revealing that F-actin restricts the mobility of proteins with a molecular mass of >100 kDa. This restriction is transiently removed during actin remodeling at the initial phase of spine structural plasticity. Photobleaching experiments combined with super-resolution imaging indicate that this increase in mobility facilitates molecular interactions, which may modulate the functions of key postsynaptic signaling molecules, such as Tiam1 and CaMKII. Thus, actin polymers in dendritic spines act as precise temporal regulators of molecular diffusion and modulate signal transduction during synaptic plasticity. Obashi et al. show that actin polymers within dendritic spines restrict mobility of large molecules using optical measurements of fluorescence correlation. Acute actin remodeling induced by plasticity-inducing stimuli increases the mobility of large postsynaptic signaling molecules, which regulate long-term changes in synaptic property.",0
"A deep learning framework for unsupervised affine and deformable image registration. Image registration, the process of aligning two or more images, is the core technique of many (semi-)automatic medical image analysis tasks. Recent studies have shown that deep learning methods, notably convolutional neural networks (ConvNets), can be used for image registration. Thus far training of ConvNets for registration was supervised using predefined example registrations. However, obtaining example registrations is not trivial. To circumvent the need for predefined examples, and thereby to increase convenience of training ConvNets for image registration, we propose the Deep Learning Image Registration (DLIR) framework for unsupervised affine and deformable image registration. In the DLIR framework ConvNets are trained for image registration by exploiting image similarity analogous to conventional intensity-based image registration. After a ConvNet has been trained with the DLIR framework, it can be used to register pairs of unseen images in one shot. We propose flexible ConvNets designs for affine image registration and for deformable image registration. By stacking multiple of these ConvNets into a larger architecture, we are able to perform coarse-to-fine image registration. We show for registration of cardiac cine MRI and registration of chest CT that performance of the DLIR framework is comparable to conventional image registration while being several orders of magnitude faster.",1
"Virtual screen for repurposing of drugs for candidate influenza a M2 ion-channel inhibitors. Influenza A virus (IAV) matrix protein 2 (M2), an ion channel, is crucial for virus infection, and therefore, an important anti-influenza drug target. Adamantanes, also known as M2 channel blockers, are one of the two classes of Food and Drug Administration-approved anti-influenza drugs, although their use was discontinued due to prevalent drug resistance. Fast emergence of resistance to current anti-influenza drugs have raised an urgent need for developing new anti-influenza drugs against resistant forms of circulating viruses. Here we propose a simple theoretical criterion for fast virtual screening of molecular libraries for candidate anti-influenza ion channel inhibitors both for wild type and adamantane-resistant influenza A viruses. After in silico screening of drug space using the EIIP/AQVN filter and further filtering of drugs by ligand based virtual screening and molecular docking we propose the best candidate drugs as potential dual inhibitors of wild type and adamantane-resistant influenza A viruses. Finally, guanethidine, the best ranked drug selected from ligand-based virtual screening, was experimentally tested. The experimental results show measurable anti-influenza activity of guanethidine in cell culture.",0
"Quantitative analysis of neural foramina in the lumbar spine: An imaging informatics and machine learning study. Purpose: To use machine learning tools and leverage big data informatics to statistically model the variation in the area of lumbar neural foramina in a large asymptomatic population. Materials and Methods: By using an electronic health record and imaging archive, lumbar MRI studies in 645 male (mean age, 50.07 years) and 511 female (mean age, 48.23 years) patients between 20 and 80 years old were identified. Machine learning algorithms were used to delineate lumbar neural foramina autonomously and measure their areas. The relationship between neural foraminal area and patient age, sex, and height was studied by using multivariable linear regression. Results: Neural foraminal areas correlated directly with patient height and inversely with patient age. The associations involved were statistically significant (P <.01). Conclusion: By using machine learning and big data techniques, a linear model encoding variation in lumbar neural foraminal areas in asymptomatic individuals has been established. This model can be used to make quantitative assessments of neural foraminal areas in patients by comparing them to the age-, sex-, and height-adjusted population averages.",1
"Applying a deep learning-based sequence labeling approach to detect attributes of medical concepts in clinical text. BACKGROUND: To detect attributes of medical concepts in clinical text, a traditional method often consists of two steps: named entity recognition of attributes and then relation classification between medical concepts and attributes. Here we present a novel solution, in which attribute detection of given concepts is converted into a sequence labeling problem, thus attribute entity recognition and relation classification are done simultaneously within one step. METHODS: A neural architecture combining bidirectional Long Short-Term Memory networks and Conditional Random fields (Bi-LSTMs-CRF) was adopted to detect various medical concept-attribute pairs in an efficient way. We then compared our deep learning-based sequence labeling approach with traditional two-step systems for three different attribute detection tasks: disease-modifier, medication-signature, and lab test-value. RESULTS: Our results show that the proposed method achieved higher accuracy than the traditional methods for all three medical concept-attribute detection tasks. CONCLUSIONS: This study demonstrates the efficacy of our sequence labeling approach using Bi-LSTM-CRFs on the attribute detection task, indicating its potential to speed up practical clinical NLP applications.",1
"Graph algorithms for condensing and consolidating gene set analysis results. Gene set analysis plays a critical role in the functional interpretation of omics data. Although this is typically done for one omics experiment at a time, there is an increasing need to combine gene set analysis results from multiple experiments performed on the same or different omics platforms, such as in multi-omics studies. Integrating results from multiple experiments is challenging, and annotation redundancy between gene sets further obscures clear conclusions. We propose to use a weighted set cover algorithm to reduce redundancy of gene sets identified in a single experiment. Next, we use affinity propagation to consolidate similar gene sets identified from multiple experiments into clusters and to automatically determine the most representative gene set for each cluster. Using three examples from over representation analysis and gene set enrichment analysis, we showed that weighted set cover outperformed a previously published set cover method and reduced the number of gene sets by 52-77%. Focusing on overlapping genes between the list of input genes and the enriched gene sets in over-representation analysis and leading-edge genes in gene set enrichment analysis further reduced the number of gene sets. A use case combining enrichment analysis results from RNASeq and proteomics data comparing basal and luminal A breast cancer samples highlighted the known difference in proliferation and DNA damage response. Finally, we used these algorithms for a pan-cancer survival analysis. Our analysis clearly revealed prognosis-related pathways common to multiple cancer types or specific to individual cancer types, as well as pathways associated with prognosis in different directions in different cancer types. We implemented these two algorithms in an R package, Sumer, which generates tables and static and interactive plots for exploration and publication.",0
"Protective role of epigallocatechin-3-gallate in NADPH oxidase-MMP2-Spm-Cer-S1P signalling axis mediated ET-1 induced pulmonary artery smooth muscle cell proliferation. The signalling pathway involving MMP-2 and sphingosine-1-phosphate (S1P) in endothelin-1 (ET-1) induced pulmonary artery smooth muscle cell (PASMC) proliferation is not clearly known. We, therefore, investigated the role of NADPH oxidase derived O2.--mediated modulation of MMP2-sphingomyeline-ceramide-S1P signalling axis in ET-1 induced increase in proliferation of PASMCs. Additionally, protective role of the tea cathechin, epigallocatechin-3-gallate (EGCG), if any, in this scenario has also been explored. ET-1 markedly increased NADPH oxidase and MMP-2 activities and proliferation of bovine pulmonary artery smooth muscle cells (BPASMCs). ET-1 also caused significant increase in sphingomyelinase (SMase) activity, ERK1/2 and sphingosine kinase (SPHK) phosphorylations, and S1P level in the cells. EGCG inhibited ET-1 induced increase in SMase activity, ERK1/2 and SPHK phosphorylations, S1P level and the SMC proliferation. EGCG also attenuated ET-1 induced activation of MMP-2 by inhibiting NADPH oxidase activity upon inhibiting the association of the NADPH oxidase components, p47phox and p67phox in the cell membrane. Molecular docking study revealed a marked binding affinity of p47phox with the galloyl group of EGCG. Overall, our study suggest that ET-1 induced proliferation of the PASMCs occurs via NADPH oxidase-MMP2- Spm- Cer-S1P signalling axis, and EGCG attenuates ET-1 induced increase in proliferation of the cells by inhibiting NADPH oxidase activity.",0
"MiRNA-disease interaction prediction based on kernel neighborhood similarity and multi-network bidirectional propagation. Background: Studies have shown that miRNAs are functionally associated with the development of many human diseases, but the roles of miRNAs in diseases and their underlying molecular mechanisms have not been fully understood. The research on miRNA-disease interaction has received more and more attention. Compared with the complexity and high cost of biological experiments, computational methods can rapidly and efficiently predict the potential miRNA-disease interaction and can be used as a beneficial supplement to experimental methods. Results: In this paper, we proposed a novel computational model of kernel neighborhood similarity and multi-network bidirectional propagation (KNMBP) for miRNA-disease interaction prediction, especially for new miRNAs and new diseases. First, we integrated multiple data sources of diseases and miRNAs, respectively, to construct a novel disease semantic similarity network and miRNA functional similarity network. Secondly, based on the modified miRNA-disease interactions, we use the kernel neighborhood similarity algorithm to calculate the disease kernel neighborhood similarity and the miRNA kernel neighborhood similarity. Finally, we utilize bidirectional propagation algorithm to predict the miRNA-disease interaction scores based on the integrated disease similarity network and miRNA similarity network. As a result, the AUC value of 5-fold cross validation for all interactions by KNMBP is 0.93126 based on the commonly used dataset, and the AUC values for all interactions, for all miRNAs, for all disease is 0.937950.863630.86937 based on another dataset extracted by ourselves, which are higher than other state-of-the-art methods. In addition, our model has good parameter robustness. The case study further demonstrated the predictive performance of the model for novel miRNA-disease interactions. Conclusions: Our KNMBP algorithm efficiently integrates multiple omics data from miRNAs and diseases to stably and efficiently predict potential miRNA-disease interactions. It is anticipated that KNMBP would be a useful tool in biomedical research.",0
"Structural and energetic understanding of novel natural inhibitors of Mycobacterium tuberculosis malate synthase. Persistent infection by Mycobacterium tuberculosis requires the glyoxylate shunt. This is a bypass to the tricarboxylic acid cycle in which isocitrate lyase (ICL) and malate synthase (MS) catalyze the net incorporation of carbon during mycobacterial growth on acetate or fatty acids as the primary carbon source. To identify a potential antitubercular compound, we performed a structure-based screening of natural compounds from the ZINC database (n = 1 67 740) against the M tuberculosis MS (MtbMS) structure. The ligands were screened against MtbMS, and 354 ligands were found to have better docking score. These compounds were assessed for Lipinski and absorption, distribution, metabolism, excretion, and toxicity prediction where 15 compounds were found to fit well for redocking studies. After refinement by molecular docking and drug-likeness analysis, four potential inhibitors (ZINC1483899, ZINC1754310, ZINC2269664, and ZINC15729522) were identified. These four ligands with phenyl-diketo acid were further subjected to molecular dynamics simulation to compare the dynamics and stability of the protein structure after ligand binding. The binding energy analysis was calculated to determine the intermolecular interactions. Our results suggested that the four compounds had a binding free energy of −201.96, −242.02, −187.03, and −169.02 kJ·mol−1, for compounds with IDs ZINC1483899, ZINC1754310, ZINC2269664, and ZINC15729522, respectively. We concluded that two compounds (ZINC1483899 and ZINC1754310) displayed considerable structural and pharmacological properties and could be probable drug candidates to fight against M tuberculosis parasites.",0
"BRCA-1 depletion impairs pro-inflammatory polarization and activation of RAW 264.7 macrophages in a NF-κB-dependent mechanism. BRCA-1 is a nuclear protein involved in DNA repair, transcriptional regulation, and cell cycle control. Its involvement in other cellular processes has been described. Here, we aimed to investigate the role of BRCA-1 in macrophages M(LPS), M(IL-4), and tumor cell-induced differentiation. We used siRNAs to knockdown BRCA-1 in RAW 264.7 macrophages exposed to LPS, IL-4, and C6 glioma cells conditioned medium (CMC6), and evaluated macrophage differentiation markers and functional phagocytic activity as well as DNA damage and cell survival in the presence and absence of BRCA-1. LPS and CMC6, but not by IL-4, increased DNA damage in macrophages, and this effect was more pronounced in BRCA-1-depleted cells, including M(IL-4). BRCA-1 depletion impaired expression of pro-inflammatory cytokines, TNF-α and IL-6, and reduced the phagocytic activity of macrophages in response to LPS. In CMC6-induced differentiation, BRCA-1 knockdown inhibited TNF-α and IL-6 expression which was accompanied by upregulation of the anti-inflammatory markers IL-10 and TGF-β and reduced phagocytosis. In contrast, M(IL-4) phenotype was not affected by BRCA-1 status. Molecular docking predicted that the conserved BRCA-1 domain BRCT can interact with the p65 subunit of NF-κB. Immunofluorescence assays showed that BRCA-1 and p65 co-localize in the nucleus of LPS-treated macrophages and reporter gene assay showed that depletion of BRCA-1 decreased LPS and CMC6-induced NF-κB transactivation. IL-4 had no effect upon NF-κB. Taken together, our findings suggest a role of BRCA-1 in macrophage differentiation and phagocytosis induced by LPS and tumor cells secretoma, but not IL-4, in a mechanism associated with inhibition of NF-κB.",0
"A user interface for optimizing radiologist engagement in image data curation for artificial intelligence. Purpose: To delineate image data curation needs and describe a locally designed graphical user interface (GUI) to aid radiologists in image annotation for artificial intelligence (AI) applications in medical imaging. Materials and Methods: GUI components support image analysis toolboxes, picture archiving and communication system integration, third-party applications, processing of scripting languages, and integration of deep learning libraries. For clinical AI applications, GUI components included two-dimensional segmentation and classification; three-dimensional segmentation and quantification; and threedimensional segmentation, quantification, and classification. To assess radiologist engagement and performance efficiency associated with GUI-related capabilities, image annotation rate (studies per day) and speed (minutes per case) were evaluated in two clinical scenarios of varying complexity: Hip fracture detection and coronary atherosclerotic plaque demarcation and stenosis grading. Results: For hip fracture, 1050 radiographs were annotated over 7 days (150 studies per day; median speed: 10 seconds per study [interquartile range, 3-21 seconds per study]). A total of 294 coronary CT angiographic studies with 1843 arteries and branches were annotated for atherosclerotic plaque over 23 days (15.2 studies [80.1 vessels] per day; median speed: 6.08 minutes per study [interquartile range, 2.8-10.6 minutes per study] and 73 seconds per vessel [interquartile range, 20.9-155 seconds per vessel]). Conclusion: GUI-component compatibility with common image analysis tools facilitates radiologist engagement in image data curation, including image annotation, supporting AI application development and evolution for medical imaging. When complemented by other GUI elements, a continuous integrated workflow supporting formation of an agile deep neural network life cycle results.",1
"Utilizing dynamic treatment information for MACE prediction of acute coronary syndrome. BACKGROUND: Main adverse cardiac events (MACE) are essentially composite endpoints for assessing safety and efficacy of treatment processes of acute coronary syndrome (ACS) patients. Timely prediction of MACE is highly valuable for improving the effects of ACS treatments. Most existing tools are specific to predict MACE by mainly using static patient features and neglecting dynamic treatment information during learning. METHODS: We address this challenge by developing a deep learning-based approach to utilize a large volume of heterogeneous electronic health record (EHR) for predicting MACE after ACS. Specifically, we obtain the deep representation of dynamic treatment features from EHR data, using the bidirectional recurrent neural network. And then, the extracted latent representation of treatment features can be utilized to predict whether a patient occurs MACE in his or her hospitalization. RESULTS: We validate the effectiveness of our approach on a clinical dataset containing 2930 ACS patient samples with 232 static feature types and 2194 dynamic feature types. The performance of our best model for predicting MACE after ACS remains robust and reaches 0.713 and 0.764 in terms of AUC and Accuracy, respectively, and has over 11.9% (1.2%) and 1.9% (7.5%) performance gain of AUC (Accuracy) in comparison with both logistic regression and a boosted resampling model presented in our previous work, respectively. The results are statistically significant. CONCLUSIONS: We hypothesize that our proposed model adapted to leverage dynamic treatment information in EHR data appears to boost the performance of MACE prediction for ACS, and can readily meet the demand clinical prediction of other diseases, from a large volume of EHR in an open-ended fashion.",1
"Ethical concerns with the use of intelligent assistive technology: findings from a qualitative study with professional stakeholders. BACKGROUND: Advances in artificial intelligence (AI), robotics and wearable computing are creating novel technological opportunities for mitigating the global burden of population ageing and improving the quality of care for older adults with dementia and/or age-related disability. Intelligent assistive technology (IAT) is the umbrella term defining this ever-evolving spectrum of intelligent applications for the older and disabled population. However, the implementation of IATs has been observed to be sub-optimal due to a number of barriers in the translation of novel applications from the designing labs to the bedside. Furthermore, since these technologies are designed to be used by vulnerable individuals with age- and multi-morbidity-related frailty and cognitive disability, they are perceived to raise important ethical challenges, especially when they involve machine intelligence, collect sensitive data or operate in close proximity to the human body. Thus, the goal of this paper is to explore and assess the ethical issues that professional stakeholders perceive in the development and use of IATs in elderly and dementia care. METHODS: We conducted a multi-site study involving semi-structured qualitative interviews with researchers and health professionals. We analyzed the interview data using a descriptive thematic analysis to inductively explore relevant ethical challenges. RESULTS: Our findings indicate that professional stakeholders find issues of patient autonomy and informed consent, quality of data management, distributive justice and human contact as ethical priorities. Divergences emerged in relation to how these ethical issues are interpreted, how conflicts between different ethical principles are resolved and what solutions should be implemented to overcome current challenges. CONCLUSIONS: Our findings indicate a general agreement among professional stakeholders on the ethical promises and challenges raised by the use of IATs among older and disabled users. Yet, notable divergences persist regarding how these ethical challenges can be overcome and what strategies should be implemented for the safe and effective implementation of IATs. These findings provide technology developers with useful information about unmet ethical needs. Study results may guide policy makers with firsthand information from relevant stakeholders about possible solutions for ethically-aligned technology governance.",0
"Disrupted apolipoprotein L1-miR193a axis dedifferentiates podocytes through autophagy blockade in an APOL1 risk milieu. We hypothesized that a functional apolipoprotein LI (APOL1)-miR193a axis (inverse relationship) preserves, but disruption alters, the podocyte molecular phenotype through the modulation of autophagy flux. Podocyte-expressing APOL1G0 (G0-podocytes) showed downregulation but podocyte-expressing APOL1G1 (G1-podocytes) and APOL1G2 (G2-podo-cytes) displayed enhanced miR193a expression. G0-, G1-, and G2-podocytes showed enhanced expression of light chain (LC) 3-II and beclin-1, but a disparate expression of p62 (low in wild-type but high in risk alleles). G0-podocytes showed enhanced, whereas G1- and G2-podocytes displayed decreased, phosphorylation of Unc-51-like autophagy-activating kinase (ULK)1 and class III phosphatidylinositol 3-kinase (PI3KC3). Podocytes overexpressing miR193a (miR193a-podocytes), G1, and G2 showed decreased transcription of PIK3R3 (PI3KC3=s regulatory unit). Since 3-methyladenine (3-MA) enhanced miR193a expression but inhibited PIK3R3 transcription, it appears that 3-MA inhibits autophagy and induces podocyte dedifferentiation via miR193a generation. miR193a-, G1-, and G2-podocytes also showed decreased phosphorylation of mammalian target of rapamycin (mTOR) that could repress lysosome reformation. G1- and G2-podocytes showed enhanced expression of run domain beclin-1interacting and cysteine-rich domain-containing protein (Rubicon); however, its silencing prevented their dedifferentiation. Docking, protein-protein interaction, and immunoprecipitation studies with an-tiautophagy-related gene (ATG)14L, anti-UV radiation resistanceassociated gene (UVRAG), or Rubicon antibodies suggested the formation of ATG14L complex I and UVRAG complex II in G0-podocytes and the formation of Rubicon complex III in G1- and G2-podocytes. These findings suggest that the APOL1 risk alleles favor podocyte dedifferentiation through blockade of multiple autophagy pathways.",0
"Different pharmacological properties of GLUT9a and GLUT9b: Potential implications in preeclampsia. Background/Aims: Glucose transporter 9 (GLUT9/SLC2A9) is the major regulator of uric acid homeostasis in humans. Hyperuricemia due to impaired regulation by GLUT9 in pregnancy is closely associated with preeclampsia. While GLUT9 is expressed in two alternative splice variants, GLUT9a and GLUT9b, with different subcellular localizations, no functional differences of the two splice variants are known to date. The aim of this study was to investigate the function of both GLUT9 isoforms. Methods: To characterize the different pharmacological properties of GLUT9a and GLUT9b electrophysiological studies of these isoforms and their modified variants, i.e. NmodGLUT9a and NmodGLUT9b, were performed using a Xenopus laevis oocytes model. Currents were measured by an electrode voltage clamp system. Results: Functional experiments unveiled that uric acid transport mediated by GLUT9a but not GLUT9b is chloride-dependent: Replacing chloride by different anions resulted in a 3.43±0.63-fold increase of GLUT9a-but not GLUT9b-mediated currents. However, replacement by iodide resulted in a loss of current for GLUT9a but not GLUT9b. Iodide inhibits GLUT9a with an IC50 of35.1±6.7μM. Modification of the N-terminal domain leads to a shift of the iodide IC50 to 1200±228μM. Using molecular docking studies, we identified two positively charged residues H23 and R31 in the N-terminal domain of hGLUT9a which can explain the observed functional differences. Conclusion: To the best of our knowledge, this is the first study showing that the N-terminal domain of hGLUT9a has a unique regulatory function and the potential to interact with small negatively charged ions like iodide. These findings may have significant implications in our understanding of hyperuricemia-associated diseases, specifically during pregnancy.",0
"Management of Thyroid Nodules Seen on US Images: Deep Learning May Match Performance of Radiologists. BackgroundManagement of thyroid nodules may be inconsistent between different observers and time consuming for radiologists. An artificial intelligence system that uses deep learning may improve radiology workflow for management of thyroid nodules.PurposeTo develop a deep learning algorithm that uses thyroid US images to decide whether a thyroid nodule should undergo a biopsy and to compare the performance of the algorithm with the performance of radiologists who adhere to American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS).Materials and MethodsIn this retrospective analysis, studies in patients referred for US with subsequent fine-needle aspiration or with surgical histologic analysis used as the standard were evaluated. The study period was from August 2006 to May 2010. A multitask deep convolutional neural network was trained to provide biopsy recommendations for thyroid nodules on the basis of two orthogonal US images as the input. In the training phase, the deep learning algorithm was first evaluated by using 10-fold cross-validation. Internal validation was then performed on an independent set of 99 consecutive nodules. The sensitivity and specificity of the algorithm were compared with a consensus of three ACR TI-RADS committee experts and nine other radiologists, all of whom interpreted thyroid US images in clinical practice.ResultsIncluded were 1377 thyroid nodules in 1230 patients with complete imaging data and conclusive cytologic or histologic diagnoses. For the 99 test nodules, the proposed deep learning algorithm achieved 13 of 15 (87%: 95% confidence interval [CI]: 67%, 100%) sensitivity, the same as expert consensus (P > .99) and higher than five of nine radiologists. The specificity of the deep learning algorithm was 44 of 84 (52%; 95% CI: 42%, 62%), which was similar to expert consensus (43 of 84; 51%; 95% CI: 41%, 62%; P = .91) and higher than seven of nine other radiologists. The mean sensitivity and specificity for the nine radiologists was 83% (95% CI: 64%, 98%) and 48% (95% CI: 37%, 59%), respectively.ConclusionSensitivity and specificity of a deep learning algorithm for thyroid nodule biopsy recommendations was similar to that of expert radiologists who used American College of Radiology Thyroid Imaging and Reporting Data System guidelines.(c) RSNA, 2019Online supplemental material is available for this article.",0
"Application of machine learning to determine top predictors of noncalcified coronary burden in psoriasis: An observational cohort study. BACKGROUND: Psoriasis is associated with elevated risk of heart attack and increased accumulation of subclinical noncalcified coronary burden by coronary computed tomography angiography (CCTA). Machine learning algorithms have been shown to effectively analyze well-characterized data sets. OBJECTIVE: In this study, we used machine learning algorithms to determine the top predictors of noncalcified coronary burden by CCTA in psoriasis. METHODS: The analysis included 263 consecutive patients with 63 available variables from the Psoriasis Atherosclerosis Cardiometabolic Initiative. The random forest algorithm was used to determine the top predictors of noncalcified coronary burden by CCTA. We evaluated our results using linear regression models. RESULTS: Using the random forest algorithm, we found that the top 10 predictors of noncalcified coronary burden were body mass index, visceral adiposity, total adiposity, apolipoprotein A1, high-density lipoprotein, erythrocyte sedimentation rate, subcutaneous adiposity, small low-density lipoprotein particle, cholesterol efflux capacity and the absolute granulocyte count. Linear regression of noncalcified coronary burden yielded results consistent with our machine learning output. LIMITATION: We were unable to provide external validation and did not study cardiovascular events. CONCLUSION: Machine learning methods identified the top predictors of noncalcified coronary burden in psoriasis. These factors were related to obesity, dyslipidemia, and inflammation, showing that these are important targets when treating comorbidities in psoriasis.",1
"Cyclin-Specific Docking Mechanisms Reveal the Complexity of M-CDK Function in the Cell Cycle. Cyclin-dependent kinases (CDKs) coordinate hundreds of molecular events during the cell cycle. Multiple cyclins are involved, but the global role of cyclin-specific phosphorylation has remained unsolved. We uncovered a cyclin docking motif, LxF, that mediates binding of replication factor Cdc6 to mitotic cyclin. This interaction leads to phospho-adaptor Cks1-mediated inhibition of M-CDK to facilitate Cdc6 accumulation and sequestration in mitosis. The LxF motif and Cks1 also mediate the mutual inhibition between M-CDK and the tyrosine kinase Swe1. Additionally, the LxF motif is critical for targeting M-CDK to phosphorylate several mitotic regulators; for example, Spo12 is targeted via LxF to release the phosphatase Cdc14. The results complete the full set of G1, S, and M-CDK docking mechanisms and outline the unified role of cyclin specificity and CDK activity thresholds. Cooperation of cyclin and Cks1 docking creates a variety of CDK thresholds and switching orders, including combinations of last in, first out (LIFO) and first in, first out (FIFO) ordering. Örd et al. find that a short linear motif in proteins enables specific targeting by the mitotic CDK. Depending on the context, this motif can lead to either enhanced phosphorylation of mitotic targets or inhibition of the CDK complex in cooperation with the phospho-adaptor subunit Cks1.",0
"A single centre prospective cohort study addressing the effect of a rule-in/rule-out troponin algorithm on routine clinical practice. Aims: In 2015, the European Society of Cardiology introduced new guidelines for the diagnosis of acute coronary syndromes in patients presenting without persistent ST-segment elevation. These guidelines included the use of high-sensitivity troponin assays for ‘rule-in’ and ‘rule-out’ of acute myocardial injury at presentation (using a ‘0 hour’ blood test). Whilst these algorithms have been extensively validated in prospective diagnostic studies, the outcome of their implementation in routine clinical practice has not been described. The present study describes the change in the patient journey resulting from implementation of such an algorithm in a busy innercity Emergency Department. Methods and results: Data were prospectively collected from electronic records at a large Central London hospital over seven months spanning the periods before, during and after the introduction of a new high-sensitivity troponin rapid diagnostic algorithm modelled on the European Society of Cardiology guideline. Over 213 days, 4644 patients had high-sensitivity troponin T measured in the Emergency Department. Of these patients, 40.4% could be ‘ruled-out’ based on the high-sensitivity troponin T concentration at presentation, whilst 7.6% could be ‘ruled-in’. Adoption of the algorithm into clinical practice was associated with a 37.5% increase of repeat high-sensitivity troponin T measurements within 1.5 h for those patients classified as ‘intermediate risk’ on presentation. Conclusions: Introduction of a 0 hour ‘rule-in’ and ‘rule-out’ algorithm in routine clinical practice enables rapid triage of 48% of patients, and is associated with more rapid repeat testing in intermediate risk patients.",0
"Quantifying Sex Bias in Clinical Studies at Scale with Automated Data Extraction. Importance: Analyses of female representation in clinical studies have been limited in scope and scale. Objective: To perform a large-scale analysis of global enrollment sex bias in clinical studies. Design, Setting, and Participants: In this cross-sectional study, clinical studies from published articles from PubMed from 1966 to 2018 and records from Aggregate Analysis of ClinicalTrials.gov from 1999 to 2018 were identified. Global disease prevalence was determined for male and female patients in 11 disease categories from the Global Burden of Disease database: cardiovascular, diabetes, digestive, hepatitis (types A, B, C, and E), HIV/AIDS, kidney (chronic), mental, musculoskeletal, neoplasms, neurological, and respiratory (chronic). Machine reading algorithms were developed that extracted sex data from tables in articles and records on December 31, 2018, at an artificial intelligence research institute. Male and female participants in 43135 articles (792004915 participants) and 13165 records (12977103 participants) were included. Main Outcomes and Measures: Sex bias was defined as the difference between the fraction of female participants in study participants minus prevalence fraction of female participants for each disease category. A total of 1000 bootstrap estimates of sex bias were computed by resampling individual studies with replacement. Sex bias was reported as mean and 95% bootstrap confidence intervals from articles and records in each disease category over time (before or during 1993 to 2018), with studies or participants as the measurement unit. Results: There were 792004915 participants, including 390470834 female participants (49%), in articles and 12977103 participants, including 6351619 female participants (49%), in records. With studies as measurement unit, substantial female underrepresentation (sex bias ≤ -0.05) was observed in 7 of 11 disease categories, especially HIV/AIDS (mean for articles, -0.17 [95% CI, -0.18 to -0.16]), chronic kidney diseases (mean, -0.17 [95% CI, -0.17 to -0.16]), and cardiovascular diseases (mean, -0.14 [95% CI, -0.14 to -0.13]). Sex bias in articles for all categories combined was unchanged over time with studies as measurement unit (range, -0.15 [95% CI, -0.16 to -0.13] to -0.10 [95% CI, -0.14 to -0.06]), but improved from before or during 1993 (mean, -0.11 [95% CI, -0.16 to -0.05]) to 2014 to 2018 (mean, -0.05 [95% CI, -0.09 to -0.02]) with participants as the measurement unit. Larger study size was associated with greater female representation. Conclusions and Relevance: Automated extraction of the number of participants in clinical reports provides an effective alternative to manual analysis of demographic bias. Despite legal and policy initiatives to increase female representation, sex bias against female participants in clinical studies persists. Studies with more participants have greater female representation. Differences between sex bias estimates with studies vs participants as measurement unit, and between articles vs records, suggest that sex bias with both measures and data sources should be reported.",1
"Establishment and validation of a novel survival prediction scoring algorithm for patients with non-small-cell lung cancer spinal metastasis. Background: This study was to develop an algorithm capable of predicting the survival of patients with NSCLC spinal metastasis for individualized therapy. Methods: We identified 176 consecutive patients with NSCLC spinal metastasis between 2006 and 2017. Twenty-four features, including age, gender, smoking, KPS, paralysis, histological subtype, tumor stage, surgery, EGFR status, CEA, CA125, CA19-9, NSE, SCC, CYFRA21-1, calcium, AKP, albumin, the number of spinal, extra-spinal bone and visceral metastasis, time to metastasis, pathological fracture, and primary or secondary metastasis, were retrospectively analyzed. Features associated with survival in the multivariate analyses were included in a scoring model, which was prospectively validated in another 63 patients (NCT03363685). Results: The median follow-up period was 12.00 months (interquartile range 6.00–23.40 months). One hundred forty-seven patients died during follow-up, with a median survival of 13.6 months being observed. Multivariate analysis revealed that the following features were associated with survival: age, smoking, CA125, SCC, KPS, and EGFR status. A scoring system based on these features was created to stratify patients into low-risk (0–3), intermediate-risk (4–6) and high-risk (7–10) groups, whose estimated median survival times 29.10, 10.40 and 3.90 months, respectively. The Harrell’s c-index was 0.72. Model validation supported this model’s validity and reproducibility. Conclusions: In patients with NSCLC spinal metastasis, survival was associated with age, smoking, CA125, SCC, KPS, and EGFR status. A validated scoring system based on these features was devised that can predict the survival times of those patients. This scoring system provides a basis for applying the NOMS framework and for facilitating individual treatment.",0
"Uncovering thousands of new peptides with sequence-mask-search hybrid de novo peptide sequencing framework. Typical analyses of mass spectrometry data only identify amino acid sequences that exist in reference databases. This restricts the possibility of discovering new peptides such as those that contain uncharacterized mutations or originate from unexpected processing of RNAs and proteins. De novo peptide sequencing approaches address this limitation but often suffer from low accuracy and require extensive validation by experts. Here, we develop SMSNet, a deep learning-based de novo peptide sequencing framework that achieves >95% amino acid accuracy while retaining good identification coverage. Applications of SMSNet on landmark proteomics and peptidomics studies reveal over 10,000 previously uncharacterized HLA antigens and phosphopeptides, and in conjunction with database-search methods, expand the coverage of peptide identification by almost 30%. The power to accurately identify new peptides of SMSNet would make it an invaluable tool for any future proteomics and peptidomics studies, including tumor neoantigen discovery, antibody sequencing, and proteome characterization of nonmodel organisms.",0
"G-1. Large-scale chromatin remodelling and transcriptional deregulation on der11 following translocation in mantle cell lymphoma. Mantle cell lymphoma (MCL) is an aggressive B-cell non-Hodgkin lymphoma characterized by poor prognosis and survival rate. Its genetic hallmark is the translocation t(11;14) which leads to the overexpression of cyclin D1 (CCND1) gene which becomes juxtaposed to the immunoglobulin heavy chain (IGH) gene on the newly formed der14 chromosome. This recurrent feature is however not sufficient to promote the development of the disease as expression of CCND1 under different known IgH enhancers in transgenic mice is not sufficient for tumor development. Additional alterations are necessary to develop a malignant phenotype. When a translocation occurs, it can induce overall nuclear reorganization, epigenetic changes and altered gene expression that may contribute to oncogenesis. Here we investigated changes in nuclear positioning of gene loci and their transcription after the t(11;14) focusing our attention on the events occurring on the der11 chromosome. Methods. 3D-immunoFISH and image analysis software were used to analyze gene loci position in nuclear space. To analyze changes of transcriptional level of genes located on the der11, quantitative RT-PCR, bioinformatic analysis and data mining were performed. ChIP was carried out to analyze specific interactions between nucleolin and the genome in MCL. Results. We demonstrated that the expression of many genes located close to the translocation breakpoint was deregulated in MCL compared to other lymphomas and to B-cells from healthy donors. Most of these genes were located on the der11 after the t(11;14). We found that the der11 is relocated in close proximity to the nucleolus. Here the nucleolin, that is part of the transcriptional factor LR-1 can deregulate gene expression by direct binding to promoters. We found that the LR-1 consensus sequence and the nucleolin binding sites are significantly enriched in the regions covered by the deregulated genes compared to the rest of chromosme11 and to cells without the t(11;14). Conclusions. We identified new epigenetic events that contribute to MCL development following t(11;14).",0
"Using a Deep Learning Algorithm and Integrated Gradients Explanation to Assist Grading for Diabetic Retinopathy. PURPOSE: To understand the impact of deep learning diabetic retinopathy (DR) algorithms on physician readers in computer-assisted settings. DESIGN: Evaluation of diagnostic technology. PARTICIPANTS: One thousand seven hundred ninety-six retinal fundus images from 1612 diabetic patients. METHODS: Ten ophthalmologists (5 general ophthalmologists, 4 retina specialists, 1 retina fellow) read images for DR severity based on the International Clinical Diabetic Retinopathy disease severity scale in each of 3 conditions: unassisted, grades only, or grades plus heatmap. Grades-only assistance comprised a histogram of DR predictions (grades) from a trained deep-learning model. For grades plus heatmap, we additionally showed explanatory heatmaps. MAIN OUTCOME MEASURES: For each experiment arm, we computed sensitivity and specificity of each reader and the algorithm for different levels of DR severity against an adjudicated reference standard. We also measured accuracy (exact 5-class level agreement and Cohen's quadratically weighted kappa), reader-reported confidence (5-point Likert scale), and grading time. RESULTS: Readers graded more accurately with model assistance than without for the grades-only condition (P < 0.001). Grades plus heatmaps improved accuracy for patients with DR (P < 0.001), but reduced accuracy for patients without DR (P = 0.006). Both forms of assistance increased readers' sensitivity moderate-or-worse DR: unassisted: mean, 79.4% [95% confidence interval (CI), 72.3%-86.5%]; grades only: mean, 87.5% [95% CI, 85.1%-89.9%]; grades plus heatmap: mean, 88.7% [95% CI, 84.9%-92.5%] without a corresponding drop in specificity (unassisted: mean, 96.6% [95% CI, 95.9%-97.4%]; grades only: mean, 96.1% [95% CI, 95.5%-96.7%]; grades plus heatmap: mean, 95.5% [95% CI, 94.8%-96.1%]). Algorithmic assistance increased the accuracy of retina specialists above that of the unassisted reader or model alone; and increased grading confidence and grading time across all readers. For most cases, grades plus heatmap was only as effective as grades only. Over the course of the experiment, grading time decreased across all conditions, although most sharply for grades plus heatmap. CONCLUSIONS: Deep learning algorithms can improve the accuracy of, and confidence in, DR diagnosis in an assisted read setting. They also may increase grading time, although these effects may be ameliorated with experience.",1
"DIAlignR provides precise retention time alignment across distant runs in DIA and targeted proteomics. Sequential Windowed Acquisition of All Theoretical Fragment Ion Mass Spectra (SWATH-MS) is widely used for proteomics analysis given its high throughput and reproducibility, but ensuring consistent quantification of analytes across large-scale studies of heterogeneous samples such as human plasma remains challenging. Heterogeneity in large-scale studies can be caused by large time intervals between data acquisition, acquisition by different operators or instruments, and intermittent repair or replacement of parts, such as the liquid chromatography column, all of which affect retention time (RT) reproducibility and, successively, performance of SWATH-MS data analysis. Here, we present a novel algorithm for RT alignment of SWATH-MS data based on direct alignment of raw MS2 chromatograms using a hybrid dynamic programming approach. The algorithm does not impose a chronological order of elution and allows for alignment of elution-order-swapped peaks. Furthermore, allowing RT mapping in a certain window around a coarse global fit makes it robust against noise. On a manually validated dataset, this strategy outperformed the current state-ofthe-art approaches. In addition, on real-world clinical data, our approach outperformed global alignment methods by mapping 98% of peaks compared with 67% cumulatively. DIAlignR reduced alignment error up to 30-fold for extremely distant runs. The robustness of technical parameters used in this pairwise alignment strategy is also demonstrated. The source code is released under the BSD license at https://github.com/Roestlab/DIAlignR.",0
"Phloretin attenuates STAT-3 activity and overcomes sorafenib resistance targeting SHP-1-mediated inhibition of STAT3 and Akt/VEGFR2 pathway in hepatocellular carcinoma. Background: Hepatocellular carcinoma (HCC) is the most common primary liver malignancy. Phloretin (PH) possesses anticancer, antitumor, and hepatoprotective effects, however, the effects and potential mechanisms of phloretin remain elusive. Methods: Five HCC cells were tested in vitro for sensitivity to PH, Sorafenib (Sor) or both and the apoptosis, signal transduction and phosphatase activity were analyzed. To validate the role of SHP-1, we used PTP inhibitor III and SHP-1 siRNA. Further, we used purified SHP-1 proteins or HCC cells expressing deletion N-SH2 domain or D61A point mutants to study the PH efficacy on SHP-1. The 'in vivo studies were conducted using HepG2 and SK-Hep1 and Sor resistant HepG2SR and Huh7SR xenografts. Molecular docking was done with Swiss dock and Auto Dock Vina. Results: PH inhibited cell growth and induced apoptosis in all HCC cells by upregulating SHP-1 expression and downregulating STAT3 expression and further inhibited pAKT/pERK signaling. PH activated SHP-1 by disruption of autoinhibition of SHP-1, leading to reduced p-STAT3Tyr705 level. PH induced apoptosis in two Sor-resistant cell lines and overcome STAT3, AKT, MAPK and VEGFR2 dependent Sor resistance in HCCs. PH potently inhibited tumor growth in both Sor-sensitive and Sor-resistant xenografts in vivo by impairing angiogenesis, cell proliferation and inducing apoptosis via targeting the SHP-1/STAT3 signaling pathway. Conclusion: Our data suggest that PH inhibits STAT3 activity in Sor-sensitive and -resistant HCCs via SHP-1-mediated inhibition of STAT3 and AKT/mTOR/JAK2/VEGFR2 pathway. Our results clearly indicate that PH may be a potent reagent for hepatocellular carcinoma and a noveltargeted therapy for further clinical investigations. Graphical abstract: [Figure not available: see fulltext].",0
"Fully automated diagnosis of anterior cruciate ligament tears on knee mr images by using deep learning. Purpose: To investigate the feasibility of using a deep learning–based approach to detect an anterior cruciate ligament (ACL) tear within the knee joint at MRI by using arthroscopy as the reference standard. Materials and Methods: A fully automated deep learning–based diagnosis system was developed by using two deep convolutional neural networks (CNNs) to isolate the ACL on MR images followed by a classification CNN to detect structural abnormalities within the isolated ligament. With institutional review board approval, sagittal proton density–weighted and fat-suppressed T2-weighted fast spinecho MR images of the knee in 175 subjects with a full-thickness ACL tear (98 male subjects and 77 female subjects; average age, 27.5 years) and 175 subjects with an intact ACL (100 male subjects and 75 female subjects; average age, 39.4 years) were retrospectively analyzed by using the deep learning approach. Sensitivity and specificity of the ACL tear detection system and five clinical radiologists for detecting an ACL tear were determined by using arthroscopic results as the reference standard. Receiver operating characteristic (ROC) analysis and two-sided exact binomial tests were used to further assess diagnostic performance. Results: The sensitivity and specificity of the ACL tear detection system at the optimal threshold were 0.96 and 0.96, respectively. In comparison, the sensitivity of the clinical radiologists ranged between 0.96 and 0.98, while the specificity ranged between 0.90 and 0.98. There was no statistically significant difference in diagnostic performance between the ACL tear detection system and clinical radiologists at P <.05. The area under the ROC curve for the ACL tear detection system was 0.98, indicating high overall diagnostic accuracy. Conclusion: There was no significant difference between the diagnostic performance of the ACL tear detection system and clinical radiologists for determining the presence or absence of an ACL tear at MRI.",1
"Transport of reactive oxygen and nitrogen species across aquaporin: A molecular level picture. Aquaporins (AQPs) are transmembrane proteins that conduct not only water molecules across the cell membrane but also other solutes, such as reactive oxygen and nitrogen species (RONS), produced (among others) by cold atmospheric plasma (CAP). These RONS may induce oxidative stress in the cell interior, which plays a role in cancer treatment. The underlying mechanisms of the transport of RONS across AQPs, however, still remain obscure. We apply molecular dynamics simulations to investigate the permeation of both hydrophilic (H2O2 and OH) and hydrophobic (NO2 and NO) RONS through AQP1. Our simulations show that these RONS can all penetrate across the pores of AQP1. The permeation free energy barrier of OH and NO is lower than that of H2O2 and NO2, indicating that these radicals may have easier access to the pore interior and interact with the amino acid residues of AQP1. We also study the effect of RONS-induced oxidation of both the phospholipids and AQP1 (i.e., sulfenylation of Cys191) on the transport of the above-mentioned RONS across AQP1. Both lipid and protein oxidation seem to slightly increase the free energy barrier for H2O2 and NO2 permeation, while for OH and NO, we do not observe a strong effect of oxidation. The simulation results help to gain insight in the underlying mechanisms of the noticeable rise of CAP-induced RONS in cancer cells, thereby improving our understanding on the role of AQPs in the selective anticancer capacity of CAP.",0
"A machine-learning approach to predict postprandial hypoglycemia. BACKGROUND: For an effective artificial pancreas (AP) system and an improved therapeutic intervention with continuous glucose monitoring (CGM), predicting the occurrence of hypoglycemia accurately is very important. While there have been many studies reporting successful algorithms for predicting nocturnal hypoglycemia, predicting postprandial hypoglycemia still remains a challenge due to extreme glucose fluctuations that occur around mealtimes. The goal of this study is to evaluate the feasibility of easy-to-use, computationally efficient machine-learning algorithm to predict postprandial hypoglycemia with a unique feature set. METHODS: We use retrospective CGM datasets of 104 people who had experienced at least one hypoglycemia alert value during a three-day CGM session. The algorithms were developed based on four machine learning models with a unique data-driven feature set: a random forest (RF), a support vector machine using a linear function or a radial basis function, a K-nearest neighbor, and a logistic regression. With 5-fold cross-subject validation, the average performance of each model was calculated to compare and contrast their individual performance. The area under a receiver operating characteristic curve (AUC) and the F1 score were used as the main criterion for evaluating the performance. RESULTS: In predicting a hypoglycemia alert value with a 30-min prediction horizon, the RF model showed the best performance with the average AUC of 0.966, the average sensitivity of 89.6%, the average specificity of 91.3%, and the average F1 score of 0.543. In addition, the RF showed the better predictive performance for postprandial hypoglycemic events than other models. CONCLUSION: In conclusion, we showed that machine-learning algorithms have potential in predicting postprandial hypoglycemia, and the RF model could be a better candidate for the further development of postprandial hypoglycemia prediction algorithm to advance the CGM technology and the AP technology further.",1
"Hesperidin-CAMKIV interaction and its impact on cell proliferation and apoptosis in the human hepatic carcinoma and neuroblastoma cells. Calcium/calmodulin-dependent protein kinase IV (CAMKIV) is a key regulatory molecule of cell signaling, and thereby controls its growth and proliferation, including expression of certain genes. The overexpression of CAMKIV is directly associated with the development of different types of cancers. Hesperidin is abundantly found in citrus fruits and exhibits wide range of pharmacological activities including anti-inflammatory, antibacterial and anticancerous effects. We have investigated binding mechanism of hesperidin with the CAMKIV using molecular docking methods followed by fluorescence quenching and isothermal titration calorimetric assays. An appreciable binding affinity of hesperidin was observed with CAMKIV during fluorescence quenching and isothermal titration calorimetric studies. Efficacy of hesperidin to inhibit the growth of human hepatic carcinoma (HepG2) and neuroblastoma (SH-SY5Y) cancer cell lines were investigated. Hesperidin has significantly reduced the proliferation of HepG2 and SH-SY5Y cells and induces apoptosis by activating the caspase-3-dependent intrinsic pathway through the upregulation of proapoptotic Bax protein. Hesperidin treatment reduces the mitochondrial membrane potential of HepG2 and SH-SY5Y cells. All these observations clearly anticipated hesperidin a potent inhibitor of CAMKIV which may be further exploited a newer therapeutic approach for the management of different cancer types.",0
"Genomic Evaluation of Multiparametric Magnetic Resonance Imaging-visible and -nonvisible Lesions in Clinically Localised Prostate Cancer. Background: The prostate cancer (PCa) diagnostic pathway is undergoing a radical change with the introduction of multiparametric magnetic resonance imaging (mpMRI), genomic testing, and different prostate biopsy techniques. It has been proposed that these tests should be used in a sequential manner to optimise risk stratification. Objective: To characterise the genomic, epigenomic, and transcriptomic features of mpMRI-visible and -nonvisible PCa in clinically localised disease. Design, setting, and participants: Multicore analysis of fresh prostate tissue sampled immediately after radical prostatectomy was performed for intermediate- to high-risk PCa. Intervention: Low-pass whole-genome, exome, methylation, and transcriptome profiling of patient tissue cores taken from microscopically benign and cancerous areas in the same prostate. Circulating free and germline DNA was assessed from the blood of five patients. Outcome measurement and statistical analysis: Correlations between preoperative mpMRI and genomic characteristics of tumour and benign prostate samples were assessed. Gene profiles for individual tumour cores were correlated with existing genomic classifiers currently used for prognostication. Results and limitations: A total of 43 prostate cores (22 tumour and 21 benign) were profiled from six whole prostate glands. Of the 22 tumour cores, 16 were tumours visible and six were tumours nonvisible on mpMRI. Intratumour genomic, epigenomic, and transcriptomic heterogeneity was found within mpMRI-visible lesions. This could potentially lead to misclassification of patients using signatures based on copy number or RNA expression. Moreover, three of the six cores obtained from mpMRI-nonvisible tumours harboured one or more genetic alterations commonly observed in metastatic castration-resistant PCa. No circulating free DNA alterations were found. Limitations include the small cohort size and lack of follow-up. Conclusions: Our study supports the continued use of systematic prostate sampling in addition to mpMRI, as avoidance of systematic biopsies in patients with negative mpMRI may mean that clinically significant tumours harbouring genetic alterations commonly seen in metastatic PCa are missed. Furthermore, there is inconsistency in individual genomics when genomic classifiers are applied. Patient summary: Our study shows that tumour heterogeneity within prostate tumours visible on multiparametric magnetic resonance imaging (mpMRI) can lead to misclassification of patients if only one core is used for genomic analysis. In addition, some cancers that were missed by mpMRI had genomic aberrations that are commonly seen in advanced metastatic prostate cancer. Avoiding biopsies in mpMRI-negative cases may mean that such potentially lethal cancers are missed. Our study supports the continued use of systematic prostate sampling in addition to multiparametric magnetic resonance imaging (mpMRI), as avoidance of systematic biopsies in patients with negative mpMRI can mean that clinically significant tumours harbouring genetic alterations commonly seen in metastatic prostate cancer can be missed. Furthermore, there is inconsistency in individual genomics when genomic classifiers are applied.",0
"Cardiovascular calcification: artificial intelligence and big data accelerate mechanistic discovery. Cardiovascular calcification is a health disorder with increasing prevalence and high morbidity and mortality. The only available therapeutic options for calcific vascular and valvular heart disease are invasive transcatheter procedures or surgeries that do not fully address the wide spectrum of these conditions; therefore, an urgent need exists for medical options. Cardiovascular calcification is an active process, which provides a potential opportunity for effective therapeutic targeting. Numerous biological processes are involved in calcific disease, including matrix remodelling, transcriptional regulation, mitochondrial dysfunction, oxidative stress, calcium and phosphate signalling, endoplasmic reticulum stress, lipid and mineral metabolism, autophagy, inflammation, apoptosis, loss of mineralization inhibition, impaired mineral resorption, cellular senescence and extracellular vesicles that act as precursors of microcalcification. Advances in molecular imaging and big data technology, including in multiomics and network medicine, and the integration of these approaches are helping to provide a more comprehensive map of human disease. In this Review, we discuss ectopic calcification processes in the cardiovascular system, with an emphasis on emerging mechanistic knowledge obtained through patient data and advances in imaging methods, experimental models and multiomics-generated big data. We also highlight the potential and challenges of artificial intelligence, machine learning and deep learning to integrate imaging and mechanistic data for drug discovery.",0
"Computational modeling of bicuspid aortopathy: Towards personalized risk strategies. This paper describes current advances on the application of in-silico for the understanding of bicuspid aortopathy and future perspectives of this technology on routine clinical care. This includes the impact that artificial intelligence can provide to develop computer-based clinical decision support system and that wearable sensors can offer to remotely monitor high-risk bicuspid aortic valve (BAV) patients. First, we discussed the benefit of computational modeling by providing tangible examples of in-silico software products based on computational fluid-dynamic (CFD) and finite-element method (FEM) that are currently transforming the way we diagnose and treat cardiovascular diseases. Then, we presented recent findings on computational hemodynamic and structural mechanics of BAV to highlight the potentiality of patient-specific metrics (not-based on aortic size) to support the clinical-decision making process of BAV-associated aneurysms. Examples of BAV-related personalized healthcare solutions are illustrated.",0
"Derivation, Validation, and Potential Treatment Implications of Novel Clinical Phenotypes for Sepsis. Importance: Sepsis is a heterogeneous syndrome. Identification of distinct clinical phenotypes may allow more precise therapy and improve care. Objective: To derive sepsis phenotypes from clinical data, determine their reproducibility and correlation with host-response biomarkers and clinical outcomes, and assess the potential causal relationship with results from randomized clinical trials (RCTs). Design, Settings, and Participants: Retrospective analysis of data sets using statistical, machine learning, and simulation tools. Phenotypes were derived among 20189 total patients (16552 unique patients) who met Sepsis-3 criteria within 6 hours of hospital presentation at 12 Pennsylvania hospitals (2010-2012) using consensus k means clustering applied to 29 variables. Reproducibility and correlation with biological parameters and clinical outcomes were assessed in a second database (2013-2014; n = 43086 total patients and n = 31160 unique patients), in a prospective cohort study of sepsis due to pneumonia (n = 583), and in 3 sepsis RCTs (n = 4737). Exposures: All clinical and laboratory variables in the electronic health record. Main Outcomes and Measures: Derived phenotype (alpha, beta, gamma, and delta) frequency, host-response biomarkers, 28-day and 365-day mortality, and RCT simulation outputs. Results: The derivation cohort included 20189 patients with sepsis (mean age, 64 [SD, 17] years; 10022 [50%] male; mean maximum 24-hour Sequential Organ Failure Assessment [SOFA] score, 3.9 [SD, 2.4]). The validation cohort included 43086 patients (mean age, 67 [SD, 17] years; 21993 [51%] male; mean maximum 24-hour SOFA score, 3.6 [SD, 2.0]). Of the 4 derived phenotypes, the alpha phenotype was the most common (n = 6625; 33%) and included patients with the lowest administration of a vasopressor; in the beta phenotype (n = 5512; 27%), patients were older and had more chronic illness and renal dysfunction; in the gamma phenotype (n = 5385; 27%), patients had more inflammation and pulmonary dysfunction; and in the delta phenotype (n = 2667; 13%), patients had more liver dysfunction and septic shock. Phenotype distributions were similar in the validation cohort. There were consistent differences in biomarker patterns by phenotype. In the derivation cohort, cumulative 28-day mortality was 287 deaths of 5691 unique patients (5%) for the alpha phenotype; 561 of 4420 (13%) for the beta phenotype; 1031 of 4318 (24%) for the gamma phenotype; and 897 of 2223 (40%) for the delta phenotype. Across all cohorts and trials, 28-day and 365-day mortality were highest among the delta phenotype vs the other 3 phenotypes (P < .001). In simulation models, the proportion of RCTs reporting benefit, harm, or no effect changed considerably (eg, varying the phenotype frequencies within an RCT of early goal-directed therapy changed the results from >33% chance of benefit to >60% chance of harm). Conclusions and Relevance: In this retrospective analysis of data sets from patients with sepsis, 4 clinical phenotypes were identified that correlated with host-response patterns and clinical outcomes, and simulations suggested these phenotypes may help in understanding heterogeneity of treatment effects. Further research is needed to determine the utility of these phenotypes in clinical care and for informing trial design and interpretation.",1
"Grab recruitment by Rab27A-Rabphilin3a triggers Rab3A activation in human sperm exocytosis. Sperm must undergo the regulated exocytosis of its dense core granule (the acrosome reaction, AR) to fertilize the egg. We have previously described that Rabs3 and 27 are organized in a RabGEF cascade within the signaling pathway elicited by exocytosis stimuli in human sperm. Here, we report the identity and the role of two molecules that link these secretory Rabs in the RabGEF cascade: Rabphilin3a and GRAB. Like Rab3 and Rab27, GRAB and Rabphilin3a are present, localize to the acrosomal region and are required for calcium-triggered exocytosis in human sperm. Sequestration of either protein with specific antibodies introduced into streptolysin O-permeabilized sperm impairs the activation of Rab3 in the acrosomal region elicited by calcium, but not that of Rab27. Biochemical and functional assays indicate that Rabphilin3a behaves as a Rab27 effector during the AR and that GRAB exhibits GEF activity toward Rab3A. Recombinant, active Rab27A pulls down Rabphilin3a and GRAB from human sperm extracts. Conversely, immobilized Rabphilin3a recruits Rab27 and GRAB; the latter promotes Rab3A activation. The enzymatic activity of GRAB toward Rab3A was also suggested by in silico and in vitro assays with purified proteins. In summary, we describe here a signaling module where Rab27A-GTP interacts with Rabphilin3a, which in turn recruits a guanine nucleotide-exchange activity toward Rab3A. This is the first description of the interaction of Rabphilin3a with a GEF. Because the machinery that drives exocytosis is highly conserved, it is tempting to hypothesize that the RabGEF cascade unveiled here might be part of the molecular mechanisms that drive exocytosis in other secretory systems.",0
"Automated diagnosis of breast ultrasonography images using deep neural networks. Ultrasonography images of breast mass aid in the detection and diagnosis of breast cancer. Manually analyzing ultrasonography images is time-consuming, exhausting and subjective. Automated analyzing such images is desired. In this study, we develop an automated breast cancer diagnosis model for ultrasonography images. Traditional methods of automated ultrasonography images analysis employ hand-crafted features to classify images, and lack robustness to the variation in the shapes, size and texture of breast lesions, leading to low sensitivity in clinical applications. To overcome these shortcomings, we propose a method to diagnose breast ultrasonography images using deep convolutional neural networks with multi-scale kernels and skip connections. Our method consists of two components: the first one is to determine whether there are malignant tumors in the image, and the second one is to recognize solid nodules. In order to let the two networks work in a collaborative way, a region enhance mechanism based on class activation maps is proposed. The mechanism helps to improve classification accuracy and sensitivity for both networks. A cross training algorithm is introduced to train the networks. We construct a large annotated dataset containing a total of 8145 breast ultrasonography images to train and evaluate the models. All of the annotations are proven by pathological records. The proposed method is compared with two state-of-the-art approaches, and outperforms both of them by a large margin. Experimental results show that our approach achieves a performance comparable to human sonographers and can be applied to clinical scenarios.",1
"Assessment of a Deep Learning Model Based on Electronic Health Record Data to Forecast Clinical Outcomes in Patients With Rheumatoid Arthritis. Importance: Knowing the future condition of a patient would enable a physician to customize current therapeutic options to prevent disease worsening, but predicting that future condition requires sophisticated modeling and information. If artificial intelligence models were capable of forecasting future patient outcomes, they could be used to aid practitioners and patients in prognosticating outcomes or simulating potential outcomes under different treatment scenarios. Objective: To assess the ability of an artificial intelligence system to prognosticate the state of disease activity of patients with rheumatoid arthritis (RA) at their next clinical visit. Design, Setting, and Participants: This prognostic study included 820 patients with RA from rheumatology clinics at 2 distinct health care systems with different electronic health record platforms: a university hospital (UH) and a public safety-net hospital (SNH). The UH and SNH had substantially different patient populations and treatment patterns. The UH has records on approximately 1 million total patients starting in January 2012. The UH data for this study were accessed on July 1, 2017. The SNH has records on 65 000 unique individuals starting in January 2013. The SNH data for the study were collected on February 27, 2018. Exposures: Structured data were extracted from the electronic health record, including exposures (medications), patient demographics, laboratories, and prior measures of disease activity. A longitudinal deep learning model was used to predict disease activity for patients with RA at their next rheumatology clinic visit and to evaluate interhospital performance and model interoperability strategies. Main Outcomes and Measures: Model performance was quantified using the area under the receiver operating characteristic curve (AUROC). Disease activity in RA was measured using a composite index score. Results: A total of 578 UH patients (mean [SD] age, 57 [15] years; 477 [82.5%] female; 296 [51.2%] white) and 242 SNH patients (mean [SD] age, 60 [15] years; 195 [80.6%] female; 30 [12.4%] white) were included in the study. Patients at the UH compared with those at the SNH were seen more frequently (median time between visits, 100 vs 180 days) and were more frequently prescribed higher-class medications (biologics) (364 [63.0%] vs 70 [28.9%]). At the UH, the model reached an AUROC of 0.91 (95% CI, 0.86-0.96) in a test cohort of 116 patients. The UH-trained model had an AUROC of 0.74 (95% CI, 0.65-0.83) in the SNH test cohort (n = 117) despite marked differences in the patient populations. In both settings, baseline prediction using each patients' most recent disease activity score had statistically random performance. Conclusions and Relevance: The findings suggest that building accurate models to forecast complex disease outcomes using electronic health record data is possible and these models can be shared across hospitals with diverse patient populations.",1
"Do no harm: a roadmap for responsible machine learning for health care. Interest in machine-learning applications within medicine has been growing, but few studies have progressed to deployment in patient care. We present a framework, context and ultimately guidelines for accelerating the translation of machine-learning-based interventions in health care. To be successful, translation will require a team of engaged stakeholders and a systematic process from beginning (problem formulation) to end (widespread deployment).",0
"Genomic Risk Predicts Molecular Imaging-detected Metastatic Nodal Disease in Prostate Cancer. Background: The Decipher genomic classifier (GC) is increasingly being used to determine metastasis risk in men with localized prostate cancer (PCa). Whether GCs predict for the presence of occult metastatic disease at presentation or subsequent metastatic progression is unknown. Objective: To determine if GC scores predict extraprostatic 68Ga prostate-specific membrane antigen (68Ga-PSMA-11) positron emission tomography (PET) positivity at presentation. Design, setting, and participants: Between December 2015 and September 2018, 91 PCa patients with both GC scores and pretreatment 68Ga-PSMA-11 PET scans were identified. Risk stratification was performed using the National Comprehensive Cancer Network (NCCN), Cancer of the Prostate Risk Assessment (CAPRA), and GC scores. Outcome measurements and statistical analysis: Logistic regression was used to identify factors correlated with PSMA-positive disease. Results and limitations: The NCCN criteria identified 23 (25.3%) and 68 patients (74.7%) as intermediate and high risk, while CAPRA scores revealed 28 (30.8%) and 63 (69.2%) as low/intermediate and high risk, respectively. By contrast, only 45 patients (49.4%) had high-risk GC scores. PSMA-avid pelvic nodal involvement was identified in 27 patients (29.7%). Higher GC score was significantly associated with pelvic nodal involvement (odds ratio [OR] 1.38 per 0.1 units; p = 0.009) and any PSMA-avid nodal involvement (pelvic or distant; OR 1.40 per 0.1 units; p = 0.007). However, higher GC score was not significantly associated with PSMA-avid osseous metastases (OR 1.11 per 0.1 units; p = 0.50). Limitations include selection bias for patients able to receive both tests and the sample size. Conclusions: Each 0.1-unit increase in GC score was associated with an approximate 40% increase in the odds of PSMA-avid lymph node involvement. These data suggest that patients with GC high risk might benefit from more nodal imaging and treatment intensification, potentially via pelvic nodal dissection, pelvic nodal irradiation, and/or the addition of chemohormonal agents. Patient summary: Patients with higher genomic classifier scores were found to have more metastatic lymph node involvement on prostate-specific membrane antigen imaging.",0
"Robot assisted training for the upper limb after stroke (RATULS): a multicentre randomised controlled trial. BACKGROUND: Loss of arm function is a common problem after stroke. Robot-assisted training might improve arm function and activities of daily living. We compared the clinical effectiveness of robot-assisted training using the MIT-Manus robotic gym with an enhanced upper limb therapy (EULT) programme based on repetitive functional task practice and with usual care. METHODS: RATULS was a pragmatic, multicentre, randomised controlled trial done at four UK centres. Stroke patients aged at least 18 years with moderate or severe upper limb functional limitation, between 1 week and 5 years after their first stroke, were randomly assigned (1:1:1) to receive robot-assisted training, EULT, or usual care. Robot-assisted training and EULT were provided for 45 min, three times per week for 12 weeks. Randomisation was internet-based using permuted block sequences. Treatment allocation was masked from outcome assessors but not from participants or therapists. The primary outcome was upper limb function success (defined using the Action Research Arm Test [ARAT]) at 3 months. Analyses were done on an intention-to-treat basis. This study is registered with the ISRCTN registry, number ISRCTN69371850. FINDINGS: Between April 14, 2014, and April 30, 2018, 770 participants were enrolled and randomly assigned to either robot-assisted training (n=257), EULT (n=259), or usual care (n=254). The primary outcome of ARAT success was achieved by 103 (44%) of 232 patients in the robot-assisted training group, 118 (50%) of 234 in the EULT group, and 85 (42%) of 203 in the usual care group. Compared with usual care, robot-assisted training (adjusted odds ratio [aOR] 1.17 [98.3% CI 0.70-1.96]) and EULT (aOR 1.51 [0.90-2.51]) did not improve upper limb function; the effects of robot-assisted training did not differ from EULT (aOR 0.78 [0.48-1.27]). More participants in the robot-assisted training group (39 [15%] of 257) and EULT group (33 [13%] of 259) had serious adverse events than in the usual care group (20 [8%] of 254), but none were attributable to the intervention. INTERPRETATION: Robot-assisted training and EULT did not improve upper limb function after stroke compared with usual care for patients with moderate or severe upper limb functional limitation. These results do not support the use of robot-assisted training as provided in this trial in routine clinical practice. FUNDING: National Institute for Health Research Health Technology Assessment Programme.",0
"The caudate nucleus undergoes dramatic and unique transcriptional changes in human prodromal Huntington's disease brain. Background: The mechanisms underlying neurodegeneration in the striatum of Huntingon's Disease (HD) brain are currently unknown. While the striatum is massively degenerated in symptomatic individuals, which makes cellular characterization difficult, it is largely intact in asymptomatic HD gene positive (HD+) individuals. Unfortunately, as striatal tissue samples from HD+ individuals are exceedingly rare, recent focus has been on the Brodmann Area 9 (BA9), a relatively unaffected region, as a surrogate tissue. In this study, we analyze gene expression in caudate nucleus (CAU) from two HD+ individuals and compare the results with healthy and symptomatic HD brains. Methods: High-throughput mRNA sequencing (mRNA-Seq) datasets were generated from post-mortem CAU of 2 asymptomatic HD+ individuals and compared with 26 HD and 56 neurologically normal controls. Datasets were analyzed using a custom bioinformatic analysis pipeline to identify and interpret differentially expressed (DE) genes. Results were compared to publicly available brain mRNA-Seq datasets from the Genotype-Tissue Expression (GTEx) project. The analysis employed current state of the art bioinformatics tools and tailored statistical and machine learning methods. Results: The transcriptional profiles in HD+ CAU and HD BA9 samples are highly similar. Differentially expressed (DE) genes related to the heat shock response, particularly HSPA6 and HSPA1A, are common between regions. The most perturbed pathways show extensive agreement when comparing disease with control. A random forest classifier predicts that the two HD+ CAU samples strongly resemble HD BA9 and not control BA9. Nonetheless, when genes were prioritized by their specificity to HD+ CAU, pathways spanning many biological processes emerge. Comparison of HD+ BA9 with HD BA9 identified NPAS4 and REST1/2 as potential early responders to disease and reflect the active disease process. Conclusions: The caudate nucleus in HD brain is dramatically affected prior to symptom onset. Gene expression patterns observed in the HD BA9 are also present in the CAU, suggesting a common response to disease. Substantial caudate-specific differences implicate many different biological pathways including metabolism, protein folding, inflammation, and neurogenic processes. While these results are at best trends due to small sample sizes, these results nonetheless provide the most detailed insight to date into the primary HD disease process.",0
"PTEN Suppresses Glycolysis by Dephosphorylating and Inhibiting Autophosphorylated PGK1. The PTEN tumor suppressor is frequently mutated or deleted in cancer and regulates glucose metabolism through the PI3K-AKT pathway. However, whether PTEN directly regulates glycolysis in tumor cells is unclear. We demonstrate here that PTEN directly interacts with phosphoglycerate kinase 1 (PGK1). PGK1 functions not only as a glycolytic enzyme but also as a protein kinase intermolecularly autophosphorylating itself at Y324 for activation. The protein phosphatase activity of PTEN dephosphorylates and inhibits autophosphorylated PGK1, thereby inhibiting glycolysis, ATP production, and brain tumor cell proliferation. In addition, knockin expression of a PGK1 Y324F mutant inhibits brain tumor formation. Analyses of human glioblastoma specimens reveals that PGK1 Y324 phosphorylation levels inversely correlate with PTEN expression status and are positively associated with poor prognosis in glioblastoma patients. This work highlights the instrumental role of PGK1 autophosphorylation in its activation and PTEN protein phosphatase activity in governing glycolysis and tumorigenesis.",0
"Global lysine crotonylation and 2-hydroxyisobutyrylation in phenotypically different Toxoplasma gondii parasites. Toxoplasma gondii is a unicellular protozoan parasite of the phylum Apicomplexa. The parasite repeatedly goes through a cycle of invasion, division and induction of host cell rupture, which is an obligatory process for proliferation inside warm-blooded animals. It is known that the biology of the parasite is controlled by a variety of mechanisms ranging from genomic to epigenetic to transcriptional regulation. In this study, we investigated the global protein posttranslational lysine crotonylation and 2-hy-droxyisobutyrylation of two T. gondii strains, RH and ME49, which represent distinct phenotypes for proliferation and pathogenicity in the host. Proteins with differential expression and modification patterns associated with parasite phenotypes were identified. Many proteins in T. gondii were crotonylated and 2-hydroxyisobutyrylated, and they were localized in diverse subcellular compartments involved in a wide variety of cellular functions such as motility, host invasion, metabolism and epigenetic gene regulation. These findings suggest that lysine crotonylation and 2-hydroxyisobutyrylation are ubiquitous throughout the T. gondii proteome, regulating critical functions of the modified proteins. These data provide a basis for identifying important proteins associated with parasite development and pathogenicity.",0
"Common binding sites for cholesterol and neurosteroids on a pentameric ligand-gated ion channel. Cholesterol is an essential component of cell membranes, and is required for mammalian pentameric ligand-gated ion channel (pLGIC) function. Computational studies suggest direct interactions between cholesterol and pLGICs but experimental evidence identifying specific binding sites is limited. In this study, we mapped cholesterol binding to Gloeobacter ligand-gated ion channel (GLIC), a model pLGIC chosen for its high level of expression, existing crystal structure, and previous use as a prototypic pLGIC. Using two cholesterol analogue photolabeling reagents with the photoreactive moiety on opposite ends of the sterol, we identified two cholesterol binding sites: an intersubunit site between TM3 and TM1 of adjacent subunits and an intrasubunit site between TM1 and TM4. In both the inter- and intrasubunit sites, cholesterol is oriented such that the 3‑OH group points toward the center of the transmembrane domains rather than toward either the cytosolic or extracellular surfaces. We then compared this binding to that of the cholesterol metabolite, allopregnanolone, a neurosteroid that allosterically modulates pLGICs. The same binding pockets were identified for allopregnanolone and cholesterol, but the binding orientation of the two ligands was markedly different, with the 3‑OH group of allopregnanolone pointing to the intra- and extracellular termini of the transmembrane domains rather than to their centers. We also found that cholesterol increases, whereas allopregnanolone decreases the thermal stability of GLIC. These data indicate that cholesterol and neurosteroids bind to common hydrophobic pockets in the model pLGIC, GLIC, but that their effects depend on the orientation and specific molecular interactions unique to each sterol.",0
"EBV microRNA-BHRF1-2-5p targets the 39UTR of immune checkpoint ligands PD-L1 and PD-L2. Epstein-Barr virus-positive (EBV1) diffuse large B-cell lymphomas (DLBCLs) express high levels of programmed death ligand 1 (PD-L1) and PD-L2. MicroRNA (miR) regulation is an important mechanism for the fine-tuning of gene expression via 39-untranslated region (39UTR) targeting, and we have previously demonstrated strong EBV miR expression in EBV1 DLBCL. Whereas the EBV latent membrane protein-1 (LMP1) is known to induce PD-L1/L2, a potential counterregulatory role of EBV miR in the fine-tuning of PD-L1/L2 expression remains to be established. To examine this, a novel in vitro model of EBV1 DLBCL was developed, using the viral strain EBV WIL, which unlike common laboratory strains retains intact noncoding regions where several EBV miRs reside. This enabled interrogation of the relationship among EBV latency genes, cell of origin (COO), PD-L1, PD-L2, and EBV miRs. The model successfully recapitulated the full spectrum of B-cell differentiation, with 4 discrete COO phases: early and late germinal center B cells (GCBs) and early and late activated B cells (ABCs). Interestingly, PD-L1/L2 levels increased markedly during transition from late GCB to early ABC phase, after LMP1 upregulation. EBV miR-BamHI fragment H rightward open reading frame 1 (BHRF1)-2-5p clustered apart from other EBV miRs, rising during late GCB phase. Bioinformatic prediction, together with functional validation, confirmed EBV miR-BHRF1-2-5p bound to PD-L1 and PD-L2 39UTRs to reduce PD-L1/L2 surface protein expression. Results indicate a novel mechanism by which EBV miR-BHRF1-2-5p plays a context-dependent counterregulatory role to fine-tune the expression of the LMP1-driven amplification of these inhibitory checkpoint ligands. Further identification of immune checkpoint-targeting miRs may enable potential novel RNA-based therapies to emerge.",0
"Hierarchical sequence labeling for extracting BEL statements from biomedical literature. BACKGROUND: Extracting relations between bio-entities from biomedical literature is often a challenging task and also an essential step towards biomedical knowledge expansion. The BioCreative community has organized a shared task to evaluate the robustness of the causal relationship extraction algorithms in Biological Expression Language (BEL) from biomedical literature. METHOD: We first map the sentence-level BEL statements in the BC-V training corpus to the corresponding text segments, thus generating hierarchically tagged training instances. A hierarchical sequence labeling model was afterwards induced from these training instances and applied to the test sentences in order to construct the BEL statements. RESULTS: The experimental results on extracting BEL statements from BioCreative V Track 4 test corpus show that our method achieves promising performance with an overall F-measure of 31.6%. Furthermore, it has the potential to be enhanced by adopting more advanced machine learning approaches. CONCLUSION: We propose a framework for hierarchical relation extraction using hierarchical sequence labeling on the instance-level training corpus derived from the original sentence-level corpus via word alignment. Its main advantage is that we can make full use of the original training corpus to induce the sequence labelers and then apply them to the test corpus.",1
"Use of Crowd Innovation to Develop an Artificial Intelligence-Based Solution for Radiation Therapy Targeting. Importance: Radiation therapy (RT) is a critical cancer treatment, but the existing radiation oncologist work force does not meet growing global demand. One key physician task in RT planning involves tumor segmentation for targeting, which requires substantial training and is subject to significant interobserver variation. Objective: To determine whether crowd innovation could be used to rapidly produce artificial intelligence (AI) solutions that replicate the accuracy of an expert radiation oncologist in segmenting lung tumors for RT targeting. Design, Setting, and Participants: We conducted a 10-week, prize-based, online, 3-phase challenge (prizes totaled $55000). A well-curated data set, including computed tomographic (CT) scans and lung tumor segmentations generated by an expert for clinical care, was used for the contest (CT scans from 461 patients; median 157 images per scan; 77942 images in total; 8144 images with tumor present). Contestants were provided a training set of 229 CT scans with accompanying expert contours to develop their algorithms and given feedback on their performance throughout the contest, including from the expert clinician. Main Outcomes and Measures: The AI algorithms generated by contestants were automatically scored on an independent data set that was withheld from contestants, and performance ranked using quantitative metrics that evaluated overlap of each algorithm's automated segmentations with the expert's segmentations. Performance was further benchmarked against human expert interobserver and intraobserver variation. Results: A total of 564 contestants from 62 countries registered for this challenge, and 34 (6%) submitted algorithms. The automated segmentations produced by the top 5 AI algorithms, when combined using an ensemble model, had an accuracy (Dice coefficient = 0.79) that was within the benchmark of mean interobserver variation measured between 6 human experts. For phase 1, the top 7 algorithms had average custom segmentation scores (S scores) on the holdout data set ranging from 0.15 to 0.38, and suboptimal performance using relative measures of error. The average S scores for phase 2 increased to 0.53 to 0.57, with a similar improvement in other performance metrics. In phase 3, performance of the top algorithm increased by an additional 9%. Combining the top 5 algorithms from phase 2 and phase 3 using an ensemble model, yielded an additional 9% to 12% improvement in performance with a final S score reaching 0.68. Conclusions and Relevance: A combined crowd innovation and AI approach rapidly produced automated algorithms that replicated the skills of a highly trained physician for a critical task in radiation therapy. These AI algorithms could improve cancer care globally by transferring the skills of expert clinicians to under-resourced health care settings.",1
"Towards cross-modal organ translation and segmentation: A cycle- and shape-consistent generative adversarial network. Synthesized medical images have several important applications. For instance, they can be used as an intermedium in cross-modality image registration or used as augmented training samples to boost the generalization capability of a classifier. In this work, we propose a generic cross-modality synthesis approach with the following targets: 1) synthesizing realistic looking 2D/3D images without needing paired training data, 2) ensuring consistent anatomical structures, which could be changed by geometric distortion in cross-modality synthesis and 3) more importantly, improving volume segmentation by using synthetic data for modalities with limited training samples. We show that these goals can be achieved with an end-to-end 2D/3D convolutional neural network (CNN) composed of mutually-beneficial generators and segmentors for image synthesis and segmentation tasks. The generators are trained with an adversarial loss, a cycle-consistency loss, and also a shape-consistency loss (supervised by segmentors) to reduce the geometric distortion. From the segmentation view, the segmentors are boosted by synthetic data from generators in an online manner. Generators and segmentors prompt each other alternatively in an end-to-end training fashion. We validate our proposed method on three datasets, including cardiovascular CT and magnetic resonance imaging (MRI), abdominal CT and MRI, and mammography X-rays from different data domains, showing both tasks are beneficial to each other and coupling these two tasks results in better performance than solving them exclusively.",1
"Expert-Level Diagnosis of Nonpigmented Skin Cancer by Combined Convolutional Neural Networks. Importance: Convolutional neural networks (CNNs) achieve expert-level accuracy in the diagnosis of pigmented melanocytic lesions. However, the most common types of skin cancer are nonpigmented and nonmelanocytic, and are more difficult to diagnose. Objective: To compare the accuracy of a CNN-based classifier with that of physicians with different levels of experience. Design, Setting, and Participants: A CNN-based classification model was trained on 7895 dermoscopic and 5829 close-up images of lesions excised at a primary skin cancer clinic between January 1, 2008, and July 13, 2017, for a combined evaluation of both imaging methods. The combined CNN (cCNN) was tested on a set of 2072 unknown cases and compared with results from 95 human raters who were medical personnel, including 62 board-certified dermatologists, with different experience in dermoscopy. Main Outcomes and Measures: The proportions of correct specific diagnoses and the accuracy to differentiate between benign and malignant lesions measured as an area under the receiver operating characteristic curve served as main outcome measures. Results: Among 95 human raters (51.6% female; mean age, 43.4 years; 95% CI, 41.0-45.7 years), the participants were divided into 3 groups (according to years of experience with dermoscopy): beginner raters (<3 years), intermediate raters (3-10 years), or expert raters (>10 years). The area under the receiver operating characteristic curve of the trained cCNN was higher than human ratings (0.742; 95% CI, 0.729-0.755 vs 0.695; 95% CI, 0.676-0.713; P < .001). The specificity was fixed at the mean level of human raters (51.3%), and therefore the sensitivity of the cCNN (80.5%; 95% CI, 79.0%-82.1%) was higher than that of human raters (77.6%; 95% CI, 74.7%-80.5%). The cCNN achieved a higher percentage of correct specific diagnoses compared with human raters (37.6%; 95% CI, 36.6%-38.4% vs 33.5%; 95% CI, 31.5%-35.6%; P = .001) but not compared with experts (37.3%; 95% CI, 35.7%-38.8% vs 40.0%; 95% CI, 37.0%-43.0%; P = .18). Conclusions and Relevance: Neural networks are able to classify dermoscopic and close-up images of nonpigmented lesions as accurately as human experts in an experimental setting.",1
"TOP-GAN: Stain-free cancer cell classification using deep learning with a small training set. We propose a new deep learning approach for medical imaging that copes with the problem of a small training set, the main bottleneck of deep learning, and apply it for classification of healthy and cancer cell lines acquired by quantitative phase imaging. The proposed method, called transferring of pre-trained generative adversarial network (TOP-GAN), is hybridization between transfer learning and generative adversarial networks (GANs). Healthy cells and cancer cells of different metastatic potential have been imaged by low-coherence off-axis holography. After the acquisition, the optical path delay maps of the cells are extracted and directly used as inputs to the networks. In order to cope with the small number of classified images, we use GANs to train a large number of unclassified images from another cell type (sperm cells). After this preliminary training, we change the last layers of the network and design automatic classifiers for the correct cell type (healthy/primary cancer/metastatic cancer) with 90-99% accuracies, although small training sets of down to several images are used. These results are better in comparison to other classic methods that aim at coping with the same problem of a small training set. We believe that our approach makes the combination of holographic microscopy and deep learning networks more accessible to the medical field by enabling a rapid, automatic and accurate classification in stain-free imaging flow cytometry. Furthermore, our approach is expected to be applicable to many other medical image classification tasks, suffering from a small training set.",1
"Automatic graph-based method for localization of cochlear implant electrode arrays in clinical CT with sub-voxel accuracy. Cochlear implants (CIs) are neural prosthetics that provide a sense of sound to people who experience severe to profound hearing loss. Recent studies have demonstrated a correlation between hearing outcomes and intra-cochlear locations of CI electrodes. Our group has been conducting investigations on this correlation and has been developing an image-guided cochlear implant programming (IGCIP) system to program CI devices to improve hearing outcomes. One crucial step that has not been automated in IGCIP is the localization of CI electrodes in clinical CTs. Existing methods for CI electrode localization do not generalize well on large-scale datasets of clinical CTs implanted with different brands of CI arrays. In this paper, we propose a novel method for localizing different brands of CI electrodes in clinical CTs. We firstly generate the candidate electrode positions at sub-voxel resolution in a whole head CT by thresholding an up-sampled feature image and voxel-thinning the result. Then, we use a graph-based path-finding algorithm to find a fixed-length path that consists of a subset of the candidates as the localization result. Validation on a large-scale dataset of clinical CTs shows that our proposed method outperforms the state-of-art CI electrode localization methods and achieves a mean error of 0.12mm when compared to expert manual localization results. This represents a crucial step in translating IGCIP from the laboratory to large-scale clinical use.",0
"How to Read Articles That Use Machine Learning: Users' Guides to the Medical Literature. In recent years, many new clinical diagnostic tools have been developed using complicated machine learning methods. Irrespective of how a diagnostic tool is derived, it must be evaluated using a 3-step process of deriving, validating, and establishing the clinical effectiveness of the tool. Machine learning-based tools should also be assessed for the type of machine learning model used and its appropriateness for the input data type and data set size. Machine learning models also generally have additional prespecified settings called hyperparameters, which must be tuned on a data set independent of the validation set. On the validation set, the outcome against which the model is evaluated is termed the reference standard. The rigor of the reference standard must be assessed, such as against a universally accepted gold standard or expert grading.",0
"Comparisons of exacerbations and mortality among regular inhaled therapies for patients with stable chronic obstructive pulmonary disease: Systematic review and Bayesian network meta-analysis. BACKGROUND: Although exacerbation and mortality are the most important clinical outcomes of stable chronic obstructive pulmonary disease (COPD), the drug classes that are the most efficacious in reducing exacerbation and mortality among all possible inhaled drugs have not been determined. METHODS AND FINDINGS: We performed a systematic review (SR) and Bayesian network meta-analysis (NMA). We searched Medline, EMBASE, the Cochrane Central Register of Controlled Trials, ClinicalTrials.gov, the European Union Clinical Trials Register, and the official websites of pharmaceutical companies (from inception to July 9, 2019). The eligibility criteria were as follows: (1) parallel-design randomized controlled trials (RCTs); (2) adults with stable COPD; (3) comparisons among long-acting muscarinic antagonists (LAMAs), long-acting beta-agonists (LABAs), inhaled corticosteroids (ICSs), combined treatment (ICS/LAMA/LABA, LAMA/LABA, or ICS/LABA), or a placebo; and (4) study duration ≥ 12 weeks. This study was prospectively registered in International Prospective Register of Systematic Reviews (PROSPERO; CRD42017069087). In total, 219 trials involving 228,710 patients were included. Compared with placebo, all drug classes significantly reduced the total exacerbations and moderate to severe exacerbations. ICS/LAMA/LABA was the most efficacious treatment for reducing the exacerbation risk (odds ratio [OR] = 0.57; 95% credible interval [CrI] 0.50-0.64; posterior probability of OR > 1 [P(OR > 1)] < 0.001). In addition, in contrast to the other drug classes, ICS/LAMA/LABA and ICS/LABA were associated with a significantly higher probability of reducing mortality than placebo (OR = 0.74, 95% CrI 0.59-0.93, P[OR > 1] = 0.004; and OR = 0.86, 95% CrI 0.76-0.98, P[OR > 1] = 0.015, respectively). The results minimally changed, even in various sensitivity and covariate-adjusted meta-regression analyses. ICS/LAMA/LABA tended to lower the risk of cardiovascular mortality but did not show significant results. ICS/LAMA/LABA increased the probability of pneumonia (OR for triple therapy = 1.56; 95% CrI 1.19-2.03; P[OR > 1] = 1.000). The main limitation is that there were few RCTs including only less symptomatic patients or patients at a low risk. CONCLUSIONS: These findings suggest that triple therapy can potentially be the best option for stable COPD patients in terms of reducing exacerbation and all-cause mortality.",0
"Molecular modeling and inhibitor docking analysis of the Na+/H+ exchanger isoform one. Na+/H+ exchanger isoform one (NHE1) is a mammalian plasma membrane protein that removes intracellular protons, thereby elevating intracellular pH (pHi). NHE1 uses the energy of allowing an extracellular sodium down its gradient into cells to remove one intracellular proton. The ubiquitous protein has several important physiological and pathological influences on mammalian cells as a result of its activity. The three-dimensional structure of human NHE1 (hNHE1) is not known. Here, we modeled NHE1 based on the structure of MjNhaP1 of Methanocaldoccocus jannaschii in combination with biochemical surface accessibility data. hNHE1 contained 12 transmembrane segments including a characteristic Na+/H+ antiporter fold of two transmembrane segments with a helix-extended region-helix conformation crossing each other within the membrane. Amino acids 363-410 mapped principally to the extracellular surface as an extracellular loop (EL5). A large preponderance of amino acids shown to be surface accessible by biochemical experiments mapped near to, or on, the extracellular surface. Docking of Na+/H+ exchanger inhibitors to the extracellular surface suggested that inhibitor binding on an extracellular site is made up from several amino acids of different regions of the protein. The results present a novel testable, three-dimensional model illustrating NHE1 structure and accounting for experimental biochemical data.",0
"Natural language processing for populating lung cancer clinical research data. BACKGROUND: Lung cancer is the second most common cancer for men and women; the wide adoption of electronic health records (EHRs) offers a potential to accelerate cohort-related epidemiological studies using informatics approaches. Since manual extraction from large volumes of text materials is time consuming and labor intensive, some efforts have emerged to automatically extract information from text for lung cancer patients using natural language processing (NLP), an artificial intelligence technique. METHODS: In this study, using an existing cohort of 2311 lung cancer patients with information about stage, histology, tumor grade, and therapies (chemotherapy, radiotherapy and surgery) manually ascertained, we developed and evaluated an NLP system to extract information on these variables automatically for the same patients from clinical narratives including clinical notes, pathology reports and surgery reports. RESULTS: Evaluation showed promising results with the recalls for stage, histology, tumor grade, and therapies achieving 89, 98, 78, and 100% respectively and the precisions were 70, 88, 90, and 100% respectively. CONCLUSION: This study demonstrated the feasibility and accuracy of automatically extracting pre-defined information from clinical narratives for lung cancer research.",1
"Substitution of venous for arterial blood sampling in the determination of regional rates of cerebral protein synthesis with L-[1-11C]leucine PET: A validation study. We developed and validated a method to estimate input functions for determination of regional rates of cerebral protein synthesis (rCPS) with L-[1-11C]leucine PET without arterial sampling. The method is based on a population-derived input function (PDIF) approach, with venous samples for calibration. Population input functions were constructed from arterial blood data measured in 25 healthy 18–24-year-old males who underwent L-[1-11C]leucine PET scans while awake. To validate the approach, three additional groups of 18–27-year-old males underwent L-[1-11C]leucine PET scans with both arterial and venous blood sampling: 13 awake healthy volunteers, 10 sedated healthy volunteers, and 5 sedated subjects with fragile X syndrome. Rate constants of the L-[1-11C]leucine kinetic model were estimated voxel-wise with measured arterial input functions and with venous-calibrated PDIFs. Venous plasma leucine measurements were used with venous-calibrated PDIFs for rCPS computation. rCPS determined with PDIFs calibrated with 30–60 min venous samples had small errors (RMSE: 4–9%), and no statistically significant differences were found in any group when compared to rCPS determined with arterial input functions. We conclude that in young adult males, PDIFs calibrated with 30–60 min venous samples can be used in place of arterial input functions for determination of rCPS with L-[1-11C]leucine PET.",0
"Prediction of forelimb reach results from motor cortex activities based on calcium imaging and deep learning. Brain-wide activities revealed by neuroimaging and recording techniques have been used to predict motor and cognitive functions in both human and animal models. However, although studies have shown the existence of micrometer-scale spatial organization of neurons in the motor cortex relevant to motor control, two-photon microscopy (TPM) calcium imaging at cellular resolution has not been fully exploited for the same purpose. Here, we ask if calcium imaging data recorded by TPM in rodent brain can provide enough information to predict features of upcoming movement. We collected calcium imaging signal from rostral forelimb area in layer 2/3 of the motor cortex while mice performed a two-dimensional lever reaching task. Images of average calcium activity collected during motion preparation period and inter-trial interval (ITI) were used to predict the forelimb reach results. The evaluation was based on a deep learning model that had been applied for object recognition. We found that the prediction accuracy for both maximum reaching location and trial outcome based on motion preparation period but not ITI were higher than the probabilities governed by chance. Our study demonstrated that imaging data encompassing information on the spatial organization of functional neuronal clusters in the motor cortex is useful in predicting motor acts even in the absence of detailed dynamics of neural activities.",0
"Deep learning: new computational modelling techniques for genomics. As a data-driven science, genomics largely utilizes machine learning to capture dependencies in data and derive novel biological hypotheses. However, the ability to extract new insights from the exponentially increasing volume of genomics data requires more expressive machine learning models. By effectively leveraging large data sets, deep learning has transformed fields such as computer vision and natural language processing. Now, it is becoming the method of choice for many genomics modelling tasks, including predicting the impact of genetic variation on gene regulatory mechanisms such as DNA accessibility and splicing.",0
"Predicting the Debonding of CAD/CAM Composite Resin Crowns with AI. A preventive measure for debonding has not been established and is highly desirable to improve the survival rate of computer-aided design/computer-aided manufacturing (CAD/CAM) composite resin (CR) crowns. The aim of this study was to assess the usefulness of deep learning with a convolution neural network (CNN) method to predict the debonding probability of CAD/CAM CR crowns from 2-dimensional images captured from 3-dimensional (3D) stereolithography models of a die scanned by a 3D oral scanner. All cases of CAD/CAM CR crowns were manufactured from April 2014 to November 2015 at the Division of Prosthodontics, Osaka University Dental Hospital (Ethical Review Board at Osaka University, approval H27-E11). The data set consisted of a total of 24 cases: 12 trouble-free and 12 debonding as known labels. A total of 8,640 images were randomly divided into 6,480 training and validation images and 2,160 test images. Deep learning with a CNN method was conducted to develop a learning model to predict the debonding probability. The prediction accuracy, precision, recall, F-measure, receiver operating characteristic, and area under the curve of the learning model were assessed for the test images. Also, the mean calculation time was measured during the prediction for the test images. The prediction accuracy, precision, recall, and F-measure values of deep learning with a CNN method for the prediction of the debonding probability were 98.5%, 97.0%, 100%, and 0.985, respectively. The mean calculation time was 2 ms/step for 2,160 test images. The area under the curve was 0.998. Artificial intelligence (AI) technology-that is, the deep learning with a CNN method established in this study-demonstrated considerably good performance in terms of predicting the debonding probability of a CAD/CAM CR crown with 3D stereolithography models of a die scanned from patients.",0
"EXTraction of EMR numerical data: an efficient and generalizable tool to EXTEND clinical research. BACKGROUND: Electronic medical records (EMR) contain numerical data important for clinical outcomes research, such as vital signs and cardiac ejection fractions (EF), which tend to be embedded in narrative clinical notes. In current practice, this data is often manually extracted for use in research studies. However, due to the large volume of notes in datasets, manually extracting numerical data often becomes infeasible. The objective of this study is to develop and validate a natural language processing (NLP) tool that can efficiently extract numerical clinical data from narrative notes. RESULTS: To validate the accuracy of the tool EXTraction of EMR Numerical Data (EXTEND), we developed a reference standard by manually extracting vital signs from 285 notes, EF values from 300 notes, glycated hemoglobin (HbA1C), and serum creatinine from 890 notes. For each parameter of interest, we calculated the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and F1 score of EXTEND using two metrics. (1) completion of data extraction, and (2) accuracy of data extraction compared to the actual values in the note verified by chart review. At the note level, extraction by EXTEND was considered correct only if it accurately detected and extracted all values of interest in a note. Using manually-annotated labels as the gold standard, the note-level accuracy of EXTEND in capturing the numerical vital sign values, EF, HbA1C and creatinine ranged from 0.88 to 0.95 for sensitivity, 0.95 to 1.0 for specificity, 0.95 to 1.0 for PPV, 0.89 to 0.99 for NPV, and 0.92 to 0.96 in F1 scores. Compared to the actual value level, the sensitivity, PPV, and F1 score of EXTEND ranged from 0.91 to 0.95, 0.95 to 1.0 and 0.95 to 0.96. CONCLUSIONS: EXTEND is an efficient, flexible tool that uses knowledge-based rules to extract clinical numerical parameters with high accuracy. By increasing dictionary terms and developing new rules, the usage of EXTEND can easily be expanded to extract additional numerical data important in clinical outcomes research.",1
"Accurate and robust deep learning-based segmentation of the prostate clinical target volume in ultrasound images. The goal of this work was to develop a method for accurate and robust automatic segmentation of the prostate clinical target volume in transrectal ultrasound (TRUS) images for brachytherapy. These images can be difficult to segment because of weak or insufficient landmarks or strong artifacts. We devise a method, based on convolutional neural networks (CNNs), that produces accurate segmentations on easy and difficult images alike. We propose two strategies to achieve improved segmentation accuracy on difficult images. First, for CNN training we adopt an adaptive sampling strategy, whereby the training process is encouraged to pay more attention to images that are difficult to segment. Secondly, we train a CNN ensemble and use the disagreement among this ensemble to identify uncertain segmentations and to estimate a segmentation uncertainty map. We improve uncertain segmentations by utilizing the prior shape information in the form of a statistical shape model. Our method achieves Hausdorff distance of 2.7+/-2.3 mm and Dice score of 93.9+/-3.5%. Comparisons with several competing methods show that our method achieves significantly better results and reduces the likelihood of committing large segmentation errors. Furthermore, our experiments show that our approach to estimating segmentation uncertainty is better than or on par with recent methods for estimation of prediction uncertainty in deep learning models. Our study demonstrates that estimation of model uncertainty and use of prior shape information can significantly improve the performance of CNN-based medical image segmentation methods, especially on difficult images.",1
"MySurgeryRisk: Development and Validation of a Machine-learning Risk Algorithm for Major Complications and Death After Surgery. OBJECTIVE: To accurately calculate the risk for postoperative complications and death after surgery in the preoperative period using machine-learning modeling of clinical data. BACKGROUND: Postoperative complications cause a 2-fold increase in the 30-day mortality and cost, and are associated with long-term consequences. The ability to precisely forecast the risk for major complications before surgery is limited. METHODS: In a single-center cohort of 51,457 surgical patients undergoing major inpatient surgery, we have developed and validated an automated analytics framework for a preoperative risk algorithm (MySurgeryRisk) that uses existing clinical data in electronic health records to forecast patient-level probabilistic risk scores for 8 major postoperative complications (acute kidney injury, sepsis, venous thromboembolism, intensive care unit admission >48 hours, mechanical ventilation >48 hours, wound, neurologic, and cardiovascular complications) and death up to 24 months after surgery. We used the area under the receiver characteristic curve (AUC) and predictiveness curves to evaluate model performance. RESULTS: MySurgeryRisk calculates probabilistic risk scores for 8 postoperative complications with AUC values ranging between 0.82 and 0.94 [99% confidence intervals (CIs) 0.81-0.94]. The model predicts the risk for death at 1, 3, 6, 12, and 24 months with AUC values ranging between 0.77 and 0.83 (99% CI 0.76-0.85). CONCLUSIONS: We constructed an automated predictive analytics framework for machine-learning algorithm with high discriminatory ability for assessing the risk of surgical complications and death using readily available preoperative electronic health records data. The feasibility of this novel algorithm implemented in real time clinical workflow requires further testing.",1
"Combined estimation of disease progression and retention on antiretroviral therapy among treated individuals with HIV in the USA: a modelling study. Background: Accurately estimating HIV disease progression and retention on antiretroviral therapy (ART) can help inform interventions to control HIV microepidemics and mathematical models used to inform health-resource allocation decisions. Our objective was to estimate the monthly probabilities of on-ART CD4 T-cell count progression, mortality, ART dropout, and ART reinitiation using a continuous-time multistate Markov model. We also aimed to validate health-state transition probability estimates to ensure they accurately reproduced the regional HIV microepidemics across the USA. Methods: In our modelling study, we considered a cohort of patients from the HIV Research Network, a consortium of 17 adult and paediatric HIV-care providers located in the northeastern (n=8), southern (n=5), and western (n=4) regions of the USA. Individuals aged 15 years or older who were in HIV care (defined as one CD4 test and one HIV-care visit in a calendar year period) with at least one ART prescription between Jan 1, 2010, and Dec 31, 2015, were included in the analysis. We used continuous-time multistate Markov models to estimate transitions between CD4 strata and between on-ART and off-ART states. We examined and adjusted for differences in probability of transition by region, race or ethnicity, sex, HIV risk group, and other baseline clinical indicators. Findings: The median age of the 32 242 individuals included in the analysis was 44 years (interquartile range 35–51). Over a median follow-up of 4·9 years (2·6–6·0), 8614 (26·7%) of 32 242 people interrupted ART and 1325 (4·1%) of 32 242 people died. Women, men who have sex with men, and individuals with no previous ART experience had greater increases in CD4 cell counts, whereas black people and people who inject drugs had increased probabilities of ART dropout and faster disease progression. Regardless of CD4 strata, individuals had increased hazard for ART dropout if they were from the south (adjusted hazard ratio [aHR] range from 1·91, 95% CI 1·71–2·13, to 2·45, 2·29–2·62) or the west (aHR range from 1·29, 1·10–1·51, to 1·66, 1·51–1·82) of the USA, compared with individuals from the northeast USA. Interpretation: Our results show heterogeneities in disease progression during ART and probability of ART retention across race and ethnicity, HIV risk groups, and regions. These differences should be viewed as targets for intervention and should be incorporated in mathematical models of regional HIV microepidemics in the USA. Funding: US National Institutes of Health, Agency for Healthcare Research and Quality, and Health Resources and Services Administration.",0
"Machine Learning to Predict the Likelihood of Acute Myocardial Infarction. BACKGROUND: Variations in cardiac troponin concentrations by age, sex and time between samples in patients with suspected myocardial infarction are not currently accounted for in diagnostic approaches. We aimed to combine these variables through machine learning to improve the assessment of risk for individual patients. METHODS: A machine learning algorithm (myocardial-ischemic-injury-index [MI(3)]) incorporating age, sex, and paired high-sensitivity cardiac troponin I concentrations, was trained on 3,013 patients and tested on 7,998 patients with suspected myocardial infarction. MI(3) uses gradient boosting to compute a value (0-100) reflecting an individual's likelihood of a diagnosis of type 1 myocardial infarction and estimates the sensitivity, negative predictive value (NPV), specificity and positive predictive value (PPV) for that individual. Assessment was by calibration and area under the receiver-operating-characteristic curve (AUC). Secondary analysis evaluated example MI(3) thresholds from the training set that identified patients as low-risk (99% sensitivity) and high-risk (75% PPV), and performance at these thresholds was compared in the test set to the 99th percentile and European Society of Cardiology (ESC) rule-out pathways. RESULTS: Myocardial infarction occurred in 404 (13.4%) patients in the training set and 849 (10.6%) patients in the test set. MI(3) was well calibrated with a very high AUC of 0.963 [0.956-0.971] in the test set and similar performance in early and late presenters. Example MI(3) thresholds identifying low-risk and high-risk patients in the training set were 1.6 and 49.7 respectively. In the test set, MI(3) values were <1.6 in 69.5% with a NPV of 99.7% (99.5%-99.8%) and sensitivity of 97.8% (96.7-98.7%), and were >/=49.7 in 10.6% with a PPV of 71.8% (68.9-75.0%) and specificity of 96.7% (96.3-97.1%). Using these thresholds, MI(3) performed better than the ESC 0/3-hour pathway (sensitivity 82.5% [74.5-88.8%], specificity 92.2% [90.7-93.5%]) and the 99th percentile at any time-point (sensitivity 89.6% [87.4-91.6%]), specificity 89.3% [88.6-90.0%]). CONCLUSIONS: Using machine learning, MI(3) provides an individualized and objective assessment of the likelihood of myocardial infarction, which can be used to identify low-risk and high-risk patients who may benefit from earlier clinical decisions. CLINICAL TRIAL REGISTRATION: Unique Identifier: Australian New Zealand Clinical Trials Registry: ACTRN12616001441404. URL: https://www.anzctr.org.au.",1
"BUGSnet: an R package to facilitate the conduct and reporting of Bayesian network Meta-analyses. BACKGROUND: Several reviews have noted shortcomings regarding the quality and reporting of network meta-analyses (NMAs). We suspect that this issue may be partially attributable to limitations in current NMA software which do not readily produce all of the output needed to satisfy current guidelines. RESULTS: To better facilitate the conduct and reporting of NMAs, we have created an R package called ""BUGSnet"" (Bayesian inference Using Gibbs Sampling to conduct a Network meta-analysis). This R package relies upon Just Another Gibbs Sampler (JAGS) to conduct Bayesian NMA using a generalized linear model. BUGSnet contains a suite of functions that can be used to describe the evidence network, estimate a model and assess the model fit and convergence, assess the presence of heterogeneity and inconsistency, and output the results in a variety of formats including league tables and surface under the cumulative rank curve (SUCRA) plots. We provide a demonstration of the functions contained within BUGSnet by recreating a Bayesian NMA found in the second technical support document composed by the National Institute for Health and Care Excellence Decision Support Unit (NICE-DSU). We have also mapped these functions to checklist items within current reporting and best practice guidelines. CONCLUSION: BUGSnet is a new R package that can be used to conduct a Bayesian NMA and produce all of the necessary output needed to satisfy current scientific and regulatory standards. We hope that this software will help to improve the conduct and reporting of NMAs.",0
"Digital pathology imaging and computer-aided diagnostics as a novel tool for standardization of evaluation of aganglionic megacolon (Hirschsprung disease) histopathology. Based on a recently introduced immunohistochemical panel (Bachmann et al. 2015) for aganglionic megacolon (AM), also known as Hirschsprung disease, histopathological diagnosis, we evaluated whether the use of digital pathology and ‘machine learning’ could help to obtain a reliable diagnosis. Slides were obtained from 31 specimens of 27 patients immunohistochemically stained for MAP2, calretinin, S100β and GLUT1. Slides were digitized by whole slide scanning. We used a Definiens Developer Tissue Studios as software for analysis. We configured necessary parameters in combination with ‘machine learning’ to identify pathological aberrations. A significant difference between AM- and non-AM-affected tissues was found for calretinin (AM 0.55% vs. non-AM 1.44%) and MAP2 (AM 0.004% vs. non-AM 0.07%) staining measurements and software-based evaluations. In contrast, S100β and GLUT1 staining measurements and software-based evaluations showed no significant differences between AM- and non-AM-affected tissues. However, no difference was found in comparison of suction biopsies with resections. Applying machine learning via an ensemble voting classifier, we achieved an accuracy of 87.5% on the test set. Automated diagnosis of AM by applying digital pathology on immunohistochemical panels was successful for calretinin and MAP2, whereas S100β and GLUT1 were not effective in diagnosis. Our method suggests that software-based approaches are capable of diagnosing AM. Our future challenge will be the improvement of efficiency by reduction of the time-consuming need for large pre-labelled training data. With increasing technical improvement, especially in unsupervised training procedures, this method could be helpful in the future.",0
"Estimating Retinal Sensitivity Using Optical Coherence Tomography With Deep-Learning Algorithms in Macular Telangiectasia Type 2. Importance: As currently used, microperimetry is a burdensome clinical testing modality for testing retinal sensitivity requiring long testing times and trained technicians. Objective: To create a deep-learning network that could directly estimate function from structure de novo to provide an en face high-resolution map of estimated retinal sensitivity. Design, Setting, and Participants: A cross-sectional imaging study using data collected between January 1, 2016, and November 30, 2017, from the Natural History Observation and Registry of macular telangiectasia type 2 (MacTel) evaluated 38 participants with confirmed MacTel from 2 centers. Main Outcomes and Measures: Mean absolute error of estimated compared with observed retinal sensitivity. Observed retinal sensitivity was obtained with fundus-controlled perimetry (microperimetry). Estimates of retinal sensitivity were made with deep-learning models that learned on superpositions of high-resolution optical coherence tomography (OCT) scans and microperimetry results. Those predictions were used to create high-density en face sensitivity maps of the macula. Training, validation, and test sets were segregated at the patient level. Results: A total of 2499 microperimetry sensitivities were mapped onto 1708 OCT B-scans from 63 eyes of 38 patients (mean [SD] age, 74.3 [9.7] years; 15 men [39.5%]). The numbers of examples for our algorithm were 67 899 (103 053 after data augmentation) for training, 1695 for validation, and 1212 for testing. Mean absolute error results were 4.51 dB (95% CI, 4.36-4.65 dB) when using linear regression and 3.66 dB (95% CI, 3.53-3.78 dB) when using the LeNet model. Using a 49.9 million-variable deep-learning model, a mean absolute error of 3.36 dB (95% CI, 3.25-3.48 dB) of retinal sensitivity for validation and test was achieved. Correlation showed a high degree of agreement (Pearson correlation r = 0.78). By paired Wilcoxon rank sum test, our model significantly outperformed these 2 baseline models (P < .001). Conclusions and Relevance: High-resolution en face maps of estimated retinal sensitivities were created in eyes with MacTel. The maps were of unequalled resolution compared with microperimetry and were able to correctly delineate functionally healthy and impaired retina. This model may be useful to monitor structural and functional disease progression and has potential as an objective surrogate outcome measure in investigational trials.",1
"Comparison of Machine Learning Methods with National Cardiovascular Data Registry Models for Prediction of Risk of Bleeding after Percutaneous Coronary Intervention. Importance: Better prediction of major bleeding after percutaneous coronary intervention (PCI) may improve clinical decisions aimed to reduce bleeding risk. Machine learning techniques, bolstered by better selection of variables, hold promise for enhancing prediction. Objective: To determine whether machine learning techniques better predict post-PCI major bleeding compared with the existing National Cardiovascular Data Registry (NCDR) models. Design, Setting, and Participants: This comparative effectiveness study used the NCDR CathPCI Registry data version 4.4 (July 1, 2009, to April 1, 2015), machine learning techniques were used (logistic regression with lasso regularization and gradient descent boosting [XGBoost, version 0.71.2]), and output was then compared with the existing simplified risk score and full NCDR models. The existing models were recreated, and then performance was evaluated through additional techniques and variables in a 5-fold cross-validation in analysis conducted from October 1, 2015, to October 27, 2017. The setting was retrospective modeling of a nationwide clinical registry of PCI. Participants were all patients undergoing PCI. Percutaneous coronary intervention procedures were excluded if they were not the index PCI of admission, if the hospital site had missing outcomes measures, or if the patient underwent subsequent coronary artery bypass grafting. Exposures: Clinical variables available at admission and diagnostic coronary angiography data were used to determine the severity and complexity of presentation. Main Outcomes and Measures: The main outcome was in-hospital major bleeding within 72 hours after PCI. Results were evaluated by comparing C statistics, calibration, and decision threshold-based metrics, including the F score (harmonic mean of positive predictive value and sensitivity) and the false discovery rate. Results: The post-PCI major bleeding rate among 3316465 procedures (patients' median age, 65 years; interquartile range, 56-73 years; 68.1% male) was 4.5%. The existing full model achieved a mean C statistic of 0.78 (95% CI, 0.78-0.78). The use of XGBoost and full range of selected variables achieved a C statistic of 0.82 (95% CI, 0.82-0.82), with an F score of 0.31 (95% CI, 0.30-0.31). XGBoost correctly identified an additional 3.7% of cases identified as high risk who experienced a bleeding event and an overall improvement of 1.0% of cases identified as low risk who did not experience a bleeding event. The data-driven decision threshold helped improve the false discovery rate of the existing techniques. The existing simplified risk score model improved the false discovery rate from more than 90% to 78.7%. Modifying the model and the data decision threshold improved this rate from 78.7% to 73.4%. Conclusions and Relevance: Machine learning techniques improved the prediction of major bleeding after PCI. These techniques may help to better identify patients who would benefit most from strategies to reduce bleeding risk.",1
"Six genes as potential diagnosis and prognosis biomarkers for hepatocellular carcinoma through data mining. Hepatocellular carcinoma (HCC) is one of the most common malignant tumors and the third of cancer mortality worldwide. Although the study of HCC has made great progress, the molecular mechanism and signal pathways of HCC are not yet clear. Therefore, it is necessary to investigate the early diagnosis and prognosis biomarkers for HCC. The aim of this study is to screen the relevant genes and study the association of gene expression with the survival status of HCC patients using bioinformatics approaches, in the hope of establishing marker genes for diagnosis and prognosis of HCC. The gene expression data and corresponding clinical information of HCC samples were downloaded from the The Cancer Genome Atlas database. We performed to study the relationship between gene expression and prognosis of HCC and screen significantly relevant genes associated with prognosis of HCC by analyzing survival and function enrichment of genes. In this study, we collected 421 samples with gene expression data, including 371 tumor samples and 50 normal samples. By using single factor Cox regression analysis, we screened 1,197 genes significantly associated with survival time in the modeling data containing 117 samples and also searched six genes as the best markers to predict living status of HCC patients. Besides, we established score system of survival risk of HCC. Our study recognized six genes (PGBD3, PGM5P3-AS1, RNF5, UTP11, BAG6, and KCND2) to be significantly associated with diagnosis and prognosis of HCC, providing novel targets for studying potential mechanism about the progression of HCC.",0
"Changes in Whole Brain Dynamics and Connectivity Patterns during Sevoflurane- A nd Propofol-induced Unconsciousness Identified by Functional Magnetic Resonance Imaging. Editor's Perspective What We Already Know about This Topic The extent to which alterations within specific brain networks impairs communication among networks remains unknown What This Article Tells Us That Is New In a volunteer functional magnetic resonance study, general anesthesia reduced activity within and among networks Specific between-network connectivity is necessary for consciousness Background: A key feature of the human brain is its capability to adapt flexibly to changing external stimuli. This capability can be eliminated by general anesthesia, a state characterized by unresponsiveness, amnesia, and (most likely) unconsciousness. Previous studies demonstrated decreased connectivity within the thalamus, frontoparietal, and default mode networks during general anesthesia. We hypothesized that these alterations within specific brain networks lead to a change of communication between networks and their temporal dynamics. Methods: We conducted a pooled spatial independent component analysis of resting-state functional magnetic resonance imaging data obtained from 16 volunteers during propofol and 14 volunteers during sevoflurane general anesthesia that have been previously published. Similar to previous studies, mean z-scores of the resulting spatial maps served as a measure of the activity within a network. Additionally, correlations of associated time courses served as a measure of the connectivity between networks. To analyze the temporal dynamics of between-network connectivity, we computed the correlation matrices during sliding windows of 1 min and applied k-means clustering to the matrices during both general anesthesia and wakefulness. Results: Within-network activity was decreased in the default mode, attentional, and salience networks during general anesthesia (P < 0.001, range of median changes:-0.34,-0.13). Average between-network connectivity was reduced during general anesthesia (P < 0.001, median change:-0.031). Distinct between-network connectivity patterns for both wakefulness and general anesthesia were observed irrespective of the anesthetic agent (P < 0.001), and there were fewer transitions in between-network connectivity patterns during general anesthesia (P < 0.001, median number of transitions during wakefulness: 4 and during general anesthesia: 0). Conclusions: These results suggest that (1) higher-order brain regions play a crucial role in the generation of specific between-network connectivity patterns and their dynamics, and (2) the capability to interact with external stimuli is represented by complex between-network connectivity patterns.",0
"Selective organ ischaemia/reperfusion identifies liver as the key driver of the post-injury plasma metabolome derangements. Background. Understanding the molecular mechanisms in perturbation of the metabolome following ischaemia and reperfusion is critical in developing novel therapeutic strategies to prevent the sequelae of post-injury shock. While the metabolic substrates fueling these alterations have been defined, the relative contribution of specific organs to the systemic metabolic reprogramming secondary to ischaemic or haemorrhagic hypoxia remains unclear. Materials and methods. A porcine model of selected organ ischaemia was employed to investigate the relative contribution of liver, kidney, spleen and small bowel ischaemia/reperfusion to the plasma metabolic phenotype, as gleaned through ultra-high performance liquid chromatography-mass spectrometry-based metabolomics. Results. Liver ischaemia/reperfusion promotes glycaemia, with increases in circulating carboxylic acid anions and purine oxidation metabolites, suggesting that this organ is the dominant contributor to the accumulation of these metabolites in response to ischaemic hypoxia. Succinate, in particular, accumulates selectively in response to the hepatic ischemia, with levels 6.5 times spleen, 8.2 times small bowel, and 6 times renal levels. Similar trends, but lower fold-change increase in comparison to baseline values, were observed upon ischaemia/reperfusion of kidney, spleen and small bowel. Discussion. These observations suggest that the liver may play a critical role in mediating the accumulation of the same metabolites in response to haemorrhagic hypoxia, especially with respect to succinate, a metabolite that has been increasingly implicated in the coagulopathy and pro-inflammatory sequelae of ischaemic and haemorrhagic shock.",0
"Training the next generation of Africa's doctors: why medical schools should embrace the team-based learning pedagogy. BACKGROUND: As far back as 1995, the Cape Town Declaration on training Africa's future doctor recognized the need for medical schools to adopt active-learning strategies in order to nurture holistic development of the doctor. However, medical education in Africa remains largely stuck with traditional pedagogies that emphasize the 'hard skills' such as knowledge and clinical acumen while doing little to develop 'soft skills' such as effective communication, teamwork, critical thinking or life-long learning skills. By reviewing literature on Africa's epidemiologic and demographic transitions, we establish the need for increasing the output of well-trained doctors in order to match the continent's complex current and future healthcare needs. Challenges that bedevil African medical education such as outdated curricula, limited educational infrastructure and chronic resource constraints are presented and discussed. Furthermore, increased student enrollments, a trend observed at many schools, coupled with chronic faculty shortages have inadvertently presented specific barriers against the success of small-group active-learning strategies such as Problem-Based and Case-Based Learning. We argue that Team-Based Learning (TBL) offers a robust alternative for delivering holistic medical education in the current setting. TBL is instructor-driven and embodies key attributes that foster development of both 'hard' and 'soft' skills. We elaborate on advantages that TBL is likely to bring to the African medical education landscape, including increased learner enthusiasm and creativity, accountability, peer mentorship, deep learning and better knowledge retention. As with all new pedagogical methods, challenges anticipated during initial implementation of TBL are discussed followed by the limited pilot experiences with TBL in Africa. CONCLUSION: For its ability to enable a student-centered, active learning experience delivered at minimum cost, we encourage individual instructors and African medical schools at large, to adopt TBL as a complementary strategy towards realizing the goal of training Africa's fit-for-purpose doctor.",0
"Assessment of risk based on variant pathways and establishment of an artificial neural network model of thyroid cancer. Background: This study aimed to establish an artificial neural network (ANN) model based on variant pathways to predict the risk of thyroid cancer. Methods: The RNASeq data of 482 thyroid cancer samples were downloaded from the TCGA database. The samples were divided into low-risk and high-risk groups, followed by identification of differentially expressed genes (DEGs). Co-expression analysis and pathway enrichment analysis were then performed. The variant pathways were screened according to the functional deviation score of each pathway, and an ANN model was established. Finally, the efficiency of the ANN model for risk assessment was validated by survival analysis and analysis of an independent microarray dataset (GSE34289) for thyroid cancer. Results: In total, 190 DEGs (85 up-regulated and 105 down-regulated) were identified between the low-risk and high-risk groups. Ten risk-related variant pathways were identified between the low-risk and high-risk groups, which were related to inflammatory and immune responses. Based on these variant pathways, an ANN model was built, consisting of an input layer, two hidden layers, and an output layer, corresponding to 15, 8, 5, and 1 neuron, respectively. Survival analysis showed that this model could effectively distinguish the samples with different risks. Analysis of microarray dataset GSE34289 showed that the accuracy of this model for predicating low-risk and high-risk samples was 77.5 and 86.0%, respectively. Conclusions: This study suggests that the ANN model based on variant pathways can be used for effectively evaluating the risk of thyroid cancer.",0
"Using poster presentation to assess large classes: a case study of a first-year undergraduate module at a South African university. BACKGROUND: The massification of higher education is often associated with poor student engagement, poor development of their critical thinking, inadequate feedback and poor student throughput. These factors necessitate the need to devise novel, innovative methods to teach, assess and provide feedback to learners to counter the restrictions imposed due to the large class learning environments. This study was conducted to ascertain the perceptions of 1st year medical students and staff at the Nelson Mandela School of Medicine regarding the value of poster presentations as a strategy to enhance learning, assessment and feedback. METHODS: This was an exploratory observational, descriptive cross-sectional, case study. Data was collected through separate student and staff questionnaires that required participant responses on a five-point Likert scale. The data was extracted into Excel spreadsheets for quantitative analysis. RESULTS: Two-hundred- and-thirty (92%) student questionnaires were returned (N = 250). Most students indicated that the design and presentation of the poster had helped them to select important material (92%), understand and describe disadvantage (86%) and to make a difference in the community (92%). The students agreed that the poster assessment was an efficient (81%) and fair method (75%) that provided opportunities for meaningful feedback. Ten staff members responded to the questionnaire. Most staff members (90%) indicated that the poster presentation had allowed students to demonstrate their engagement in a meaningful and appropriate way around issues of disadvantage and HIV and agreed that the poster presentations allowed for immediate and effective feedback. CONCLUSION: Students' interactions in the tasks promoted active engagement with others and course material; the development of higher order thinking and skills which added to students' accounts of transformative learning experiences. They could describe and illustrate the difference that they had made in their chosen community. The poster presentations allowed for quick and efficient marking, immediate feedback and an opportunity to validate the students' participation. Poster presentations offered an innovative way to encourage deep meaningful engagement and learning amongst peers and facilitators. Poster presentations should be more widely considered as an innovative way of encouraging deeper engagement and learning in a large class setting.",0
"Deep Learning-Assisted Diagnosis of Cerebral Aneurysms Using the HeadXNet Model. Importance: Deep learning has the potential to augment clinician performance in medical imaging interpretation and reduce time to diagnosis through automated segmentation. Few studies to date have explored this topic. Objective: To develop and apply a neural network segmentation model (the HeadXNet model) capable of generating precise voxel-by-voxel predictions of intracranial aneurysms on head computed tomographic angiography (CTA) imaging to augment clinicians' intracranial aneurysm diagnostic performance. Design, Setting, and Participants: In this diagnostic study, a 3-dimensional convolutional neural network architecture was developed using a training set of 611 head CTA examinations to generate aneurysm segmentations. Segmentation outputs from this support model on a test set of 115 examinations were provided to clinicians. Between August 13, 2018, and October 4, 2018, 8 clinicians diagnosed the presence of aneurysm on the test set, both with and without model augmentation, in a crossover design using randomized order and a 14-day washout period. Head and neck examinations performed between January 3, 2003, and May 31, 2017, at a single academic medical center were used to train, validate, and test the model. Examinations positive for aneurysm had at least 1 clinically significant, nonruptured intracranial aneurysm. Examinations with hemorrhage, ruptured aneurysm, posttraumatic or infectious pseudoaneurysm, arteriovenous malformation, surgical clips, coils, catheters, or other surgical hardware were excluded. All other CTA examinations were considered controls. Main Outcomes and Measures: Sensitivity, specificity, accuracy, time, and interrater agreement were measured. Metrics for clinician performance with and without model augmentation were compared. Results: The data set contained 818 examinations from 662 unique patients with 328 CTA examinations (40.1%) containing at least 1 intracranial aneurysm and 490 examinations (59.9%) without intracranial aneurysms. The 8 clinicians reading the test set ranged in experience from 2 to 12 years. Augmenting clinicians with artificial intelligence-produced segmentation predictions resulted in clinicians achieving statistically significant improvements in sensitivity, accuracy, and interrater agreement when compared with no augmentation. The clinicians' mean sensitivity increased by 0.059 (95% CI, 0.028-0.091; adjusted P = .01), mean accuracy increased by 0.038 (95% CI, 0.014-0.062; adjusted P = .02), and mean interrater agreement (Fleiss κ) increased by 0.060, from 0.799 to 0.859 (adjusted P = .05). There was no statistically significant change in mean specificity (0.016; 95% CI, -0.010 to 0.041; adjusted P = .16) and time to diagnosis (5.71 seconds; 95% CI, 7.22-18.63 seconds; adjusted P = .19). Conclusions and Relevance: The deep learning model developed successfully detected clinically significant intracranial aneurysms on CTA. This suggests that integration of an artificial intelligence-assisted diagnostic model may augment clinician performance with dependable and accurate predictions and thereby optimize patient care.",1
"The Chylomicronemia Syndrome Is Most Often Multifactorial: A Narrative Review of Causes and Treatment. The chylomicronemia syndrome occurs when triglyceride levels are severely elevated (usually >16.95 mmol/L [1500 mg/dL]) and is characterized by such clinical features as abdominal pain, acute pancreatitis, eruptive xanthomas, and lipemia retinalis. It may result from 1 of 3 conditions: the presence of secondary forms of hypertriglyceridemia concurrent with genetic causes of hypertriglyceridemia, termed multifactorial chylomicronemia syndrome (MFCS); a deficiency in the enzyme lipoprotein lipase and some associated proteins, termed familial chylomicronemia syndrome (FCS); or familial partial lipodystrophy. Most chylomicronemia syndrome cases are the result of MFCS; FCS is very rare. In all these conditions, triglyceride-rich lipoproteins accumulate because of impaired plasma clearance. This review describes the 3 major causes of the chylomicronemia syndrome; their consequences; and the approaches to treatment, which differ considerably by group.",0
"AI-Assisted Forward Modeling of Biological Structures. The rise of machine learning and deep learning technologies have allowed researchers to automate image classification. We describe a method that incorporates automated image classification and principal component analysis to evaluate computational models of biological structures. We use a computational model of the kinetochore to demonstrate our artificial-intelligence (AI)-assisted modeling method. The kinetochore is a large protein complex that connects chromosomes to the mitotic spindle to facilitate proper cell division. The kinetochore can be divided into two regions: the inner kinetochore, including proteins that interact with DNA; and the outer kinetochore, comprised of microtubule-binding proteins. These two kinetochore regions have been shown to have different distributions during metaphase in live budding yeast and therefore act as a test case for our forward modeling technique. We find that a simple convolutional neural net (CNN) can correctly classify fluorescent images of inner and outer kinetochore proteins and show a CNN trained on simulated, fluorescent images can detect difference in experimental images. A polymer model of the ribosomal DNA locus serves as a second test for the method. The nucleolus surrounds the ribosomal DNA locus and appears amorphous in live-cell, fluorescent microscopy experiments in budding yeast, making detection of morphological changes challenging. We show a simple CNN can detect subtle differences in simulated images of the ribosomal DNA locus, demonstrating our CNN-based classification technique can be used on a variety of biological structures.",0
"Improving rare disease classification using imperfect knowledge graph. BACKGROUND: Accurately recognizing rare diseases based on symptom description is an important task in patient triage, early risk stratification, and target therapies. However, due to the very nature of rare diseases, the lack of historical data poses a great challenge to machine learning-based approaches. On the other hand, medical knowledge in automatically constructed knowledge graphs (KGs) has the potential to compensate the lack of labeled training examples. This work aims to develop a rare disease classification algorithm that makes effective use of a knowledge graph, even when the graph is imperfect. METHOD: We develop a text classification algorithm that represents a document as a combination of a ""bag of words"" and a ""bag of knowledge terms,"" where a ""knowledge term"" is a term shared between the document and the subgraph of KG relevant to the disease classification task. We use two Chinese disease diagnosis corpora to evaluate the algorithm. The first one, HaoDaiFu, contains 51,374 chief complaints categorized into 805 diseases. The second data set, ChinaRe, contains 86,663 patient descriptions categorized into 44 disease categories. RESULTS: On the two evaluation data sets, the proposed algorithm delivers robust performance and outperforms a wide range of baselines, including resampling, deep learning, and feature selection approaches. Both classification-based metric (macro-averaged F1 score) and ranking-based metric (mean reciprocal rank) are used in evaluation. CONCLUSION: Medical knowledge in large-scale knowledge graphs can be effectively leveraged to improve rare diseases classification models, even when the knowledge graph is incomplete.",1
"Evidential MACE prediction of acute coronary syndrome using electronic health records. BACKGROUND: Major adverse cardiac event (MACE) prediction plays a key role in providing efficient and effective treatment strategies for patients with acute coronary syndrome (ACS) during their hospitalizations. Existing prediction models have limitations to cope with imprecise and ambiguous clinical information such that clinicians cannot reach to reliable MACE prediction results for individuals. METHODS: To remedy it, this study proposes a hybrid method using Rough Set Theory (RST) and Dempster-Shafer Theory (DST) of evidence. In details, four state-of-the-art models, including one traditional ACS risk scoring model, i.e., GRACE, and three machine learning based models, i.e., Support Vector Machine, L1-Logistic Regression, and Classification and Regression Tree, are employed to generate initial MACE prediction results, and then RST is applied to determine the weights of the four single models. After that, the acquired prediction results are assumed as basic beliefs for the problem propositions and in this way, an evidential prediction result is generated based on DST in an integrative manner. RESULTS: Having applied the proposed method on a clinical dataset consisting of 2930 ACS patient samples, our model achieves 0.715 AUC value with competitive standard deviation, which is the best prediction results comparing with the four single base models and two baseline ensemble models. CONCLUSIONS: Facing with the limitations in traditional ACS risk scoring models, machine learning models and the uncertainties of EHR data, we present an ensemble approach via RST and DST to alleviate this problem. The experimental results reveal that our proposed method achieves better performance for the problem of MACE prediction when compared with the single models.",1
"Molecular mechanisms of lineage decisions in metabolite-specific T cells. Mucosal-associated invariant T cells (MAIT cells) recognize the microbial metabolite 5-(2-oxopropylideneamino)-6-d-ribitylaminouracil (5-OP-RU) presented by the MHC class Ib molecule, MR1. MAIT cells acquire effector functions during thymic development, but the mechanisms involved are unclear. Here we used single-cell RNA-sequencing to characterize the developmental path of 5-OP-RU-specific thymocytes. In addition to the known MAIT1 and MAIT17 effector subsets selected on bone-marrow-derived hematopoietic cells, we identified 5-OP-RU-specific thymocytes that were selected on thymic epithelial cells and differentiated into CD44− naive T cells. MAIT cell positive selection required signaling through the adapter, SAP, that controlled the expression of the transcription factor, ZBTB16. Pseudotemporal ordering of single cells revealed transcriptional trajectories of 5-OP-RU-specific thymocytes selected on either thymic epithelial cells or hematopoietic cells. The resulting model illustrates T cell lineage decisions.",0
"Fast and accurate bacterial species identification in urine specimens using LC-MS/MS mass spectrometry and machine learning. Fast identification of microbial species in clinical samples is essential to provide an appropriate antibiotherapy to the patient and reduce the prescription of broad-spectrum antimicrobials leading to antibioresistances. MALDITOF- MS technology has become a tool of choice for microbial identification but has several drawbacks: It requires a long step of bacterial culture before analysis (>24 h), has a low specificity and is not quantitative. We developed a new strategy for identifying bacterial species in urine using specific LC-MS/MS peptidic signatures. In the first training step, libraries of peptides are obtained on pure bacterial colonies in DDA mode, their detection in urine is then verified in DIA mode, followed by the use of machine learning classifiers (NaiveBayes, BayesNet and Hoeffding tree) to define a peptidic signature to distinguish each bacterial species from the others. Then, in the second step, this signature is monitored in unknown urine samples using targeted proteomics. This method, allowing bacterial identification in less than 4 h, has been applied to fifteen species representing 84% of all Urinary Tract Infections. More than 31,000 peptides in 190 samples were quantified by DIA and classified by machine learning to determine an 82 peptides signature and build a prediction model. This signature was validated for its use in routine using Parallel Reaction Monitoring on two different instruments. Linearity and reproducibility of the method were demonstrated as well as its accuracy on donor specimens. Within 4h and without bacterial culture, our method was able to predict the predominant bacteria infecting a sample in 97% of cases and 100% above the standard threshold. This work demonstrates the efficiency of our method for the rapid and specific identification of the bacterial species causing UTI and could be extended in the future to other biological specimens and to bacteria having specific virulence or resistance factors.",0
"Comparative assessment of CNN architectures for classification of breast FNAC images. Fine needle aspiration cytology (FNAC) entails using a narrow gauge (25-22 G) needle to collect a sample of a lesion for microscopic examination. It allows a minimally invasive, rapid diagnosis of tissue but does not preserve its histological architecture. FNAC is commonly used for diagnosis of breast cancer, with traditional practice being based on the subjective visual assessment of the breast cytopathology cell samples under a microscope to evaluate the state of various cytological features. Therefore, there are many challenges in maintaining consistency and reproducibility of findings. However, the advent of digital imaging and computational aid in diagnosis can improve the diagnostic accuracy and reduce the effective workload of pathologists. This paper presents a comparison of various deep convolutional neural network (CNN) based fine-tuned transfer learned classification approach for the diagnosis of the cell samples. The proposed approach has been tested using VGG16, VGG19, ResNet-50 and GoogLeNet-V3 (aka Inception V3) architectures of CNN on an image dataset of 212 images (99 benign and 113 malignant), later augmented and cleansed to 2120 images (990 benign and 1130 malignant), where the network was trained using images of 80% cell samples and tested on the rest. This paper presents a comparative assessment of the models giving a new dimension to FNAC study where GoogLeNet-V3 (fine-tuned) achieved an accuracy of 96.25% which is highly satisfactory.",0
"High-spatial-resolution diffusion MRI in Parkinson disease: Lateral asymmetry of the substantia nigra. Background: Motor symptoms in Parkinson disease (PD) have exhibited lateral asymmetry, suggesting asymmetric neuronal loss in the substantia nigra (SN). Diffusion MRI may be able to help confirm tissue microstructural alterations in the substantia nigra to probe for the presence of asymmetry. Purpose: To investigate lateral asymmetry in the SN of patients with PD by using diffusion MRI with both Gaussian and non-Gaussian models. Materials and Methods: In this cross-sectional study conducted from March 2015 to March 2017, 27 participants with PD and 27 age-matched healthy control (HC) participants, all right handed, underwent MRI at 3.0 T. High-spatial-resolution diffusion images were acquired with a reduced field of view by using seven b values up to 3000 sec/mm2. A continuous-time random-walk (CTRW) non-Gaussian diffusion model was used to produce anomalous diffusion coefficient (Dm) and temporal (a) and spatial (b) diffusion heterogeneity indexes followed by a Gaussian diffusion model to yield an apparent diffusion coefficient (ADC). Individual or linear combinations of diffusion parameters in the SN were unilaterally and bilaterally compared between the PD and HC groups. Results: In the bilateral comparison between the PD and HC groups, differences were observed in b (0.67 6 0.06 [standard deviation] vs 0.64 6 0.04, respectively; P = .016), ADC (0.48 mm2/msec 6 0.08 vs 0.53 mm2/msec 6 0.06, respectively; P = .03), and the combination of CTRW parameters (P = .02). In the unilateral comparison, differences were observed in all diffusion parameters on the left SN (P , .03), but not on the right (P . .20). In a receiver operating characteristic (ROC) analysis to delineate left SN abnormality in PD, the combination of Dm, a, and b produced the best sensitivity (sensitivity, 0.78); the combination of Dm and b produced the best specificity (specificity, 0.85); and the combination of a and b produced the largest area under the ROC curve (area under the ROC curve, 0.73). Conclusion: These results suggest that quantitative diffusion MRI is sensitive to brain tissue changes in participants with Parkinson disease and provide evidence of substantia nigra lateral asymmetry in this disease.",0
"Multi-Dimensional Screening Strategy for Drug Repurposing with Statistical Framework-A New Road to Influenza Drug discovery. Influenza virus is known for its intermittent outbreaks affecting billions of people worldwide. Several neuraminidase inhibitors have been used in practice to overcome this situation. However, advent of new resistant mutants has limited its clinical utilization. In the recent years drug repurposing technique has attained the limelight as it is cost effective and reduces the time consumed for drug discovery. Here, we present multi-dimensional repurposing strategy that integrates the results of ligand-, energy-, receptor cavity, and shape-based pharmacophore algorithm to effectively identify novel drug candidate for influenza. The pharmacophore hypotheses were generated by utilizing the PHASE module of Schrödinger. The generated hypotheses such as AADP, AADDD, and DDRRNH, respectively, for ligand-, e-pharmacophore and receptor cavity based approach alongside shape of oseltamivir were successfully utilized to screen the DrugBank database. Subsequently, these models were evaluated for their differentiating ability using Enrichment calculation. Receiver operating curve and enrichment factors from the analysis indicate that the models possess better capability to screen actives from decoy set of molecules. Eventually, the hits retrieved from different hypotheses were subjected to molecular docking using Glide module of Schrödinger Suite. The results of different algorithms were then combined to eliminate false positive hits and to demonstrate reliable prediction performance than existing approaches. Of note, Pearson's correlation coefficients were calculated to examine the extent of correlation between the glide score and IC50 values. Further, the interaction profile, pharmacokinetic, and pharmacodynamics properties were analyzed for the hit compounds. The results from our analysis showed that alprostadil (DB00770) exhibits better binding affinity toward NA protein than the existing drug molecules. The biological activity of the hit was also predicted using PASS algorithm that renders the antiviral activity of the compound. Further, the results were validated using mutation analysis and molecular dynamic simulation studies. Indeed, this integrative filtering is able to exceed accuracy of other state-of-the-art methods for the drug discovery.",0
"Combination of peri- and intratumoral radiomic features on baseline CT scans predicts response to chemotherapy in lung adenocarcinoma. Purpose: To identify the role of radiomics texture features both within and outside the nodule in predicting (a) time to progression (TTP) and overall survival (OS) as well as (b) response to chemotherapy in patients with non–small cell lung cancer (NSCLC). Materials and Methods: Data in a total of 125 patients who had been treated with pemetrexed-based platinum doublet chemotherapy at Cleveland Clinic were retrospectively analyzed. The patients were divided randomly into two sets with the constraint that there were an equal number of responders and nonresponders in the training set. The training set comprised 53 patients with NSCLC, and the validation set comprised 72 patients. A machine learning classifier trained with radiomic texture features extracted from intra- and peritumoral regions of non–contrast-enhanced CT images was used to predict response to chemotherapy. The radiomic risk-score signature was generated by using least absolute shrinkage and selection operator with the Cox regression model; association of the radiomic signature with TTP and OS was also evaluated. Results: A combination of radiomic features in conjunction with a quadratic discriminant analysis classifier yielded a mean maximum area under the receiver operating characteristic curve (AUC) of 0.82 ± 0.09 (standard deviation) in the training set and a corresponding AUC of 0.77 in the independent testing set. The radiomics signature was also significantly associated with TTP (hazard ratio [HR], 2.8; 95% confidence interval [CI]: 1.95, 4.00; P <.0001) and OS (HR, 2.35; 95% CI: 1.41, 3.94; P =.0011). Additionally, decision curve analysis demonstrated that in terms of clinical usefulness, the radiomics signature had a higher overall net benefit in prediction of high-risk patients to receive treatment than the clinicopathologic measurements. Conclusion: This study suggests that radiomic texture features extracted from within and around the nodule on baseline CT scans are (a) predictive of response to chemotherapy and (b) associated with TTP and OS for patients with NSCLC.",1
"Covalent Docking Identifies a Potent and Selective MKK7 Inhibitor. The c-Jun NH2-terminal kinase (JNK) signaling pathway is central to the cell response to stress, inflammatory signals, and toxins. While selective inhibitors are known for JNKs and for various upstream MAP3Ks, no selective inhibitor is reported for MKK7––one of two direct MAP2Ks that activate JNK. Here, using covalent virtual screening, we identify selective MKK7 covalent inhibitors. We optimized these compounds to low-micromolar inhibitors of JNK phosphorylation in cells. The crystal structure of a lead compound bound to MKK7 demonstrated that the binding mode was correctly predicted by docking. We asserted the selectivity of our inhibitors on a proteomic level and against a panel of 76 kinases, and validated an on-target effect using knockout cell lines. Lastly, we show that the inhibitors block activation of primary mouse B cells by lipopolysaccharide. These MKK7 tool compounds will enable better investigation of JNK signaling and may serve as starting points for therapeutics.",0
"Lack of Association Between CTLA-4 Genetic Polymorphisms and Noncardiac Gastric Cancer in a Chinese Population. Cytotoxic T lymphocyte antigen 4 (CTLA-4) is a key negative immunoregulatory molecule with characteristics of gene polymorphisms. Genetically predisposed CTLA-4 alteration in humans was associated with gastric cancer (GC) development. To explore the association of CTLA-4 polymorphism with susceptibility of noncardiac GC (NCGC), 490 NCGC patients and 1476 control individuals were studied. Four CTLA-4 polymorphisms were genotyped with SNPscan genotyping assays and the haplotypes were constructed with SHESIS software. Frequencies of the CTLA-4 haplotypes were estimated using an expectation-maximization algorithm. The CTLA-4 polymorphism genotype distribution and allele frequencies were not significantly different between the NCGC patients and the control subjects. The CTLA-4 haplotypes did not exhibit a significantly increased risk for NCGC patients. Adjusting status of age, sex, smoking status, alcohol use, and body mass index could not moderate any of the relationships. Data suggested that CTLA-4 polymorphisms (rs3087243, rs16840252, rs733618, and rs231775) were not significantly associated with the risk of NCGC in this Chinese population studied.",0
"MiR-181c-5p exacerbates hypoxia/reoxygenation-induced cardiomyocyte apoptosis via targeting PTPN4. Background. Activation of cell apoptosis is a major form of cell death during myocardial ischemia/reperfusion injury (I/RI). Therefore, examining ways to control cell apoptosis has important clinical significance for improving postischemic recovery. Clinical evidence demonstrated that miR-181c-5p was significantly upregulated in the early phase of myocardial infarction. However, whether or not miR-181c-5p mediates cardiac I/RI through cell apoptosis pathway is unknown. Thus, the present study is aimed at investigating the role and the possible mechanism of miR-181c-5p in apoptosis during I/R injury by using H9C2 cardiomyocytes. Methods and Results. The rat origin H9C2 cardiomyocytes were subjected to hypoxia/reoxygenation (H/R, 6 hours hypoxia followed by 6 hours reoxygenation) to induce cell injury. The results showed that H/R significantly increased the expression of miR-181c-5p but not miR-181c-3p in H9C2 cells. In line with this, in an in vivo rat cardiac I/RI model, miR-181c-5p expression was also significantly increased. The overexpression of miR-181c-5p by its agomir transfection significantly aggravated H/R-induced cell injury (increased lactate dehydrogenase level and reduced cell viability) and exacerbated H/R-induced cell apoptosis (greater cleaved caspases 3 expression, Bax/Bcl-2 and more TUNEL-positive cells). In contrast, inhibition of miR-181c-5p in vitro had the opposite effect. By using computational prediction algorithms, protein tyrosine phosphatase nonreceptor type 4 (PTPN4) was predicted as a potential target gene of miR-181c-5p and was verified by the luciferase reporter assay. The overexpression of miR-181c-5p significantly attenuated the mRNA and protein expression of PTPN4 in H9C2 cardiomyocytes. Moreover, knockdown of PTPN4 significantly aggravated H/R-induced enhancement of LDH level, cleaved caspase 3 expression, and apoptotic cell death, which mimicked the proapoptotic effects of miR-181c-5p in H9C2 cardiomyocytes. Conclusions. These findings suggested that miR-181c-5p exacerbates H/R-induced cardiomyocyte injury and apoptosis via targeting PTPN4 and that miR-181c-5p/PTPN4 signaling may yield novel strategies to combat myocardial I/R injury.",0
"Whole Blood Transcriptome Analysis for Lifelong Monitoring in Elite Sniffer Dogs Produced by Somatic Cell Nuclear Transfer. Reproductive cloning by somatic cell nuclear transfer (SCNT) is a valuable method to propagate service dogs with desirable traits because of higher selection rates in cloned dogs. However, incomplete reprogramming is a major barrier to SCNT, and the assessment of reprogramming is limited to preimplantation embryos and tissues from dead and/or adult tissue. Thus, lifelong monitoring in SCNT dogs can be useful to evaluate the SCNT service dogs for propagation. We applied microarray and qRT-PCR to profile of mRNA and miRNA in whole blood samples collected from four cloned dogs (S), three age-matched control dogs (A), and a donor dog (D). In the analysis of differentially expressed genes in S-A, A-D, and S-D pairs, most genomes were completely reprogrammed and rejuvenated in the cloned offspring. However, several RNAs were differentially expressed. Interestingly, the altered genes are associated with aging and senescence. Furthermore, we identified potential biomarkers such as mirR-223 (NFIB; CLIC4), miRN-494 (ARHGEF12), miR-106b (PPP1R3B; CC2D1A), miR-20a (CC2D1A; PPP1R3B), miR-30e (IGJ; HIRA), and miR-19a (TNRC6A) by miRNA-target mRNA pairing for monitoring rejuvenation, aging/senescence, and reprogramming in cloned dogs. The novel comparative transcriptomic information about SCNT and age-matched dogs can be used to assess the lifelong health of cloned dogs and to facilitate the selection of training animals with minimal invasive procedures.",0
"Equalization of four cardiovascular risk algorithms after systematic recalibration: individual-participant meta-analysis of 86 prospective studies. AIMS: There is debate about the optimum algorithm for cardiovascular disease (CVD) risk estimation. We conducted head-to-head comparisons of four algorithms recommended by primary prevention guidelines, before and after 'recalibration', a method that adapts risk algorithms to take account of differences in the risk characteristics of the populations being studied. METHODS AND RESULTS: Using individual-participant data on 360 737 participants without CVD at baseline in 86 prospective studies from 22 countries, we compared the Framingham risk score (FRS), Systematic COronary Risk Evaluation (SCORE), pooled cohort equations (PCE), and Reynolds risk score (RRS). We calculated measures of risk discrimination and calibration, and modelled clinical implications of initiating statin therapy in people judged to be at 'high' 10 year CVD risk. Original risk algorithms were recalibrated using the risk factor profile and CVD incidence of target populations. The four algorithms had similar risk discrimination. Before recalibration, FRS, SCORE, and PCE over-predicted CVD risk on average by 10%, 52%, and 41%, respectively, whereas RRS under-predicted by 10%. Original versions of algorithms classified 29-39% of individuals aged >/=40 years as high risk. By contrast, recalibration reduced this proportion to 22-24% for every algorithm. We estimated that to prevent one CVD event, it would be necessary to initiate statin therapy in 44-51 such individuals using original algorithms, in contrast to 37-39 individuals with recalibrated algorithms. CONCLUSION: Before recalibration, the clinical performance of four widely used CVD risk algorithms varied substantially. By contrast, simple recalibration nearly equalized their performance and improved modelled targeting of preventive action to clinical need.",0
"Efficient method for isolation of reticulocyte RNA from healthy individuals and hemolytic anaemia patients. Despite enormous progress and development of high-throughput methods in genome-wide mRNA analyses, data on the erythroid transcriptome are still limited, even though they could be useful in medical diagnostics and personalized therapy as well as in research on normal and pathological erythroid maturation. Although obtaining normal and pathological reticulocyte transcriptome profiles should contribute greatly to our understanding of the molecular bases of terminal erythroid differentiation as well as the mechanisms of the hematological diseases, a basic limitation of these studies is the difficulty of efficient reticulocyte RNA isolation from human peripheral blood. The restricted number of possible parallel experiments primarily concern healthy individuals with the lowest number of reticulocytes in the peripheral blood and a low RNA content. In the present study, an efficient method for reticulocyte RNA isolation from healthy individuals and hemolytic anaemia patients is presented. The procedure includes leukofiltration, Ficoll-Paque gradient centrifugation, Percoll gradient centrifugation, and negative (CD45 and CD61) immunomagnetic separation. This relatively fast and simple four-stage method was successfully applied to obtain a reticulocyte-rich population from healthy subjects, which was used to efficiently isolate the high-quality RNA essential for successful NGS-based transcriptome analysis.",0
"A Transcriptome-wide Translational Program Defined by LIN28B Expression Level. Tan et al. show that changes in LIN28B expression can upregulate and downregulate gene expression. By suppressing let-7 family miRNA biogenesis, LIN28B liberates Argonaute to bind non-let-7 miRNA families with greater frequency, causing downstream changes because of redistributed miRNA activity. These effects affect a significant portion of the transcriptome.",0
"Common brain disorders are associated with heritable patterns of apparent aging of the brain. Common risk factors for psychiatric and other brain disorders are likely to converge on biological pathways influencing the development and maintenance of brain structure and function across life. Using structural MRI data from 45,615 individuals aged 3-96 years, we demonstrate distinct patterns of apparent brain aging in several brain disorders and reveal genetic pleiotropy between apparent brain aging in healthy individuals and common brain disorders.",0
"A data-driven approach to predicting diabetes and cardiovascular disease with machine learning. BACKGROUND: Diabetes and cardiovascular disease are two of the main causes of death in the United States. Identifying and predicting these diseases in patients is the first step towards stopping their progression. We evaluate the capabilities of machine learning models in detecting at-risk patients using survey data (and laboratory results), and identify key variables within the data contributing to these diseases among the patients. METHODS: Our research explores data-driven approaches which utilize supervised machine learning models to identify patients with such diseases. Using the National Health and Nutrition Examination Survey (NHANES) dataset, we conduct an exhaustive search of all available feature variables within the data to develop models for cardiovascular, prediabetes, and diabetes detection. Using different time-frames and feature sets for the data (based on laboratory data), multiple machine learning models (logistic regression, support vector machines, random forest, and gradient boosting) were evaluated on their classification performance. The models were then combined to develop a weighted ensemble model, capable of leveraging the performance of the disparate models to improve detection accuracy. Information gain of tree-based models was used to identify the key variables within the patient data that contributed to the detection of at-risk patients in each of the diseases classes by the data-learned models. RESULTS: The developed ensemble model for cardiovascular disease (based on 131 variables) achieved an Area Under - Receiver Operating Characteristics (AU-ROC) score of 83.1% using no laboratory results, and 83.9% accuracy with laboratory results. In diabetes classification (based on 123 variables), eXtreme Gradient Boost (XGBoost) model achieved an AU-ROC score of 86.2% (without laboratory data) and 95.7% (with laboratory data). For pre-diabetic patients, the ensemble model had the top AU-ROC score of 73.7% (without laboratory data), and for laboratory based data XGBoost performed the best at 84.4%. Top five predictors in diabetes patients were 1) waist size, 2) age, 3) self-reported weight, 4) leg length, and 5) sodium intake. For cardiovascular diseases the models identified 1) age, 2) systolic blood pressure, 3) self-reported weight, 4) occurrence of chest pain, and 5) diastolic blood pressure as key contributors. CONCLUSION: We conclude machine learned models based on survey questionnaire can provide an automated identification mechanism for patients at risk of diabetes and cardiovascular diseases. We also identify key contributors to the prediction, which can be further explored for their implications on electronic health records.",1
"Improved homology modeling of the human & rat EP4 prostanoid receptors. Background: The EP4 prostanoid receptor is one of four GPCRs that mediate the diverse actions of prostaglandin E2 (PGE2). Novel selective EP4 receptor agonists would assist to further elucidate receptor sub-type function and promote development of therapeutics for bone healing, heart failure, and other receptor associated conditions. The rat EP4 (rEP4) receptor has been used as a surrogate for the human EP4 (hEP4) receptor in multiple SAR studies. To better understand the validity of this traditional approach, homology models were generated by threading for both receptors using the RaptorX server. These models were fit to an implicit membrane using the PPM server and OPM database with refinement of intra and extracellular loops by Prime (Schrödinger). To understand the interaction between the receptors and known agonists, induced-fit docking experiments were performed using Glide and Prime (Schrödinger), with both endogenous agonists and receptor sub-type selective, small-molecule agonists. The docking scores and observed interactions were compared with radioligand displacement experiments and receptor (rat & human) activation assays monitoring cAMP. Results: Rank-ordering of in silico compound docking scores aligned well with in vitro activity assay EC50 and radioligand binding Ki. We observed variations between rat and human EP4 binding pockets that have implications in future small-molecule receptor-modulator design and SAR, specifically a S103G mutation within the rEP4 receptor. Additionally, these models helped identify key interactions between the EP4 receptor and ligands including PGE2 and several known sub-type selective agonists while serving as a marked improvement over the previously reported models. Conclusions: This work has generated a set of novel homology models of the rEP4 and hEP4 receptors. The homology models provide an improvement upon the previously reported model, largely due to improved solvation. The hEP4 docking scores correlates best with the cAMP activation data, where both data sets rank order Rivenprost>CAY10684 > PGE1 ≈ PGE2 > 11-deoxy-PGE1 ≈ 11-dexoy-PGE2 > 8-aza-11-deoxy-PGE1. This rank-ordering matches closely with the rEP4 receptor as well. Species-specific differences were noted for the weak agonists Sulprostone and Misoprostol, which appear to dock more readily within human receptor versus rat receptor.",0
"Quality-based UnwRap of SUbdivided Large Arrays (URSULA) for high-resolution MRI data. In Magnetic Resonance Imaging, mapping of the static magnetic field and the magnetic susceptibility is based on multidimensional phase measurements. Phase data are ambiguous and have to be unwrapped to their true range in order to exhibit a correct representation of underlying features. High-resolution imaging at ultra-high fields, where susceptibility and phase contrast are natural tools, can generate large datasets, which tend to dramatically increase computing time demands for spatial unwrapping algorithms. This article describes a novel method, URSULA, which introduces an artificial volume compartmentalisation that allows large-scale unwrapping problems to be broken down, making URSULA ideally suited for computational parallelisation. In the presented study, URSULA is illustrated with a quality-guided unwrapping approach. Validation is performed on numerical data and an application on a high-resolution measurement, at the clinical field strength of 3T is demonstrated. In conclusion, URSULA allows for a reduction of the problem size, a substantial speed-up and for handling large data sets without sacrificing the overall accuracy of the resulting phase information.",0
"Artificial intelligence estimates the importance of baseline factors in predicting response to anti-PD1 in metastatic melanoma. Objective: Prognosis of patients with metastatic melanoma has dramatically improved over recent years because of the advent of antibodies targeting programmed cell death protein-1 (PD1). However, the response rate is 40% and baseline biomarkers for the outcome are yet to be identified. Here, we aimed to determine whether artificial intelligence might be useful in weighting the importance of baseline variables in predicting response to anti-PD1. Methods: This is a retrospective study evaluating 173 patients receiving anti-PD1 for melanoma. Using an artificial neuronal network analysis, the importance of different variables was estimated and used in predicting response rate and overall survival. Results: After a mean follow-up of 12.8 (±11.9) months, disease control rate was 51%. Using artificial neuronal network, we observed that 3 factors predicted response to anti-PD1: neutrophil-to-lymphocyte ratio (NLR) (importance: 0.195), presence of ≥3 metastatic sites (importance: 0.156), and baseline lactate dehydrogenase (LDH) > upper limit of normal (importance: 0.154). Looking at connections between different covariates and overall survival, the most important variables influencing survival were: presence of ≥3 metastatic sites (importance: 0.202), age (importance: 0.189), NLR (importance: 0.164), site of primary melanoma (cutaneous vs. noncutaneous) (importance: 0.112), and LDH > upper limit of normal (importance: 0.108). Conclusions: NLR, presence of ≥3 metastatic sites, LDH levels, age, and site of primary melanoma are important baseline factors influencing response and survival. Further studies are warranted to estimate a model to drive the choice to administered anti-PD1 treatments in patients with melanoma.",1
"Evaluating the role of RAD52 and its interactors as novel potential molecular targets for hepatocellular carcinoma. Background: Radiation sensitive 52 (RAD52) is an important protein that mediates DNA repair in tumors. However, little is known about the impact of RAD52 on hepatocellular carcinoma (HCC). We investigated the expression of RAD52 and its values in HCC. Some proteins that might be coordinated with RAD52 in HCC were also analyzed. Methods: Global RAD52 mRNA levels in HCC were assessed using The Cancer Genome Atlas (TCGA) database. RAD52 expression was analyzed in 70 HCC tissues and adjacent tissues by quantitative real-time PCR (qRT-PCR), Western blotting and immunohistochemistry. The effect of over-expressed RAD52 in Huh7 HCC cells was investigated. The String database was then used to perform enrichment and functional analysis of RAD52 and its interactome. Cytoscape software was used to create a protein-protein interaction network. Molecular interaction studies with RAD52 and its interactome were performed using the molecular docking tools in Hex8.0.0. Finally, these DNA repair proteins, which interact with RAD52, were also analyzed using the TCGA dataset and were detected by qRT-PCR. Based on the TCGA database, algorithms combining ROC between RAD52 and RAD52 interactors were used to diagnose HCC by binary logistic regression. Results: In TCGA, upregulated RAD52 related to gender was obtained in HCC. The area under the receiver operating characteristic curve (AUC) of RAD52 was 0.704. The results of overall survival (OS) and recurrence-free survival (RFS) indicated no difference in the prognosis between patients with high and low RAD52 gene expression. We validated that RAD52 expression was increased at the mRNA and protein levels in Chinese HCC tissues compared with adjacent tissues. Higher RAD52 was associated with older age, without correlation with other clinicopathological factors. In vitro, over-expressed RAD52 significantly promoted the proliferation and migration of Huh7 cells. Furthermore, RAD52 interactors (radiation sensitive 51, RAD51; X-ray repair cross complementing 6, XRCC6; Cofilin, CFL1) were also increased in HCC and participated in some biological processes with RAD52. Protein structure analysis showed that RAD52-RAD51 had the firmest binding structure with the lowest E-total energy (- 1120.5 kcal/mol) among the RAD52-RAD51, RAD52-CFL1, and RAD52-XRCC6 complexes. An algorithm combining ROC between RAD52 and its interactome indicated a greater specificity and sensitivity for HCC screening. Conclusions: Overall, our study suggested that RAD52 plays a vital role in HCC pathogenesis and serves as a potential molecular target for HCC diagnosis and treatment. This study's findings regarding the multigene prediction and diagnosis of HCC are valuable.",0
"Impact of a decreasing pre-test probability on the performance of diagnostic tests for coronary artery disease. Aims: To provide a pooled estimation of contemporary pre-test probabilities (PTPs) of significant coronary artery disease (CAD) across clinical patient categories, re-evaluate the utility of the application of diagnostic techniques according to such estimates, and propose a comprehensive diagnostic technique selection tool for suspected CAD. Methods and results: Estimates of significant CAD prevalence across sex, age, and type of chest pain categories from three large-scale studies were pooled (n = 15 815). The updated PTPs and diagnostic performance profiles of exercise electrocardiogram, invasive coronary angiography, coronary computed tomography angiography (CCTA), positron emission tomography (PET), stress cardiac magnetic resonance (CMR), and SPECT were integrated to define the PTP ranges in which ruling-out CAD is possible with a post-test probability of <10% and <5%. These ranges were then integrated in a new colour-coded tabular diagnostic technique selection tool. The Bayesian relationship between PTP and the rate of diagnostic false positives was explored to complement the characterization of their utility. Pooled CAD prevalence was 14.9% (range = 1-52), clearly lower than that used in current clinical guidelines. Ruling-out capabilities of non-invasive imaging were good overall. The greatest ruling-out capacity (i.e. post-test probability <5%) was documented by CCTA, PET, and stress CMR. With decreasing PTP, the fraction of false positive findings rapidly increased, although a lower CAD prevalence partially cancels out such effect. Conclusion: The contemporary PTP of significant CAD across symptomatic patient categories is substantially lower than currently assumed. With a low prevalence of the disease, non-invasive testing can rarely rule-in the disease and focus should shift to ruling-out obstructive CAD. The large proportion of false positive findings must be taken into account when patients with low PTP are investigated.",0
"Bisecting GlcNAc is a general suppressor of terminal modification of N-glycan. Glycoproteins are decorated with complex glycans for protein functions. However, regulation mechanisms of complex glycan biosynthesis are largely unclear. Here we found that bisecting GlcNAc, a branching sugar residue in N-glycan, suppresses the biosynthesis of various types of terminal epitopes in N-glycans, including fucose, sialic acid and human natural killer-1. Expression of these epitopes in N-glycan was elevated in mice lacking the biosynthetic enzyme of bisecting GlcNAc, GnT-III, and was conversely suppressed by GnT-III overexpression in cells. Many glycosyltransferases for N-glycan terminals were revealed to prefer a nonbisected N-glycan as a substrate to its bisected counterpart, whereas no up-regulation of their mRNAs was found. This indicates that the elevated expression of the terminal N-glycan epitopes in GnT-III-deficient mice is attributed to the substrate specificity of the biosynthetic enzymes. Molecular dynamics simulations further confirmed that nonbisected glycans were preferentially accepted by those glycosyltransferases. These findings unveil a new regulation mechanism of protein N-glycosylation.",0
"LI-RADS Treatment Response Algorithm: Performance and Diagnostic Accuracy. Background In 2017, the Liver Imaging Reporting and Data System (LI-RADS) included an algorithm for the assessment of hepatocellular carcinoma (HCC) treated with local-regional therapy. The aim of the algorithm was to enable standardized evaluation of treatment response to guide subsequent therapy. However, the performance of the algorithm has not yet been validated in the literature. Purpose To evaluate the performance of the LI-RADS 2017 Treatment Response algorithm for assessing the histopathologic viability of HCC treated with bland arterial embolization. Materials and Methods This retrospective study included patients who underwent bland arterial embolization for HCC between 2006 and 2016 and subsequent liver transplantation. Three radiologists independently assessed all treated lesions by using the CT/MRI LI-RADS 2017 Treatment Response algorithm. Radiology and posttransplant histopathology reports were then compared. Lesions were categorized on the basis of explant pathologic findings as either completely (100%) or incompletely (<100%) necrotic, and performance characteristics and predictive values for the LI-RADS Treatment Response (LR-TR) Viable and Nonviable categories were calculated for each reader. Interreader association was calculated by using the Fleiss kappa. Results A total of 45 adults (mean age, 57.1 years +/- 8.2; 13 women) with 63 total lesions were included. For predicting incomplete histopathologic tumor necrosis, the accuracy of the LR-TR Viable category for the three readers was 60%-65%, and the positive predictive value was 86%-96%. For predicting complete histopathologic tumor necrosis, the accuracy of the LR-TR Nonviable category was 67%-71%, and the negative predictive value was 81%-87%. By consensus, 17 (27%) of 63 lesions were categorized as LR-TR Equivocal, and 12 of these lesions were incompletely necrotic. Interreader association for the LR-TR category was moderate (kappa = 0.55; 95% confidence interval: 0.47, 0.67). Conclusion The Liver Imaging Reporting and Data System 2017 Treatment Response algorithm had high predictive value and moderate interreader association for the histopathologic viability of hepatocellular carcinoma treated with bland arterial embolization when lesions were assessed as Viable or Nonviable. (c) RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Gervais in this issue.",0
"CHASMplus Reveals the Scope of Somatic Missense Mutations Driving Human Cancers. Tokheim et al. introduce a computational approach to accurately separate driver from passenger mutations in cancer. Their analysis revealed that most driver mutations occur only in a few patients, presenting a challenge for precision medicine, and several cancer types will benefit from additional sequencing to identify these rare driver mutations.",0
"Combination of active transfer learning and natural language processing to improve liver volumetry using surrogate metrics with deep learning. Purpose: To determine if weakly supervised learning with surrogate metrics and active transfer learning can hasten clinical deployment of deep learning models. Materials and Methods: By leveraging Liver Tumor Segmentation (LiTS) challenge 2017 public data (n = 131 studies), natural language processing of reports, and an active learning method, a model was trained to segment livers on 239 retrospectively collected portal venous phase abdominal CT studies obtained between January 1, 2014, and December 31, 2016. Absolute volume differences between predicted and originally reported liver volumes were used to guide active learning and assess accuracy. Overall survival based on liver volumes predicted by this model (n = 34 patients) versus radiology reports and Model for End-Stage Liver Disease with sodium (MELD-Na) scores was assessed. Differences in absolute liver volume were compared by using the paired Student t test, Bland-Altman analysis, and intraclass correlation; survival analysis was performed with the Kaplan-Meier method and a Mantel-Cox test. Results: Data from patients with poor liver volume prediction (n = 10) with a model trained only with publicly available data were incorporated into an active learning method that trained a new model (LiTS data plus over-and underestimated active learning cases [LiTS-OU]) that performed significantly better on a held-out institutional test set (absolute volume difference of 231 vs 176 mL, P =.0005). In overall survival analysis, predicted liver volumes using the best active learning-trained model (LiTS-OU) were at least comparable with liver volumes extracted from radiology reports and MELD-Na scores in predicting survival. Conclusion: Active transfer learning using surrogate metrics facilitated deployment of deep learning models for clinically meaningful liver segmentation at a major liver transplant center.",1
"A draft map of the human ovarian proteome for tissue engineering and clinical applications. Fertility preservation research in women today is increasingly taking advantage of bioengineering techniques to develop new biomimetic materials and solutions to safeguard ovarian cell function and microenvironment in vitro and in vivo. However, available data on the human ovary are limited and fundamental differences between animal models and humans are hampering researchers in their quest for more extensive knowledge of human ovarian physiology and key reproductive proteins that need to be preserved. We therefore turned to multi-dimensional label-free mass spectrometry to analyze human ovarian cortex, as it is a high-throughput and conclusive technique providing information on the proteomic composition of complex tissues like the ovary. In-depth proteomic profiling through two-dimensional liquid chromatography-mass spectrometry, Western blotting, histological and immunohistochemical analyses, and data mining helped us to confidently identify 1508 proteins. Moreover, our method allowed us to chart the most complete representation so far of the ovarian matrisome, defined as the ensemble of extracellular matrix proteins and associated factors, including more than 80 proteins. In conclusion, this study will provide a better understanding of ovarian proteomics, with a detailed characterization of the ovarian follicle microenvironment, in order to enable bioengineers to create biomimetic scaffolds for transplantation and three-dimensional in vitro culture. By publishing our proteomic data, we also hope to contribute to accelerating biomedical research into ovarian health and disease in general. Molecular & Cellular Proteomics 18: 10.1074/ mcp.RA117.000469, S159–S173, 2019.",0
"Identification of a 26-lncRNAs Risk Model for Predicting Overall Survival of Cervical Squamous Cell Carcinoma Based on Integrated Bioinformatics Analysis. As a common malignancy in women, cervical squamous cell carcinoma is a major cause of cancer-related mortality globally. Recent studies have demonstrated that long non-coding RNA (lncRNA) can function as potential biomarkers in cancer prognosis; however, little is known about its role in cervical cancer. In this study, we downloaded the gene expression profiles along with the clinical data of patients with cervical squamous cell carcinoma from The Cancer Genome Atlas. By applying bioinformatics analysis including random forest selection and Least Absolute Shrinkage and Selection Operator (LASSO) cox regression model along with 10-fold cross-validation, we constructed a 26-lncRNAs risk model that can be used to predict the overall survival of cervical squamous cell carcinoma. After that, Kaplan-Meier analysis combined with log-rank p test was applied to assess the predictive accuracy of the 26-lncRNAs risk model. Further analysis showed that the prognostic value of 26-lncRNAs risk model was independent of other clinicopathological factors. At last, lncRNAs in the model were put into gene ontology biological process enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathways analysis, which suggested that these lncRNAs might contribute to cancer-associated processes such as cell cycle and apoptosis. This study indicated that lncRNAs signature could be a useful marker to predict the prognosis of cervical squamous cell carcinoma.",0
"Mammographic Breast Density Assessment Using Deep Learning: Clinical Implementation. Purpose To develop a deep learning (DL) algorithm to assess mammographic breast density. Materials and Methods In this retrospective study, a deep convolutional neural network was trained to assess Breast Imaging Reporting and Data System (BI-RADS) breast density based on the original interpretation by an experienced radiologist of 41 479 digital screening mammograms obtained in 27 684 women from January 2009 to May 2011. The resulting algorithm was tested on a held-out test set of 8677 mammograms in 5741 women. In addition, five radiologists performed a reader study on 500 mammograms randomly selected from the test set. Finally, the algorithm was implemented in routine clinical practice, where eight radiologists reviewed 10 763 consecutive mammograms assessed with the model. Agreement on BI-RADS category for the DL model and for three sets of readings-(a) radiologists in the test set, (b) radiologists working in consensus in the reader study set, and (c) radiologists in the clinical implementation set-were estimated with linear-weighted kappa statistics and were compared across 5000 bootstrap samples to assess significance. Results The DL model showed good agreement with radiologists in the test set (kappa = 0.67; 95% confidence interval [CI]: 0.66, 0.68) and with radiologists in consensus in the reader study set (kappa = 0.78; 95% CI: 0.73, 0.82). There was very good agreement (kappa = 0.85; 95% CI: 0.84, 0.86) with radiologists in the clinical implementation set; for binary categorization of dense or nondense breasts, 10 149 of 10 763 (94%; 95% CI: 94%, 95%) DL assessments were accepted by the interpreting radiologist. Conclusion This DL model can be used to assess mammographic breast density at the level of an experienced mammographer. (c) RSNA, 2018 Online supplemental material is available for this article . See also the editorial by Chan and Helvie in this issue.",1
"Association of Early Interventions with Birth Outcomes and Child Linear Growth in Low-Income and Middle-Income Countries: Bayesian Network Meta-analyses of Randomized Clinical Trials. Importance: The first 1000 days of life represent a critical window for child development. Pregnancy, exclusive breastfeeding (EBF) period (0-6 months), and complementary feeding (CF) period (6-24 months) have different growth requirements, so separate considerations for intervention strategies are needed. No synthesis to date has attempted to quantify the associations of interventions under multiple domains of micronutrient and balanced energy protein and food supplements, deworming, maternal education, water sanitation, and hygiene across these 3 life periods with birth and growth outcomes. Objective: To determine the magnitude of association of interventions with birth and growth outcomes based on randomized clinical trials (RCTs) conducted in low-income and middle-income countries (LMICs) using Bayesian network meta-analyses. Data Sources: MEDLINE, Embase, and Cochrane databases were searched from their inception up to August 14, 2018. Study Selection: Included were LMIC-based RCTs of interventions provided to pregnant women, infants (0-6 months), and children (6-24 months). Data Extraction and Synthesis: Two independent reviewers used a standardized data extraction and quality assessment form. Random-effects network meta-analyses were performed for each life period. Effect sizes are reported as odds ratios (ORs) and mean differences (MeanDiffs) for dichotomous and continuous outcomes, with 95% credible intervals (CrIs). This study calculated probabilities of interventions being superior to standard of care by at least a minimal clinically important difference. Main Outcomes and Measures: The study compared ORs on preterm birth and MeanDiffs on birth weight for pregnancy, length for age (LAZ) for EBF, and height for age (HAZ) for CF. Results: Among 302 061 participants in 169 randomized clinical trials, the network meta-analyses found several nutritional interventions that demonstrated greater association with improved birth and growth outcomes compared with standard of care. For instance, compared with standard of care, maternal supplements of multiple micronutrients showed reduced odds for preterm birth (OR, 0.54; 95% CrI, 0.27-0.97) and improved mean birth weight (MeanDiff, 0.08 kg; 95% CrI, 0.00-0.17 kg) but not LAZ during EBF (MeanDiff, -0.02; 95% CrI, -0.18 to 0.14). Supplementing infants and children with multiple micronutrients showed improved LAZ (MeanDiff, 0.20; 95% CrI, 0.03-0.35) and HAZ (MeanDiff, 0.14; 95% CrI, 0.02-0.25). The study found that pregnancy interventions generally had higher probabilities of a minimal clinically importance difference than the interventions for the EBF or CF in the first 1000 days of life. Conclusions and Relevance: These analyses highlight the importance of intervening early for child development, during pregnancy if possible. Results of this study suggest that there is a need to combine interventions from multiple domains and test for their effectiveness as a package..",0
"Using artificial intelligence to reduce diagnostic workload without compromising detection of urinary tract infections. BACKGROUND: A substantial proportion of microbiological screening in diagnostic laboratories is due to suspected urinary tract infections (UTIs), yet approximately two thirds of urine samples typically yield negative culture results. By reducing the number of query samples to be cultured and enabling diagnostic services to concentrate on those in which there are true microbial infections, a significant improvement in efficiency of the service is possible. METHODOLOGY: Screening process for urine samples prior to culture was modelled in a single clinical microbiology laboratory covering three hospitals and community services across Bristol and Bath, UK. Retrospective analysis of all urine microscopy, culture, and sensitivity reports over one year was used to compare two methods of classification: a heuristic model using a combination of white blood cell count and bacterial count, and a machine learning approach testing three algorithms (Random Forest, Neural Network, Extreme Gradient Boosting) whilst factoring in independent variables including demographics, historical urine culture results, and clinical details provided with the specimen. RESULTS: A total of 212,554 urine reports were analysed. Initial findings demonstrated the potential for using machine learning algorithms, which outperformed the heuristic model in terms of relative workload reduction achieved at a classification sensitivity > 95%. Upon further analysis of classification sensitivity of subpopulations, we concluded that samples from pregnant patients and children (age 11 or younger) require independent evaluation. First the removal of pregnant patients and children from the classification process was investigated but this diminished the workload reduction achieved. The optimal solution was found to be three Extreme Gradient Boosting algorithms, trained independently for the classification of pregnant patients, children, and then all other patients. When combined, this system granted a relative workload reduction of 41% and a sensitivity of 95% for each of the stratified patient groups. CONCLUSION: Based on the considerable time and cost savings achieved, without compromising the diagnostic performance, the heuristic model was successfully implemented in routine clinical practice in the diagnostic laboratory at Severn Pathology, Bristol. Our work shows the potential application of supervised machine learning models in improving service efficiency at a time when demand often surpasses resources of public healthcare providers.",1
"Predicting hospital-acquired pneumonia among schizophrenic patients: a machine learning approach. BACKGROUND: Medications are frequently used for treating schizophrenia, however, anti-psychotic drug use is known to lead to cases of pneumonia. The purpose of our study is to build a model for predicting hospital-acquired pneumonia among schizophrenic patients by adopting machine learning techniques. METHODS: Data related to a total of 185 schizophrenic in-patients at a Taiwanese district mental hospital diagnosed with pneumonia between 2013 ~ 2018 were gathered. Eleven predictors, including gender, age, clozapine use, drug-drug interaction, dosage, duration of medication, coughing, change of leukocyte count, change of neutrophil count, change of blood sugar level, change of body weight, were used to predict the onset of pneumonia. Seven machine learning algorithms, including classification and regression tree, decision tree, k-nearest neighbors, naïve Bayes, random forest, support vector machine, and logistic regression were utilized to build predictive models used in this study. Accuracy, area under receiver operating characteristic curve, sensitivity, specificity, and kappa were used to measure overall model performance. RESULTS: Among the seven adopted machine learning algorithms, random forest and decision tree exhibited the optimal predictive accuracy versus the remaining algorithms. Further, six most important risk factors, including, dosage, clozapine use, duration of medication, change of neutrophil count, change of leukocyte count, and drug-drug interaction, were also identified. CONCLUSIONS: Although schizophrenic patients remain susceptible to the threat of pneumonia whenever treated with anti-psychotic drugs, our predictive model may serve as a useful support tool for physicians treating such patients.",1
"Development and validation of an esophageal squamous cell carcinoma detection model by large-scale microrna profiling. IMPORTANCE Patients with late-stage esophageal squamous cell carcinoma (ESCC) have a poor prognosis. Noninvasive screening tests using serum microRNAs (miRNAs) to accurately detect earlystage ESCC are needed to improve mortality. OBJECTIVE To establish a model using serum miRNAs to distinguish patients with ESCC from noncancer controls. DESIGN, SETTING, AND PARTICIPANTS In this case-control study, serum miRNA expression profiles of patients with ESCC (n = 566) and control patients without cancer (n = 4965) were retrospectively analyzed to establish a diagnostic model, which was tested in a training set and confirmed in a validation set. Patients histologically diagnosed as having ESCC who did not receive prior therapy or have a past or concurrent cancer other than ESCC were enrolled from the National Cancer Center Hospital in Tokyo, Japan. Between October 2010 and November 2015, control samples were collected from the National Cancer Center Biobank, the Biobank of the National Center for Geriatrics and Gerontology, and the general population undergoing routine health examination. Data analysis was performed between August 2015 and October 2018. Serum samples were randomly divided into discovery and validation sets. MAIN OUTCOMES AND MEASURES The expression of 2565 miRNAs was assessed in each sample. The discriminant model (named the EC index) was evaluated in the training set using Fisher linear discriminant analysis with a greedy algorithm. Receiver operating characteristic curve analysis evaluated the diagnostic ability of the model in the validation set. RESULTS In the training set, 283 patients with esophageal cancer (median age, 67 years [range, 37-90 years]; 83.4%male) were compared with 283 control patients (median age, 54 years [range, 22-100 years]; 43.1%male), and the EC index was constructed using 6 miRNAs (miR-8073, miR-6820-5p, miR-6794-5p, miR-3196, miR-744-5p, and miR-6799-5p). The area under the receiver operating characteristic curvewas 1.00, with sensitivity of 1.00 and specificity of 0.98. The validation set included 283 patients (median age, 66 years [range, 42-87 years]; 83.0%male) and 4682 control patients (median age, 68 years [range, 20-98 years]; 44.7%male), and the area under the receiver operating characteristic curve for the EC index was 1.00, with sensitivity of 0.96 and specificity of 0.98. CONCLUSIONS AND RELEVANCE What appears to be novel serum miRNA discriminant model was developed for the diagnosis of ESCC. A multicenter prospective study is ongoing to confirm the present observations.",1
"Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Microsatellite instability determines whether patients with gastrointestinal cancer respond exceptionally well to immunotherapy. However, in clinical practice, not every patient is tested for MSI, because this requires additional genetic or immunohistochemical tests. Here we show that deep residual learning can predict MSI directly from H&E histology, which is ubiquitously available. This approach has the potential to provide immunotherapy to a much broader subset of patients with gastrointestinal cancer.",1
"Development and validation of machine learning models in prediction of remission in patients with moderate to severe Crohn disease. IMPORTANCE Biological therapies have revolutionized inflammatory bowel disease management, but many patients do not respond to biological monotherapy. Identification of likely responders could reduce costs and delays in remission. OBJECTIVE To identify patients with Crohn disease likely to be durable responders to ustekinumab before committing to long-term treatment. DESIGN, SETTING, AND PARTICIPANTS This cohort study analyzed data from 3 phase 3 randomized clinical trials (UNITI-1, UNITI-2, and IM-UNITI) conducted from 2011 to 2015. Participants (n = 401) were individuals with active (C-reactive protein [CRP] measurement of_5mg/L at enrollment) Crohn disease who received ustekinumab therapy. Data analysis was performed from November 1, 2017, to June 1, 2018. EXPOSURES All included patients were exposed to 1 or more dose of ustekinumab for 8 weeks or more. MAIN OUTCOMES AND MEASURES Random forest methods were used in building 2 models for predicting Crohn disease remission, with a CRP level lower than 5mg/dL as a proxy for biological remission, beyond week 42 of ustekinumab treatment. The first model used only baseline data, and the second used data through week 8. RESULTS In total, 401 participants, with a mean (SD) age of 36.3 (12.6) years and 170 male (42.4%), were included. Theweek-8 model had a mean area under the receiver operating characteristic curve (AUROC) of 0.78 (95%CI, 0.69-0.87). In the testing data set, 27 of 55 participants (49.1%) classified as likely to have treatment success achieved success with a CRP level lower than 5mg/L after week 42, and 7 of 65 participants (10.8%) classified as likely to have treatment failure achieved this outcome. In the full cohort, 87 patients (21.7%) attained remission after week 42. A prediction model using the week-6 albumin to CRP ratio had an AUROC of 0.76 (95%CI, 0.71-0.82). Baseline ustekinumab serum levels did not improve the model's prediction performance. CONCLUSIONS AND RELEVANCE In patients with active Crohn disease, demographic and laboratory data before week 8 of treatment appeared to allow the prompt identification of likely nonresponders to ustekinumab without the need for costly drug-level monitoring.",1
"Deep learning-based prescription of cardiac MRI planes. Purpose: To develop and evaluate a system to prescribe imaging planes for cardiac MRI based on deep learning (DL)−based localization of key anatomic landmarks. Materials and Methods: Annotated landmarks on 892 long-axis (LAX) and 493 short-axis (SAX) cine steady-state free precession series from cardiac MR images were retrospectively collected between February 2012 and June 2017. U-Net-based heatmap regression was used for localization of cardiac landmarks, which were used to compute cardiac MRI planes. Performance was evaluated by comparing localization distances and plane angle differences between DL predictions and ground truth. The plane angulations from DL were compared with those prescribed by the technologist at the original time of acquisition. Data were split into 80% for training and 20% for testing, and results confirmed with fivefold cross-validation. Results: On LAX images, DL localized the apex within mean 12.56 mm ± 19.11 (standard deviation) and the mitral valve (MV) within 7.68 mm ± 6.91. On SAX images, DL localized the aortic valve within 5.78 mm ± 5.68, MV within 5.90 mm ± 5.24, pulmonary valve within 6.55 mm ± 6.39, and tricuspid valve within 6.39 mm ± 5.89. On the basis of these localizations, average angle bias and mean error of DL-predicted imaging planes relative to ground truth annotations were as follows: SAX, −1.27° ± 6.81 and 4.93° ± 4.86; four chambers, 0.38° ± 6.45 and 5.16° ± 3.80; three chambers, 0.13° ± 12.70 and 9.02° ± 8.83; and two chamber, 0.25° ± 9.08 and 6.53° ± 6.28, respectively. Conclusion: DL-based anatomic localization is a feasible strategy for planning cardiac MRI planes. This approach can produce imaging planes comparable to those defined by ground truth landmarks.",1
"The ai-2/luxsquorum sensing system affects the growth characteristics, biofilm formation, and virulence of haemophilus parasuis. Haemophilus parasuis (H. parasuis) is a kind of opportunistic pathogen of the upper respiratory tract of piglets. Under certain circumstances, virulent strains can breach the mucosal barrier and enter the bloodstream, causing severe Glässer's disease. Many virulence factors are found to be related to the pathogenicity of H. parasuis strain, but the pathogenic mechanism remains unclear. LuxS/AI-2, as a kind of very important quorum sensing system, affects the growth characteristics, biofilm formation, antibiotic production, virulence, and metabolism of different strains. In order to investigate the effect of luxS/AI-2 quorum sensing system on the virulence of H. parasuis, a deletion mutant strain (luxS) and complemented strain (C-luxS) were constructed and characterized. The results showed that the luxS gene participated in regulating and controlling stress resistance, biofilm formation and virulence. Compared with wild-Type strain, luxS strain decreased the production of AI-2 molecules and the tolerance toward oxidative stress and heat shock, and it reduced the abilities of autoagglutination, hemagglutination, and adherence, whereas it increased the abilities to form biofilm in vitro. In vivo experiments showed that luxS strain attenuated its virulence about 10-folds and significantly decreased its tissue burden of bacteria in mice, compared with the wild-Type strain. Taken together, the luxS/AI-2 quorum sensing system in H. parasuis not only plays an important role in growth and biofilm formation, but also affects the pathogenicity of H. parasuis.",0
"Automated profiling of growth cone heterogeneity defines relations between morphology and motility. Growth cones are complex, motile structures at the tip of an outgrowing neurite. They often exhibit a high density of filopodia (thin actin bundles), which complicates the unbiased quantification of their morphologies by software. Contemporary image processing methods require extensive tuning of segmentation parameters, require significant manual curation, and are often not sufficiently adaptable to capture morphology changes associated with switches in regulatory signals. To overcome these limitations, we developed Growth Cone Analyzer (GCA). GCA is designed to quantify growth cone morphodynamics from time-lapse sequences imaged both in vitro and in vivo, but is sufficiently generic that it may be applied to nonneuronal cellular structures. We demonstrate the adaptability of GCA through the analysis of growth cone morphological variation and its relation to motility in both an unperturbed system and in the context of modified Rho GTPase signaling. We find that perturbations inducing similar changes in neurite length exhibit underappreciated phenotypic nuance at the scale of the growth cone.",0
"A machine learning model to predict hepatocellular carcinoma response to transcatheter arterial chemoembolization. Purpose: To evaluate a fully automated machine learning algorithm that uses pretherapeutic quantitative CT image features and clinical factors to predict hepatocellular carcinoma (HCC) response to transcatheter arterial chemoembolization (TACE). Materials and Methods: Outcome information from 105 patients receiving first-line treatment with TACE was evaluated retrospectively. The primary clinical endpoint was time to progression (TTP) based on follow-up CT radiologic criteria (modified Response Evaluation Criteria in Solid Tumors). A 14-week cutoff was used to classify patients as TACE-susceptible (TTP 14 weeks) or TACE-refractory (TTP, 14 weeks). Response to TACE was predicted using a random forest classifier with the Barcelona Clinic Liver Cancer (BCLC) stage and quantitative image features as input, as well as the BCLC stage alone as a control. Results: The model’s response prediction accuracy rate was 74.2% (95% confidence interval [CI]: 64%, 82%) using a combination of the BCLC stage plus quantitative image features versus 62.9% (95% CI: 52%, 72%) using the BCLC stage alone. Shape image features of the tumor and background liver were the dominant features correlated to the TTP as selected by the Boruta method and were used to predict the outcome. Conclusion: This preliminary study demonstrated that quantitative image features obtained prior to therapy can improve the accuracy of predicting response of HCC to TACE. This approach is likely to provide useful information for aiding in selection of patients with HCC for TACE.",1
"Cortical pain processing in the rat anterior cingulate cortex and primary somatosensory cortex. Pain is a complex multidimensional experience encompassing sensory-discriminative, affective-motivational and cognitive-emotional components mediated by different neural mechanisms. Investigations of neurophysiological signals from simultaneous recordings of two or more cortical circuits may reveal important circuit mechanisms on cortical pain processing. The anterior cingulate cortex (ACC) and primary somatosensory cortex (S1) represent two most important cortical circuits related to sensory and affective processing of pain. Here, we recorded in vivo extracellular activity of the ACC and S1 simultaneously from male adult Sprague-Dale rats (n = 5), while repetitive noxious laser stimulations were delivered to animalõs hindpaw during pain experiments. We identified spontaneous pain-like events based on stereotyped pain behaviors in rats. We further conducted systematic analyses of spike and local field potential (LFP) recordings from both ACC and S1 during evoked and spontaneous pain episodes. From LFP recordings, we found stronger phase-amplitude coupling (theta phase vs. gamma amplitude) in the S1 than the ACC (n = 10 sessions), in both evoked (p = 0.058) and spontaneous pain-like behaviors (p = 0.017, paired signed rank test). In addition, pain-modulated ACC and S1 neuronal firing correlated with the amplitude of stimulus-induced event-related potentials (ERPs) during evoked pain episodes. We further designed statistical and machine learning methods to detect pain signals by integrating ACC and S1 ensemble spikes and LFPs. Together, these results reveal differential coding roles between the ACC and S1 in cortical pain processing, as well as point to distinct neural mechanisms between evoked and putative spontaneous pain at both LFP and cellular levels.",0
"A guide to deep learning in healthcare. Here we present deep-learning techniques for healthcare, centering our discussion on deep learning in computer vision, natural language processing, reinforcement learning, and generalized methods. We describe how these computational techniques can impact a few key areas of medicine and explore how to build end-to-end systems. Our discussion of computer vision focuses largely on medical imaging, and we describe the application of natural language processing to domains such as electronic health record data. Similarly, reinforcement learning is discussed in the context of robotic-assisted surgery, and generalized deep-learning methods for genomics are reviewed.",0
"Molecular Mechanism for Ligand Recognition and Subtype Selectivity of α2C Adrenergic Receptor. Adrenergic G-protein-coupled receptors (GPCRs) mediate different cellular signaling pathways in the presence of endogenous catecholamines and play important roles in both physiological and pathological conditions. Extensive studies have been carried out to investigate the structure and function of β adrenergic receptors (βARs). However, the structure of α adrenergic receptors (αARs) remains to be determined. Here, we report the structure of the human α2C adrenergic receptor (α2CAR) with the non-selective antagonist, RS79948, at 2.8 Å. Our structure, mutations, modeling, and functional experiments indicate that a α2CAR-specific D206ECL2-R409ECL3-Y4056.58 network plays a role in determining α2 adrenergic subtype selectivity. Furthermore, our results show that a specific loosened helix at the top of TM4 in α2CAR is involved in receptor activation. Together, our structure of human α2CAR-RS79948 provides key insight into the mechanism underlying the α2 adrenergic receptor activation and subtype selectivity.",0
"Characterization of disease-specific cellular abundance profiles of chronic inflammatory skin conditions from deconvolution of biopsy samples. Background: Psoriasis and atopic dermatitis are two inflammatory skin diseases with a high prevalence and a significant burden on the patients. Underlying molecular mechanisms include chronic inflammation and abnormal proliferation. However, the cell types contributing to these molecular mechanisms are much less understood. Recently, deconvolution methodologies have allowed the digital quantification of cell types in bulk tissue based on mRNA expression data from biopsies. Using these methods to study the cellular composition of the skin enables the rapid enumeration of multiple cell types, providing insight into the numerical changes of cell types associated with chronic inflammatory skin conditions. Here, we use deconvolution to enumerate the cellular composition of the skin and estimate changes related to onset, progress, and treatment of these skin diseases. Methods: A novel signature matrix, i.e. DerM22, containing expression data from 22 reference cell types, is used, in combination with the CIBERSORT algorithm, to identify and quantify the cellular subsets within whole skin biopsy samples. We apply the approach to public microarray mRNA expression data from the skin layers and 648 samples from healthy subjects and patients with psoriasis or atopic dermatitis. The methodology is validated by comparison to experimental results from flow cytometry and immunohistochemistry studies, and the deconvolution of independent data from isolated cell types. Results: We derived the relative abundance of cell types from healthy, lesional, and non-lesional skin and observed a marked increase in the abundance of keratinocytes and leukocytes in the lesions of both inflammatory dermatological conditions. The relative fraction of these cells varied from healthy to diseased skin and from non-lesional to lesional skin. We show that changes in the relative abundance of skin-related cell types can be used to distinguish between mild and severe cases of psoriasis and atopic dermatitis, and trace the effect of treatment. Conclusions: Our analysis demonstrates the value of this new resource in interpreting skin-derived transcriptomics data by enabling the direct quantification of cell types in a skin sample and the characterization of pathological changes in tissue composition.",0
"Receptor tyrosine kinases (RTKs) consociate in regulatory clusters in Alzheimer’s disease and type 2 diabetes. Alzheimer’s disease (AD) and type 2 diabetes (T2D) share the common hallmark of insulin resistance. It is conjectured that receptor tyrosine kinases (RTKs) play definitive roles in the process. To decipher the signaling overlap behind this phenotypic resemblance, the activity status of RTKs is probed in post-mortem AD and T2D tissues and cell models. Activities of only about one-third changed in a similar fashion, whereas about half of them showed opposite outcomes when exposed to contrasting signals akin to AD and T2D. Interestingly, irrespective of disease type, RTKs with enhanced and compromised activities clustered distinctly, indicating separate levels of regulations. Similar regulatory mechanisms within an activity cluster could be inferred, which have potential to impact future therapeutic developments.",0
"Phenotypic Screening Combined with Machine Learning for Efficient Identification of Breast Cancer-Selective Therapeutic Targets. The lack of functional understanding of most mutations in cancer, combined with the non-druggability of most proteins, challenge genomics-based identification of oncology drug targets. We implemented a machine-learning-based approach (idTRAX), which relates cell-based screening of small-molecule compounds to their kinase inhibition data, to directly identify effective and readily druggable targets. We applied idTRAX to triple-negative breast cancer cell lines and efficiently identified cancer-selective targets. For example, we found that inhibiting AKT selectively kills MFM-223 and CAL148 cells, while inhibiting FGFR2 only kills MFM-223. Since the effects of catalytically inhibiting a protein can diverge from those of reducing its levels, targets identified by idTRAX frequently differ from those identified through gene knockout/knockdown methods. This is critical if the purpose is to identify targets specifically for small-molecule drug development, whereby idTRAX may produce fewer false-positives. The rapid nature of the approach suggests that it may be applicable in personalizing therapy.",0
"Antibiotic Lethality Is Impacted by Nutrient Availabilities: New Insights from Machine Learning. In this issue of Cell, Yang, Wright et al. describe a machine learning approach that that can provide mechanistic insight from chemical screens. They use this approach to uncover how the nutritional availability for Escherichia coli impacts lethality toward three widely used antibiotics.",0
"Comparison of a clinical-laboratory algorithm, 4t and heparin-induced thrombocytopenia expert probability scores in the diagnosis of heparin-induced thrombocytopenia in the critical care setting. Background: Several scoring systems are utilized to calculate the pre-test probability of heparin-induced thrombocytopenia (HIT). We hypothesize that a clinical-laboratory algorithm combining the 4Ts score with the optical density (OD) of anti-PF4-heparin antibody is more accurate than either the 4Ts or HIT expert probability (HEP) scores in the critical care setting. Methods: A single-institution retrospective review of adult patients admitted to the intensive care unit (ICU) that were evaluated for HIT was conducted. Two reviewers independently rated the proposed algorithm, 4Ts and HEP score. Summary, univariate and area under receiver operator characteristic analyses were performed. Results: A total of 88 patients with a mean (SD) age of 62 (15) years were included. The sensitivity, positive predictive value and negative predictive value were superior in our clinical-laboratory algorithm compared to the 4Ts score ≥ 4 and the HEP score ≥ 2. The algorithm’s specificity was non-inferior to the 4Ts score and HEP score. There was no significant difference between our clinical-laboratory algorithm and the 4Ts score or the HEP score in predicting HIT. Conclusion: Our study confirms that the combination of clinical and laboratory criteria is crucial in the presumable diagnosis of HIT. This is the first study that validates different HIT scores in an isolated ICU population.",0
"Evaluating reinforcement learning agents for anatomical landmark detection. Automatic detection of anatomical landmarks is an important step for a wide range of applications in medical image analysis. Manual annotation of landmarks is a tedious task and prone to observer errors. In this paper, we evaluate novel deep reinforcement learning (RL) strategies to train agents that can precisely and robustly localize target landmarks in medical scans. An artificial RL agent learns to identify the optimal path to the landmark by interacting with an environment, in our case 3D images. Furthermore, we investigate the use of fixed- and multi-scale search strategies with novel hierarchical action steps in a coarse-to-fine manner. Several deep Q-network (DQN) architectures are evaluated for detecting multiple landmarks using three different medical imaging datasets: fetal head ultrasound (US), adult brain and cardiac magnetic resonance imaging (MRI). The performance of our agents surpasses state-of-the-art supervised and RL methods. Our experiments also show that multi-scale search strategies perform significantly better than fixed-scale agents in images with large field of view and noisy background such as in cardiac MRI. Moreover, the novel hierarchical steps can significantly speed up the searching process by a factor of 4-5 times.",1
"Quantifying homologous proteins and proteoforms. Many proteoforms-arising from alternative splicing, post-translational modifications (PTM), or paralogous genes-have distinct biological functions, such as histone PTM proteoforms. However, their quantification by existing bottom-up mass-spectrometry (MS) methods is undermined by peptide-specific biases. To avoid these biases, we developed and implemented a first-principles model (HIquant) for quantifying proteoform stoichiometries. We characterized when MS data allow inferring proteoform stoichiometries by HIquant and derived an algorithm for optimal inference. We applied this algorithm to infer proteoform stoichiometries in two experimental systems that supported rigorous bench-marking: alkylated proteoforms spiked-in at known ratios and endogenous histone 3 PTM proteoforms quantified relative to internal heavy standards. When compared with the benchmarks, the proteoform stoichiometries interfered by HIquant without using external standards had relative error of 5-15% for simple proteoforms and 20-30% for complex proteoforms.",0
"Comparing radiomic classifiers and classifier ensembles for detection of peripheral zone prostate tumors on T2-weighted MRI: a multi-site study. BACKGROUND: For most computer-aided diagnosis (CAD) problems involving prostate cancer detection via medical imaging data, the choice of classifier has been largely ad hoc, or been motivated by classifier comparison studies that have involved large synthetic datasets. More significantly, it is currently unknown how classifier choices and trends generalize across multiple institutions, due to heterogeneous acquisition and intensity characteristics (especially when considering MR imaging data). In this work, we empirically evaluate and compare a number of different classifiers and classifier ensembles in a multi-site setting, for voxel-wise detection of prostate cancer (PCa) using radiomic texture features derived from high-resolution in vivo T2-weighted (T2w) MRI. METHODS: Twelve different supervised classifier schemes: Quadratic Discriminant Analysis (QDA), Support Vector Machines (SVMs), naïve Bayes, Decision Trees (DTs), and their ensemble variants (bagging, boosting), were compared in terms of classification accuracy as well as execution time. Our study utilized 85 prostate cancer T2w MRI datasets acquired from across 3 different institutions (1 for discovery, 2 for independent validation), from patients who later underwent radical prostatectomy. Surrogate ground truth for disease extent on MRI was established by expert annotation of pre-operative MRI through spatial correlation with corresponding ex vivo whole-mount histology sections. Classifier accuracy in detecting PCa extent on MRI on a per-voxel basis was evaluated via area under the ROC curve. RESULTS: The boosted DT classifier yielded the highest cross-validated AUC (= 0.744) for detecting PCa in the discovery cohort. However, in independent validation, the boosted QDA classifier was identified as the most accurate and robust for voxel-wise detection of PCa extent (AUCs of 0.735, 0.683, 0.768 across the 3 sites). The next most accurate and robust classifier was the single QDA classifier, which also enjoyed the advantage of significantly lower computation times compared to any of the other methods. CONCLUSIONS: Our results therefore suggest that simpler classifiers (such as QDA and its ensemble variants) may be more robust, accurate, and efficient for prostate cancer CAD problems, especially in the context of multi-site validation.",1
"Taxifolin prevents postprandial hyperglycemia by regulating the activity of α-amylase: Evidence from an in vivo and in silico studies. There has been a dramatic increase in the prevalence of diabetes mellitus (DM) and its associated complications globally. The postprandial stage of DM involves prompt elevation in the levels of blood glucose and α-amylase, a carbohydrate-metabolizing enzyme is mainly involved in the regulation of postprandial hyperglycemia. This study was designed to assess the ability of a well-known flavonoid, taxifolin (TFN), against postprandial hyperglycemia and its inhibitory effects on α-amylase activity through the assessment of therapeutic potentials of TFN in an alloxan-induced diabetic animal model. The binding potential TFN with an α-amylase receptor was also investigated through molecular dynamics (MD) simulation and docking of to compare the binding affinities and energies of TFN and standard drug acarbose (ACB) with target enzyme. TFN significantly improved the postprandial hyperglycemia, lipid profile, and serum levels of α-amylase, lipase, and C-reactive protein in a dose-dependent manner when compared with that of either DM-induced and ACB-treated alloxan-induced diabetic rats. Moreover, TFN also enhanced the anti-oxidant status and normal functioning of the liver in alloxan-induced diabetic rats more efficiently as compared to that of ACB-treated alloxan-induced diabetic rats. Therapeutic potentials of TFN were also verified by MD simulation and docking results, which exhibited that the binding energy and affinity of TFN to bind with receptor was significantly higher as compared to that of ACB. Hence, the results of this study signify that TFN might be a potent inhibitor of α-amylase that has the potential to regulate the postprandial hyperglycemia along with its anti-inflammatory and anti-oxidant properties during the treatment of DM.",0
"Artificial Intelligence and Surgical Decision-Making. Importance: Surgeons make complex, high-stakes decisions under time constraints and uncertainty, with significant effect on patient outcomes. This review describes the weaknesses of traditional clinical decision-support systems and proposes that artificial intelligence should be used to augment surgical decision-making. Observations: Surgical decision-making is dominated by hypothetical-deductive reasoning, individual judgment, and heuristics. These factors can lead to bias, error, and preventable harm. Traditional predictive analytics and clinical decision-support systems are intended to augment surgical decision-making, but their clinical utility is compromised by time-consuming manual data management and suboptimal accuracy. These challenges can be overcome by automated artificial intelligence models fed by livestreaming electronic health record data with mobile device outputs. This approach would require data standardization, advances in model interpretability, careful implementation and monitoring, attention to ethical challenges involving algorithm bias and accountability for errors, and preservation of bedside assessment and human intuition in the decision-making process. Conclusions and Relevance: Integration of artificial intelligence with surgical decision-making has the potential to transform care by augmenting the decision to operate, informed consent process, identification and mitigation of modifiable risk factors, decisions regarding postoperative management, and shared decisions regarding resource use.",0
"miR-9 Upregulation Integrates Post-ischemic Neuronal Survival and Regeneration In Vitro. The irrefutable change in the expression of brain-enriched microRNAs (miRNAs) following ischemic stroke has promoted the development of radical miRNA-based therapeutics encompassing neuroprotection and neuronal restoration. Our previous report on the systems-level prediction of miR-9 in post-stroke-induced neurogenesis served as a premise to experimentally uncover the functional role of miR-9 in post-ischemic neuronal survival and regeneration. The oxygen-glucose deprivation (OGD) in SH-SY5Y cells significantly reduced miR-9 expression, while miR-9 mimic transfection enhanced post-ischemic neuronal cell viability. The next major objective involved the execution of a drug repositioning strategy to augment miR-9 expression via structure-based screening of Food and Drug Administration (FDA)-approved drugs that bind to Histone Deacetylase 4 (HDAC4), a known miR-9 target. Glucosamine emerged as the top hit and its binding potential to HDAC4 was verified by Molecular Dynamics (MD) Simulation, Drug Affinity Responsive Target Stability (DARTS) assay, and MALDI-TOF MS. It was intriguing that the glucosamine treatment 1-h post-OGD was associated with the increased miR-9 level as well as enhanced neuronal viability. miR-9 mimic or post-OGD glucosamine treatment significantly increased the cellular proliferation (BrdU assay), while the neurite outgrowth assay displayed elongated neurites. The enhanced BCL2 and VEGF parallel with the reduced NFκB1, TNF-α, IL-1β, and iNOS mRNA levels in miR-9 mimic or glucosamine-treated cells further substantiated their post-ischemic neuroprotective and regenerative efficacy. Hence, this study unleashes a potential therapeutic approach that integrates neuronal survival and regeneration via small-molecule-based regulation of miR-9 favoring long-term recovery against ischemic stroke.",0
"Evaluation of an Algorithm for Identifying Ocular Conditions in Electronic Health Record Data. Importance: For research involving big data, researchers must accurately identify patients with ocular diseases or phenotypes of interest. Reliance on administrative billing codes alone for this purpose is limiting. Objective: To develop a method to accurately identify the presence or absence of ocular conditions of interest using electronic health record (EHR) data. Design, Setting, and Participants: This study is a retrospective analysis of the EHR data of patients (n = 122339) in the Sight Outcomes Research Collaborative Ophthalmology Data Repository who received eye care at participating academic medical centers between August 1, 2012, and August 31, 2017. An algorithm that searches structured and unstructured (free-text) EHR data for conditions of interest was developed and then tested to determine how well it could detect the presence or absence of exfoliation syndrome (XFS). The algorithm was trained to search for evidence of XFS among a sample of patients with and without XFS (n = 200) by reviewing International Classification of Diseases, Ninth Revision or International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-9 or ICD-10) billing codes, the patient's problem list, and text within the ocular examination section and unstructured (free-text) data in the EHR. The likelihood that each patient had XFS was estimated using logistic least absolute shrinkage and selection operator (LASSO) regression. The EHR data of all patients were run through the algorithm to generate an XFS probability score for each patient. The algorithm was validated with review of EHRs by glaucoma specialists. Main Outcomes and Measures: Positive predictive value (PPV) and negative predictive value (NPV) of the algorithm were computed as the proportion of patients correctly classified with XFS or without XFS. Results: This study included 122 339 patients, with a mean (SD) age of 52.4 (25.1) years. Of these patients, 69 002 (56.4%) were female and 99 579 (81.4%) were white. The algorithm assigned a less than 10% probability of XFS for 121 085 patients (99.0%) as well as an XFS probability score of more than 75% for 543 patients (0.4%), more than 90% for 353 patients (0.3%), and more than 99% for 83 patients (0.07%). Validated by glaucoma specialists, the algorithm had a PPV of 95.0% (95% CI, 89.5%-97.7%) and an NPV of 100% (95% CI, 91.2%-100%). When there was ICD-9 or ICD-10 billing code documentation of XFS, in 86% or 96% of the records, respectively, evidence of XFS was also recorded elsewhere in the EHR. Conversely, when there was clinical examination or free-text evidence of XFS, it was documented with ICD-9 codes only approximately 40% of the time and even less often with ICD-10 codes. Conclusions and Relevance: The algorithm developed, tested, and validated in this study appears to be better at identifying the presence or absence of XFS in EHR data than the conventional approach of assessing only billing codes; such an algorithm may enhance the ability of investigators to use EHR data to study patients with ocular diseases.",1
"Segmentation and analysis of surface characteristics of oral tissues obtained by scanning electron microscopy to differentiate normal and oral precancerous condition. Abnormal epithelial stratification is a sign of oral dysplasia and hence evaluation of surface characteristics of oral epithelial region can help in detection of cancerous progression. Surface characteristics can be better visualised by Scanning Electron Microscopy (SEM) in comparison to light microscopy. In our study we have developed automated image processing algorithms i.e. Gaussian with median filtering and Gradient filtering, using MATLAB 2016b, to segment the surface characteristics i.e. the ridges and pits in the SEM images of oral tissue of normal (13 samples) and Oral Submucous Fibrosis (OSF) (36 samples) subjects. After segmentation, quantitative measurement of the parameters like area, thickness and textural features like entropy, contrast and range filter of ridges as well as area of pit and the ratio of area of ridge vs. area of pit was done. Statistical significant differences were obtained in between normal and OSF study groups for thickness (p=0.0107), entropy (p<0.00001) and contrast of ridge (p<0.00001) for Gaussian with median filtering and for all the parameters except thickness of the ridge(p=1.386), for Gradient filtering. Thus, computer aided image processing by Gradient filter followed by quantitative measurement of the surface characteristics provided precise differentiation between normal and precancerous oral condition.",0
"Bleeding in cardiac patients prescribed antithrombotic drugs: electronic health record phenotyping algorithms, incidence, trends and prognosis. BACKGROUND: Clinical guidelines and public health authorities lack recommendations on scalable approaches to defining and monitoring the occurrence and severity of bleeding in populations prescribed antithrombotic therapy. METHODS: We examined linked primary care, hospital admission and death registry electronic health records (CALIBER 1998-2010, England) of patients with newly diagnosed atrial fibrillation, acute myocardial infarction, unstable angina or stable angina with the aim to develop algorithms for bleeding events. Using the developed bleeding phenotypes, Kaplan-Meier plots were used to estimate the incidence of bleeding events and we used Cox regression models to assess the prognosis for all-cause mortality, atherothrombotic events and further bleeding. RESULTS: We present electronic health record phenotyping algorithms for bleeding based on bleeding diagnosis in primary or hospital care, symptoms, transfusion, surgical procedures and haemoglobin values. In validation of the phenotype, we estimated a positive predictive value of 0.88 (95% CI 0.64, 0.99) for hospitalised bleeding. Amongst 128,815 patients, 27,259 (21.2%) had at least 1 bleeding event, with 5-year risks of bleeding of 29.1%, 21.9%, 25.3% and 23.4% following diagnoses of atrial fibrillation, acute myocardial infarction, unstable angina and stable angina, respectively. Rates of hospitalised bleeding per 1000 patients more than doubled from 1.02 (95% CI 0.83, 1.22) in January 1998 to 2.68 (95% CI 2.49, 2.88) in December 2009 coinciding with the increased rates of antiplatelet and vitamin K antagonist prescribing. Patients with hospitalised bleeding and primary care bleeding, with or without markers of severity, were at increased risk of all-cause mortality and atherothrombotic events compared to those with no bleeding. For example, the hazard ratio for all-cause mortality was 1.98 (95% CI 1.86, 2.11) for primary care bleeding with markers of severity and 1.99 (95% CI 1.92, 2.05) for hospitalised bleeding without markers of severity, compared to patients with no bleeding. CONCLUSIONS: Electronic health record bleeding phenotyping algorithms offer a scalable approach to monitoring bleeding in the population. Incidence of bleeding has doubled in incidence since 1998, affects one in four cardiovascular disease patients, and is associated with poor prognosis. Efforts are required to tackle this iatrogenic epidemic.",0
"Identification and validation of l-asparaginase as a potential metabolic target against Mycobacterium tuberculosis. Multidrug-resistant Mycobacterium tuberculosis (Mtb) has emerged as a major health challenge, necessitating the search for new molecular targets. A secretory amidohydrolase, l-asparaginase of Mtb (MtA), originally implicated in nitrogen assimilation and neutralization of acidic microenvironment inside human alveolar macrophages, has been proposed as a crucial metabolic enzyme. To investigate whether this enzyme could serve as a potential drug target, it was studied for structural details and active site–specific inhibitors were tested on cultured Mycobacterial strain. The structural details of MtA obtained through comparative modeling and molecular dynamics simulations provided insights about the orchestration of an alternate reaction mechanism at the active site. This was contrary to the critical Tyr flipping mechanism reported in other asparaginases. We report the novel finding of Tyr to Val replacement in catalytic triad I along with the structural reorganization of a β-hairpin loop upon substrate binding in MtA active site. Further, 5 MtA-specific, active-site–based inhibitors were obtained by following a rigorous differential screening protocol. When tested on Mycobacterium culture, 3 of these, M3 (ZINC 4740895), M26 (ZINC 33535), and doxorubicin showed promising results with inhibitory concentrations (IC 50) of 431, 100, and 56 µM, respectively. Based on our findings and considering stark differences with human asparaginase, we project MtA as a promising molecular target against which the selected inhibitors may be used to counteract Mtb infection effectively.",0
"A deep learning model incorporating part of speech and self-matching attention for named entity recognition of Chinese electronic medical records. BACKGROUND: The Named Entity Recognition (NER) task as a key step in the extraction of health information, has encountered many challenges in Chinese Electronic Medical Records (EMRs). Firstly, the casual use of Chinese abbreviations and doctors' personal style may result in multiple expressions of the same entity, and we lack a common Chinese medical dictionary to perform accurate entity extraction. Secondly, the electronic medical record contains entities from a variety of categories of entities, and the length of those entities in different categories varies greatly, which increases the difficult in the extraction for the Chinese NER. Therefore, the entity boundary detection becomes the key to perform accurate entity extraction of Chinese EMRs, and we need to develop a model that supports multiple length entity recognition without relying on any medical dictionary. METHODS: In this study, we incorporate part-of-speech (POS) information into the deep learning model to improve the accuracy of Chinese entity boundary detection. In order to avoid the wrongly POS tagging of long entities, we proposed a method called reduced POS tagging that reserves the tags of general words but not of the seemingly medical entities. The model proposed in this paper, named SM-LSTM-CRF, consists of three layers: self-matching attention layer - calculating the relevance of each character to the entire sentence; LSTM (Long Short-Term Memory) layer - capturing the context feature of each character; CRF (Conditional Random Field) layer - labeling characters based on their features and transfer rules. RESULTS: The experimental results at a Chinese EMRs dataset show that the F1 value of SM-LSTM-CRF is increased by 2.59% compared to that of the LSTM-CRF. After adding POS feature in the model, we get an improvement of about 7.74% at F1. The reduced POS tagging reduces the false tagging on long entities, thus increases the F1 value by 2.42% and achieves an F1 score of 80.07%. CONCLUSIONS: The POS feature marked by the reduced POS tagging together with self-matching attention mechanism puts a stranglehold on entity boundaries and has a good performance in the recognition of clinical entities.",1
"Predicting patient-reported outcomes following hip and knee replacement surgery using supervised machine learning. BACKGROUND: Machine-learning classifiers mostly offer good predictive performance and are increasingly used to support shared decision-making in clinical practice. Focusing on performance and practicability, this study evaluates prediction of patient-reported outcomes (PROs) by eight supervised classifiers including a linear model, following hip and knee replacement surgery. METHODS: NHS PRO data (130,945 observations) from April 2015 to April 2017 were used to train and test eight classifiers to predict binary postoperative improvement based on minimal important differences. Area under the receiver operating characteristic, J-statistic and several other metrics were calculated. The dependent outcomes were generic and disease-specific improvement based on the EQ-5D-3L visual analogue scale (VAS) as well as the Oxford Hip and Knee Score (Q score). RESULTS: The area under the receiver operating characteristic of the best training models was around 0.87 (VAS) and 0.78 (Q score) for hip replacement, while it was around 0.86 (VAS) and 0.70 (Q score) for knee replacement surgery. Extreme gradient boosting, random forests, multistep elastic net and linear model provided the highest overall J-statistics. Based on variable importance, the most important predictors for post-operative outcomes were preoperative VAS, Q score and single Q score dimensions. Sensitivity analysis for hip replacement VAS evaluated the influence of minimal important difference, patient selection criteria as well as additional data years. Together with a small benchmark of the NHS prediction model, robustness of our results was confirmed. CONCLUSIONS: Supervised machine-learning implementations, like extreme gradient boosting, can provide better performance than linear models and should be considered, when high predictive performance is needed. Preoperative VAS, Q score and specific dimensions like limping are the most important predictors for postoperative hip and knee PROMs.",1
"Structural determinants governing β-arrestin2 interaction with PDZ proteins and recruitment to CRFR1. β-Arrestins are multifunctional adaptor proteins best know for their vital role in regulating G protein coupled receptor (GPCR) trafficking and signaling. β-arrestin2 recruitment and receptor internalization of corticotropin-releasing factor receptor 1 (CRFR1), a GPCR whose antagonists have been shown to demonstrate both anxiolytic- and antidepressant-like effects, have previously been shown to be modulated by PDZ proteins. Thus, a structural characterization of the interaction between β-arrestins and PDZ proteins can delineate potential mechanism of PDZ-dependent regulation of GPCR trafficking. Here, we find that the PDZ proteins PSD-95, MAGI1, and PDZK1 interact with β-arrestin2 in a PDZ domain-dependent manner. Further investigation of such interaction using mutational analyses revealed that mutating the alanine residue at 175 residue of β-arrestin2 to phenylalanine impairs interaction with PSD-95. Additionally, A175F mutant of β-arrestin2 shows decreased CRF-stimulated recruitment to CRFR1 and reduced receptor internalization. Thus, our findings show that the interaction between β-arrestins and PDZ proteins is key for CRFR1 trafficking and may be targeted to mitigate impaired CRFR1 signaling in mental and psychiatric disorders.",0
"Assessing the effectiveness of artificial intelligence methods for melanoma: A retrospective review. BACKGROUND: Artificial intelligence methods for the classification of melanoma have been studied extensively. However, few studies compare these methods under the same standards. OBJECTIVE: To seek the best artificial intelligence method for diagnosis of melanoma. METHODS: The contrast test used 2200 dermoscopic images. Image segmentations, feature extractions, and classifications were performed in sequence for evaluation of traditional machine learning algorithms. The recent popular convolutional neural network frameworks were used for transfer learning training classification. RESULTS: The region growing algorithm has the best segmentation performance, with an intersection over union of 70.06% and a false-positive rate of 17.67%. Classification performance was better with logistic regression, with a sensitivity of 76.36% and a specificity of 87.04%. The Inception V3 model (Google, Mountain View, CA) worked best in deep learning algorithms: the accuracy was 93.74%, the sensitivity was 94.36%, and the specificity was 85.64%. LIMITATIONS: There was no division in the severity of melanoma samples used in this experiment. The data set was relatively small for deep learning. CONCLUSION: The performance of traditional machine learning is satisfactory for the small data set of melanoma dermoscopic images, and the potential for deep learning in the future big data era is enormous.",0
"Evidence for the impact of BAG3 on electrophysiological activity of primary culture of neonatal cardiomyocytes. Homeostasis of proteins involved in contractility of individual cardiomyocytes and those coupling adjacent cells is of critical importance as any abnormalities in cardiac electrical conduction may result in cardiac irregular activity and heart failure. Bcl2-associated athanogene 3 (BAG3) is a stress-induced protein whose role in stabilizing myofibril proteins as well as protein quality control pathways, especially in the cardiac tissue, has captured much attention. Mutations of BAG3 have been implicated in the pathogenesis of cardiac complications such as dilated cardiomyopathy. In this study, we have used an in vitro model of neonatal rat ventricular cardiomyocytes to investigate potential impacts of BAG3 on electrophysiological activity by employing the microelectrode array (MEA) technology. Our MEA data showed that BAG3 plays an important role in the cardiac signal generation as reduced levels of BAG3 led to lower signal frequency and amplitude. Our analysis also revealed that BAG3 is essential to the signal propagation throughout the myocardium, as the MEA data-based conduction velocity, connectivity degree, activation time, and synchrony were adversely affected by BAG3 knockdown. Moreover, BAG3 deficiency was demonstrated to be connected with the emergence of independently beating clusters of cardiomyocytes. On the other hand, BAG3 overexpression improved the activity of cardiomyocytes in terms of electrical signal amplitude and connectivity degree. Overall, by providing more in-depth analyses and characterization of electrophysiological parameters, this study reveals that BAG3 is of critical importance for electrical activity of neonatal cardiomyocytes.",0
"A functional agonist of insect olfactory receptors: Behavior, physiology and structure. Chemical signaling is ubiquitous and employs a variety of receptor types to detect the cacophony of molecules relevant for each living organism. Insects, our most diverse taxon, have evolved unique olfactory receptors with as little as 10% sequence identity between receptor types. We have identified a promiscuous volatile, 2-methyltetrahydro-3-furanone (coffee furanone), that elicits chemosensory and behavioral activity across multiple insect orders and receptors. In vivo and in vitro physiology showed that coffee furanone was detected by roughly 80% of the recorded neurons expressing the insect-specific olfactory receptor complex in the antenna of Drosophila melanogaster, at concentrations similar to other known, and less promiscuous, ligands. Neurons expressing specialized receptors, other chemoreceptor types, or mutants lacking the complex entirely did not respond to this compound. This indicates that coffee furanone is a promiscuous ligand for the insect olfactory receptor complex itself and did not induce non-specific cellular responses. In addition, we present homology modeling and docking studies with selected olfactory receptors that suggest conserved interaction regions for both coffee furanone and known ligands. Apart from its physiological activity, this known food additive elicits a behavioral response for several insects, including mosquitoes, flies, and cockroaches. A broad-scale behaviorally active molecule non-toxic to humans thus has significant implications for health and agriculture. Coffee furanone serves as a unique tool to unlock molecular, physiological, and behavioral relationships across this diverse receptor family and animal taxa.",0
"Genomic data analysis workflows for tumors from patient-derived xenografts (PDXs): Challenges and guidelines. Background: Patient-derived xenograft (PDX) models are in vivo models of human cancer that have been used for translational cancer research and therapy selection for individual patients. The Jackson Laboratory (JAX) PDX resource comprises 455 models originating from 34 different primary sites (as of 05/08/2019). The models undergo rigorous quality control and are genomically characterized to identify somatic mutations, copy number alterations, and transcriptional profiles. Bioinformatics workflows for analyzing genomic data obtained from human tumors engrafted in a mouse host (i.e., Patient-Derived Xenografts; PDXs) must address challenges such as discriminating between mouse and human sequence reads and accurately identifying somatic mutations and copy number alterations when paired non-tumor DNA from the patient is not available for comparison. Results: We report here data analysis workflows and guidelines that address these challenges and achieve reliable identification of somatic mutations, copy number alterations, and transcriptomic profiles of tumors from PDX models that lack genomic data from paired non-tumor tissue for comparison. Our workflows incorporate commonly used software and public databases but are tailored to address the specific challenges of PDX genomics data analysis through parameter tuning and customized data filters and result in improved accuracy for the detection of somatic alterations in PDX models. We also report a gene expression-based classifier that can identify EBV-transformed tumors. We validated our analytical approaches using data simulations and demonstrated the overall concordance of the genomic properties of xenograft tumors with data from primary human tumors in The Cancer Genome Atlas (TCGA). Conclusions: The analysis workflows that we have developed to accurately predict somatic profiles of tumors from PDX models that lack normal tissue for comparison enable the identification of the key oncogenic genomic and expression signatures to support model selection and/or biomarker development in therapeutic studies. A reference implementation of our analysis recommendations is available at https://github.com/TheJacksonLaboratory/PDX-Analysis-Workflows.",0
"Yield and Efficiency of Novel Intensified Tuberculosis Case-Finding Algorithms for People Living with HIV. RATIONALE: The recommended tuberculosis (TB) intensified case finding (ICF) algorithm for people living with HIV (symptom-based screening followed by Xpert MTB/RIF [Xpert] testing) is insufficiently sensitive and results in unnecessary Xpert testing. OBJECTIVES: To evaluate whether novel ICF algorithms combining C-reactive protein (CRP)-based screening with urine Determine TB-LAM (TB-LAM), sputum Xpert, and/or sputum culture could improve ICF yield and efficiency. METHODS: We compared the yield and efficiency of novel ICF algorithms inclusive of point-of-care CRP-based TB screening and confirmatory testing with urine TB-LAM (if CD4 count </=100 cells/mul), sputum Xpert, and/or a single sputum culture among consecutive people living with HIV with CD4 counts less than or equal to 350 cells/mul initiating antiretroviral therapy in Uganda. MEASUREMENTS AND MAIN RESULTS: Of 1,245 people living with HIV, 203 (16%) had culture-confirmed TB including 101 (49%) patients with CD4 counts less than or equal to 100 cells/mul. Compared with the current ICF algorithm, point-of-care CRP-based TB screening followed by Xpert testing had similar yield (56% [95% confidence interval, 49-63] vs. 59% [95% confidence interval, 51-65]) but consumed less than half as many Xpert assays per TB case detected (9 vs. 4). Addition of TB-LAM did not significantly increase diagnostic yield relative to the current ICF algorithm but provided same-day diagnosis for 26% of TB patients with advanced HIV. Addition of a single culture to TB-LAM and Xpert substantially improved ICF yield, identifying 78% of all TB cases. CONCLUSIONS: Point-of-care CRP-based screening can improve ICF efficiency among people living with HIV. Addition of TB-LAM and a single culture to Xpert confirmatory testing could enable HIV programs to increase the speed of TB diagnosis and ICF yield.",0
"Monitoring Disease Progression With a Quantitative Severity Scale for Retinopathy of Prematurity Using Deep Learning. Importance: Retinopathy of prematurity (ROP) is a leading cause of childhood blindness worldwide, but clinical diagnosis is subjective and qualitative. Objective: To describe a quantitative ROP severity score derived using a deep learning algorithm designed to evaluate plus disease and to assess its utility for objectively monitoring ROP progression. Design, Setting, and Participants: This retrospective cohort study included images from 5255 clinical examinations of 871 premature infants who met the ROP screening criteria of the Imaging and Informatics in ROP (i-ROP) Consortium, which comprises 9 tertiary care centers in North America, from July 1, 2011, to December 31, 2016. Data analysis was performed from July 2017 to May 2018. Exposure: A deep learning algorithm was used to assign a continuous ROP vascular severity score from 1 (most normal) to 9 (most severe) at each examination based on a single posterior photograph compared with a reference standard diagnosis (RSD) simplified into 4 categories: no ROP, mild ROP, type 2 ROP or pre-plus disease, or type 1 ROP. Disease course was assessed longitudinally across multiple examinations for all patients. Main Outcomes and Measures: Mean ROP vascular severity score progression over time compared with the RSD. Results: A total of 5255 clinical examinations from 871 infants (mean [SD] gestational age, 27.0 [2.0] weeks; 493 [56.6%] male; mean [SD] birth weight, 949 [271] g) were analyzed. The median severity scores for each category were as follows: 1.1 (interquartile range [IQR], 1.0-1.5) (no ROP), 1.5 (IQR, 1.1-3.4) (mild ROP), 4.6 (IQR, 2.4-5.3) (type 2 and pre-plus), and 7.5 (IQR, 5.0-8.7) (treatment-requiring ROP) (P < .001). When the long-term differences in the median severity scores across time between the eyes progressing to treatment and those who did not eventually require treatment were compared, the median score was higher in the treatment group by 0.06 at 30 to 32 weeks, 0.75 at 32 to 34 weeks, 3.56 at 34 to 36 weeks, 3.71 at 36 to 38 weeks, and 3.24 at 38 to 40 weeks postmenstrual age (P < .001 for all comparisons). Conclusions and Relevance: The findings suggest that the proposed ROP vascular severity score is associated with category of disease at a given point in time and clinical progression of ROP in premature infants. Automated image analysis may be used to quantify clinical disease progression and identify infants at high risk for eventually developing treatment-requiring ROP. This finding has implications for quality and delivery of ROP care and for future approaches to disease classification.",1
"Proof-of-concept study: Homomorphically encrypted data can support real-time learning in personalized cancer medicine. BACKGROUND: The successful introduction of homomorphic encryption (HE) in clinical research holds promise for improving acceptance of data-sharing protocols, increasing sample sizes, and accelerating learning from real-world data (RWD). A well-scoped use case for HE would pave the way for more widespread adoption in healthcare applications. Determining the efficacy of targeted cancer treatments used off-label for a variety of genetically defined conditions is an excellent candidate for introduction of HE-based learning systems because of a significant unmet need to share and combine confidential data, the use of relatively simple algorithms, and an opportunity to reach large numbers of willing study participants. METHODS: We used published literature to estimate the numbers of patients who might be eligible to receive treatments approved for other indications based on molecular profiles. We then estimated the sample size and number of variables that would be required for a successful system to detect exceptional responses with sufficient power. We generated an appropriately sized, simulated dataset (n = 5000) and used an established HE algorithm to detect exceptional responses and calculate total drug exposure, while the data remained encrypted. RESULTS: Our results demonstrated the feasibility of using an HE-based system to identify exceptional responders and perform calculations on patient data during a hypothetical 3-year study. Although homomorphically encrypted computations are time consuming, the required basic computations (i.e., addition) do not pose a critical bottleneck to the analysis. CONCLUSION: In this proof-of-concept study, based on simulated data, we demonstrate that identifying exceptional responders to targeted cancer treatments represents a valuable and feasible use case. Past solutions to either completely anonymize data or restrict access through stringent data use agreements have limited the utility of abundant and valuable data. Because of its privacy protections, we believe that an HE-based learning system for real-world cancer treatment would entice thousands more patients to voluntarily contribute data through participation in research studies beyond the currently available secondary data populated from hospital electronic health records and administrative claims. Forming collaborations between technical experts, physicians, patient advocates, payers, and researchers, and testing the system on existing RWD are critical next steps to making HE-based learning a reality in healthcare.",0
"Application of a Neural Network Whole Transcriptome-Based Pan-Cancer Method for Diagnosis of Primary and Metastatic Cancers. Importance: A molecular diagnostic method that incorporates information about the transcriptional status of all genes across multiple tissue types can strengthen confidence in cancer diagnosis. Objective: To determine the practical use of a whole transcriptome-based pan-cancer method in diagnosing primary and metastatic cancers and resolving complex diagnoses. Design, Setting, and Participants: This cross-sectional diagnostic study assessed Supervised Cancer Origin Prediction Using Expression (SCOPE), a machine learning method using whole-transcriptome RNA sequencing data. Training was performed on publicly available primary cancer data sets, including The Cancer Genome Atlas. Testing was performed retrospectively on untreated primary cancers and treated metastases from volunteer adult patients at BC Cancer in Vancouver, British Columbia, from January 1, 2013, to March 31, 2016, and testing spanned 10 822 samples and 66 output classes representing untreated primary cancers (n = 40) and adjacent normal tissues (n = 26). SCOPE's performance was demonstrated on 211 untreated primary mesothelioma cancers and 201 treatment-resistant metastatic cancers. Finally, SCOPE was used to identify the putative site of origin in 15 cases with initial presentation as cancers with unknown primary of origin. Results: A total of 10 688 adult patient samples representing 40 untreated primary tumor types and 26 adjacent-normal tissues were used for training. Demographic data were not available for all data sets. Among the training data set, 5157 of 10 244 (50.3%) were male and the mean (SD) age was 58.9 (14.5) years. Testing was performed on 211 patients with untreated primary mesothelioma (173 [82.0%] male; mean [SD] age, 64.5 [11.3] years); 201 patients with treatment-resistant cancers (141 [70.1%] female; mean [SD] age, 55.6 [12.9] years); and 15 patients with cancers of unknown primary of origin; among the treatment-resistant cancers, 168 were metastatic, and 33 were the primary presentation. An accuracy rate of 99% was obtained for primary epithelioid mesotheliomas tested (125 of 126). The remaining 85 mesotheliomas had a mixed etiology (sarcomatoid mesotheliomas) and were correctly identified as a mixture of their primary components, with potential implications in resolving subtypes and incidences of mixed histology. SCOPE achieved an overall mean (SD) accuracy rate of 86% (11%) and F1 score of 0.79 (0.12) on the 201 treatment-resistant cancers and matched 12 of 15 of the putative diagnoses for cancers with indeterminate diagnosis from conventional pathology. Conclusions and Relevance: These results suggest that machine learning approaches incorporating multiple tumor profiles can more accurately identify the cancerous state and discriminate it from normal cells. SCOPE uses the whole transcriptomes from normal and tumor tissues, and results of this study suggest that it performs well for rare cancer types, primary cancers, treatment-resistant metastatic cancers, and cancers of unknown primary of origin. Genes most relevant in SCOPE's decision making were examined, and several are known biological markers of respective cancers. SCOPE may be applied as an orthogonal diagnostic method in cases where the site of origin of a cancer is unknown, or when standard pathology assessment is inconclusive.",1
"Learning the implicit strain reconstruction in ultrasound elastography using privileged information. Quasi-static ultrasound elastography is an importance imaging technology to assess the conditions of various diseases through reconstructing the tissue strain from radio frequency data. State-of-the-art strain reconstruction techniques suffer from the inexperienced user unfriendliness, high model bias, and low effectiveness-to-efficiency ratio. The three challenges result from the explicitness characteristic (i.e. explicit formulation of the reconstruction model) in these techniques. For these challenges, we are the first to develop an implicit strain reconstruction framework by a deep neural network architecture. However, the classic neural network methods are unsuitable to the strain reconstruction task because they are difficult to impose any direct influence on the intermediate state of the learning process. This may lead the map learned by the neural network to be biased with the desired map. In order to correct the intermediate state of the learning process, our framework proposes the learning-using-privileged-information (LUPI) paradigm with causality in the network. It provides the causal privileged information besides the training examples to help the network learning, while makes these privileged information unavailable at the test stage. This improvement can narrow the search region of the map learned by the network, and thus prompts the network to evolve towards the actual ultrasound elastography process. Moreover, in order to ensure the causality in LUPI, our framework proposes a physically-based data generation strategy to produce the triplets of privileged information, training examples and labels. This data generation process can approximately describes the actual ultrasound elastography process by the numerical simulation based on the tissue biomechanics and ultrasound physics. It thus can build the causal relationship between the privileged information and training examples/labels. It can also address the medical data insufficiency problem. The performance of our framework has been validated on 100 simulation data, 42 phantom data and 4 real clinical data by comparing with the ground truth performed by an ultrasound simulation system and four state-of-the-art methods. The experimental results show that our framework is well agreed (average bias is 0.065 for strain reconstruction) with the ground truth, as well as superior to these state-of-the-art methods. These results can demonstrate the effectiveness of our framework in the strain reconstruction.",1
"The effect of motion correction interpolation on quantitative T1 mapping with MRI. Quantitative magnetic resonance imaging (qMRI) is a technique for mapping the physical properties of the underlying tissue using several MR images with different contrasts. To overcome subject motion between the acquired images, it is necessary to register the images to a common reference frame. A drawback of registration is the use of interpolation and resampling techniques, which can introduce artifacts into the interpolated data. These artifacts could have unfavorable effects on the accuracy of the estimated tissue's physical properties. Here, we quantified the error of interpolation and resampling on T1-weighted images and studied its effects on the mapping of the longitudinal relaxation time (T1) using variable flip angles. We simulated T1-weighted images and calculated the transformation error resulting from interpolation and resampling. We found that the error is a function of the image contrast (i.e., flip angle) and of the translation and rotation of the image. Furthermore, we found that the error in the T1-weighted images has a substantial effect on the T1 estimation, of the order of 10% of the signal in the brain's gray and white matter. Hence, minimizing the registration error can enable more accurate in vivo modeling of brain microstructure.",0
"Patient-reported outcomes following enzalutamide or placebo in men with non-metastatic, castration-resistant prostate cancer (PROSPER): a multicentre, randomised, double-blind, phase 3 trial. Background: In the PROSPER trial, enzalutamide significantly improved metastasis-free survival in patients with non-metastatic, castration-resistant prostate cancer. Here, we report the results of patient-reported outcomes of this study. Methods: In the randomised, double-blind, placebo-controlled, phase 3 PROSPER trial, done at 254 study sites worldwide, patients aged 18 years or older with non-metastatic, castration-resistant prostate cancer and a prostate-specific antigen doubling time of up to 10 months were randomly assigned (2:1) via an interactive voice web recognition system to receive oral enzalutamide (160 mg per day) or placebo. Randomisation was stratified by prostate-specific antigen doubling time and baseline use of a bone-targeting agent. The primary endpoint was metastasis-free survival, reported elsewhere. Secondary efficacy endpoints, reported here, were pain progression (assessed by the Brief Pain Inventory Short Form [BPI-SF] questionnaire) and health-related quality of life (assessed with the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire [EORTC QLQ-PR25], the EuroQoL 5-Dimensions 5-Levels health questionnaire visual analogue scale [EQ-5D-FL, EQ-VAS], and the Functional Assessment of Cancer Therapy-Prostate [FACT-P] questionnaires). Patients completed questionnaires at baseline, week 17, and every 16 weeks thereafter until treatment discontinuation. We used predefined questionnaire thresholds to identify clinically meaningful changes. Enrolment for PROSPER is complete and follow-up continues. This trial is registered with ClinicalTrials.gov, number NCT02003924. Findings: Between Nov 26, 2013, and June 28, 2017, 1401 patients were enrolled and randomly assigned to receive enzalutamide (n=933) or placebo (n=468). Median follow-up was 18·5 months (IQR 10·7–29·2) in the enzalutamide group and 15·1 months (7·4–25·9) in the placebo group. Patient-reported outcome scores at baseline were similar between groups. Changes in least squares mean from baseline to week 97 favoured enzalutamide versus placebo for FACT-P social and family wellbeing (0·30 [95% CI −0·25 to 0·85] vs −0·64 [−1·51 to 0·24]; difference 0·94 [95% CI 0·02 to 1·85]; p=0·045) and disfavoured enzalutamide versus placebo for EORTC QLQ-PR25 hormonal treatment-related symptoms (1·55 [0·26 to 2·83) vs −1·83 [−3·86 to 0·20]; difference 3·38 [1·24 to 5·51]; p=0·0020); neither of these changes were clinically meaningful. No significant differences were observed between treatments for changes from baseline to week 97 in any other patient-reported outcome score. Time to clinically meaningful pain progression as assessed by BPI-SF pain severity was longer with enzalutamide than with placebo (median 36·83 months, [95% CI 34·69 to not reached [NR] vs NR; hazard ratio [HR] 0·75 [95% CI 0·57 to 0·97]; p=0·028); there was no significant difference for BPI-SF item 3 or pain interference. Time to clinically meaningful symptom worsening was longer with enzalutamide than with placebo for EORTC QLQ-PR25 urinary symptoms (median 36·86 months [95% CI 33·35 to NR] vs 25·86 [18·53 to 29·47]; HR 0·58 [95% CI 0·46 to 0·72]; p<0·0001) and bowel symptoms (33·15 [29·50 to NR] vs 25·89 [18·43 to 29·67]; 0·72 [0·59 to 0·89]; p=0·0018), and clinically meaningful health-related quality of life as assessed by FACT-P total score (22·11 [18·63 to 25·86] vs 18·43 [14·85–19·35]; 0·83 [0·69 to 0·99]; p=0·037), emotional wellbeing (36·73 [33·12 to 38·21] vs 29·47 [22·18 to 33·15]; 0·69 [0·55 to 0·86]; p=0·0008), and prostate cancer subscale (18·43 [14·85 to 18·66] vs 14·69 [11·07 to 16·20]; 0·79 [0·67 to 0·93]; p=0·0042), although there was no significant difference for other FACT-P scores. Time to clinically meaningful deterioration in EORTC QLQ-PR25 hormonal treatment-related symptoms was shorter with enzalutamide than with placebo (median 33·15 months [95% CI 29·60 to NR] vs 36·83 [29·47 to NR]; HR 1·29 [95% CI 1·02 to 1·63]; p=0· 35). Time to deterioration of EQ-VAS was significantly longer for enzalutamide than for placebo (median 22·11 months [95% CI 18·46 to 25·66] vs 14·75 [11·07 to 18·17]; HR 0·75 [95% CI 0·63 to 0·90]; p=0·0013). Interpretation: Patients with non-metastatic, castration-resistant prostate cancer receiving enzalutamide had longer metastasis-free survival than did those who received placebo, while maintaining low pain levels and prostate cancer symptom burden and high health-related quality of life. Enzalutamide showed a clinical benefit by delaying pain progression, symptom worsening, and decrease in functional status, compared with placebo. These findings suggest that enzalutamide is a treatment option that should be discussed with patients presenting with high-risk, non- metastatic, castration-resistant prostate cancer. Funding: Astellas Pharma Inc, Medivation LLC (a Pfizer Company).",0
"In silico identification and in vitro validation of nogalamycin N-oxide (NSC116555) as a potent anticancer compound against non–small-cell lung cancer cells. The epidermal growth factor receptor (EGFR) was found to be overexpressed in several cancers, especially in lung cancers. Finding new effective drug against EGFR is the key to cancer treatment. In this study, the GOLD docking algorithm was used to virtually screen for novel human EGFR inhibitors from the NCI database. Thirty-four hit compounds were tested for EGFR-tyrosine kinase (TK) inhibition. Two potent compounds, 1-amino-4-(4-[4-amino-2-sulfophenyl]anilino)-9,10-dioxoanthracene-2-sulfonic acid (NSC125910), and nogalamycin N-oxide (NSC116555) were identified with IC50 values against EGFR-TK comparable to gefitinib; 16.14 and 37.71 nM, respectively. However, only NSC116555 demonstrated cytotoxic effects against non–small-cell lung cancer, A549, shown in the cell cytotoxicity assay with an IC50 of 0.19 + 0.01 µM, which was more potent than gefitinib. Furthermore, NSC116555 showed cytotoxicity against A549 via apoptosis in a dose-dependent manner.",0
"Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia from the Electrocardiogram. Importance: For patients with chronic kidney disease (CKD), hyperkalemia is common, associated with fatal arrhythmias, and often asymptomatic, while guideline-directed monitoring of serum potassium is underused. A deep-learning model that enables noninvasive hyperkalemia screening from the electrocardiogram (ECG) may improve detection of this life-threatening condition. Objective: To evaluate the performance of a deep-learning model in detection of hyperkalemia from the ECG in patients with CKD. Design, Setting, and Participants: A deep convolutional neural network (DNN) was trained using 1576581 ECGs from 449380 patients seen at Mayo Clinic, Rochester, Minnesota, from 1994 to 2017. The DNN was trained using 2 (leads I and II) or 4 (leads I, II, V3, and V5) ECG leads to detect serum potassium levels of 5.5 mEq/L or less (to convert to millimoles per liter, multiply by 1) and was validated using retrospective data from the Mayo Clinic in Minnesota, Florida, and Arizona. The validation included 61965 patients with stage 3 or greater CKD. Each patient had a serum potassium count drawn within 4 hours after their ECG was recorded. Data were analyzed between April 12, 2018, and June 25, 2018. Exposures: Use of a deep-learning model. Main Outcomes and Measures: Area under the receiver operating characteristic curve (AUC) and sensitivity and specificity, with serum potassium level as the reference standard. The model was evaluated at 2 operating points, 1 for equal specificity and sensitivity and another for high (90%) sensitivity. Results: Of the total 1638546 ECGs, 908000 (55%) were from men. The prevalence of hyperkalemia in the 3 validation data sets ranged from 2.6% (n = 1282 of 50099; Minnesota) to 4.8% (n = 287 of 6011; Florida). Using ECG leads I and II, the AUC of the deep-learning model was 0.883 (95% CI, 0.873-0.893) for Minnesota, 0.860 (95% CI, 0.837-0.883) for Florida, and 0.853 (95% CI, 0.830-0.877) for Arizona. Using a 90% sensitivity operating point, the sensitivity was 90.2% (95% CI, 88.4%-91.7%) and specificity was 63.2% (95% CI, 62.7%-63.6%) for Minnesota; the sensitivity was 91.3% (95% CI, 87.4%-94.3%) and specificity was 54.7% (95% CI, 53.4%-56.0%) for Florida; and the sensitivity was 88.9% (95% CI, 84.5%-92.4%) and specificity was 55.0% (95% CI, 53.7%-56.3%) for Arizona. Conclusions and Relevance: In this study, using only 2 ECG leads, a deep-learning model detected hyperkalemia in patients with renal disease with an AUC of 0.853 to 0.883. The application of artificial intelligence to the ECG may enable screening for hyperkalemia. Prospective studies are warranted..",1
"A Novel N Staging System for Predicting Survival in Patients with Medullary Thyroid Cancer. Introduction: Despite the crucially prognostic value of lymph node metastasis (LNM) in patients with medullary thyroid cancer (MTC), only the LNM compartment alone was reflected in the 8th edition of the American Joint Committee on Cancer (AJCC) system. Objective: This study aimed to incorporate the metastatic lymph node number and metastatic lymph node ratio to generate a more accurate and appropriate N staging system for patients with MTC based on recursive partitioning analysis. Design, Setting, and Patients: Two cohorts were included in the analysis, including 1374 MTC patients from the Surveillance, Epidemiology, and End Results database as the derivation cohort, and 164 patients from Fudan University Shanghai Cancer Center as the validation cohort. The predictive performance of the alternative proposed N staging system was compared with that of the 8th AJCC system by using the Harrell concordance index (C-index) and the area under the receiver operating characteristic curve (AUC). Results: In the derivation cohort, the C-index and the AUC at 10 years were 0.778 and 0.789, respectively, for the novel N staging system, and 0.749 and 0.741, respectively, for the 8th AJCC N staging system. Similar trends were also observed in the validation cohort. The proposed N staging system had a better prognostic performance. Conclusion: With some improvements, the novel N staging system for MTC suggested from this research may be assessed for potential adoption in the next edition of the AJCC N staging system.",0
"IDH1-R132H acts as a tumor suppressor in glioma via epigenetic up-regulation of the DNA damage response. Patients with glioma whose tumors carry a mutation in isocitrate dehydrogenase 1 (IDH1(R132H)) are younger at diagnosis and live longer. IDH1 mutations co-occur with other molecular lesions, such as 1p/19q codeletion, inactivating mutations in the tumor suppressor protein 53 (TP53) gene, and loss-of-function mutations in alpha thalassemia/mental retardation syndrome X-linked gene (ATRX). All adult low-grade gliomas (LGGs) harboring ATRX loss also express the IDH1(R132H) mutation. The current molecular classification of LGGs is based, partly, on the distribution of these mutations. We developed a genetically engineered mouse model harboring IDH1(R132H), TP53 and ATRX inactivating mutations, and activated NRAS G12V. Previously, we established that ATRX deficiency, in the context of wild-type IDH1, induces genomic instability, impairs nonhomologous end-joining DNA repair, and increases sensitivity to DNA-damaging therapies. In this study, using our mouse model and primary patient-derived glioma cultures with IDH1 mutations, we investigated the function of IDH1(R132H) in the context of TP53 and ATRX loss. We discovered that IDH1(R132H) expression in the genetic context of ATRX and TP53 gene inactivation (i) increases median survival in the absence of treatment, (ii) enhances DNA damage response (DDR) via epigenetic up-regulation of the ataxia-telangiectasia-mutated (ATM) signaling pathway, and (iii) elicits tumor radioresistance. Accordingly, pharmacological inhibition of ATM or checkpoint kinases 1 and 2, essential kinases in the DDR, restored the tumors' radiosensitivity. Translation of these findings to patients with IDH1(132H) glioma harboring TP53 and ATRX loss could improve the therapeutic efficacy of radiotherapy and, consequently, patient survival.",0
"Predicting life expectancy with a long short-term memory recurrent neural network using electronic medical records. BACKGROUND: Life expectancy is one of the most important factors in end-of-life decision making. Good prognostication for example helps to determine the course of treatment and helps to anticipate the procurement of health care services and facilities, or more broadly: facilitates Advance Care Planning. Advance Care Planning improves the quality of the final phase of life by stimulating doctors to explore the preferences for end-of-life care with their patients, and people close to the patients. Physicians, however, tend to overestimate life expectancy, and miss the window of opportunity to initiate Advance Care Planning. This research tests the potential of using machine learning and natural language processing techniques for predicting life expectancy from electronic medical records. METHODS: We approached the task of predicting life expectancy as a supervised machine learning task. We trained and tested a long short-term memory recurrent neural network on the medical records of deceased patients. We developed the model with a ten-fold cross-validation procedure, and evaluated its performance on a held-out set of test data. We compared the performance of a model which does not use text features (baseline model) to the performance of a model which uses features extracted from the free texts of the medical records (keyword model), and to doctors' performance on a similar task as described in scientific literature. RESULTS: Both doctors and the baseline model were correct in 20% of the cases, taking a margin of 33% around the actual life expectancy as the target. The keyword model, in comparison, attained an accuracy of 29% with its prognoses. While doctors overestimated life expectancy in 63% of the incorrect prognoses, which harms anticipation to appropriate end-of-life care, the keyword model overestimated life expectancy in only 31% of the incorrect prognoses. CONCLUSIONS: Prognostication of life expectancy is difficult for humans. Our research shows that machine learning and natural language processing techniques offer a feasible and promising approach to predicting life expectancy. The research has potential for real-life applications, such as supporting timely recognition of the right moment to start Advance Care Planning.",1
"Identification of dysregulated miRNAs in triple negative breast cancer: A meta-analysis approach. Triple negative breast cancer (TNBC) is an aggressive subtype of breast cancer with poor clinical outcomes and lack of approved targeted therapy. Dysregulated microRNAs (miRNAs) have been considered a promising biomarker, which plays an important role in the tumorigenesis of human cancer. Due to the increase in miRNA profiling datasets of TNBC, a proper analysis is required for studying. Therefore, this study used meta-analysis to amalgamate ten miRNA profiling studies of TNBC. By the robust rank aggregation method, metasignatures of six miRNAs (4 upregulated: hsa-miR-135b-5p, hsa-miR-18a-5p, hsa-miR-9-5p and hsa-miR-522-3p; 2 downregulated: hsa-miR-190b and hsa-miR-449a) were obtained. The gene ontology analysis revealed that target genes regulated by miRNAs were associated with processes like the regulation of transcription, DNA dependent, and signal transduction. Also, it is noted from the pathway analysis that signaling and cancer pathways were associated with the progression of TNBC. A Naïve Bayes-based classifier built with miRNA signatures discriminates TNBC and non-TNBC samples in test data set with high diagnostic sensitivity and specificity. From the analysis carried out by the study, it is suggested that the identified miRNAs are of great importance in improving the diagnostics and therapeutics for TNBC.",0
"Defining skin cancer local recurrence. Definitions of skin cancer recurrence are variable and nonstandardized, which can lead to inconsistent and potentially inappropriate management of tumors of uncertain recurrence status. Defining recurrence is important given the potential association with metastasis in both melanoma and nonmelanoma skin cancer. A review of the literature across multiple disciplines involved in the care of skin cancer patients reveals that although criteria for recurrence are provided in the majority of cases, most are vague and inconsistent. Given the presumably increased morbidity and mortality associated with recurrent tumors, accurate identification and appropriate management is paramount. In addition, value-based health care necessitates validated and relevant outcome measures that are standardized and, thus, enable tracking of comparable and corresponding outcomes. A universal definition of localized skin cancer recurrence would ultimately allow for improved surveillance and informed therapeutic strategies to decrease morbidity and mortality of patients afflicted with skin cancer, the most common cancer nationwide.",0
"SarcTrack. RATIONALE: Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) in combination with CRISPR/Cas9 genome editing provide unparalleled opportunities to study cardiac biology and disease. However, sarcomeres, the fundamental units of myocyte contraction, are immature and nonlinear in hiPSC-CMs, which technically challenge accurate functional interrogation of contractile parameters in beating cells. Furthermore, existing analysis methods are relatively low-throughput, indirectly assess contractility, or only assess well-aligned sarcomeres found in mature cardiac tissues. OBJECTIVE: We aimed to develop an analysis platform that directly, rapidly, and automatically tracks sarcomeres in beating cardiomyocytes. The platform should assess sarcomere content, contraction and relaxation parameters, and beat rate. METHODS AND RESULTS: We developed SarcTrack, a MatLab software that monitors fluorescently tagged sarcomeres in hiPSC-CMs. The algorithm determines sarcomere content, sarcomere length, and returns rates of sarcomere contraction and relaxation. By rapid measurement of hundreds of sarcomeres in each hiPSC-CM, SarcTrack provides large data sets for robust statistical analyses of multiple contractile parameters. We validated SarcTrack by analyzing drug-treated hiPSC-CMs, confirming the contractility effects of compounds that directly activate (CK-1827452) or inhibit (MYK-461) myosin molecules or indirectly alter contractility (verapamil and propranolol). SarcTrack analysis of hiPSC-CMs carrying a heterozygous truncation variant in the myosin-binding protein C ( MYBPC3) gene, which causes hypertrophic cardiomyopathy, recapitulated seminal disease phenotypes including cardiac hypercontractility and diminished relaxation, abnormalities that normalized with MYK-461 treatment. CONCLUSIONS: SarcTrack provides a direct and efficient method to quantitatively assess sarcomere function. By improving existing contractility analysis methods and overcoming technical challenges associated with functional evaluation of hiPSC-CMs, SarcTrack enhances translational prospects for sarcomere-regulating therapeutics and accelerates interrogation of human cardiac genetic variants.",0
"Pan-cancer Convergence to a Small-Cell Neuroendocrine Phenotype that Shares Susceptibilities with Hematological Malignancies. Small-cell neuroendocrine cancers (SCNCs) are an aggressive cancer subtype. Transdifferentiation toward an SCN phenotype has been reported as a resistance route in response to targeted therapies. Here, we identified a convergence to an SCN state that is widespread across epithelial cancers and is associated with poor prognosis. More broadly, non-SCN metastases have higher expression of SCN-associated transcription factors than non-SCN primary tumors. Drug sensitivity and gene dependency screens demonstrate that these convergent SCNCs have shared vulnerabilities. These common vulnerabilities are found across unannotated SCN-like epithelial cases, small-round-blue cell tumors, and unexpectedly in hematological malignancies. The SCN convergent phenotype and common sensitivity profiles with hematological cancers can guide treatment options beyond tissue-specific targeted therapies. Balanis, Sheu, et al. identify a small-cell neuroendocrine (SCN) state across various epithelial cancer types that shares genome-wide expression, methylation, and copy-number alteration patterns and associates with poor prognosis. SCN cancers have common vulnerabilities that, unexpectedly, are shared with blood cancers.",0
"Fully automated CT quantification of Epicardial adipose tissue by deep learning: A multicenter study. Purpose: To evaluate the performance of deep learning for robust and fully automated quantification of epicardial adipose tissue (EAT) from multicenter cardiac CT data. Materials and Methods: In this multicenter study, a convolutional neural network approach was trained to quantify EAT on non-contrast material-enhanced calcium-scoring CT scans from multiple cohorts, scanners, and protocols (n = 850). Deep learning performance was compared with the performance of three expert readers and with interobserver variability in a subset of 141 scans. The deep learning algorithm was incorporated into research software. Automated EAT progression was compared with expert measurements for 70 patients with baseline and follow-up scans. Results: Automated quantification was performed in a mean (6 standard deviation) time of 1.57 seconds 6 0.49, compared with 15 minutes for experts. Deep learning provided high agreement with expert manual quantification for all scans (R = 0.974; P, 001), with no significant bias (0.53 cm3; P =. 13). Manual EAT volumes measured by two experienced readers were highly correlated (R = 0.984; P, 001) but with a bias of 4.35 cm3 (P, 001). Deep learning quantifications were highly correlated with the measurements of both experts (R = 0.973 and R = 0.979; P, 001), with significant bias for reader 1 (5.11 cm3; P, 001) but not for reader 2 (0.88 cm3; P =. 26). EAT progression by deep learning correlated strongly with manual EAT progression (R = 0.905; P, 001) in 70 patients, with no significant bias (0.64 cm3; P =. 43), and was related to an increased noncalcified plaque burden quantified from coronary CT angiography (5.7% vs 1.8%; P =. 026). Conclusion: Deep learning allows rapid, robust, and fully automated quantification of EAT from calcium scoring CT. It performs as well as an expert reader and can be implemented for routine cardiovascular risk assessment.",1
"Strong binding active constituents of phytochemical to BMPR1A promote bone regeneration: In vitro, in silico docking, and in vivo studies. Two of the most problematic orthopedic and neurosurgeon visits are associated with spine and craniofacial fractures. Therefore, more attention needs to be paid to finding a medicine to repair these fractures. Amongst the most mysterious herbs, Aloe vera stands out. In the present study, the ameliorating function of A. vera on osteogenesis was studied in vitro and in vivo. Osteoblast-like cells were exposed to A. vera, followed by analysis of cell viability, lactate dehydrogenase release, and intracellular reactive oxygen species (ROS) production. The results showed an enhanced cell biocompatibility in a dose-dependent manner due to attenuated intracellular ROS production. Furthermore, a docking study indicated that the strong affinity of A. vera constituents to type I bone morphogenic protein receptor (BMPR1A) without the involvement of the BMPR1A chain B. The induction of osteogenesis prompts extracellular calcium deposition by osteoblasts, which affirms successful in vitro bone regeneration. However, injection of A. vera in rats with critical size calvarial defects induced Runx2, alkaline phosphatase (ALP), OCN, and BMP2 genes overexpression, which led to the formation of victorious bone with enhanced bone density and ALP activity. It is worthy to note that Aloin has the highest affinity to BMPR1A, whereas there are no reports regarding the impact of Aloenin, Aloesin, and γ-sitosterol on osteogenesis. Furthermore, some of them have antitumor potency, and it might be proposed that they are considered as a bone substitute in the osteotomy site of osteosarcoma with the aim of bone recovery and suppression of osteosarcoma. The whole consequences of this investigation manifests the plausibility of using A. vera as an antioxidant and osteoconductive substitute.",0
"Combined treatment with emodin and a telomerase inhibitor induces significant telomere damage/dysfunction and cell death. G-quadruplex telomeric secondary structures represent natural replication fork barriers and must be resolved to permit efficient replication. Stabilization of telomeric G4 leads to telomere dysfunctions demonstrated by telomere shortening or damage, resulting in genome instability and apoptosis. Chemical compounds targeting G4 structures have been reported to induce telomere disturbance and tumor suppression. Here, virtual screening was performed in a natural compound library using PyRx to identify novel G4 ligands. Emodin was identified as one of the best candidates, showing a great G4-binding potential. Subsequently, we confirmed that emodin could stabilize G4 structures in vitro and trigger telomere dysfunctions including fragile telomeres, telomere loss, and telomeric DNA damage. However, this telomere disturbance could be rescued by subsequent elevation of telomerase activity; in contrast, when we treated the cells with the telomerase inhibitor BIBR1532 upon emodin treatment, permanent telomere disturbance and obvious growth inhibition of 4T1-cell xenograft tumors were observed in mice. Taken together, our results show for the first time that emodin-induced telomeric DNA damage can upregulate telomerase activity, which may weaken its anticancer effect. The combined use of emodin and the telomerase inhibitor synergistically induced telomere dysfunction and inhibited tumor generation.",0
"Quantitative pixel intensity- and color-based image analysis on minimally compressed files: implications for whole-slide imaging. Current best practice in the quantitative analysis of microscopy images dictates that image files should be saved in a lossless format such as TIFF. Use of lossy files, including those processed with the JPEG algorithm, is highly discouraged due to effects of compression on pixel characteristics. However, with the growing popularity of whole-slide imaging (WSI) and its attendant large file sizes, compressed image files are becoming more prevelent. This prompted us to perform a color-based quantitative pixel analysis of minimally compressed WSI images. Sections from three tissues stained with one of three reagents representing the colors blue (hematoxylin), red (Oil-Red-O), and brown (immunoperoxidase) were scanned with a whole slide imager in triplicate at 20x, 40x, and 63x magnifications. The resulting files were in the form of a BigTIFF with a JPEG compression automatically applied during acquisition. Images were imported into analysis software, six regions of interest were applied to various morphological locations, and the areas assessed for the color of interest. Whereas the number of designated weakly or strongly positive pixels was variable across the triplicate scans for the individual regions of interest, the total number of positive pixels was consistent. These results suggest that total positivity for a specific color representing a histochemical or immunohistochemical stain can be adequately quantitated on compressed images, but degrees of positivity (e.g., weak vs. strong) may not be as reliable. However, it is important to assess individual whole-slide imagers, file compression level and algorithm, and analysis software for reproducibility.",0
"CONFIGURE: A pipeline for identifying context specific regulatory modules from gene expression data and its application to breast cancer. Background: Gene expression data is widely used for identifying subtypes of diseases such as cancer. Differentially expressed gene analysis and gene set enrichment analysis are widely used for identifying biological mechanisms at the gene level and gene set level, respectively. However, the results of differentially expressed gene analysis are difficult to interpret and gene set enrichment analysis does not consider the interactions among genes in a gene set. Results: We present CONFIGURE, a pipeline that identifies context specific regulatory modules from gene expression data. First, CONFIGURE takes gene expression data and context label information as inputs and constructs regulatory modules. Then, CONFIGURE makes a regulatory module enrichment score (RMES) matrix of enrichment scores of the regulatory modules on samples using the single-sample GSEA method. CONFIGURE calculates the importance scores of the regulatory modules on each context to rank the regulatory modules. We evaluated CONFIGURE on the Cancer Genome Atlas (TCGA) breast cancer RNA-seq dataset to determine whether it can produce biologically meaningful regulatory modules for breast cancer subtypes. We first evaluated whether RMESs are useful for differentiating breast cancer subtypes using a multi-class classifier and one-vs-rest binary SVM classifiers. The multi-class and one-vs-rest binary classifiers were trained using the RMESs as features and outperformed baseline classifiers. Furthermore, we conducted literature surveys on the basal-like type specific regulatory modules obtained by CONFIGURE and showed that highly ranked modules were associated with the phenotypes of basal-like type breast cancers. Conclusions: We showed that enrichment scores of regulatory modules are useful for differentiating breast cancer subtypes and validated the basal-like type specific regulatory modules by literature surveys. In doing so, we found regulatory module candidates that have not been reported in previous literature. This demonstrates that CONFIGURE can be used to predict novel regulatory markers which can be validated by downstream wet lab experiments. We validated CONFIGURE on the breast cancer RNA-seq dataset in this work but CONFIGURE can be applied to any gene expression dataset containing context information.",0
"Pyrazinamide drug resistance in RpsA mutant (∆438A) of Mycobacterium tuberculosis: Dynamics of essential motions and free-energy landscape analysis. Pyrazinamide is an essential first-line antitubercular drug which plays pivotal role in tuberculosis treatment. It is a prodrug that requires amide hydrolysis by mycobacterial pyrazinamidase enzyme for conversion into pyrazinoic acid (POA). POA is known to target ribosomal protein S1 (RpsA), aspartate decarboxylase (PanD), and some other mycobacterial proteins. Spontaneous chromosomal mutations in RpsA have been reported for phenotypic resistance against pyrazinamide. We have constructed and validated 3D models of the native and Δ438A mutant form of RpsA protein. RpsA protein variants were then docked to POA and long range molecular dynamics simulations were carried out. Per residue binding free-energy calculations, free-energy landscape analysis, and essential dynamics analysis were performed to outline the mechanism underlying the high-level PZA resistance conferred by the most frequently occurring deletion mutant of RpsA. Our study revealed the conformational modulation of POA binding site due to the disruptive collective modes of motions and increased conformational flexibility in the mutant than the native form. Residue wise MMPBSA decomposition and protein-drug interaction pattern revealed the difference of energetically favorable binding site in the wild-type (WT) protein in comparison with the mutant. Analysis of size and shape of minimal energy landscape area delineated higher stability of the WT complex than the mutant form. Our study provides mechanistic insights into pyrazinamide resistance in Δ438A RpsA mutant, and the results arising out of this study will pave way for design of novel and effective inhibitors targeting the resistant strains of Mycobacterium tuberculosis.",0
"Gene-based analysis of ADHD using PASCAL: A biological insight into the novel associated genes. Background: Attention-Deficit Hyperactivity Disorder (ADHD) is a complex neurodevelopmental disorder (NDD) which may significantly impact on the affected individual's life. ADHD is acknowledged to have a high heritability component (70-80%). Recently, a meta-analysis of GWAS (Genome Wide Association Studies) has demonstrated the association of several independent loci. Our main aim here, is to apply PASCAL (pathway scoring algorithm), a new gene-based analysis (GBA) method, to the summary statistics obtained in this meta-analysis. PASCAL will take into account the linkage disequilibrium (LD) across genomic regions in a different way than the most commonly employed GBA methods (MAGMA or VEGAS (Versatile Gene-based Association Study)). In addition to PASCAL analysis a gene network and an enrichment analysis for KEGG and GO terms were carried out. Moreover, GENE2FUNC tool was employed to create gene expression heatmaps and to carry out a (DEG) (Differentially Expressed Gene) analysis using GTEX v7 and BrainSpan data. Results: PASCAL results have revealed the association of new loci with ADHD and it has also highlighted other genes previously reported by MAGMA analysis. PASCAL was able to discover new associations at a gene level for ADHD: FEZF1 (p-value: 2.2 × 10- 7) and FEZF1-AS1 (p-value: 4.58 × 10- 7). In addition, PASCAL has been able to highlight association of other genes that share the same LD block with some previously reported ADHD susceptibility genes. Gene network analysis has revealed several interactors with the associated ADHD genes and different GO and KEGG terms have been associated. In addition, GENE2FUNC has demonstrated the existence of several up and down regulated expression clusters when the associated genes and their interactors were considered. Conclusions: PASCAL has been revealed as an efficient tool to extract additional information from previous GWAS using their summary statistics. This study has identified novel ADHD associated genes that were not previously reported when other GBA methods were employed. Moreover, a biological insight into the biological function of the ADHD associated genes across brain regions and neurodevelopmental stages is provided.",0
"Ear biometrics for patient identification in global health: a field study to test the effectiveness of an image stabilization device in improving identification accuracy. BACKGROUND: In many low and middle-income countries (LMICs), difficulties in patient identification are a major obstacle to the delivery of longitudinal care. In absence of unique identifiers, biometrics have emerged as an attractive solution to the identification problem. We developed an mHealth App for subject identification using pattern recognition around ear morphology (Project SEARCH (Scanning EARS for Child Health). Early field work with the SEARCH App revealed that image stabilization would be required for optimum performance. METHODS: To improve image capture, we designed and tested a device (the 'Donut'), which standardizes distance, angle, rotation and lighting. We then ran an experimental trial with 194 participants to measure the impact of the Donut on identification rates. Images of the participant's left ear were taken both with and without use of the Donut, then processed by the SEARCH algorithm, measuring the top one and top ten most likely matches. RESULTS: With the Donut, the top one identification rate and top ten identification rates were 99.5 and 99.5%, respectively, vs. 38.4 and 24.1%, respectively, without the Donut (P < 0.0001 for each comparison). In sensitivity analyses, crop technique during pre-processing of images had a powerful impact on identification rates, but this too was facilitated through the Donut. CONCLUSIONS: By standardizing lighting, angle and spatial location of the ear, the Donut achieved near perfect identification rates on a cohort of 194 participants, proving the feasibility and effectiveness of using the ear as a biometric identifier. TRIAL REGISTRATION: This study did not include a medical intervention or assess a medical outcome, and therefore did not meet the definition of a human subjects research study as defined by FDAAA. We did not register our study under clinicaltrials.gov .",0
"Comparison and validation of accelerometer wear time and non-wear time algorithms for assessing physical activity levels in children and adolescents. BACKGROUND: Accelerometers are widely used to measure sedentary time and daily physical activity (PA). However, data collection and processing criteria, such as non-wear time rules might affect the assessment of total PA and sedentary time and the associations with health variables. The study aimed to investigate whether the choice of different non-wear time definitions would affect the outcomes of PA levels in youth. METHODS: Seventy-seven healthy youngsters (44 boys), aged 10-17 years, wore an accelerometer and kept a non-wear log diary during 4 consecutives days. We compared 7 published algorithms (10, 15, 20, 30, 60 min of continuous zeros, Choi, and Troiano algorithms). Agreements of each algorithm with the log diary method were assessed using Bland-Altmans plots and by calculating the concordance correlation coefficient for repeated measures. RESULTS: Variations in time spent in sedentary and moderate to vigorous PA (MVPA) were 30 and 3.7%. Compared with the log diary method, greater discrepancies were found for the algorithm 10 min (p < 0.001). For the time assessed in sedentary, the agreement with diary was excellent for the 4 algorithms (Choi, r = 0.79; Troiano, r = 0.81; 30 min, r = 0.79; 60 min, r = 0.81). Concordance for each method was excellent for the assessment of time spent in MVPA (> 0.86). The agreement for the wear time assessment was excellent for 5 algorithms (Choi r = 0.79; Troiano r = 0.79; 20 min r = 0.77; 30 min r = 0.80; 60 min r = 0.80). CONCLUSIONS: The choice of non-wear time rules may considerably affect the sedentary time assessment in youth. Using of appropriate data reduction decision in youth is needed to limit differences in associations between health outcomes and sedentary behaviors and may improve comparability for future studies. Based on our results, we recommend the use of the algorithm of 30 min of continuous zeros for defining non-wear time to improve the accuracy in assessing PA levels in youth. TRIAL REGISTRATION: NCT02844101 (retrospectively registered at July 13th 2016).",0
"Investigation of structural stability and functionality of homodimeric gramicidin towards peptide-based drug: a molecular simulation approach. Increasing death rates due to antibiotic resistance deteriorate the existing treatment measures. Antimicrobial peptides have turned into the emerging cure for multidrug resistance. However, the stability and functionality determine an antimicrobial peptide as a drug. Analyses of the homodimeric β-helical peptide, gramicidin have suggested the significant role of gramicidin-A, gramicidin-B, and gramicidin-C as antimicrobial compounds, but the structural basis for understanding the stability and functionality is insufficient to resolve multidrug resistance. To identify the best template among gramicidin types as a therapeutic product, we combined a detailed comparative static analysis and dynamic analysis along with conformational free energy and secondary structure prediction. We observed that the high intramolecular interactions and the geometrical features favored gramicidin-A among other types of gramicidin. Our analyses further revealed that the secondary structure of gramicidin-A showed β sheets with coils along the conformations without any disruption, thereby enhanced its membrane interactions in terms of binding free energy. In conclusion, gramicidin-A has definitely showed enhanced structural stability and functionality; this could be considered the best template for a potential therapeutic product.",0
"A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography. BACKGROUND: Coronary inflammation induces dynamic changes in the balance between water and lipid content in perivascular adipose tissue (PVAT), as captured by perivascular Fat Attenuation Index (FAI) in standard coronary CT angiography (CCTA). However, inflammation is not the only process involved in atherogenesis and we hypothesized that additional radiomic signatures of adverse fibrotic and microvascular PVAT remodelling, may further improve cardiac risk prediction. METHODS AND RESULTS: We present a new artificial intelligence-powered method to predict cardiac risk by analysing the radiomic profile of coronary PVAT, developed and validated in patient cohorts acquired in three different studies. In Study 1, adipose tissue biopsies were obtained from 167 patients undergoing cardiac surgery, and the expression of genes representing inflammation, fibrosis and vascularity was linked with the radiomic features extracted from tissue CT images. Adipose tissue wavelet-transformed mean attenuation (captured by FAI) was the most sensitive radiomic feature in describing tissue inflammation (TNFA expression), while features of radiomic texture were related to adipose tissue fibrosis (COL1A1 expression) and vascularity (CD31 expression). In Study 2, we analysed 1391 coronary PVAT radiomic features in 101 patients who experienced major adverse cardiac events (MACE) within 5 years of having a CCTA and 101 matched controls, training and validating a machine learning (random forest) algorithm (fat radiomic profile, FRP) to discriminate cases from controls (C-statistic 0.77 [95%CI: 0.62-0.93] in the external validation set). The coronary FRP signature was then tested in 1575 consecutive eligible participants in the SCOT-HEART trial, where it significantly improved MACE prediction beyond traditional risk stratification that included risk factors, coronary calcium score, coronary stenosis, and high-risk plaque features on CCTA (Delta[C-statistic] = 0.126, P < 0.001). In Study 3, FRP was significantly higher in 44 patients presenting with acute myocardial infarction compared with 44 matched controls, but unlike FAI, remained unchanged 6 months after the index event, confirming that FRP detects persistent PVAT changes not captured by FAI. CONCLUSION: The CCTA-based radiomic profiling of coronary artery PVAT detects perivascular structural remodelling associated with coronary artery disease, beyond inflammation. A new artificial intelligence (AI)-powered imaging biomarker (FRP) leads to a striking improvement of cardiac risk prediction over and above the current state-of-the-art.",1
"A step beyond the hygiene hypothesis-immune-mediated classes determined in a population-based study. BACKGROUND: Comorbidity patterns of childhood infections, atopic diseases, and adverse childhood experiences (ACE) are related to immune system programming conditions. The aim of this study was to make a step beyond the hygiene hypothesis and to comprehensively classify these patterns with latent class analysis (LCA). A second aim was to characterize the classes by associations with immunological, clinical, and sociodemographic variables. METHODS: LCA was applied to data from the CoLaus|PsyCoLaus study (N = 4874, age range 35-82 years) separately for men and women. It was based on survey information on chickenpox, measles, mumps, rubella, herpes simplex, pertussis, scarlet fever, hay fever, asthma, eczema, urticaria, drug allergy, interparental violence, parental maltreatment, and trauma in early childhood. Subsequently, we examined how immune-mediated classes were reflected in leukocyte counts, inflammatory markers (IL-1beta, IL-6, TNF-alpha, hsCRP), chronic inflammatory diseases, and mental disorders, and how they differed across social classes and birth cohorts. RESULTS: LCA results with five classes were selected for further analysis. Latent classes were similar in both sexes and were labeled according to their associations as neutral, resilient, atopic, mixed (comprising infectious and atopic diseases), and ACE class. They came across with specific differences in biomarker levels. Mental disorders typically displayed increased lifetime prevalence rates in the atopic, the mixed, and the ACE classes, and decreased rates in the resilient class. The same patterns were apparent in chronic inflammatory diseases, except that the ACE class was relevant specifically in women but not in men. CONCLUSIONS: This is the first study to systematically determine immune-mediated classes that evolve early in life. They display characteristic associations with biomarker levels and somatic and psychiatric diseases occurring later in life. Moreover, they show different distributions across social classes and allow to better understand the mechanisms beyond the changes in the prevalence of chronic somatic and psychiatric diseases.",0
"Integrated phosphoproteomics and transcriptional classifiers reveal hidden RAS signaling dynamics in multiple myeloma. A major driver of multiple myeloma (MM) is thought to be aberrant signaling, yet no kinase inhibitors have proven successful in the clinic. Here, we employed an integrated, systems approach combining phosphoproteomic and transcriptome analysis to dissect cellular signaling in MM to inform precision medicine strategies. Unbiased phosphoproteomics initially revealed differential activation of kinases across MM cell lines and that sensitivity to mammalian target of rapamycin (mTOR) inhibition may be particularly dependent on mTOR kinase baseline activity. We further noted differential activity of immediate downstream effectors of Ras as a function of cell line genotype. We extended these observations to patient transcriptome data in the Multiple Myeloma Research Foundation CoMMpass study. A machine-learning–based classifier identified surprisingly divergent transcriptional outputs between NRAS- and KRAS-mutated tumors. Genetic dependency and gene expression analysis revealed mutated Ras as a selective vulnerability, but not other MAPK pathway genes. Transcriptional analysis further suggested that aberrant MAPK pathway activation is only present in a fraction of RAS-mutated vs wild-type RAS patients. These high-MAPK patients, enriched for NRAS Q61 mutations, have inferior outcomes, whereas RAS mutations overall carry no survival impact. We further developed an interactive software tool to relate pharmacologic and genetic kinase dependencies in myeloma. Collectively, these predictive models identify vulnerable signaling signatures and highlight surprising differences in functional signaling patterns between NRAS and KRAS mutants invisible to the genomic landscape. These results will lead to improved stratification of MM patients in precision medicine trials while also revealing unexplored modes of Ras biology in MM.",1
"Postmortem Cortex Samples Identify Distinct Molecular Subtypes of ALS: Retrotransposon Activation, Oxidative Stress, and Activated Glia. Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by the progressive loss of motor neurons. While several pathogenic mutations have been identified, the vast majority of ALS cases have no family history of disease. Thus, for most ALS cases, the disease may be a product of multiple pathways contributing to varying degrees in each patient. Using machine learning algorithms, we stratify the transcriptomes of 148 ALS postmortem cortex samples into three distinct molecular subtypes. The largest cluster, identified in 61% of patient samples, displays hallmarks of oxidative and proteotoxic stress. Another 19% of the samples shows predominant signatures of glial activation. Finally, a third group (20%) exhibits high levels of retrotransposon expression and signatures of TARDBP/TDP-43 dysfunction. We further demonstrate that TDP-43 (1) directly binds a subset of retrotransposon transcripts and contributes to their silencing in vitro, and (2) pathological TDP-43 aggregation correlates with retrotransposon de-silencing in vivo.",0
"Neural network-derived perfusion maps for the assessment of lesions in patients with acute ischemic stroke. Purpose: To perform a proof-of-concept study to investigate the clinical utility of perfusion maps derived from convolutional neural networks (CNNs) for the workup of patients with acute ischemic stroke presenting with a large vessel occlusion. Materials and Methods: Data on endovascularly treated patients with acute ischemic stroke (n = 151; median age, 68 years [interquartile range, 59-75 years]; 82 of 151 [54.3%] women) were retrospectively extracted from a single-center institutional prospective registry (between January 2011 and December 2015). Dynamic susceptibility perfusion imaging data were processed by applying a commercially available reference method and in parallel by a recently proposed CNN method to automatically infer time to maximum of the tissue residue function (Tmax) perfusion maps. The outputs were compared by using quantitative markers of tissue at risk derived from manual segmentations of perfusion lesions from two expert raters. Results: Strong correlations of lesion volumes (Tmax. 4 seconds, 6 seconds, and. 8 seconds; R = 0.865-0.914; P 001) and good spatial overlap of respective lesion segmentations (Dice coefficients, 0.70-0.85) between the CNN method and reference output were observed. Eligibility for late-window reperfusion treatment was feasible with use of the CNN method, with complete interrater agreement for the CNN method (Cohen k = 1; P. 001), although slight discrepancies compared with the reference-based output were observed (Cohen k = 0.609-0.64; P 001). The CNN method tended to underestimate smaller lesion volumes, leading to a disagreement between the CNN and reference method in five of 45 patients (9%). Conclusion: Compared with standard deconvolution-based processing of raw perfusion data, automatic CNN-derived Tmax perfusion maps can be applied to patients who have acute ischemic large vessel occlusion stroke, with similar clinical utility.",1
"Systematic identification and analysis of dysregulated miRNA and transcription factor feed-forward loops in hypertrophic cardiomyopathy. Hypertrophic cardiomyopathy (HCM) is the most common genetic cardiovascular disease. Although some genes and miRNAs related with HCM have been studied, the molecular regulatory mechanisms between miRNAs and transcription factors (TFs) in HCM have not been systematically elucidated. In this study, we proposed a novel method for identifying dysregulated miRNA-TF feed-forward loops (FFLs) by integrating sample matched miRNA and gene expression profiles and experimentally verified interactions of TF-target gene and miRNA-target gene. We identified 316 dysregulated miRNA-TF FFLs in HCM, which were confirmed to be closely related with HCM from various perspectives. Subpathway enrichment analysis demonstrated that the method was outperformed by the existing method. Furthermore, we systematically analysed the global architecture and feature of gene regulation by miRNAs and TFs in HCM, and the FFL composed of hsa-miR-17-5p, FASN and STAT3 was inferred to play critical roles in HCM. Additionally, we identified two panels of biomarkers defined by three TFs (CEBPB, HIF1A, and STAT3) and four miRNAs (hsa-miR-155-5p, hsa-miR-17-5p, hsa-miR-20a-5p, and hsa-miR-181a-5p) in a discovery cohort of 126 samples, which could differentiate HCM patients from healthy controls with better performance. Our work provides HCM-related dysregulated miRNA-TF FFLs for further experimental study, and provides candidate biomarkers for HCM diagnosis and treatment.",0
"A review of head and neck cancer staging system in the TNM classification of malignant tumors (eighth edition). A number of major modifications were made to the classification of head and neck carcinomas in the eighth edition of the American Joint Committee on Cancer, Cancer Staging Manual and Union for International Cancer Control TNM classification of Malignant Tumors. These modifications were aimed at improving the prognosis prediction accuracy of the system. In this article, we review the new edition of the TNM classification system. Among the several changes in the new system, a separate algorithm for p16-positive oropharyngeal carcinoma was included, as were new chapters on ‘Head and Neck Skin Carcinoma’ and ‘Unknown Primary Carcinoma—Cervical Nodes.’ Changes to Tumor (T) classification were made by introducing the depth of invasion of oral carcinoma, whereas changes to Node (N) classification were made by adding extra-nodal extension. It is believed that these changes will help improve the accuracy of the system in the prediction of prognosis. However, it is necessary to verify their validity through further clinical research.",0
"Microvesicle proteomic profiling of uterine liquid biopsy for ovarian cancer early detection. High-grade ovarian cancer (HGOC) is the leading cause of mortality from gynecological malignancies, because of diagnosis at a metastatic stage. Current screening options fail to improve mortality because of the absence of early-stage-specific biomarkers. We postulated that a liquid biopsy, such as utero-tubal lavage (UtL), may identify localized lesions better than systemic approaches of serum/plasma analysis. Further, while mutation-based assays are challenged by the rarity of tumor DNA within nonmutated DNA, analyzing the proteomic profile, is expected to enable earlier detection, as it reveals perturbations in both the tumor as well as in its microenvironment. To attain deep proteomic coverage and overcome the high dynamic range of this body fluid, we applied our method for microvesicle proteomics to the UtL samples. Liquid biopsies from HGOC patients (n 49) and controls (n 127) were divided into a discovery and validation sets. Data-dependent analysis of the samples on the Q-Exactive mass spectrometer provided depth of 8578 UtL proteins in total, and on average 3000 proteins per sample. We used support vector machine algorithms for sample classification, and crossed three feature-selection algorithms, to construct and validate a 9-protein classifier with 70% sensitivity and 76.2% specificity. The signature correctly identified all Stage I lesions. These results demonstrate the potential power of microvesicle-based proteomic biomarkers for early cancer diagnosis.",0
"Contriving multiepitope subunit vaccine by exploiting structural and nonstructural viral proteins to prevent Epstein–Barr virus-associated malignancy. Cancer is one of the common lifestyle diseases and is considered to be the leading cause of death worldwide. Epstein–Barr virus (EBV)-infected individuals remain asymptomatic; but under certain stress conditions, EBV may lead to the development of cancers such as Burkitt’s and Hodgkin’s lymphoma and nasopharyngeal carcinoma. EBV-associated cancers result in a large number of deaths in Asian and African population, and no effective cure has still been developed. We, therefore, tried to devise a subunit vaccine with the help of immunoinformatic approaches that can be used for the prevention of EBV-associated malignancies. The epitopes were predicted through B-cell, cytotoxic T lymphocytes (CTL), and helper T lymphocytes (HTL) from the different oncogenic proteins of EBV. A vaccine was designed by combining the B-cell and T-cell (HTL and CTL) epitopes through linkers, and for the enhancement of immunogenicity, an adjuvant was added at the N-terminal. Further, homology modeling was performed to generate the 3D structure of the designed vaccine. Moreover, molecular docking was performed between the designed vaccine and immune receptor (TLR-3) to determine the interaction between the final vaccine construct and the immune receptor complex. In addition, molecular dynamics was performed to analyze the stable interactions between the ligand final vaccine model and receptor TLR-3 molecule. Lastly, to check the expression of our vaccine construct, we performed in silico cloning. This study needed experimental validation to ensure its effectiveness and potency to control malignancy.",0
"Disparities in Acceptance of Deceased Donor Kidneys between the United States and France and Estimated Effects of Increased US Acceptance. Importance: Approximately 3500 donated kidneys are discarded in the United States each year, drawing concern from Medicare and advocacy groups. Objective: To estimate the effects of more aggressive allograft acceptance practices on the donor pool and allograft survival for the population of US wait-listed kidney transplant candidates. Design, Setting, and Participants: A nationwide study using validated registries from the United States and France comprising comprehensive cohorts of deceased donors with organs offered to kidney transplant centers between January 1, 2004, and December 31, 2014. Data were analyzed between September 1, 2018, and April 5, 2019. Main Outcomes and Measures: The primary outcome was kidney allograft discard. The secondary outcome was allograft failure after transplantation. We used logistic regression to model organ acceptance and discard practices in both countries. We then quantified using computer simulation models the number of kidneys discarded in the United States that a more aggressive system would have instead used for transplantation. Finally, based on actual survival data, we quantified the additional years of allograft life that a redesigned US system would have saved. Findings: In the United States, 156089 kidneys were recovered from deceased donors between 2004 and 2014, of which 128102 were transplanted, and 27987 (17.9%) were discarded. In France, among the 29984 kidneys recovered between 2004 and 2014, 27252 were transplanted, and 2732 (9.1%, P <.001 vs United States) were discarded. The mean (SD) age of kidneys transplanted in the United States was 36.51 (17.02) years vs 50.91 (17.34) years in France (P <.001). Kidney quality showed little change in the United States over time (mean [SD] kidney donor risk index [KDRI], 1.30 [0.48] in 2004 vs 1.32 [0.46] in 2014), whereas a steadily rising KDRI in France reflected a temporal trend of more aggressive organ use (mean [SD] KDRI, 1.37 [0.47] in 2004 vs 1.74 [0.72] in 2014; P <.001). We applied the French-based allocation model to the population of US deceased donor kidneys and found that 17435 (62%) of kidneys discarded in the United States would have instead been transplanted under the French system. We further determined that a redesigned system with more aggressive organ acceptance practices would generate an additional 132445 allograft life-years in the United States over the 10-year observation period. Conclusions and Relevance: Greater acceptance of kidneys from deceased donors who are older and have more comorbidities could provide major survival benefits to the population of US wait-listed patients.",0
"The Cancer Genome Atlas Expression Subtypes Stratify Response to Checkpoint Inhibition in Advanced Urothelial Cancer and Identify a Subset of Patients with High Survival Probability. Analysis of the IMvigor 210 trials involving patients with platinum-refractory or cisplatin-ineligible urothelial carcinoma who were treated with the PD-L1 inhibitor atezolizumab identified a resistance signature as an immune biomarker. Transcriptome profiling of 368 tumor samples from this trial revealed that the “genomically unstable” Lund subtype classification was associated with the best response. We developed and applied a novel single-patient subtype classifier based on The Cancer Genome Atlas 2017 expression-based molecular subtypes. We identified 11 patients with a neuronal subtype, with a 100% response rate in eight confirmed cases (2 complete response, 6 partial response), and 72% overall, including 3/11 patients with an unconfirmed response. The survival probability was extraordinarily high for the neuronal subtype, which represents a high-risk cohort with advanced disease, and may be secondary to low levels of TGFβ expression and high mutation/neoantigen burden. Patient summary: We describe a methodology for genomic classification of an individual patient's bladder cancer tumor and have identified a subtype that is associated with a high response rate to immunotherapy. This is an important step forward in identifying the right treatment for the right patient, which is the goal of personalized precision medicine. We describe a novel single-patient classifier based on The Cancer Genome Atlas 2017 scheme that identified the neuronal subtype of urothelial carcinoma as an extreme responder to anti-PD-L1 therapy. In the future, trials targeting subtype-based therapy may improve precision delivery of care for urothelial carcinoma.",0
"Novel putative drugs and key initiating genes for neurodegenerative disease determined using network-based genetic integrative analysis. Understanding the genetic causes of neurodegenerative disease (ND) can be useful for their prevention and treatment. Among the genetic variations responsible for ND, heritable germline variants have been discovered in genome-wide association studies (GWAS), and nonheritable somatic mutations have been discovered in sequencing projects. Distinguishing the important initiating genes in ND and comparing the importance of heritable and nonheritable genetic variants for treating ND are important challenges. In this study, we analysed GWAS results, somatic mutations and drug targets of ND from large databanks by performing directed network-based analysis considering a randomised network hypothesis testing procedure. A disease-associated biological network was created in the context of the functional interactome, and the nonrandom topological characteristics of directed-edge classes were interpreted. Hierarchical network analysis indicated that drug targets tend to lie upstream of somatic mutations and germline variants. Furthermore, using directed path length information and biological explanations, we provide information on the most important genes in these created node classes and their associated drugs. Finally, we identified nine germline variants overlapping with drug targets for ND, seven somatic mutations close to drug targets from the hierarchical network analysis and six crucial genes in controlling other genes from the network analysis. Based on these findings, some drugs have been proposed for treating ND via drug repurposing. Our results provide new insights into the therapeutic actionability of GWAS results and somatic mutations for ND. The interesting properties of each node class and the existing relationships between them can broaden our knowledge of ND.",0
"Rare disease knowledge enrichment through a data-driven approach. BACKGROUND: Existing resources to assist the diagnosis of rare diseases are usually curated from the literature that can be limited for clinical use. It often takes substantial effort before the suspicion of a rare disease is even raised to utilize those resources. The primary goal of this study was to apply a data-driven approach to enrich existing rare disease resources by mining phenotype-disease associations from electronic medical record (EMR). METHODS: We first applied association rule mining algorithms on EMR to extract significant phenotype-disease associations and enriched existing rare disease resources (Human Phenotype Ontology and Orphanet (HPO-Orphanet)). We generated phenotype-disease bipartite graphs for HPO-Orphanet, EMR, and enriched knowledge base HPO-Orphanet + and conducted a case study on Hodgkin lymphoma to compare performance on differential diagnosis among these three graphs. RESULTS: We used disease-disease similarity generated by the eRAM, an existing rare disease encyclopedia, as a gold standard to compare the three graphs with sensitivity and specificity as (0.17, 0.36, 0.46) and (0.52, 0.47, 0.51) for three graphs respectively. We also compared the top 15 diseases generated by the HPO-Orphanet + graph with eRAM and another clinical diagnostic tool, the Phenomizer. CONCLUSIONS: Per our evaluation results, our approach was able to enrich existing rare disease knowledge resources with phenotype-disease associations from EMR and thus support rare disease differential diagnosis.",1
"NeoMutate: An ensemble machine learning framework for the prediction of somatic mutations in cancer. Background: The accurate screening of tumor genomic landscapes for somatic mutations using high-throughput sequencing involves a crucial step in precise clinical diagnosis and targeted therapy. However, the complex inherent features of cancer tissue, especially, tumor genetic intra-heterogeneity coupled with the problem of sequencing and alignment artifacts, makes somatic variant calling a challenging task. Current variant filtering strategies, such as rule-based filtering and consensus voting of different algorithms, have previously helped to increase specificity, although comes at the cost of sensitivity. Methods: In light of this, we have developed the NeoMutate framework which incorporates 7 supervised machine learning (ML) algorithms to exploit the strengths of multiple variant callers, using a non-redundant set of biological and sequence features. We benchmarked NeoMutate by simulating more than 10,000 bona fide cancer-related mutations into three well-characterized Genome in a Bottle (GIAB) reference samples. Results: A robust and exhaustive evaluation of NeoMutate's performance based on 5-fold cross validation experiments, in addition to 3 independent tests, demonstrated a substantially improved variant detection accuracy compared to any of its individual composite variant callers and consensus calling of multiple tools. Conclusions: We show here that integrating multiple tools in an ensemble ML layer optimizes somatic variant detection rates, leading to a potentially improved variant selection framework for the diagnosis and treatment of cancer.",0
"Development and Performance of a Checklist for Initial Triage After an Anthrax Mass Exposure Event. Background: Population exposure to Bacillus anthracis spores could cause mass casualties requiring complex medical care. Rapid identification of patients needing anthrax-specific therapies will improve patient outcomes and resource use. Objective: To develop a checklist that rapidly distinguishes most anthrax from nonanthrax illnesses on the basis of clinical presentation and identifies patients requiring diagnostic testing after a population exposure. Design: Comparison of published anthrax case reports from 1880 through 2013 that included patients seeking anthrax-related care at 2 epicenters of the 2001 U.S. anthrax attacks. Setting: Outpatient and inpatient. Patients: 408 case patients with inhalation, ingestion, and cutaneous anthrax and primary anthrax meningitis, and 657 control patients. Measurements: Diagnostic test characteristics, including positive and negative likelihood ratios (LRs) and patient triage assignation. Results: Checklist-directed triage without diagnostic testing correctly classified 95% (95% CI, 93% to 97%) of 353 adult anthrax case patients and 76% (CI, 73% to 79%) of 647 control patients (positive LR, 3.96 [CI, 3.45 to 4.55]; negative LR, 0.07 [CI, 0.04 to 0.11]; false-negative rate, 5%; false-positive rate, 24%). Diagnostic testing was needed for triage in up to 5% of case patients and 15% of control patients and improved overall test characteristics (positive LR, 8.90 [CI, 7.05 to 11.24]; negative LR, 0.06 [CI, 0.04 to 0.09]; false-negative rate, 5%; false-positive rate, 11%). Checklist sensitivity and specificity were minimally affected by inclusion of pediatric patients. Sensitivity increased to 97% (CI, 94% to 100%) and 98% (CI, 96% to 100%), respectively, when only inhalation anthrax cases or higher-quality case reports were investigated. Limitations: Data on case patients were limited to nonstandardized, published observational reports, many of which lacked complete data on symptoms and signs of interest. Reporting bias favoring more severe cases and lack of intercurrent outbreaks (such as influenza) in the control populations may have improved test characteristics. Conclusion: A brief checklist covering symptoms and signs can distinguish anthrax from other conditions with minimal need for diagnostic testing after known or suspected population exposure. Primary Funding Source: U.S. Department of Health and Human Services.",0
"Reproducing the molecular subclassification of peripheral T-cell lymphoma-NOS by immunohistochemistry. Peripheral T-cell lymphoma (PTCL) is a heterogeneous group of mature T-cell malignancies; approximately one-third of cases are designated as PTCL-not otherwise specified (PTCL-NOS). Using gene-expression profiling (GEP), we have previously defined 2 major molecular subtypes of PTCL-NOS, PTCL-GATA3 and PTCL-TBX21, which have distinct biological differences in oncogenic pathways and prognosis. In the current study, we generated an immunohistochemistry (IHC) algorithm to identify the 2 subtypes in paraffin tissue using antibodies to key transcriptional factors (GATA3 and TBX21) and their target proteins (CCR4 and CXCR3). In a training cohort of 49 cases of PTCL-NOS with corresponding GEP data, the 2 subtypes identified by the IHC algorithm matched the GEP results with high sensitivity (85%) and showed a significant difference in overall survival (OS) (P = .03). The IHC algorithm classification showed high interobserver reproducibility among pathologists and was validated in a second PTCL-NOS cohort (n = 124), where a significant difference in OS between the PTCL-GATA3 and PTCL-TBX21 subtypes was confirmed (P = .003). In multivariate analysis, a high International Prognostic Index score (3-5) and the PTCL-GATA3 subtype identified by IHC were independent adverse predictors of OS (P = .0015). Additionally, the 2 IHC-defined subtypes were significantly associated with distinct morphological features (P < .001), and there was a significant enrichment of an activated CD8+ cytotoxic phenotype in the PTCL-TBX21 subtype (P = .03). The IHC algorithm will aid in identifying the 2 subtypes in clinical practice, which will aid the future clinical management of patients and facilitate risk stratification in clinical trials.",0
"Cardio-Oncology Rehabilitation to Manage Cardiovascular Outcomes in Cancer Patients and Survivors: A Scientific Statement from the American Heart Association. Cardiovascular disease is a competing cause of death in patients with cancer with early-stage disease. This elevated cardiovascular disease risk is thought to derive from both the direct effects of cancer therapies and the accumulation of risk factors such as hypertension, weight gain, cigarette smoking, and loss of cardiorespiratory fitness. Effective and viable strategies are needed to mitigate cardiovascular disease risk in this population; a multimodal model such as cardiac rehabilitation may be a potential solution. This statement from the American Heart Association provides an overview of the existing knowledge and rationale for the use of cardiac rehabilitation to provide structured exercise and ancillary services to cancer patients and survivors. This document introduces the concept of cardio-oncology rehabilitation, which includes identification of patients with cancer at high risk for cardiac dysfunction and a description of the cardiac rehabilitation infrastructure needed to address the unique exposures and complications related to cancer care. In this statement, we also discuss the need for future research to fully implement a multimodal model of cardiac rehabilitation for patients with cancer and to determine whether reimbursement of these services is clinically warranted.",0
"A deep neural network approach to predicting clinical outcomes of neuroblastoma patients. Background: The availability of high-throughput omics datasets from large patient cohorts has allowed the development of methods that aim at predicting patient clinical outcomes, such as survival and disease recurrence. Such methods are also important to better understand the biological mechanisms underlying disease etiology and development, as well as treatment responses. Recently, different predictive models, relying on distinct algorithms (including Support Vector Machines and Random Forests) have been investigated. In this context, deep learning strategies are of special interest due to their demonstrated superior performance over a wide range of problems and datasets. One of the main challenges of such strategies is the ""small n large p"" problem. Indeed, omics datasets typically consist of small numbers of samples and large numbers of features relative to typical deep learning datasets. Neural networks usually tackle this problem through feature selection or by including additional constraints during the learning process. Methods: We propose to tackle this problem with a novel strategy that relies on a graph-based method for feature extraction, coupled with a deep neural network for clinical outcome prediction. The omics data are first represented as graphs whose nodes represent patients, and edges represent correlations between the patients' omics profiles. Topological features, such as centralities, are then extracted from these graphs for every node. Lastly, these features are used as input to train and test various classifiers. Results: We apply this strategy to four neuroblastoma datasets and observe that models based on neural networks are more accurate than state of the art models (DNN: 85%-87%, SVM/RF: 75%-82%). We explore how different parameters and configurations are selected in order to overcome the effects of the small data problem as well as the curse of dimensionality. Conclusions: Our results indicate that the deep neural networks capture complex features in the data that help predicting patient clinical outcomes.",0
"Visualizing Deep Learning Models for the Detection of Referable Diabetic Retinopathy and Glaucoma. Importance: Convolutional neural networks have recently been applied to ophthalmic diseases; however, the rationale for the outputs generated by these systems is inscrutable to clinicians. A visualization tool is needed that would enable clinicians to understand important exposure variables in real time. Objective: To systematically visualize the convolutional neural networks of 2 validated deep learning models for the detection of referable diabetic retinopathy (DR) and glaucomatous optic neuropathy (GON). Design, Setting, and Participants: The GON and referable DR algorithms were previously developed and validated (holdout method) using 48116 and 66790 retinal photographs, respectively, derived from a third-party database (LabelMe) of deidentified photographs from various clinical settings in China. In the present cross-sectional study, a random sample of 100 true-positive photographs and all false-positive cases from each of the GON and DR validation data sets were selected. All data were collected from March to June 2017. The original color fundus images were processed using an adaptive kernel visualization technique. The images were preprocessed by applying a sliding window with a size of 28 x 28 pixels and a stride of 3 pixels to crop images into smaller subimages to produce a feature map. Threshold scales were adjusted to optimal levels for each model to generate heat maps highlighting localized landmarks on the input image. A single optometrist allocated each image to predefined categories based on the generated heat map. Main Outcomes and Measures: Visualization regions of the fundus. Results: In the GON data set, 90 of 100 true-positive cases (90%; 95% CI, 82%-95%) and 15 of 22 false-positive cases (68%; 95% CI, 45%-86%) displayed heat map visualization within regions of the optic nerve head only. Lesions typically seen in cases of referable DR (exudate, hemorrhage, or vessel abnormality) were identified as the most important prognostic regions in 96 of 100 true-positive DR cases (96%; 95% CI, 90%-99%). In 39 of 46 false-positive DR cases (85%; 95% CI, 71%-94%), the heat map displayed visualization of nontraditional fundus regions with or without retinal venules. Conclusions and Relevance: These findings suggest that this visualization method can highlight traditional regions in disease diagnosis, substantiating the validity of the deep learning models investigated. This visualization technique may promote the clinical adoption of these models.",1
"Keratinocytic Skin Cancer Detection on the Face Using Region-Based Convolutional Neural Network. Importance: Detection of cutaneous cancer on the face using deep-learning algorithms has been challenging because various anatomic structures create curves and shades that confuse the algorithm and can potentially lead to false-positive results. Objective: To evaluate whether an algorithm can automatically locate suspected areas and predict the probability of a lesion being malignant. Design, Setting, and Participants: Region-based convolutional neural network technology was used to create 924538 possible lesions by extracting nodular benign lesions from 182348 clinical photographs. After manually or automatically annotating these possible lesions based on image findings, convolutional neural networks were trained with 1106886 image crops to locate and diagnose cancer. Validation data sets (2844 images from 673 patients; mean [SD] age, 58.2 [19.9] years; 308 men [45.8%]; 185 patients with malignant tumors, 305 with benign tumors, and 183 free of tumor) were obtained from 3 hospitals between January 1, 2010, and September 30, 2018. Main Outcomes and Measures: The area under the receiver operating characteristic curve, F1 score (mean of precision and recall; range, 0.000-1.000), and Youden index score (sensitivity + specificity -1; 0%-100%) were used to compare the performance of the algorithm with that of the participants. Results: The algorithm analyzed a mean (SD) of 4.2 (2.4) photographs per patient and reported the malignancy score according to the highest malignancy output. The area under the receiver operating characteristic curve for the validation data set (673 patients) was 0.910. At a high-sensitivity cutoff threshold, the sensitivity and specificity of the model with the 673 patients were 76.8% and 90.6%, respectively. With the test partition (325 images; 80 patients), the performance of the algorithm was compared with the performance of 13 board-certified dermatologists, 34 dermatology residents, 20 nondermatologic physicians, and 52 members of the general public with no medical background. When the disease screening performance was evaluated at high sensitivity areas using the F1 score and Youden index score, the algorithm showed a higher F1 score (0.831 vs 0.653 [0.126], P < .001) and Youden index score (0.675 vs 0.417 [0.124], P < .001) than that of nondermatologic physicians. The accuracy of the algorithm was comparable with that of dermatologists (F1 score, 0.831 vs 0.835 [0.040]; Youden index score, 0.675 vs 0.671 [0.100]). Conclusions and Relevance: The results of the study suggest that the algorithm could localize and diagnose skin cancer without preselection of suspicious lesions by dermatologists.",1
"Cognitive refractory state caused by spontaneous epileptic high-frequency oscillations in the human brain. Epileptic brain tissue is often considered physiologically dysfunctional, and the optimal treatment of many patients with uncontrollable seizures involves surgical removal of the epileptic tissue. However, it is unclear to what extent the epileptic tissue is capable of generating physiological responses to cognitive stimuli and how cognitive deficits ensuing surgical resections can be determined using state-of-the-art computational methods. To address these unknowns, we recruited six patients with nonlesional epilepsies and identified the epileptic focus in each patient with intracranial electrophysiological monitoring. We measured spontaneous epileptic activity in the form of high-frequency oscillations (HFOs), recorded stimulus-locked physiological responses in the form of physiological high-frequency broadband activity, and explored the interaction of the two as well as their behavioral correlates. Across all patients, we found abundant normal physiological responses to relevant cognitive stimuli in the epileptic sites. However, these physiological responses were more likely to be ""seized"" (delayed or missed) when spontaneous HFOs occurred about 850 to 1050 ms before, until about 150 to 250 ms after, the onset of relevant cognitive stimuli. Furthermore, spontaneous HFOs in medial temporal lobe affected the subjects' memory performance. Our findings suggest that nonlesional epileptic sites are capable of generating normal physiological responses and highlight a compelling mechanism for cognitive deficits in these patients. The results also offer clinicians a quantitative tool to differentiate pathological and physiological high-frequency activities in epileptic sites and to indirectly assess their possible cognitive reserve function and approximate the risk of resective surgery.",0
"Multi-task exclusive relationship learning for alzheimer's disease progression prediction with longitudinal data. Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive impairment of memory and other cognitive functions. Currently, many multi-task learning approaches have been proposed to predict the disease progression at the early stage using longitudinal data, with each task corresponding to a particular time point. However, the underlying association among different time points in disease progression is still under-explored in previous studies. To this end, we propose a multi-task exclusive relationship learning model to automatically capture the intrinsic relationship among tasks at different time points for estimating clinical measures based on longitudinal imaging data. The proposed method can select the most discriminative features for different tasks and also model the intrinsic relatedness among different time points, by utilizing an exclusive lasso regularization and a relationship induced regularization. Specifically, the exclusive lasso regularization enables partial group structure feature selection among the longitudinal data, while the relationship induced regularization efficiently introduces the relationship information from data to guide knowledge transfer. We further develop an efficient optimization algorithm to solve the proposed objective function. Extensive experiments on both synthetic and real datasets demonstrate the effectiveness of our proposed method. In comparison with several state-of-the-art methods, our proposed method can achieve promising performance for cognitive status prediction and also can help discover disease-related biomarkers.",0
"Anticancer effect of deuterium depleted water - Redox disbalance leads to oxidative stress. Despite the convincing empirical evidence that deuterium depleted water (DDW, 25-125 ppm deuterium) has anticancer effect, the molecular mechanism remains unclear. Here, redox proteomics investigation of the DDW action in A549 cells revealed an increased level of oxidative stress, whereas expression proteomics in combination with thermal profiling uncovered crucial role of mitochondrial proteins. In the proposed scenario, reversal of the normally positive deuterium gradient across the inner membrane leads to an increased export of protons from the matrix to intermembrane space and an increase in the mitochondrial membrane potential, enhancing the production of reactive oxygen species (ROS). The resulting oxidative stress leads to slower growth and can induce apoptosis. However, further deuterium depletion in ambient water triggers a feedback mechanism, which leads to restoration of the redox equilibrium and resumed growth. The DDW-induced oxidative stress, verified by traditional biochemical assays, may be helpful as an adjuvant to ROS-inducing anticancer therapy.",0
"Augmented Bladder Tumor Detection Using Deep Learning. Adequate tumor detection is critical in complete transurethral resection of bladder tumor (TURBT) to reduce cancer recurrence, but up to 20% of bladder tumors are missed by standard white light cystoscopy. Deep learning augmented cystoscopy may improve tumor localization, intraoperative navigation, and surgical resection of bladder cancer. We aimed to develop a deep learning algorithm for augmented cystoscopic detection of bladder cancer. Patients undergoing cystoscopy/TURBT were recruited and white light videos were recorded. Video frames containing histologically confirmed papillary urothelial carcinoma were selected and manually annotated. We constructed CystoNet, an image analysis platform based on convolutional neural networks, for automated bladder tumor detection using a development dataset of 95 patients for algorithm training and five patients for testing. Diagnostic performance of CystoNet was validated prospectively in an additional 54 patients. In the validation dataset, per-frame sensitivity and specificity were 90.9% (95% confidence interval [CI], 90.3-91.6%) and 98.6% (95% CI, 98.5-98.8%), respectively. Per-tumor sensitivity was 90.9% (95% CI, 90.3-91.6%). CystoNet detected 39 of 41 papillary and three of three flat bladder cancers. With high sensitivity and specificity, CystoNet may improve the diagnostic yield of cystoscopy and efficacy of TURBT. PATIENT SUMMARY: Conventional cystoscopy has recognized shortcomings in bladder cancer detection, with implications for recurrence. Cystoscopy augmented with artificial intelligence may improve cancer detection and resection.",1
"Identification of a novel class of RIP1/RIP3 dual inhibitors that impede cell death and inflammation in mouse abdominal aortic aneurysm models. Receptor interacting protein kinase-1 and -3 (RIP1 and RIP3) are essential mediators of cell death processes and participate in inflammatory responses. Our group recently demonstrated that gene deletion of Rip3 or pharmacological inhibition of RIP1 attenuated pathogenesis of abdominal aortic aneurysm (AAA), a life-threatening degenerative vascular disease characterized by depletion of smooth muscle cells (SMCs), inflammation, negative extracellular matrix remodeling, and progressive expansion of aorta. The goal of this study was to develop drug candidates for AAA and other disease conditions involving cell death and inflammation. We screened 1141 kinase inhibitors for their ability to block necroptosis using the RIP1 inhibitor Necrostatin-1s (Nec-1s) as a selection baseline. Positive compounds were further screened for cytotoxicity and virtual binding to RIP3. A cluster of top hits, represented by GSK2593074A (GSK’074), displayed structural similarity to the established RIP3 inhibitor GSK’843. In multiple cell types including mouse SMCs, fibroblasts (L929), bone marrow derived macrophages (BMDM), and human colon epithelial cells (HT29), GSK’074 inhibited necroptosis with an IC50 of ~3 nM. Furthermore, GSK’074, but not Nec-1s, blocked cytokine production by SMCs. Biochemical analyses identified both RIP1 and RIP3 as the biological targets of GSK’074. Unlike GSK’843 which causes profound apoptosis at high doses (>3 µM), GSK’074 showed no detectable cytotoxicity even at 20 µM. Daily intraperitoneal injection of GSK’074 at 0.93 mg/kg significantly attenuated aortic expansion in two mouse models of AAA (calcium phosphate: DMSO 66.06 ± 9.17% vs GSK’074 27.36 ± 8.25%, P < 0.05; Angiotensin II: DMSO 85.39 ± 15.76% vs GSK’074 36.28 ± 5.76%, P < 0.05). Histologically, GSK’074 treatment diminished cell death and macrophage infiltration in aneurysm-prone aortae. Together, our data suggest that GSK’074 represents a new class of necroptosis inhibitors with dual targeting ability to both RIP1 and RIP3. The high potency and minimum cytotoxicity make GSK’074 a desirable drug candidate of pharmacological therapies to attenuate AAA progression and other necroptosis related diseases.",0
"The diagnostic accuracy of dermoscopy for basal cell carcinoma: A systematic review and meta-analysis. Background: Dermoscopy is a noninvasive technique for the diagnosis of skin lesions. Its accuracy for basal cell carcinoma (BCC) has not been systematically studied. Objective: We sought to systematically investigate the accuracy of dermoscopy for the diagnosis of BCC compared with examination with the naked eye. Methods: A systematic review of studies reporting the accuracy of naked eye examination and dermoscopy for the diagnosis of BCC was conducted. A meta-analysis for sensitivity and specificity was performed using a bivariate mixed-effects logistic regression modeling framework. Results: Seventeen studies were identified. The pooled sensitivity and specificity of dermoscopy for the diagnosis of BCC were 91.2% and 95%, respectively. In studies comparing test performance, adding dermoscopy to naked eye examination improved sensitivity from 66.9% to 85% (P =.0001) and specificity from 97.2% to 98.2% (P =.006). The sensitivity and specificity of dermoscopy were higher for pigmented than nonpigmented BCC. Sensitivity increased when dermoscopy was performed by experts and when the diagnosis was based on in-person dermoscopy as opposed to dermoscopic photographs. Limitations: Significant heterogeneity among studies with a medium-to-high risk of bias. Conclusion: Dermoscopy is a sensitive and specific add-on tool for the diagnosis of BCC. It is especially valuable for pigmented BCC.",0
"Modeling DNA Unwinding by AddAB Helicase–Nuclease and Modulation by Chi Sequences: Comparison with AdnAB and RecBCD. Introduction: AddAB enzyme is a helicase–nuclease complex that initiates recombinational repair of double-stranded DNA breaks. It catalyzes processive DNA unwinding and concomitant resection of the unwound strands, which are modulated by the recognition of a recombination hotspot called Chi in the 3′-terminated strand. Despite extensive structural, biochemical and single molecule studies, the detailed molecular mechanism of DNA unwinding by the complex and modulation by Chi sequence remains unclear. Methods: A model of DNA unwinding by the AddAB complex and modulation by Chi recognition was presented, based on which the dynamics of AddAB complex was studied analytically. Results: The theoretical results explain well the available experimental data on effect of DNA sequence on velocity, effect of Chi recognition on velocity, static disorder peculiar to the AddAB complex, and dynamics of pausing of wild-type and mutant AddAB complexes occurring at Chi or Chi-like sequence. Predictions were provided. Comparisons of AddAB complex with other helicase–nuclease complexes such as RecBCD and AdnAB were made. Conclusions: The study has strong implications for the molecular mechanism of DNA unwinding by the AddAB complex. The intriguing issues are addressed of why Chi recognition is an inefficient process, how AddAB complex pauses upon recognizing Chi sequence, how the paused state transits to the translocating state, why the mutant AddAB with a stronger affinity to Chi sequence has a shorter pausing lifetime, why the pausing lifetime is sensitive to the solution temperature, and so on.",0
"Relation path feature embedding based convolutional neural network method for drug discovery. BACKGROUND: Drug development is an expensive and time-consuming process. Literature-based discovery has played a critical role in drug development and may be a supplementary method to help scientists speed up the discovery of drugs. METHODS: Here, we propose a relation path features embedding based convolutional neural network model with attention mechanism for drug discovery from literature, which we denote as PACNN. First, we use predications from biomedical abstracts to construct a biomedical knowledge graph, and then apply a path ranking algorithm to extract drug-disease relation path features on the biomedical knowledge graph. After that, we use these drug-disease relation features to train a convolutional neural network model which combined with the attention mechanism. Finally, we employ the trained models to mine drugs for treating diseases. RESULTS: The experiment shows that the proposed model achieved promising results, comparing to several random walk algorithms. CONCLUSIONS: In this paper, we propose a relation path features embedding based convolutional neural network with attention mechanism for discovering potential drugs from literature. Our method could be an auxiliary method for drug discovery, which can speed up the discovery of new drugs for the incurable diseases.",1
"HiSSI: High-order SNP-SNP interactions detection based on efficient significant pattern and differential evolution. Background: Detecting single nucleotide polymorphism (SNP) interactions is an important and challenging task in genome-wide association studies (GWAS). Various efforts have been devoted to detect SNP interactions. However, the large volume of SNP datasets results in such a big number of high-order SNP combinations that restrict the power of detecting interactions. Methods: In this paper, to combat with this challenge, we propose a two-stage approach (called HiSSI) to detect high-order SNP-SNP interactions. In the screening stage, HiSSI employs a statistically significant pattern that takes into account family wise error rate, to control false positives and to effectively screen two-locus combinations candidate set. In the searching stage, HiSSI applies two different search strategies (exhaustive search and heuristic search based on differential evolution along with χ 2-test) on candidate pairwise SNP combinations to detect high-order SNP interactions. Results: Extensive experiments on simulated datasets are conducted to evaluate HiSSI and recently proposed and related approaches on both two-locus and three-locus disease models. A real genome-wide dataset: Breast cancer dataset collected from the Wellcome Trust Case Control Consortium (WTCCC) is also used to test HiSSI. Conclusions: Simulated experiments on both two-locus and three-locus disease models show that HiSSI is more powerful than other related approaches. Real experiment on breast cancer dataset, in which HiSSI detects some significantly two-locus and three-locus interactions associated with breast cancer, again corroborate the effectiveness of HiSSI in high-order SNP-SNP interaction identification.",0
"Spread of alpha-synuclein pathology through the brain connectome is modulated by selective vulnerability and predicted by network analysis. Studies of patients afflicted by neurodegenerative diseases suggest that misfolded proteins spread through the brain along anatomically connected networks, prompting progressive decline. Recently, mouse models have recapitulated the cell-to-cell transmission of pathogenic proteins and neuron death observed in patients. However, the factors regulating the spread of pathogenic proteins remain a matter of debate due to an incomplete understanding of how vulnerability functions in the context of spread. Here we use quantitative pathology mapping in the mouse brain, combined with network modeling to understand the spatiotemporal pattern of spread. Patterns of alpha-synuclein pathology are well described by a network model that is based on two factors: anatomical connectivity and endogenous alpha-synuclein expression. The map and model allow the assessment of selective vulnerability to alpha-synuclein pathology development and neuron death. Finally, we use quantitative pathology to understand how the G2019S LRRK2 genetic risk factor affects the spread and toxicity of alpha-synuclein pathology.",0
"The intrinsic attractor manifold and population dynamics of a canonical cognitive circuit across waking and sleep. Neural circuits construct distributed representations of key variables-external stimuli or internal constructs of quantities relevant for survival, such as an estimate of one's location in the world-as vectors of population activity. Although population activity vectors may have thousands of entries (dimensions), we consider that they trace out a low-dimensional manifold whose dimension and topology match the represented variable. This manifold perspective enables blind discovery and decoding of the represented variable using only neural population activity (without knowledge of the input, output, behavior or topography). We characterize and directly visualize manifold structure in the mammalian head direction circuit, revealing that the states form a topologically nontrivial one-dimensional ring. The ring exhibits isometry and is invariant across waking and rapid eye movement sleep. This result directly demonstrates that there are continuous attractor dynamics and enables powerful inference about mechanism. Finally, external rather than internal noise limits memory fidelity, and the manifold approach reveals new dynamical trajectories during sleep.",0
"Deep learning for cardiovascular medicine: a practical primer. Deep learning (DL) is a branch of machine learning (ML) showing increasing promise in medicine, to assist in data classification, novel disease phenotyping and complex decision making. Deep learning is a form of ML typically implemented via multi-layered neural networks. Deep learning has accelerated by recent advances in computer hardware and algorithms and is increasingly applied in e-commerce, finance, and voice and image recognition to learn and classify complex datasets. The current medical literature shows both strengths and limitations of DL. Strengths of DL include its ability to automate medical image interpretation, enhance clinical decision-making, identify novel phenotypes, and select better treatment pathways in complex diseases. Deep learning may be well-suited to cardiovascular medicine in which haemodynamic and electrophysiological indices are increasingly captured on a continuous basis by wearable devices as well as image segmentation in cardiac imaging. However, DL also has significant weaknesses including difficulties in interpreting its models (the 'black-box' criticism), its need for extensive adjudicated ('labelled') data in training, lack of standardization in design, lack of data-efficiency in training, limited applicability to clinical trials, and other factors. Thus, the optimal clinical application of DL requires careful formulation of solvable problems, selection of most appropriate DL algorithms and data, and balanced interpretation of results. This review synthesizes the current state of DL for cardiovascular clinicians and investigators, and provides technical context to appreciate the promise, pitfalls, near-term challenges, and opportunities for this exciting new area.",0
"Early inhibition of endothelial retinoid uptake upon myocardial infarction restores cardiac function and prevents cell, tissue, and animal death. Physiologically, following myocardial infarction (MI), retinoid levels elevate locally in the infarcted area. Whereas therapeutic systemic application of retinoids was shown to reduce the progression of ventricular dilatation and the onset of heart failure, the role of acute physiologically increased retinoids in the infarction zone is unknown to date. To reveal the role of local retinoids in the MI zone is the central aim of this study. Using human cell culture and co-culture models for hypoxia as well as various assays systems, lentivirus-based transgene expression, in silico molecular docking studies, and an MI model in rats, we analysed the impact of the retinoid all-trans retinoic acid (ATRA) on cell signalling, cell viability, tissue survival, heart function, and MI-induced death in rats. Based on our results, ATRA-mediated signalling does aggravate the MI phenotype (e.g. 2.5-fold increased mortality compared to control), whereas 5′-methoxyleoligin (5ML), a new agent which interferes with ATRA-signalling rescues the ATRA-dependent phenotype. On the molecular level, ATRA signalling causes induction of TXNIP, a potent inhibitor of the physiological antioxidant thioredoxin (TRX1) and sensitizes cells to necrotic cell death upon hypoxia. 5ML-mediated prevention of ATRA effects were shown to be based on the inhibition of cellular ATRA uptake by interference with the cholesterol (and retinol) binding motif of the transmembrane protein STRA6. 5ML-mediated inhibition of ATRA uptake led to a strong reduction of ATRA-dependent gene expression, reduced ROS formation, and protection from necrotic cell death. As 5ML exerted a cardioprotective effect, also independent of its inhibition of cellular ATRA uptake, the agent likely has another cardioprotective property, which may rely on the induction of TRX1 activity. In summary, this is the first study to show i) that local retinoids in the early MI zone may worsen disease outcome, ii) that inhibition of endothelial retinoid uptake using 5ML may constitute a novel treatment strategy, and iii) that targeting endothelial and myocardial retinoid uptake (e.g. via STRA6 inhibition) may constitute a novel treatment target in acute MI.",0
"Scrublet: Computational Identification of Cell Doublets in Single-Cell Transcriptomic Data. Single-cell RNA-sequencing has become a widely used, powerful approach for studying cell populations. However, these methods often generate multiplet artifacts, where two or more cells receive the same barcode, resulting in a hybrid transcriptome. In most experiments, multiplets account for several percent of transcriptomes and can confound downstream data analysis. Here, we present Single-Cell Remover of Doublets (Scrublet), a framework for predicting the impact of multiplets in a given analysis and identifying problematic multiplets. Scrublet avoids the need for expert knowledge or cell clustering by simulating multiplets from the data and building a nearest neighbor classifier. To demonstrate the utility of this approach, we test Scrublet on several datasets that include independent knowledge of cell multiplets. Scrublet is freely available for download at github.com/AllonKleinLab/scrublet.",0
"AGA Clinical Practice Update on Diagnosis and Monitoring of Celiac Disease-Changing Utility of Serology and Histologic Measures: Expert Review. PURPOSE: The purpose of this clinical practice update is to define key modalities in the diagnosis and monitoring of celiac disease (CD) in adults as well as in children and adolescents. METHODS: The recommendations outlined in this expert review are based on available published evidence, including cohort and case-control studies of the diagnostic process as well as controlled and descriptive studies of disease management. Best Practice Advice 1: Serology is a crucial component of the detection and diagnosis of CD, particularly tissue transglutaminase-immunoglobulin A (TG2-IgA), IgA testing, and less frequently, endomysial IgA testing. Best Practice Advice 2: Thorough histological analysis of duodenal biopsies with Marsh classification, counting of lymphocytes per high-power field, and morphometry is important for diagnosis as well as for differential diagnosis. Best Practice Advice 2a: TG2-IgA, at high levels (> x10 upper normal limit) is a reliable and accurate test for diagnosing active CD. When such a strongly positive TG2-IgA is combined with a positive endomysial antibody in a second blood sample, the positive predictive value for CD is virtually 100%. In adults, esophagogastroduodenoscopy (EGD) and duodenal biopsies may then be performed for purposes of differential diagnosis. Best Practice Advice 3: IgA deficiency is an infrequent but important explanation for why patients with CD may be negative on IgA isotype testing despite strong suspicion. Measuring total IgA levels, IgG deamidated gliadin antibody tests, and TG2-IgG testing in that circumstance is recommended. Best Practice Advice 4: IgG isotype testing for TG2 antibody is not specific in the absence of IgA deficiency. Best Practice Advice 5: In patients found to have CD first by intestinal biopsies, celiac-specific serology should be undertaken as a confirmatory test before initiation of a gluten-free diet (GFD). Best Practice Advice 6: In patients in whom CD is strongly suspected in the face of negative biopsies, TG2-IgA should still be performed and, if positive, repeat biopsies might be considered either at that time or sometime in the future. Best Practice Advice 7: Reduction or avoidance of gluten before diagnostic testing is discouraged, as it may reduce the sensitivity of both serology and biopsy testing. Best Practice Advice 8: When patients have already started on a GFD before diagnosis, we suggest that the patient go back on a normal diet with 3 slices of wheat bread daily preferably for 1 to 3 months before repeat determination of TG2-IgA. Best Practice Advice 9: Determination of HLA-DQ2/DQ8 has a limited role in the diagnosis of CD. Its value is largely related to its negative predictive value to rule out CD in patients who are seronegative in the face of histologic changes, in patients who did not have serologic confirmation at the time of diagnosis, and in those patients with a historic diagnosis of CD; especially as very young children before the introduction of celiac-specific serology. MANAGEMENT: Best Practice Advice 10: Celiac serology has a guarded role in the detection of continued intestinal injury, in particular as to sensitivity, as negative serology in a treated patient does not guarantee that the intestinal mucosa has healed. Persistently positive serology usually indicates ongoing intestinal damage and gluten exposure. Follow-up serology should be performed 6 and 12 months after diagnosis, and yearly thereafter. Best Practice Advice 11: Patients with persistent or relapsing symptoms, without other obvious explanations for those symptoms, should undergo endoscopic biopsies to determine healing even in the presence of negative TG2-IgA.",0
"Evaluation of algorithms for Multi-Modality Whole Heart Segmentation: An open-access grand challenge. Knowledge of whole heart anatomy is a prerequisite for many clinical applications. Whole heart segmentation (WHS), which delineates substructures of the heart, can be very valuable for modeling and analysis of the anatomy and functions of the heart. However, automating this segmentation can be challenging due to the large variation of the heart shape, and different image qualities of the clinical data. To achieve this goal, an initial set of training data is generally needed for constructing priors or for training. Furthermore, it is difficult to perform comparisons between different methods, largely due to differences in the datasets and evaluation metrics used. This manuscript presents the methodologies and evaluation results for the WHS algorithms selected from the submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017. The challenge provided 120 three-dimensional cardiac images covering the whole heart, including 60 CT and 60 MRI volumes, all acquired in clinical environments with manual delineation. Ten algorithms for CT data and eleven algorithms for MRI data, submitted from twelve groups, have been evaluated. The results showed that the performance of CT WHS was generally better than that of MRI WHS. The segmentation of the substructures for different categories of patients could present different levels of challenge due to the difference in imaging and variations of heart shapes. The deep learning (DL)-based methods demonstrated great potential, though several of them reported poor results in the blinded evaluation. Their performance could vary greatly across different network structures and training strategies. The conventional algorithms, mainly based on multi-atlas segmentation, demonstrated good performance, though the accuracy and computational efficiency could be limited. The challenge, including provision of the annotated training data and the blinded evaluation for submitted algorithms on the test data, continues as an ongoing benchmarking resource via its homepage (www.sdspeople.fudan.edu.cn/zhuangxiahai/0/mmwhs/).",1
"Improving automated pediatric bone age estimation using ensembles of models from the 2017 RSNA machine learning challenge. Purpose: To investigate improvements in performance for automatic bone age estimation that can be gained through model ensembling. Materials and Methods: A total of 48 submissions from the 2017 RSNA Pediatric Bone Age Machine Learning Challenge were used. Participants were provided with 12 611 pediatric hand radiographs with bone ages determined by a pediatric radiologist to develop models for bone age determination. The final results were determined using a test set of 200 radiographs labeled with the weighted average of six ratings. The mean pairwise model correlation and performance of all possible model combinations for ensembles of up to 10 models using the mean absolute deviation (MAD) were evaluated. A bootstrap analysis using the 200 test radiographs was conducted to estimate the true generalization MAD. Results: The estimated generalization MAD of a single model was 4.55 months. The best-performing ensemble consisted of four models with an MAD of 3.79 months. The mean pairwise correlation of models within this ensemble was 0.47. In comparison, the lowest achievable MAD by combining the highest-ranking models based on individual scores was 3.93 months using eight models with a mean pairwise model correlation of 0.67. Conclusion: Combining less-correlated, high-performing models resulted in better performance than naively combining the top-performing models. Machine learning competitions within radiology should be encouraged to spur development of heterogeneous models whose predictions can be combined to achieve optimal performance.",1
"Attention gated networks: Learning to leverage salient regions in medical images. We propose a novel attention gate (AG) model for medical image analysis that automatically learns to focus on target structures of varying shapes and sizes. Models trained with AGs implicitly learn to suppress irrelevant regions in an input image while highlighting salient features useful for a specific task. This enables us to eliminate the necessity of using explicit external tissue/organ localisation modules when using convolutional neural networks (CNNs). AGs can be easily integrated into standard CNN models such as VGG or U-Net architectures with minimal computational overhead while increasing the model sensitivity and prediction accuracy. The proposed AG models are evaluated on a variety of tasks, including medical image classification and segmentation. For classification, we demonstrate the use case of AGs in scan plane detection for fetal ultrasound screening. We show that the proposed attention mechanism can provide efficient object localisation while improving the overall prediction performance by reducing false positives. For segmentation, the proposed architecture is evaluated on two large 3D CT abdominal datasets with manual annotations for multiple organs. Experimental results show that AG models consistently improve the prediction performance of the base architectures across different datasets and training sizes while preserving computational efficiency. Moreover, AGs guide the model activations to be focused around salient regions, which provides better insights into how model predictions are made. The source code for the proposed AG models is publicly available.",1
"Clinical correlates of longitudinal MRI changes in CADASIL. Previous studies showed that various types of cerebral lesions, as assessed on MRI, largely contribute to the clinical severity of CADASIL. However, the clinical impact of longitudinal changes of classical markers of small vessel disease on conventional MRI has been only poorly investigated. One hundred sixty NOTCH3 mutation carriers (mean age ± SD, 49.8 ± 10.9 years) were followed over three years. Validated methods were used to determine the percent brain volume change (PBVC), number of incident lacunes, change of volume of white matter hyperintensities and change of number of cerebral microbleeds. Multivariable logistic regression analyses were performed to assess the independent association between changes of these MRI markers and incident clinical events. Mixed-effect multiple linear regression analyses were used to assess their association with changes of clinical scales. Over a mean period of 3.1 ± 0.2 years, incident lacunes are found independently associated with incident stroke and change of Trail Making Test Part B. PBVC is independently associated with all incident events and clinical scale changes except the modified Rankin Scale at three years. Our results suggest that, on conventional MRI, PBVC and the number of incident lacunes are the most sensitive and independent correlates of clinical worsening over three years in CADASIL.",0
"Activation of Caspase-6 Is Promoted by a Mutant Huntingtin Fragment and Blocked by an Allosteric Inhibitor Compound. Aberrant activation of caspase-6 (C6) in the absence of other hallmarks of apoptosis has been demonstrated in cells and tissues from patients with Huntington disease (HD) and animal models. C6 activity correlates with disease progression in patients with HD and the cleavage of mutant huntingtin (mHTT) protein is thought to strongly contribute to disease pathogenesis. Here we show that the mHTT1-586 fragment generated by C6 cleavage interacts with the zymogen form of the enzyme, stabilizing a conformation that contains an active site and is prone to full activation. This shift toward enhanced activity can be prevented by a small-molecule inhibitor that blocks the interaction between C6 and mHTT1-586. Molecular docking studies suggest that the inhibitor binds an allosteric site in the C6 zymogen. The interaction of mHTT1-586 with C6 may therefore promote a self-reinforcing, feedforward cycle of C6 zymogen activation and mHTT cleavage driving HD pathogenesis.",0
"Deep Learning for Chest Radiograph Diagnosis in the Emergency Department. BackgroundThe performance of a deep learning (DL) algorithm should be validated in actual clinical situations, before its clinical implementation.PurposeTo evaluate the performance of a DL algorithm for identifying chest radiographs with clinically relevant abnormalities in the emergency department (ED) setting.Materials and MethodsThis single-center retrospective study included consecutive patients who visited the ED and underwent initial chest radiography between January 1 and March 31, 2017. Chest radiographs were analyzed with a commercially available DL algorithm. The performance of the algorithm was evaluated by determining the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity at predefined operating cutoffs (high-sensitivity and high-specificity cutoffs). The sensitivities and specificities of the algorithm were compared with those of the on-call radiology residents who interpreted the chest radiographs in the actual practice by using McNemar tests. If there were discordant findings between the algorithm and resident, the residents reinterpreted the chest radiographs by using the algorithm's output.ResultsA total of 1135 patients (mean age, 53 years +/- 18; 582 men) were evaluated. In the identification of abnormal chest radiographs, the algorithm showed an AUC of 0.95 (95% confidence interval [CI]: 0.93, 0.96), a sensitivity of 88.7% (227 of 256 radiographs; 95% CI: 84.1%, 92.3%), and a specificity of 69.6% (612 of 879 radiographs; 95% CI: 66.5%, 72.7%) at the high-sensitivity cutoff and a sensitivity of 81.6% (209 of 256 radiographs; 95% CI: 76.3%, 86.2%) and specificity of 90.3% (794 of 879 radiographs; 95% CI: 88.2%, 92.2%) at the high-specificity cutoff. Radiology residents showed lower sensitivity (65.6% [168 of 256 radiographs; 95% CI: 59.5%, 71.4%], P < .001) and higher specificity (98.1% [862 of 879 radiographs; 95% CI: 96.9%, 98.9%], P < .001) compared with the algorithm. After reinterpretation of chest radiographs with use of the algorithm's outputs, the sensitivity of the residents improved (73.4% [188 of 256 radiographs; 95% CI: 68.0%, 78.8%], P = .003), whereas specificity was reduced (94.3% [829 of 879 radiographs; 95% CI: 92.8%, 95.8%], P < .001).ConclusionA deep learning algorithm used with emergency department chest radiographs showed diagnostic performance for identifying clinically relevant abnormalities and helped improve the sensitivity of radiology residents' evaluation.Published under a CC BY 4.0 license.Online supplemental material is available for this article.See also the editorial by Munera and Infante in this issue.",1
"Deep learning in ophthalmology: The technical and clinical considerations. The advent of computer graphic processing units, improvement in mathematical models and availability of big data has allowed artificial intelligence (AI) using machine learning (ML) and deep learning (DL) techniques to achieve robust performance for broad applications in social-media, the internet of things, the automotive industry and healthcare. DL systems in particular provide improved capability in image, speech and motion recognition as well as in natural language processing. In medicine, significant progress of AI and DL systems has been demonstrated in image-centric specialties such as radiology, dermatology, pathology and ophthalmology. New studies, including pre-registered prospective clinical trials, have shown DL systems are accurate and effective in detecting diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD), retinopathy of prematurity, refractive error and in identifying cardiovascular risk factors and diseases, from digital fundus photographs. There is also increasing attention on the use of AI and DL systems in identifying disease features, progression and treatment response for retinal diseases such as neovascular AMD and diabetic macular edema using optical coherence tomography (OCT). Additionally, the application of ML to visual fields may be useful in detecting glaucoma progression. There are limited studies that incorporate clinical data including electronic health records, in AL and DL algorithms, and no prospective studies to demonstrate that AI and DL algorithms can predict the development of clinical eye disease. This article describes global eye disease burden, unmet needs and common conditions of public health importance for which AI and DL systems may be applicable. Technical and clinical aspects to build a DL system to address those needs, and the potential challenges for clinical adoption are discussed. AI, ML and DL will likely play a crucial role in clinical ophthalmology practice, with implications for screening, diagnosis and follow up of the major causes of vision impairment in the setting of ageing populations globally.",0
"Development of a Selective CDK7 Covalent Inhibitor Reveals Predominant Cell-Cycle Phenotype. Cyclin-dependent kinase 7 (CDK7) regulates both cell cycle and transcription, but its precise role remains elusive. We previously described THZ1, a CDK7 inhibitor, which dramatically inhibits superenhancer-associated gene expression. However, potent CDK12/13 off-target activity obscured CDK7s contribution to this phenotype. Here, we describe the discovery of a highly selective covalent CDK7 inhibitor. YKL-5-124 causes arrest at the G1/S transition and inhibition of E2F-driven gene expression; these effects are rescued by a CDK7 mutant unable to covalently engage YKL-5-124, demonstrating on-target specificity. Unlike THZ1, treatment with YKL-5-124 resulted in no change to RNA polymerase II C-terminal domain phosphorylation; however, inhibition could be reconstituted by combining YKL-5-124 and THZ531, a selective CDK12/13 inhibitor, revealing potential redundancies in CDK control of gene transcription. These findings highlight the importance of CDK7/12/13 polypharmacology for anti-cancer activity of THZ1 and posit that selective inhibition of CDK7 may be useful for treatment of cancers marked by E2F misregulation. Olson et al. describe the development and characterization of YKL-5-124, a potent, selective, and covalent CDK7 inhibitor. YKL-5-124 displays biochemical and cellular selectivity for CDK7 over CDK12/13, structurally related kinases. CDK7 inhibition by YKL-5-124 induces a strong cell-cycle arrest and a surprisingly weak effect on RNA Pol II phosphorylation.",0
"Dysregulation of RNA Splicing in Tauopathies. Pathological aggregation of RNA binding proteins (RBPs) is associated with dysregulation of RNA splicing in PS19 P301S tau transgenic mice and in Alzheimer's disease brain tissues. The dysregulated splicing particularly affects genes involved in synaptic transmission. The effects of neuroprotective TIA1 reduction on PS19 mice are also examined. TIA1 reduction reduces disease-linked alternative splicing events for the major synaptic mRNA transcripts examined, suggesting that normalization of RBP functions is associated with the neuroprotection. Use of the NetDecoder informatics algorithm identifies key upstream biological targets, including MYC and EGFR, underlying the transcriptional and splicing changes in the protected compared to tauopathy mice. Pharmacological inhibition of MYC and EGFR activity in neuronal cultures tau recapitulates the neuroprotective effects of TIA1 reduction. These results demonstrate that dysfunction of RBPs and RNA splicing processes are major elements of the pathophysiology of tauopathies, as well as potential therapeutic targets for tauopathies.",0
"Intracellular MLCK1 diversion reverses barrier loss to restore mucosal homeostasis. Epithelial barrier loss is a driver of intestinal and systemic diseases. Myosin light chain kinase (MLCK) is a key effector of barrier dysfunction and a potential therapeutic target, but enzymatic inhibition has unacceptable toxicity. Here, we show that a unique domain within the MLCK splice variant MLCK1 directs perijunctional actomyosin ring (PAMR) recruitment. Using the domain structure and multiple screens, we identify a domain-binding small molecule (divertin) that blocks MLCK1 recruitment without inhibiting enzymatic function. Divertin blocks acute, tumor necrosis factor (TNF)-induced MLCK1 recruitment as well as downstream myosin light chain (MLC) phosphorylation, barrier loss, and diarrhea in vitro and in vivo. Divertin corrects barrier dysfunction and prevents disease development and progression in experimental inflammatory bowel disease. Beyond applications of divertin in gastrointestinal disease, this general approach to enzymatic inhibition by preventing access to specific subcellular sites provides a new paradigm for safely and precisely targeting individual properties of enzymes with multiple functions.",0
"Incorporating medical code descriptions for diagnosis prediction in healthcare. BACKGROUND: Diagnosis aims to predict the future health status of patients according to their historical electronic health records (EHR), which is an important yet challenging task in healthcare informatics. Existing diagnosis prediction approaches mainly employ recurrent neural networks (RNN) with attention mechanisms to make predictions. However, these approaches ignore the importance of code descriptions, i.e., the medical definitions of diagnosis codes. We believe that taking diagnosis code descriptions into account can help the state-of-the-art models not only to learn meaning code representations, but also to improve the predictive performance, especially when the EHR data are insufficient. METHODS: We propose a simple, but general diagnosis prediction framework, which includes two basic components: diagnosis code embedding and predictive model. To learn the interpretable code embeddings, we apply convolutional neural networks (CNN) to model medical descriptions of diagnosis codes extracted from online medical websites. The learned medical embedding matrix is used to embed the input visits into vector representations, which are fed into the predictive models. Any existing diagnosis prediction approach (referred to as the base model) can be cast into the proposed framework as the predictive model (called the enhanced model). RESULTS: We conduct experiments on two real medical datasets: the MIMIC-III dataset and the Heart Failure claim dataset. Experimental results show that the enhanced diagnosis prediction approaches significantly improve the prediction performance. Moreover, we validate the effectiveness of the proposed framework with insufficient EHR data. Finally, we visualize the learned medical code embeddings to show the interpretability of the proposed framework. CONCLUSIONS: Given the historical visit records of a patient, the proposed framework is able to predict the next visit information by incorporating medical code descriptions.",1
"Identification and weighting of kidney allocation criteria: a novel multi-expert fuzzy method. BACKGROUND: Kidney allocation is a multi-criteria and complex decision-making problem, which should also consider ethical issues in addition to the medical aspects. Leading countries in this field use a point scoring system to allocate kidneys. Hence, the purpose of this study is to identify and weight the kidney allocation criteria considering the balance between utility and equity. METHODS: To do this, a new fuzzy hybrid approach is proposed, which consists of two steps: In the first step, Fuzzy Delphi Method (FDM) is used to identify the effective criteria in the kidney allocation algorithm. In the second step, Intuitionistic Fuzzy Analytic Hierarchy Process (IF-AHP) is employed to determine the weight of the criteria. RESULTS: The results showed that the highest weight belongs to ""Medical emergency"" criterion and the lowest weight to ""5 HLA mismatches"", which is similar to Euro-transplant kidney allocation system (ETKAS). The developed method is evaluated in two steps. First, the proposed model is implemented using a real case study from the Iranian Kidney Allocation System. It was shown that the proposed model has the potential to improve allocation outcome. Second, the proposed model's superiority to the current model is approved by the experts using the results display in the profile matrix. Finally, sensitivity analysis is performed to check the robustness of the proposed model. CONCLUSIONS: This paper contributes to the kidney allocation literature by doing the following: (a) developing a comprehensive framework for identification and weightings of criteria for kidney allocation, (b) using, for the first time, the IF-AHP technique to consider hesitancy of decision makers and uncertainty in organ allocation, and (c) proposing an appropriate framework for the countries that intend to improve or modify their organ allocation system.",0
"DoubletDecon: Deconvoluting Doublets from Single-Cell RNA-Sequencing Data. Methods for single-cell RNA sequencing (scRNA-seq) have greatly advanced in recent years. While droplet- and well-based methods have increased the capture frequency of cells for scRNA-seq, these technologies readily produce technical artifacts, such as doublet cell captures. Doublets occurring between distinct cell types can appear as hybrid scRNA-seq profiles, but do not have distinct transcriptomes from individual cell states. We introduce DoubletDecon, an approach that detects doublets with a combination of deconvolution analyses and the identification of unique cell-state gene expression. We demonstrate the ability of DoubletDecon to identify synthetic, mixed-species, genetic, and cell-hashing cell doublets from scRNA-seq datasets of varying cellular complexity with a high sensitivity relative to alternative approaches. Importantly, this algorithm prevents the prediction of valid mixed-lineage and transitional cell states as doublets by considering their unique gene expression. DoubletDecon has an easy-to-use graphical user interface and is compatible with diverse species and unsupervised population detection algorithms.",0
"Genetically elevated circulating homocysteine concentrations increase the risk of diabetic kidney disease in Chinese diabetic patients. Diabetic kidney disease (DKD) is a devastating and frequent complication of diabetes mellitus. Here, we first adopted methylenetetrahytrofolate reductase (MTHFR) gene C677T polymorphism as an instrument to infer the possible causal relevance between circulating homocysteine and DKD risk in a Chinese population and next attempted to build a risk prediction model for DKD. This is a hospital-based case-control association study. Total 1107 study participants were diagnosed with type 2 diabetes mellitus, including 547 patients with newly diagnosed and histologically confirmed DKD. MTHFR gene C677T polymorphism was determined using the TaqMan method. Carriers of 677TT genotype (14.55 μmol/L) had significantly higher homocysteine concentrations than carriers of 677CT genotype (12.88 μmol/L) (P < 0.001). Carriers of 677TT genotype had a 1.57-fold increased risk of DKD (odds ratio: 1.57, 95% CI: 1.21-2.05, P = 0.001) relative to carriers of 677CT genotype after adjusting for confounders. Mendelian randomization analysis revealed that the odds ratio for DKD relative to diabetes mellitus per 5 μmol/L increment of circulating homocysteine concentrations was 3.86 (95% confidence interval: 1.21-2.05, P < 0.001). In the Logistic regression analysis, hypertension, homocysteine and triglyceride were significantly associated with an increased risk of DKD and they constituted a risk prediction model with good test performance and discriminatory capacity. Taken together, our findings provide evidence that elevated circulating homocysteine concentrations were causally associated with an increased risk of DKD in Chinese diabetic patients.",0
"Deep Learning and Glaucoma Specialists: The Relative Importance of Optic Disc Features to Predict Glaucoma Referral in Fundus Photographs. PURPOSE: To develop and validate a deep learning (DL) algorithm that predicts referable glaucomatous optic neuropathy (GON) and optic nerve head (ONH) features from color fundus images, to determine the relative importance of these features in referral decisions by glaucoma specialists (GSs) and the algorithm, and to compare the performance of the algorithm with eye care providers. DESIGN: Development and validation of an algorithm. PARTICIPANTS: Fundus images from screening programs, studies, and a glaucoma clinic. METHODS: A DL algorithm was trained using a retrospective dataset of 86 618 images, assessed for glaucomatous ONH features and referable GON (defined as ONH appearance worrisome enough to justify referral for comprehensive examination) by 43 graders. The algorithm was validated using 3 datasets: dataset A (1205 images, 1 image/patient; 18.1% referable), images adjudicated by panels of GSs; dataset B (9642 images, 1 image/patient; 9.2% referable), images from a diabetic teleretinal screening program; and dataset C (346 images, 1 image/patient; 81.7% referable), images from a glaucoma clinic. MAIN OUTCOME MEASURES: The algorithm was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity for referable GON and glaucomatous ONH features. RESULTS: The algorithm's AUC for referable GON was 0.945 (95% confidence interval [CI], 0.929-0.960) in dataset A, 0.855 (95% CI, 0.841-0.870) in dataset B, and 0.881 (95% CI, 0.838-0.918) in dataset C. Algorithm AUCs ranged between 0.661 and 0.973 for glaucomatous ONH features. The algorithm showed significantly higher sensitivity than 7 of 10 graders not involved in determining the reference standard, including 2 of 3 GSs, and showed higher specificity than 3 graders (including 1 GS), while remaining comparable to others. For both GSs and the algorithm, the most crucial features related to referable GON were: presence of vertical cup-to-disc ratio of 0.7 or more, neuroretinal rim notching, retinal nerve fiber layer defect, and bared circumlinear vessels. CONCLUSIONS: A DL algorithm trained on fundus images alone can detect referable GON with higher sensitivity than and comparable specificity to eye care providers. The algorithm maintained good performance on an independent dataset with diagnoses based on a full glaucoma workup.",1
"Sample size calculations for model validation in linear regression analysis. BACKGROUND: Linear regression analysis is a widely used statistical technique in practical applications. For planning and appraising validation studies of simple linear regression, an approximate sample size formula has been proposed for the joint test of intercept and slope coefficients. METHODS: The purpose of this article is to reveal the potential drawback of the existing approximation and to provide an alternative and exact solution of power and sample size calculations for model validation in linear regression analysis. RESULTS: A fetal weight example is included to illustrate the underlying discrepancy between the exact and approximate methods. Moreover, extensive numerical assessments were conducted to examine the relative performance of the two distinct procedures. CONCLUSIONS: The results show that the exact approach has a distinct advantage over the current method with greater accuracy and high robustness.",0
"Clinical practice recommendations for the diagnosis and management of X-linked hypophosphataemia. X-linked hypophosphataemia (XLH) is the most common cause of inherited phosphate wasting and is associated with severe complications such as rickets, lower limb deformities, pain, poor mineralization of the teeth and disproportionate short stature in children as well as hyperparathyroidism, osteomalacia, enthesopathies, osteoarthritis and pseudofractures in adults. The characteristics and severity of XLH vary between patients. Because of its rarity, the diagnosis and specific treatment of XLH are frequently delayed, which has a detrimental effect on patient outcomes. In this Evidence-Based Guideline, we recommend that the diagnosis of XLH is based on signs of rickets and/or osteomalacia in association with hypophosphataemia and renal phosphate wasting in the absence of vitamin D or calcium deficiency. Whenever possible, the diagnosis should be confirmed by molecular genetic analysis or measurement of levels of fibroblast growth factor 23 (FGF23) before treatment. Owing to the multisystemic nature of the disease, patients should be seen regularly by multidisciplinary teams organized by a metabolic bone disease expert. In this article, we summarize the current evidence and provide recommendations on features of the disease, including new treatment modalities, to improve knowledge and provide guidance for diagnosis and multidisciplinary care.",0
"A network embedding model for pathogenic genes prediction by multi-path random walking on heterogeneous network. Background: Prediction of pathogenic genes is crucial for disease prevention, diagnosis, and treatment. But traditional genetic localization methods are often technique-difficulty and time-consuming. With the development of computer science, computational biology has gradually become one of the main methods for finding candidate pathogenic genes. Methods: We propose a pathogenic genes prediction method based on network embedding which is called Multipath2vec. Firstly, we construct an heterogeneous network which is called GP-network. It is constructed based on three kinds of relationships between genes and phenotypes, including correlations between phenotypes, interactions between genes and known gene-phenotype pairs. Then in order to embedding the network better, we design the multi-path to guide random walk in GP-network. The multi-path includes multiple paths between genes and phenotypes which can capture complex structural information of heterogeneous network. Finally, we use the learned vector representation of each phenotype and protein to calculate the similarities and rank according to the similarities between candidate genes and the target phenotype. Results: We implemented Multipath2vec and four baseline approaches (i.e., CATAPULT, PRINCE, Deepwalk and Metapath2vec) on many-genes gene-phenotype data, single-gene gene-phenotype data and whole gene-phenotype data. Experimental results show that Multipath2vec outperformed the state-of-the-art baselines in pathogenic genes prediction task. Conclusions: We propose Multipath2vec that can be utilized to predict pathogenic genes and experimental results show the higher accuracy of pathogenic genes prediction.",0
"Apolipoprotein AI) Promotes Atherosclerosis Regression in Diabetic Mice by Suppressing Myelopoiesis and Plaque Inflammation. BACKGROUND: Despite robust cholesterol lowering, cardiovascular disease risk remains increased in patients with diabetes mellitus. Consistent with this, diabetes mellitus impairs atherosclerosis regression after cholesterol lowering in humans and mice. In mice, this is attributed in part to hyperglycemia-induced monocytosis, which increases monocyte entry into plaques despite cholesterol lowering. In addition, diabetes mellitus skews plaque macrophages toward an atherogenic inflammatory M1 phenotype instead of toward the atherosclerosis-resolving M2 state typical with cholesterol lowering. Functional high-density lipoprotein (HDL), typically low in patients with diabetes mellitus, reduces monocyte precursor proliferation in murine bone marrow and has anti-inflammatory effects on human and murine macrophages. Our study aimed to test whether raising functional HDL levels in diabetic mice prevents monocytosis, reduces the quantity and inflammation of plaque macrophages, and enhances atherosclerosis regression after cholesterol lowering. METHODS: Aortic arches containing plaques developed in Ldlr(-/-) mice were transplanted into either wild-type, diabetic wild-type, or diabetic mice transgenic for human apolipoprotein AI, which have elevated functional HDL. Recipient mice all had low levels of low-density lipoprotein cholesterol to promote plaque regression. After 2 weeks, plaques in recipient mouse aortic grafts were examined. RESULTS: Diabetic wild-type mice had impaired atherosclerosis regression, which was normalized by raising HDL levels. This benefit was linked to suppressed hyperglycemia-driven myelopoiesis, monocytosis, and neutrophilia. Increased HDL improved cholesterol efflux from bone marrow progenitors, suppressing their proliferation and monocyte and neutrophil production capacity. In addition to reducing circulating monocytes available for recruitment into plaques, in the diabetic milieu, HDL suppressed the general recruitability of monocytes to inflammatory sites and promoted plaque macrophage polarization to the M2, atherosclerosis-resolving state. There was also a decrease in plaque neutrophil extracellular traps, which are atherogenic and increased by diabetes mellitus. CONCLUSIONS: Raising apolipoprotein AI and functional levels of HDL promotes multiple favorable changes in the production of monocytes and neutrophils and in the inflammatory environment of atherosclerotic plaques of diabetic mice after cholesterol lowering and may represent a novel approach to reduce cardiovascular disease risk in people with diabetes mellitus.",0
"A network clustering based feature selection strategy for classifying autism spectrum disorder. Background: Advanced non-invasive neuroimaging techniques offer new approaches to study functions and structures of human brains. Whole-brain functional networks obtained from resting state functional magnetic resonance imaging has been widely used to study brain diseases like autism spectrum disorder (ASD). Auto-classification of ASD has become an important issue. Existing classification methods for ASD are based on features extracted from the whole-brain functional networks, which may be not discriminant enough for good performance. Methods: In this study, we propose a network clustering based feature selection strategy for classifying ASD. In our proposed method, we first apply symmetric non-negative matrix factorization to divide brain networks into four modules. Then we extract features from one of four modules called default mode network (DMN) and use them to train several classifiers for ASD classification. Results: The computational experiments show that our proposed method achieves better performances than those trained with features extracted from the whole brain network. Conclusion: It is a good strategy to train the classifiers for ASD based on features from the default mode subnetwork.",0
"Heterotypic docking compatibility of human connexin37 with other vascular connexins. Human vascular connexins (Cx37, Cx40, Cx43, and Cx45) can form various types of gap junction channels to synchronize vasodilation/constriction to control local circulation. Most of our knowledge on heterotypic gap junctions of these vascular connexins was from studies on rodent connexins. In human vasculature, the same four homolog connexins exist, but whether these human connexins can form heterotypic GJs as those of rodents have not been fully studied. Here we used in vitro expression system to study the coupling status and GJ channel properties of human heterotypic Cx37/Cx40, Cx37/Cx43, and Cx37/Cx45 GJs. Our results showed that Cx37/Cx43 and Cx37/Cx45 GJs, but not Cx37/Cx40 GJs, were functional and each with unique rectifying channel properties. The failure of docking between Cx37 and Cx40 could be rescued by designed Cx40 variants. Characterization of the heterotypic Cx37/Cx43 and Cx37/Cx45 GJs may help us in understanding the intercellular communication at the myoendothelial junction.",0
"Single-Cell Transcriptomics Uncovers Glial Progenitor Diversity and Cell Fate Determinants during Development and Gliomagenesis. By applying lineage-targeted, single-cell transcriptomics analysis, Weng and colleagues uncover distinct intermediate glial progenitors in the neonatal brain and their malignant counterparts in murine and human gliomas. Lineage-driving network analysis further identifies Zfp36l1 as a pivotal regulator for glial fate specification and glioma growth.",0
"NBS1 interacts with HP1 to ensure genome integrity. Heterochromatin Protein 1 (HP1) and the Mre11-Rad50-Nbs1 (MRN) complex are conserved factors that play crucial role in genome stability and integrity. Despite their involvement in overlapping cellular functions, ranging from chromatin organization, telomere maintenance to DNA replication and repair, a tight functional relationship between HP1 and the MRN complex has never been elucidated. Here we show that the Drosophila HP1a protein binds to the MRN complex through its chromoshadow domain (CSD). In addition, loss of any of the MRN members reduces HP1a levels indicating that the MRN complex acts as regulator of HP1a stability. Moreover, overexpression of HP1a in nbs (but not in rad50 or mre11) mutant cells drastically reduces DNA damage associated with the loss of Nbs suggesting that HP1a and Nbs work in concert to maintain chromosome integrity in flies. We have also found that human HP1α and NBS1 interact with each other and that, similarly to Drosophila, siRNA-mediated inhibition of NBS1 reduces HP1α levels in human cultured cells. Surprisingly, fibroblasts from Nijmegen Breakage Syndrome (NBS) patients, carrying the 657del5 hypomorphic mutation in NBS1 and expressing the p26 and p70 NBS1 fragments, accumulate HP1α indicating that, differently from NBS1 knockout cells, the presence of truncated NBS1 extends HP1α turnover and/or promotes its stability. Remarkably, an siRNA-mediated reduction of HP1α in NBS fibroblasts decreases the hypersensitivity to irradiation, a characteristic of the NBS syndrome. Overall, our data provide an unanticipated evidence of a close interaction between HP1 and NBS1 that is essential for genome stability and point up HP1α as a potential target to counteract chromosome instability in NBS patient cells.",0
"NMR structure determination of Ixolaris and factor X(a) interaction reveals a noncanonical mechanism of Kunitz inhibition. Ixolaris is a potent tick salivary anticoagulant that binds coagulation factor Xa (FXa) and zymogen FX, with formation of a quaternary tissue factor (TF)/FVIIa/ FX(a)/Ixolaris inhibitory complex. Ixolaris blocks TF-induced coagulation and PAR2 signaling and prevents thrombosis, tumor growth, and immune activation. We present a high-resolution structure and dynamics of Ixolaris and describe the structural basis for recognition of FX. Ixolaris consists of 2 Kunitz domains (K1 and K2) in which K2 is strikingly dynamic and encompasses several residues involved in FX binding. This indicates that the backbone plasticity of K2 is critical for Ixolaris biological activity. Notably, a nuclear magnetic resonance–derived model reveals a mechanism for an electrostatically guided, high-affinity interaction between Ixolaris and FX heparin-binding (pro)exosite, resulting in an allosteric switch in the catalytic site. This is the first report revealing the structure-function relationship of an anticoagulant targeting a zymogen serving as a scaffold for TF inhibition.",0
"Automatic needle detection and real-time Bi-planar needle visualization during 3D ultrasound scanning of the liver. 2D ultrasound (US) image guidance is used in minimally invasive procedures in the liver to visualize the target and the needle. Needle insertion using 2D ultrasound keeping the transducer position to view needle and reach target is challenging. Dedicated needle holders attached to the US transducer help to target in plane and at a specific angle. A drawback of this is that, the probe is fixed to the needle and cannot be rotated to assess the position of the needle in a perpendicular plane. In this study, we propose an automatic needle detection and tracking method using 3D US imaging to improve image guidance and visualization of the target in the liver with respect to the needle during these interventional procedures. The method utilizes a convolutional neural network for detection of the needle in 3D US images. In a subsequent step, the output of the convolutional neural network is used to detect needle candidates, which are fed into a final tracking step to determine the real needle position. The needle position is used to present two perpendicular cross-sectional planes of the 3D US image containing the needle in both directions. Performance of the method was evaluated in phantoms and in-vivo data by calculating the needle position distance and needle orientation angle between segmented needles and reference ground truth needles, which were manually annotated by an observer. The method successfully detects the needle position and orientation with mean errors of 1mm and 2 degrees , respectively. The proposed method yields a robust automatic needle detection and visualization at a frame rate of 3Hz in 3D ultrasound imaging of the liver.",1
"Phase contrast mapping MRI measurements of global cerebral blood flow across different perfusion states – A direct comparison with 15O-H2O positron emission tomography using a hybrid PET/MR system. Phase-contrast mapping (PCM) magnetic resonance imaging (MRI) provides easy-access non-invasive quantification of global cerebral blood flow (gCBF) but its accuracy in altered perfusion states is not established. We aimed to compare paired PCM MRI and 15O-H2O positron emission tomography (PET) measurements of gCBF in different perfusion states in a single scanning session. Duplicate combined gCBF PCM-MRI and 15O-H2O PET measurements were performed in the resting condition, during hyperventilation and after acetazolamide administration (post-ACZ) using a 3T hybrid PET/MR system. A total of 62 paired gCBF measurements were acquired in 14 healthy young male volunteers. Average gCBF in resting state measured by PCM-MRI and 15O-H2O PET were 58.5 ± 10.7 and 38.6 ± 5.7 mL/100 g/min, respectively, during hyperventilation 33 ± 8.6 and 24.7 ± 5.8 mL/100 g/min, respectively, and post-ACZ 89.6 ± 27.1 and 57.3 ± 9.6 mL/100 g/min, respectively. On average, gCBF measured by PCM-MRI was 49% higher compared to 15O-H2O PET. A strong correlation between the two methods across all states was observed (R2 = 0.72, p < 0.001). Bland–Altman analysis suggested a perfusion dependent relative bias resulting in higher relative difference at higher CBF values. In conclusion, measurements of gCBF by PCM-MRI in healthy volunteers show a strong correlation with 15O-H2O PET, but are associated with a large and non-linear perfusion-dependent difference.",0
"Variation in Follow-up Imaging Recommendations in Radiology Reports: Patient, Modality, and Radiologist Predictors. Background Variation between radiologists when making recommendations for additional imaging and associated factors are, to the knowledge of the authors, unknown. Clear identification of factors that account for variation in follow-up recommendations might prevent unnecessary tests for incidental or ambiguous image findings. Purpose To determine incidence and identify factors associated with follow-up recommendations in radiology reports from multiple modalities, patient care settings, and imaging divisions. Materials and Methods This retrospective study analyzed 318 366 reports obtained from diagnostic imaging examinations performed at a large urban quaternary care hospital from January 1 to December 31, 2016, excluding breast and US reports. A subset of 1000 reports were randomly selected and manually annotated to train and validate a machine learning algorithm to predict whether a report included a follow-up imaging recommendation (training-and-validation set consisted of 850 reports and test set of 150 reports). The trained algorithm was used to classify 318 366 reports. Multivariable logistic regression was used to determine the likelihood of follow-up recommendation. Additional analysis by imaging subspecialty division was performed, and intradivision and interradiologist variability was quantified. Results The machine learning algorithm classified 38 745 of 318 366 (12.2%) reports as containing follow-up recommendations. Average patient age was 59 years +/- 17 (standard deviation); 45.2% (143 767 of 318 366) of reports were from male patients. Among 65 radiologists, 57% (37 of 65) were men. At multivariable analysis, older patients had higher rates of follow-up recommendations (odds ratio [OR], 1.01 [95% confidence interval {CI}: 1.01, 1.01] for each additional year), male patients had lower rates of follow-up recommendations (OR, 0.9; 95% CI: 0.9, 1.0), and follow-up recommendations were most common among CT studies (OR, 4.2 [95% CI: 4.0, 4.4] compared with radiography). Radiologist sex (P = .54), presence of a trainee (P = .45), and years in practice (P = .49) were not significant predictors overall. A division-level analysis showed 2.8-fold to 6.7-fold interradiologist variation. Conclusion Substantial interradiologist variation exists in the probability of recommending a follow-up examination in a radiology report, after adjusting for patient, examination, and radiologist factors. (c) RSNA, 2019 See also the editorial by Russell in this issue.",1
"Urinary stone detection on ct images using deep convolutional neural networks: Evaluation of model performance and generalization. Purpose: To investigate the diagnostic accuracy of cascading convolutional neural network (CNN) for urinary stone detection on unenhanced CT images and to evaluate the performance of pretrained models enriched with labeled CT images across different scanners. Materials and Methods: This HIPAA-compliant, institutional review board–approved, retrospective clinical study used unenhanced abdominopelvic CT scans from 535 adults suspected of having urolithiasis. The scans were obtained on two scanners (scanner 1 [hereafter S1] and scanner 2 [hereafter S2]). A radiologist reviewed clinical reports and labeled cases for determination of reference standard. Stones were present on 279 (S1, 131; S2, 148) and absent on 256 (S1, 158; S2, 98) scans. One hundred scans (50 from each scanner) were randomly reserved as the test dataset, and the rest were used for developing a cascade of two CNNs: The first CNN identified the extent of the urinary tract, and the second CNN detected presence of stone. Nine variations of models were developed through the combination of different training data sources (S1, S2, or both [hereafter SB]) with (ImageNet, GrayNet) and without (Random) pretrained CNNs. First, models were compared for generalizability at the section level. Second, models were assessed by using area under the receiver operating characteristic curve (AUC) and accuracy at the patient level with test dataset from both scanners (n = 100). Results: The GrayNet-pretrained model showed higher classifier exactness than did ImageNet-pretrained or Random-initialized models when tested by using data from the same or different scanners at section level. At the patient level, the AUC for stone detection was 0.92–0.95, depending on the model. Accuracy of GrayNet-SB (95%) was higher than that of ImageNet-SB (91%) and Random-SB (88%). For stones larger than 4 mm, all models showed similar performance (false-negative results: Two of 34). For stones smaller than 4 mm, the number of false-negative results for GrayNet-SB, ImageNet-SB, and Random-SB were one of 16, three of 16, and five of 16, respectively. GrayNet-SB identified stones in all 22 test cases that had obstructive uropathy. Conclusion: A cascading model of CNNs can detect urinary tract stones on unenhanced CT scans with a high accuracy (AUC, 0.954). Performance and generalization of CNNs across scanners can be enhanced by using transfer learning with datasets enriched with labeled medical images.",1
"Modeling Surgical Technical Skill Using Expert Assessment for Automated Computer Rating. OBJECTIVE: Computer vision was used to predict expert performance ratings from surgeon hand motions for tying and suturing tasks. SUMMARY BACKGROUND DATA: Existing methods, including the objective structured assessment of technical skills (OSATS), have proven reliable, but do not readily discriminate at the task level. Computer vision may be used for evaluating distinct task performance throughout an operation. METHODS: Open surgeries was videoed and surgeon hands were tracked without using sensors or markers. An expert panel of 3 attending surgeons rated tying and suturing video clips on continuous scales from 0 to 10 along 3 task measures adapted from the broader OSATS: motion economy, fluidity of motion, and tissue handling. Empirical models were developed to predict the expert consensus ratings based on the hand kinematic data records. RESULTS: The predicted versus panel ratings for suturing had slopes from 0.73 to 1, and intercepts from 0.36 to 1.54 (Average R2 = 0.81). Predicted versus panel ratings for tying had slopes from 0.39 to 0.88, and intercepts from 0.79 to 4.36 (Average R2 = 0.57). The mean square error among predicted and expert ratings was consistently less than the mean squared difference among individual expert ratings and the eventual consensus ratings. CONCLUSIONS: The computer algorithm consistently predicted the panel ratings of individual tasks, and were more objective and reliable than individual assessment by surgical experts.",1
"Simulation-based training of junior doctors in handling critically ill patients facilitates the transition to clinical practice: an interview study. BACKGROUND: Junior doctors lack confidence and competence in handling the critically ill patient including diagnostic skills, decision-making and team working with other health care professionals. Simulation-based training on managing emergency situations can have substantial effects on satisfaction and learning. However, there are indications of problems when applying learned skills to practice. Our aim was to identify first-year doctors' perceptions, reflections and experiences on transfer of skills to a clinical setting after simulation-based training in handling critically ill patients. METHODS: We used a qualitative approach and conducted semi-structured telephone interviews with a sample of twenty first-year doctors six months after a 4-day simulation-based training course in handling critically ill patients. Interviews were transcribed verbatim. A content-analysis approach was used to analyse the data. RESULTS: The following main themes were identified from the interviews: preparedness for clinical practice, organisational readiness, use of algorithms, communication, teamwork, situational awareness and decision making. The doctors gave several examples of simulation-based training increasing their preparedness for clinical practice and handling the critically ill patient. The usefulness of algorithms and the appreciation of non-technical skills were highlighted and found to be helpful in managing clinical difficulties. Concern was expressed related to staff willingness and preparedness in using these tools. CONCLUSIONS: Overall, the simulation-based training seemed to facilitate the transition from being a medical student to become a junior doctor. The doctors experienced an ability to transfer the use of algorithms and non-technical skills trained in the simulated environment to the clinical environment. However, the application of these skills was more difficult if these skills were unfamiliar to the surrounding clinical staff. TRIAL REGISTRATION: Not applicable.",0
"In silico analysis and molecular dynamics simulation of human superoxide dismutase 3 (SOD3) genetic variants. Oxidative stress is a major factor in aging processes. Superoxide dismutase 3 (SOD3) plays a key role in the protection of extracellular oxidative stress. Missense mutations in SOD3 have been described to be associated with the occurrence of pulmonary, cardiovascular, and neoplastic diseases. This study aims to analyze the effects of missense mutations on the SOD3 structure and function by modeling a complete SOD3 structure as well as analyzing the differences between the wild-types and mutants using computational simulations. Here, ten algorithms were used to predict the structural and functional effects of missense mutations. A complete model of SOD3 protein was made by ab initio and comparative modeling using the Rosetta algorithm and validated by PROCHECK, Verify 3D, QMEAN, and ProSa. Molecular dynamics (MD) simulations were performed and analyzed using the GROMACS package. The deleterious potential of the A58T and R231G mutants was not predicted by the majority of the used algorithms. The analyzed mutations were predicted as destabilizing by at least one algorithm. The MD analyses indicated that protein flexibility may be increased by all of the analyzed mutations, while the protein-ligand stability may be decreased. They also suggested that the variants A91T and R231G increase the overall dimensions of SOD3 and decrease its accessible surface area. Our findings, therefore, indicated that the analyzed mutations could affect the protein structure and its ability to interact with other molecules, which may be related to the functional impairment of SOD3 upon A58T and R231G mutations, as well as their involvement in pathologies.",0
"Small molecule glycomimetics inhibit vascular calcification via c-Met/Notch3/HES1 signalling. Background/Aims: Vascular calcification represents a huge clinical problem contributing to adverse cardiovascular events, with no effective treatment currently available. Upregulation of hepatocyte growth factor has been linked with vascular calcification, and thus, represent a potential target in the development of a novel therapeutic strategy. Glycomimetics have been shown to interrupt HGF-receptor signalling, therefore this study investigated the effect of novel glycomimetics on osteogenic signalling and vascular calcification in vitro. Methods: Primary human vascular smooth muscle cells (HVSMCs) were induced by β-glycerophosphate (β-GP) and treated with 4 glycomimetic compounds (C1-C4). The effect of β-GP and C1-C4 on alkaline phosphatase (ALP), osteogenic markers and c-Met/Notch3/HES1 signalling was determined using colorimetric assays, qRT-PCR and western blotting respectively. Results: C1-C4 significantly attenuated β-GP-induced calcification, as shown by Alizarin Red S staining and calcium content by day 14. In addition, C1-C4 reduced ALP activity and prevented upregulation of the osteogenic markers, BMP-2, Runx2, Msx2 and OPN. Furthermore, β-GP increased c-Met phosphorylation at day 21, an effect ameliorated by C2 and C4 and the c-Met inhibitor, crizotinib. We next interrogated the effects of the Notch inhibitor DAPT and confirmed an inhibition of β-GP up-regulated Notch3 protein by C2, DAPT and crizotinib compared to controls. Hes-1 protein upregulation by β-GP, was also significantly downregulated by C2 and DAPT. GOLD docking analysis identified a potential binding interaction of C1-C4 to HGF which will be investigated further. Conclusion: These findings demonstrate that glycomimetics have potent anti-calcification properties acting via HGF/c-Met and Notch signalling.",0
"Gliomasphere marker combinatorics: multidimensional flow cytometry detects CD44+/CD133+/ITGA6+/CD36+ signature. Glioblastoma is the most dangerous brain cancer. One reason for glioblastoma's aggressiveness are glioblastoma stem-like cells. To target them, a number of markers have been proposed (CD133, CD44, CD15, A2B5, CD36, CXCR4, IL6R, L1CAM, and ITGA6). A comprehensive study of co-expression patterns of them has, however, not been performed so far. Here, we mapped the multidimensional co-expression profile of these stemness-associated molecules. Gliomaspheres – an established model of glioblastoma stem-like cells – were used. Seven different gliomasphere systems were subjected to multicolor flow cytometry measuring the nine markers CD133, CD44, CD15, A2B5, CD36, CXCR4, IL6R, L1CAM, and ITGA6 all simultaneously based on a novel 9-marker multicolor panel developed for this study. The viSNE dimensionality reduction algorithm was applied for analysis. All gliomaspheres were found to express at least five different glioblastoma stem-like cell markers. Multi-dimensional analysis showed that all studied gliomaspheres consistently harbored a cell population positive for the molecular signature CD44+/CD133+/ITGA6+/CD36+. Glioblastoma patients with an enrichment of this combination had a significantly worse survival outcome when analyzing the two largest available The Cancer Genome Atlas datasets (MIT/Harvard Affymetrix: P = 0.0015, University of North Carolina Agilent: P = 0.0322). In sum, we detected a previously unknown marker combination – demonstrating feasibility, usefulness, and importance of high-dimensional gliomasphere marker combinatorics.",0
"Use of electronic health record data and machine learning to identify candidates for HIV pre-exposure prophylaxis: a modelling study. BACKGROUND: The limitations of existing HIV risk prediction tools are a barrier to implementation of pre-exposure prophylaxis (PrEP). We developed and validated an HIV prediction model to identify potential PrEP candidates in a large health-care system. METHODS: Our study population was HIV-uninfected adult members of Kaiser Permanente Northern California, a large integrated health-care system, who were not yet using PrEP and had at least 2 years of previous health plan enrolment with at least one outpatient visit from Jan 1, 2007, to Dec 31, 2017. Using 81 electronic health record (EHR) variables, we applied least absolute shrinkage and selection operator (LASSO) regression to predict incident HIV diagnosis within 3 years on a subset of patients who entered the cohort in 2007-14 (development dataset), assessing ten-fold cross-validated area under the receiver operating characteristic curve (AUC) and 95% CIs. We compared the full model to simpler models including only men who have sex with men (MSM) status and sexually transmitted infection (STI) positivity, testing, and treatment. Models were validated prospectively with data from an independent set of patients who entered the cohort in 2015-17. We computed predicted probabilities of incident HIV diagnosis within 3 years (risk scores), categorised as low risk (<0.05%), moderate risk (0.05% to <0.20%), high risk (0.20% to <1.0%), and very high risk (>/=1.0%), for all patients in the validation dataset. FINDINGS: Of 3 750 664 patients in 2007-17 (3 143 963 in the development dataset and 606 701 in the validation dataset), there were 784 incident HIV cases within 3 years of baseline. The LASSO procedure retained 44 predictors in the full model, with an AUC of 0.86 (95% CI 0.85-0.87) for incident HIV cases in 2007-14. Model performance remained high in the validation dataset (AUC 0.84, 0.80-0.89). The full model outperformed simpler models including only MSM status and STI positivity. For the full model, flagging 13 463 (2.2%) patients with high or very high HIV risk scores in the validation dataset identified 32 (38.6%) of the 83 incident HIV cases, including 32 (46.4%) of 69 male cases and none of the 14 female cases. The full model had equivalent sensitivity by race whereas simpler models identified fewer black than white HIV cases. INTERPRETATION: Prediction models using EHR data can identify patients at high risk of HIV acquisition who could benefit from PrEP. Future studies should optimise EHR-based HIV risk prediction tools and evaluate their effect on prescription of PrEP. FUNDING: Kaiser Permanente Community Benefit Research Program and the US National Institutes of Health.",1
"FtsA as a cidal target for Staphylococcus aureus: Molecular docking and dynamics studies. Staphylococcus aureus infection is a healthcare problem to mankind for a considerable period of time. Once when it enters the bloodstream of an individual, it may potentially result in life-threatening conditions. The resistance of S. aureus to various drugs such as penicillin, methicillin, gentamicin, erythromycin, and tetracycline have been well documented. Presently vancomycin is the drug of choice for methicillin resistant S. aureus. Scientists believe that S. aureus would completely develop resistance to vancomycin as well. Therefore there is a commensurate need to develop a drug to replace vancomycin. In the current study, we have focussed on FtsA, an important and vital cell division protein, which is found only in S. aureus and in other prokaryotic cells. We have carried out virtual screening process for FtsA against ZINC database, the best hit molecules obtained from the preliminary docking studies were subjected to SYBYL X 2.0 docking. The molecules ZINC74432848, ZINC37769607, and ZINC96896268 displayed the highest C-score value of 4.89, 4.49, and 4.22, respectively. The top ranked molecule ZINC74432848 was observed to form 4 hydrogen bonds with FtsA. The simulation study reveals the greater stability of the FtsA-ZINC74432848 complex. If the in vitro and in vivo study turns out affirmative, then ZINC74432848 could be developed as a potent drug for FtsA.",0
"Fine structure and dynamics of EB3 binding zones on microtubules in fibroblast cells. End-binding (EB) proteins associate with the growing tips of microtubules (MTs) and modulate their dynamics directly and indirectly, by recruiting essential factors to fine-tune MTs for their many essential roles in cells. Previously EB proteins have been shown to recognize a stabilizing GTP/GDP-Pi cap at the tip of growing MTs, but information about additional EB-binding zones on MTs has been limited. In this work, we studied fluorescence intensity profiles of one of the three mammalian EB-proteins, EB3, fused with red fluorescent protein (RFP). The distribution of EB3 on MTs in mouse fibroblasts frequently deviated from single exponential decay and exhibited secondary peaks. Those secondary peaks, which we refer to as EB3-islands, were detected on 56% comets of growing MTs and were encountered once per 44 s of EB3-RFP comet growth time with about 5 s half-lifetime. The majority of EB3-islands in the vicinity of MT tips was stationary and originated from EB3 comets moving with the growing MT tips. Computational modeling of the decoration of dynamic MT tips by EB3 suggested that the EB3-islands could not be explained simply by a stochastic first-order GTP hydrolysis/phosphate release. We speculate that additional protein factors contribute to EB3 residence time on MTs in cells, likely affecting MT dynamics.",0
"Medical education trends for future physicians in the era of advanced technology and artificial intelligence: an integrative review. BACKGROUND: Medical education must adapt to different health care contexts, including digitalized health care systems and a digital generation of students in a hyper-connected world. The aims of this study are to identify and synthesize the values that medical educators need to implement in the curricula and to introduce representative educational programs. METHODS: An integrative review was conducted to combine data from various research designs. We searched for articles on PubMed, Scopus, Web of Science, and EBSCO ERIC between 2011 and 2017. Key search terms were ""undergraduate medical education,"" ""future,"" ""twenty-first century,"" ""millennium,"" ""curriculum,"" ""teaching,"" ""learning,"" and ""assessment."" We screened and extracted them according to inclusion and exclusion criteria from titles and abstracts. All authors read the full texts and discussed them to reach a consensus about the themes and subthemes. Data appraisal was performed using a modified Hawker 's evaluation form. RESULTS: Among the 7616 abstracts initially identified, 28 full-text articles were selected to reflect medical education trends and suggest suitable educational programs. The integrative themes and subthemes of future medical education are as follows: 1) a humanistic approach to patient safety that involves encouraging humanistic doctors and facilitating collaboration; 2) early experience and longitudinal integration by early exposure to patient-oriented integration and longitudinal integrated clerkships; 3) going beyond hospitals toward society by responding to changing community needs and showing respect for diversity; and 4) student-driven learning with advanced technology through active learning with individualization, social interaction, and resource accessibility. CONCLUSIONS: This review integrated the trends in undergraduate medical education in readiness for the anticipated changes in medical environments. The detailed programs introduced in this study could be useful for medical educators in the development of curricula. Further research is required to integrate the educational trends into graduate and continuing medical education, and to investigate the status or effects of innovative educational programs in each medical school or environment.",0
"A graph-based lesion characterization and deep embedding approach for improved computer-aided diagnosis of nonmass breast MRI lesions. Nonmass-like enhancements are a common but diagnostically challenging finding in breast MRI. Nonmass-like lesions can be described as clusters of spatially and temporally inter-connected regions of enhancements, so they can be modeled as networks and their properties characterized via network-based connectivity. In this work, we represented nonmass lesions as graphs using a link formation energy model that favors linkages between regions of similar enhancement and closer spatial proximity. However, adding graph features to an existing computer-aided diagnosis (CAD) pipeline incurs an increase of feature space dimensionality, which poses additional challenges to traditional supervised machine learning techniques due to the inability to increase accordingly the number of training datasets. We propose the combination of unsupervised dimensionality reduction and embedded space clustering followed by a supervised classifier to improve the performance of a CAD system for nonmass-like lesions in breast MRI. Our work extends a previoulsy proposed framework for deep embedded unsupervised clustering (DEC) to embedding space classification, with the joint optimization of objective functions for DEC and supervised multi-layered perceptron (MLP) classification. The strength of the method lies in the ability to learn and further optimize an embedded feature representation of lower dimensionality that maximizes the diagnostic accuracy of a CAD lesion classifier to discriminate between benign and malignant lesions. We identified 792 nonmass-like enhancements (267 benign, 110 malignant and 415 unknown) in 411 patients undergoing breast MRI at our institution. The diagnostic performance of the proposed method was evaluated and compared to the performance of a conventional supervised MLP classifier in original feature space. A statistically significant increase in diagnostic area under the ROC curve (AUC) was achieved. Generalization AUC increased from 0.67+/-0.08 to 0.81+/-0.10 (21% increase, p-value=4.2x10(-8)) with the proposed graph-based lesion characterization and deep embedding framework.",1
"Assessment of Machine Learning Detection of Environmental Enteropathy and Celiac Disease in Children. Importance: Duodenal biopsies from children with enteropathies associated with undernutrition, such as environmental enteropathy (EE) and celiac disease (CD), display significant histopathological overlap. Objective: To develop a convolutional neural network (CNN) to enhance the detection of pathologic morphological features in diseased vs healthy duodenal tissue. Design, Setting, and Participants: In this prospective diagnostic study, a CNN consisting of 4 convolutions, 1 fully connected layer, and 1 softmax layer was trained on duodenal biopsy images. Data were provided by 3 sites: Aga Khan University Hospital, Karachi, Pakistan; University Teaching Hospital, Lusaka, Zambia; and University of Virginia, Charlottesville. Duodenal biopsy slides from 102 children (10 with EE from Aga Khan University Hospital, 16 with EE from University Teaching Hospital, 34 with CD from University of Virginia, and 42 with no disease from University of Virginia) were converted into 3118 images. The CNN was designed and analyzed at the University of Virginia. The data were collected, prepared, and analyzed between November 2017 and February 2018. Main Outcomes and Measures: Classification accuracy of the CNN per image and per case and incorrect classification rate identified by aggregated 10-fold cross-validation confusion/error matrices of CNN models. Results: Overall, 102 children participated in this study, with a median (interquartile range) age of 31.0 (20.3-75.5) months and a roughly equal sex distribution, with 53 boys (51.9%). The model demonstrated 93.4% case-detection accuracy and had a false-negative rate of 2.4%. Confusion metrics indicated most incorrect classifications were between patients with CD and healthy patients. Feature map activations were visualized and learned distinctive patterns, including microlevel features in duodenal tissues, such as alterations in secretory cell populations. Conclusions and Relevance: A machine learning-based histopathological analysis model demonstrating 93.4% classification accuracy was developed for identifying and differentiating between duodenal biopsies from children with EE and CD. The combination of the CNN with a deconvolutional network enabled feature recognition and highlighted secretory cells' role in the model's ability to differentiate between these histologically similar diseases.",1
"Radiomic versus Convolutional Neural Networks Analysis for Classification of Contrast-enhancing Lesions at Multiparametric Breast MRI. Purpose To compare the diagnostic performance of radiomic analysis (RA) and a convolutional neural network (CNN) to radiologists for classification of contrast agent-enhancing lesions as benign or malignant at multiparametric breast MRI. Materials and Methods Between August 2011 and August 2015, 447 patients with 1294 enhancing lesions (787 malignant, 507 benign; median size, 15 mm +/- 20) were evaluated. Lesions were manually segmented by one breast radiologist. RA was performed by using L1 regularization and principal component analysis. CNN used a deep residual neural network with 34 layers. All algorithms were also retrained on half the number of lesions (n = 647). Machine interpretations were compared with prospective interpretations by three breast radiologists. Standard of reference was histologic analysis or follow-up. Areas under the receiver operating curve (AUCs) were used to compare diagnostic performance. Results CNN trained on the full cohort was superior to training on the half-size cohort (AUC, 0.88 vs 0.83, respectively; P = .01), but there was no difference for RA and L1 regularization (AUC, 0.81 vs 0.80, respectively; P = .76) or RA and principal component analysis (AUC, 0.78 vs 0.78, respectively; P = .93). By using the full cohort, CNN performance (AUC, 0.88; 95% confidence interval: 0.86, 0.89) was better than RA and L1 regularization (AUC, 0.81; 95% confidence interval: 0.79, 0.83; P < .001) and RA and principal component analysis (AUC, 0.78; 95% confidence interval: 0.76, 0.80; P < .001). However, CNN was inferior to breast radiologist interpretation (AUC, 0.98; 95% confidence interval: 0.96, 0.99; P < .001). Conclusion A convolutional neural network was superior to radiomic analysis for classification of enhancing lesions as benign or malignant at multiparametric breast MRI. Both approaches were inferior to radiologists' performance; however, more training data will further improve performance of convolutional neural network, but not that of radiomics algorithms. (c) RSNA, 2018 Online supplemental material is available for this article.",1
"Computational aided mechanistic understanding of Camellia sinensis bioactive compounds against co-chaperone p23 as potential anticancer agent. Co-chaperon p23 has been well established as molecular chaperon for the heat shock protein 90 (Hsp90) that further leads to immorality in cancer cells by providing defense against Hsp90 inhibitors, and as stimulating agent for generating overexpressed antiapoptotic proteins, that is, Hsp70 and Hsp27. The natural compounds such as catechins from Camellia sinensis (green tea) are also well known for inhibition activity against various cancer. However, molecular interaction profile and potential lead bioactive compounds against co-chaperon p23 from green tea are not yet reported. To this context, we study the various secondary metabolites of green tea against co-chaperon p23 using structure-based virtual screening from Traditional Chinese Medicine (TCM) database. Following 26 compounds were obtained from TCM database and further studied for extra precision molecular docking that showed binding score between −10.221 and −2.276 kcal/mol with co-chaperon p23. However, relative docking score to known inhibitors, that is, ailanthone (−4.54 kcal/mol) and gedunin (3.60 kcal/mol) along with ADME profile analysis concluded epicatechin (−7.013 kcal/mol) and cis-theaspirone (−4.495 kcal/mol) as potential lead inhibitors from green tea against co-chaperone p23. Furthermore, molecular dynamics simulation and molecular mechanics generalized born surface area calculations validated that epicatechin and cis-theaspirone have significantly occupied the active region of co-chaperone p23 by hydrogen and hydrophobic interactions with various residues including most substantial amino acids, that is, Thr90, Ala94, and Lys95. Hence, these results supported the fact that green tea contained potential compounds with an ability to inhibit the cancer by disrupting the co-chaperon p23 activity.",0
"Functional mimetic peptide discovery isolated by phage display interacts selectively to fibronectin domain and inhibits gelatinase. Matrix metalloproteinases (MMPs) play critical roles in a multiple number of autoimmunity diseases progression and metastasis of solid tumor. Gelatinases including MMP-2 and MMP-9 are extremely overexpressed in multiple pathological processes. MMP-9 and MMP-2 breakdown the extracellular matrix component gelatin very efficaciously. Therefore, designing and expansion of MMPs inhibitors can be an engrossing plan for therapeutic intermediacy. Anyway, a wide range of MMPs inhibitors face failure in several clinical trials. Due to sequence and structural conservation across the various MMPs, achieving specific and selective inhibitors is very demanding. In the current study, a phage-displayed peptide library was screened using active human recombinant MMP-9 protein and evaluated by enzyme-linked immunosorbent assay. Here, we isolate novel peptide sequence from phage display peptide libraries that can be a specific gelatinase inhibitor. Interestingly, in silico molecular docking showed strong interactions between the peptide three-dimensional models and some important residues of the MMP-9 and MMP-2 proteins at the fibronectin domain. A consensus peptide sequence was then synthesized (named as RSH-12) to evaluate its inhibitory potency by in vitro assays. Zymography assay was employed to evaluate the effect of RSH-12 on gelatinolysis activity of MMP-2 and MMP-9 secretion from the HT1080 cells using different concentrations of RSH-12 and inhibiting MMP-9- and MMP-2-driven gelatin proteolysis, measured by fluorescein isothiocyanate-gelatin degradation assay and HT1080 cell invasion assay on Matrigel (gelatinous protein mixture). The negative control peptide (CP) with the irrelevant sequence and no MMP inhibition properties and the positive control compound (GM6001) as a potent inhibitor of MMPs were used to assess the selectivity and specificity of gelatinases inhibition by RSH-12. Therefore, RSH-12 decreased the gelatin degradation by specifically preventing gelatin binding to MMP-9 and MMP-2. Selective gelatinase inhibitors may prove the usefulness of the new peptide discovered in tumor targeting and anticancer and anti-inflammation therapies.",0
"Prior dengue virus infection and risk of Zika: A pediatric cohort in Nicaragua. Background: Zika virus (ZIKV) emerged in northeast Brazil in 2015 and spread rapidly across the Americas, in populations that have been largely exposed to dengue virus (DENV). The impact of prior DENV infection on ZIKV infection outcome remains unclear. To study this potential impact, we analyzed the large 2016 Zika epidemic in Managua, Nicaragua, in a pediatric cohort with well-characterized DENV infection histories. Methods and findings: Symptomatic ZIKV infections (Zika cases) were identified by real-time reverse transcription PCR and serology in a community-based cohort study that follows approximately 3,700 children aged 2-14 years old. Annual blood samples were used to identify clinically inapparent ZIKV infections using a novel, well-characterized serological assay. Multivariable Poisson regression was used to examine the relation between prior DENV infection and incidence of symptomatic and inapparent ZIKV infection. The generalized-growth method was used to estimate the effective reproduction number. From January 1, 2016, to February 28, 2017, 560 symptomatic ZIKV infections and 1,356 total ZIKV infections (symptomatic and inapparent) were identified, for an overall incidence of 14.0 symptomatic infections (95% CI: 12.9, 15.2) and 36.5 total infections (95% CI: 34.7, 38.6) per 100 person-years. Effective reproduction number estimates ranged from 3.3 to 3.4, depending on the ascending wave period. Incidence of symptomatic and total ZIKV infections was higher in females and older children. Analysis of the effect of prior DENV infection was performed on 3,027 participants with documented DENV infection histories, of which 743 (24.5%) had experienced at least 1 prior DENV infection during cohort follow-up. Prior DENV infection was inversely associated with risk of symptomatic ZIKV infection in the total cohort population (incidence rate ratio [IRR]: 0.63; 95% CI: 0.48, 0.81; p < 0.005) and with risk of symptomatic presentation given ZIKV infection (IRR: 0.62; 95% CI: 0.44, 0.86) when adjusted for age, sex, and recent DENV infection (1-2 years before ZIKV infection). Recent DENV infection was significantly associated with decreased risk of symptomatic ZIKV infection when adjusted for age and sex, but not when adjusted for prior DENV infection. Prior or recent DENV infection did not affect the rate of total ZIKV infections. Our findings are limited to a pediatric population and constrained by the epidemiology of the site. Conclusions: These findings support that prior DENV infection may protect individuals from symptomatic Zika. More research is needed to address the possible immunological mechanism(s) of cross-protection between ZIKV and DENV and whether DENV immunity also modulates other ZIKV infection outcomes such as neurological or congenital syndromes.",0
"The unfolded protein response modulators GSK2606414 and KIRA6 are potent KIT inhibitors. IRE1, PERK, and ATF6 are the three transducers of the mammalian canonical unfolded protein response (UPR). GSK2606414 is a potent inhibitor of PERK, while KIRA6 inhibits the kinase activity of IRE1. Both molecules are frequently used to probe the biological roles of the UPR in mammalian cells. In a direct binding assay, GSK2606414 bound to the cytoplasmic domain of KIT with dissociation constants (Kd) value of 664 ± 294 nM whereas KIRA6 showed a Kd value of 10.8 ± 2.9 µM. In silico docking studies confirmed a compact interaction of GSK2606414 and KIRA6 with KIT ATP binding pocket. In cultured cells, GSK2606414 inhibited KIT tyrosine kinase activity at nanomolar concentrations and in a PERK-independent manner. Moreover, in contrast to other KIT inhibitors, GSK2606414 enhanced KIT endocytosis and its lysosomal degradation. Although KIRA6 also inhibited KIT at nanomolar concentrations, it did not prompt KIT degradation, and rescued KIT from GSK2606414-mediated degradation. Consistent with KIT inhibition, nanomolar concentrations of GSK2606414 and KIRA6 were sufficient to induce cell death in a KIT signaling-dependent mast cell leukemia cell line. Our data show for the first time that KIT is a shared target for two seemingly unrelated UPR inhibitors at concentrations that overlap with PERK and IRE1 inhibition. Furthermore, these data underscore discrepancies between in vitro binding measurements of kinase inhibitors and inhibition of the tyrosine kinase receptors in living cells.",0
"Odanacatib for the treatment of postmenopausal osteoporosis: results of the LOFT multicentre, randomised, double-blind, placebo-controlled trial and LOFT Extension study. Background: Odanacatib, a cathepsin K inhibitor, reduces bone resorption while maintaining bone formation. Previous work has shown that odanacatib increases bone mineral density in postmenopausal women with low bone mass. We aimed to investigate the efficacy and safety of odanacatib to reduce fracture risk in postmenopausal women with osteoporosis. Methods: The Long-term Odanacatib Fracture Trial (LOFT) was a multicentre, randomised, double-blind, placebo-controlled, event-driven study at 388 outpatient clinics in 40 countries. Eligible participants were women aged at least 65 years who were postmenopausal for 5 years or more, with a femoral neck or total hip bone mineral density T-score between −2·5 and −4·0 if no previous radiographic vertebral fracture, or between −1·5 and −4·0 with a previous vertebral fracture. Women with a previous hip fracture, more than one vertebral fracture, or a T-score of less than −4·0 at the total hip or femoral neck were not eligible unless they were unable or unwilling to use approved osteoporosis treatment. Participants were randomly assigned (1:1) to either oral odanacatib (50 mg once per week) or matching placebo. Randomisation was done using an interactive voice recognition system after stratification for previous radiographic vertebral fracture, and treatment was masked to study participants, investigators and their staff, and sponsor personnel. If the study completed before 5 years of double-blind treatment, consenting participants could enrol in a double-blind extension study (LOFT Extension), continuing their original treatment assignment for up to 5 years from randomisation. Primary endpoints were incidence of vertebral fractures as assessed using radiographs collected at baseline, 6 and 12 months, yearly, and at final study visit in participants for whom evaluable radiograph images were available at baseline and at least one other timepoint, and hip and non-vertebral fractures adjudicated as being a result of osteoporosis as assessed by clinical history and radiograph. Safety was assessed in participants who received at least one dose of study drug. The adjudicated cardiovascular safety endpoints were a composite of cardiovascular death, myocardial infarction, or stroke, and new-onset atrial fibrillation or flutter. Individual cardiovascular endpoints and death were also assessed. LOFT and LOFT Extension are registered with ClinicalTrials.gov (number NCT00529373) and the European Clinical Trials Database (EudraCT number 2007-002693-66). Findings: Between Sept 14, 2007, and Nov 17, 2009, we randomly assigned 16 071 evaluable patients to treatment: 8043 to odanacatib and 8028 to placebo. After a median follow-up of 36·5 months (IQR 34·43–40·15) 4297 women assigned to odanacatib and 3960 assigned to placebo enrolled in LOFT Extension (total median follow-up 47·6 months, IQR 35·45–60·06). In LOFT, cumulative incidence of primary outcomes for odanacatib versus placebo were: radiographic vertebral fractures 3·7% (251/6770) versus 7·8% (542/6910), hazard ratio (HR) 0·46, 95% CI 0·40–0·53; hip fractures 0·8% (65/8043) versus 1·6% (125/8028), 0·53, 0·39–0·71; non-vertebral fractures 5·1% (412/8043) versus 6·7% (541/8028), 0·77, 0·68–0·87; all p<0·0001. Combined results from LOFT plus LOFT Extension for cumulative incidence of primary outcomes for odanacatib versus placebo were: radiographic vertebral fractures 4·9% (341/6909) versus 9·6% (675/7011), HR 0·48, 95% CI 0·42–0·55; hip fractures 1·1% (86/8043) versus 2·0% (162/8028), 0·52, 0·40–0·67; non-vertebral fractures 6·4% (512/8043) versus 8·4% (675/8028), 0·74, 0·66–0·83; all p<0·0001. In LOFT, the composite cardiovascular endpoint of cardiovascular death, myocardial infarction, or stroke occurred in 273 (3·4%) of 8043 patients in the odanacatib group versus 245 (3·1%) of 8028 in the placebo group (HR 1·12, 95% CI 0·95–1·34; p=0·18). New-onset atrial fibrillation or flutter occurred in 112 (1·4%) of 8043 patients in the odanacatib group versus 96 (1·2% of 8028 in the placebo group (HR 1·18, 0·90–1·55; p=0·24). Odanacatib was associated with an increased risk of stroke (1·7% [136/8043] vs 1·3% [104/8028], HR 1·32, 1·02–1·70; p=0·034), but not myocardial infarction (0·7% [60/8043] vs 0·9% [74/8028], HR 0·82, 0·58–1·15; p=0·26). The HR for all-cause mortality was 1·13 (5·0% [401/8043] vs 4·4% [356/8028], 0·98–1·30; p=0·10). When data from LOFT Extension were included, the composite of cardiovascular death, myocardial infarction, or stroke occurred in significantly more patients in the odanacatib group than in the placebo group (401 [5·0%] of 8043 vs 343 [4·3%] of 8028, HR 1·17, 1·02–1·36; p=0·029, as did stroke (2·3% [187/8043] vs 1·7% [137/8028], HR 1·37, 1·10–1·71; p=0·0051). Interpretation: Odanacatib reduced the risk of fracture, but was associated with an increased risk of cardiovascular events, specifically stroke, in postmenopausal women with osteoporosis. Based on the overall balance between benefit and risk, the study's sponsor decided that they would no longer pursue development of odanacatib for treatment of osteoporosis. Funding: Merck Sharp & Dohme Corp, a subsidiary of Merck & Co, Inc, Kenilworth, NJ, USA.",0
"RNA splicing analysis in genomic medicine. High-throughput next-generation sequencing technologies have led to a rapid increase in the number of sequence variants identified in clinical practice via diagnostic genetic tests. Current bioinformatic analysis pipelines fail to take adequate account of the possible splicing effects of such variants, particularly where variants fall outwith canonical splice site sequences, and consequently the pathogenicity of such variants may often be missed. The regulation of splicing is highly complex and as a result, in silico prediction tools lack sufficient sensitivity and specificity for reliable use. Variants of all kinds can be linked to aberrant splicing in disease and the need for correct identification and diagnosis grows ever more crucial as novel splice-switching antisense oligonucleotide therapies start to enter clinical usage. RT-PCR provides a useful targeted assay of the splicing effects of identified variants, while minigene assays, massive parallel reporter assays and animal models can also be used for more detailed study of a particular splicing system, given enough time and resources. However, RNA-sequencing (RNA-seq) has the potential to be used as a rapid diagnostic tool in genomic medicine. By utilising data science approaches and machine learning, it may prove possible to finally understand and interpret the 'splicing code’ and apply this knowledge in human disease diagnostics.",0
"All over the place: deciphering HRAS signaling from different subcellular compartments. RAS (rat sarcoma virus oncogene homolog) oncogenes regulate fundamental biological processes through an ever-expanding signaling network. Using interaction proteomics, phosphoproteomics, transcriptomics, and integration of these datasets with a novel biostatistics approach, we have investigated Harvey-RAS (HRAS) signaling from different subcellular sites. The results reveal highly diversified signaling networks that regulate different aspects of HRAS functions.",0
"A Deep Neural Network for Predicting and Engineering Alternative Polyadenylation. Alternative polyadenylation (APA) is a major driver of transcriptome diversity in human cells. Here, we use deep learning to predict APA from DNA sequence alone. We trained our model (APARENT, APA REgression NeT) on isoform expression data from over 3 million APA reporters. APARENT's predictions are highly accurate when tasked with inferring APA in synthetic and human 3'UTRs. Visualizing features learned across all network layers reveals that APARENT recognizes sequence motifs known to recruit APA regulators, discovers previously unknown sequence determinants of 3' end processing, and integrates these features into a comprehensive, interpretable, cis-regulatory code. We apply APARENT to forward engineer functional polyadenylation signals with precisely defined cleavage position and isoform usage and validate predictions experimentally. Finally, we use APARENT to quantify the impact of genetic variants on APA. Our approach detects pathogenic variants in a wide range of disease contexts, expanding our understanding of the genetic origins of disease.",0
"Incidence of neonatal neutropenia and leukopenia after in utero exposure to chemotherapy for maternal cancer. Objective: The main purpose of this article was to report the incidence of neonatal neutropenia or leukopenia after chemotherapy exposure during pregnancy according to the time elapsed between treatment during pregnancy and birth. Background: A single study reports 33% of infants exposed to chemotherapy within the last month of pregnancy are born with neutropenia, which can place the newborn at risk for nosocomial infections. On the basis of this report, chemotherapy is typically stopped by 34 weeks of pregnancy to avoid maternal or neonatal myelosuppression at delivery. Such a pause in treatment may affect maternal health. Determining the true incidence of neutropenia after chemotherapy in relation to the time of this lapse in treatment is important to support this practice. Materials and Methods: Complete blood counts are collected for newborn whose mothers were treated for cancer during pregnancy and enrolled in the Cancer and Pregnancy Registry. Neutropenia was defined as absolute neutrophil count< 1000 mm3 and leukopenia was defined as white blood cells < 5000 cells /μL. Incidence of neutropenia was calculated according to the time elapsed from last chemotherapy treatment until birth. Fisher's exact test is used to determine if neutropenia or leukopenia is related to the time elapsed between chemotherapy during pregnancy and newborn birth. A Bayesian analysis evaluated the occurrence of neutropenia and leukopenia according to the number of days between the initiation of chemotherapy and birth. Results: A total of 135 infants exposed to chemotherapy in utero with a complete blood count collected at birth were identified from the database. Only 7.3% and 2.9% of infants were born with neutropenia or leukopenia, respectively. The highest incidence of newborn neutropenia occurred in infants delivered 22 to 28 days after chemotherapy. Conclusions: The incidence of neutropenia peaks when chemotherapy is given 22 to 28 days before birth, while leukopenia is highest if delivery is < 7 days from chemotherapy.",0
"Predicting factors for survival of breast cancer patients using machine learning techniques. BACKGROUND: Breast cancer is one of the most common diseases in women worldwide. Many studies have been conducted to predict the survival indicators, however most of these analyses were predominantly performed using basic statistical methods. As an alternative, this study used machine learning techniques to build models for detecting and visualising significant prognostic indicators of breast cancer survival rate. METHODS: A large hospital-based breast cancer dataset retrieved from the University Malaya Medical Centre, Kuala Lumpur, Malaysia (n = 8066) with diagnosis information between 1993 and 2016 was used in this study. The dataset contained 23 predictor variables and one dependent variable, which referred to the survival status of the patients (alive or dead). In determining the significant prognostic factors of breast cancer survival rate, prediction models were built using decision tree, random forest, neural networks, extreme boost, logistic regression, and support vector machine. Next, the dataset was clustered based on the receptor status of breast cancer patients identified via immunohistochemistry to perform advanced modelling using random forest. Subsequently, the important variables were ranked via variable selection methods in random forest. Finally, decision trees were built and validation was performed using survival analysis. RESULTS: In terms of both model accuracy and calibration measure, all algorithms produced close outcomes, with the lowest obtained from decision tree (accuracy = 79.8%) and the highest from random forest (accuracy = 82.7%). The important variables identified in this study were cancer stage classification, tumour size, number of total axillary lymph nodes removed, number of positive lymph nodes, types of primary treatment, and methods of diagnosis. CONCLUSION: Interestingly the various machine learning algorithms used in this study yielded close accuracy hence these methods could be used as alternative predictive tools in the breast cancer survival studies, particularly in the Asian region. The important prognostic factors influencing survival rate of breast cancer identified in this study, which were validated by survival curves, are useful and could be translated into decision support tools in the medical domain.",1
"Big data and machine learning algorithms for health-care delivery. Analysis of big data by machine learning offers considerable advantages for assimilation and evaluation of large amounts of complex health-care data. However, to effectively use machine learning tools in health care, several limitations must be addressed and key issues considered, such as its clinical implementation and ethics in health-care delivery. Advantages of machine learning include flexibility and scalability compared with traditional biostatistical methods, which makes it deployable for many tasks, such as risk stratification, diagnosis and classification, and survival predictions. Another advantage of machine learning algorithms is the ability to analyse diverse data types (eg, demographic data, laboratory findings, imaging data, and doctors' free-text notes) and incorporate them into predictions for disease risk, diagnosis, prognosis, and appropriate treatments. Despite these advantages, the application of machine learning in health-care delivery also presents unique challenges that require data pre-processing, model training, and refinement of the system with respect to the actual clinical problem. Also crucial are ethical considerations, which include medico-legal implications, doctors' understanding of machine learning tools, and data privacy and security. In this Review, we discuss some of the benefits and challenges of big data and machine learning in health care.",0
"Selective inhibition of P-gp transporter by goniothalamin derivatives sensitizes resistant cancer cells to chemotherapy. Overexpression of efflux transporters of the ATP-binding cassette (ABC) transporter family, primarily P-glycoprotein (P-gp), is a frequent cause of multidrug resistance in cancer and leads to failure of current chemotherapies. Thus, identification of selective P-gp inhibitors might provide a basis for the development of novel anticancer drug candidates. The natural product goniothalamin and 21 derivatives were characterized regarding their ability to inhibit ABC transporter function. Among the goniothalamins, selective inhibitors of P-gp were discovered. The two most potent inhibitors (R)-3 and (S)-3 displayed the ability to increase intracellular accumulation of doxorubicin, thereby sensitizing P-gp-overexpressing tumor cells to chemotherapy by decreasing doxorubicin IC50 value up to 15-fold. Molecular docking studies indicated these compounds to inhibit P-gp by acting as transporter substrates. In conclusion, our findings revealed a novel role of goniothalamin derivatives in reversing P-gp-mediated chemotherapy resistance.",0
"Matrix metalloproteinases as target genes for gene regulatory networks driving molecular and cellular pathways related to a multistep pathogenesis of cerebrovascular disease. The present study investigated a joint contribution of matrix metalloproteinases (MMPs) genes to ischemic stroke (IS) development and analyzed interactions between MMP genes and genome-wide associated loci for IS. A total of 1288 unrelated Russians (600 IS patients and 688 healthy individuals) from Central Russia were recruited for the study. Genotyping of seven single nucleotide polymorphisms (SNPs) of MMP genes (rs1799750, rs243865, rs3025058, rs11225395, rs17576, rs486055, and rs2276109) and eight genome-wide associated loci for IS were done using Taq-Man–based assays and MALDI-TOF mass spectrometry iPLEX platform, respectively. Allele − 799T at rs11225395 of the MMP8 gene was significantly associated with a decreased risk of IS after adjustment for sex and age (OR = 0.82; 95%CI, 0.70-0.96; P = 0.016). The model-based multifactor dimensionality reduction method has revealed 21 two-order, 124 three-order, and 474 four-order gene-gene (G×G) interactions models meaningfully (Pperm < 0.05) associated with the IS risk. The bioinformatic analysis enabled establishing the studied MMP gene polymorphisms possess a clear regulatory potential and may be targeted by gene regulatory networks driving molecular and cellular pathways related to the pathogenesis of IS. In conclusion, the present study was the first to identify an association between polymorphism rs11225395 of the MMP8 gene and IS risk. The study findings also indicate that MMPs deserve special attention as a potential class of genes influencing the multistep mechanisms of cerebrovascular disease including atherosclerosis in cerebral arteries, acute cerebral artery occlusion as well as the ischemic injury of the brain and its recovery.",0
"Breath hold effect on cardiovascular brain pulsations – A multimodal magnetic resonance encephalography study. Ultra-fast functional magnetic resonance encephalography (MREG) enables separate assessment of cardiovascular, respiratory, and vasomotor waves from brain pulsations without temporal aliasing. We examined effects of breath hold- (BH) related changes on cardiovascular brain pulsations using MREG to study the physiological nature of cerebrovascular reactivity. We used alternating 32 s BH and 88 s resting normoventilation (NV) to change brain pulsations during MREG combined with simultaneously measured respiration, continuous non-invasive blood pressure, and cortical near-infrared spectroscopy (NIRS) in healthy volunteers. Changes in classical resting-state network BOLD-like signal and cortical blood oxygenation were reproduced based on MREG and NIRS signals. Cardiovascular pulsation amplitudes of MREG signal from anterior cerebral artery, oxygenated hemoglobin concentration in frontal cortex, and blood pressure decreased after BH. MREG cardiovascular pulse amplitudes in cortical areas and sagittal sinus increased, while cerebrospinal fluid and white matter remained unchanged. Respiratory centers in the brainstem – hypothalamus – thalamus – amygdala network showed strongest increases in cardiovascular pulsation amplitude. The spatial propagation of averaged cardiovascular impulses altered as a function of successive BH runs. The spread of cardiovascular pulse cycles exhibited a decreasing spatial similarity over time. MREG portrayed spatiotemporally accurate respiratory network activity and cardiovascular pulsation dynamics related to BH challenges at an unpreceded high temporal resolution.",0
"Identification of long non-coding RNA-related and -coexpressed mRNA biomarkers for hepatocellular carcinoma. Background: While changes in mRNA expression during tumorigenesis have been used widely as molecular biomarkers for the diagnosis of a number of cancers, the approach has limitations. For example, traditional methods do not consider the regulatory and positional relationship between mRNA and lncRNA. The latter has been largely shown to possess tumor suppressive or oncogenic properties. The combined analysis of mRNA and lncRNA is likely to facilitate the identification of biomarkers with higher confidence. Results: Therefore, we have developed an lncRNA-related method to identify traditional mRNA biomarkers. First we identified mRNAs that are differentially expressed in Hepatocellular Carcinoma (HCC) by comparing cancer and matched adjacent non-tumorous liver tissues. Then, we performed mRNA-lncRNA relationship and coexpression analysis and obtained 41 lncRNA-related and -coexpressed mRNA biomarkers. Next, we performed network analysis, gene ontology analysis and pathway analysis to unravel the functional roles and molecular mechanisms of these lncRNA-related and -coexpressed mRNA biomarkers. Finally, we validated the prediction and performance of the 41 lncRNA-related and -coexpressed mRNA biomarkers using Support Vector Machine model with five-fold cross-validation in an independent HCC dataset from RNA-seq. Conclusions: Our results suggested that mRNAs expression profiles coexpressed with positionally related lncRNAs can provide important insights into early diagnosis and specific targeted gene therapy of HCC.",0
"Machine Learning Identification of Surgical and Operative Factors Associated with Surgical Expertise in Virtual Reality Simulation. Importance: Despite advances in the assessment of technical skills in surgery, a clear understanding of the composites of technical expertise is lacking. Surgical simulation allows for the quantitation of psychomotor skills, generating data sets that can be analyzed using machine learning algorithms. Objective: To identify surgical and operative factors selected by a machine learning algorithm to accurately classify participants by level of expertise in a virtual reality surgical procedure. Design, Setting, and Participants: Fifty participants from a single university were recruited between March 1, 2015, and May 31, 2016, to participate in a case series study at McGill University Neurosurgical Simulation and Artificial Intelligence Learning Centre. Data were collected at a single time point and no follow-up data were collected. Individuals were classified a priori as expert (neurosurgery staff), seniors (neurosurgical fellows and senior residents), juniors (neurosurgical junior residents), and medical students, all of whom participated in 250 simulated tumor resections. Exposures: All individuals participated in a virtual reality neurosurgical tumor resection scenario. Each scenario was repeated 5 times. Main Outcomes and Measures: Through an iterative process, performance metrics associated with instrument movement and force, resection of tissues, and bleeding generated from the raw simulator data output were selected by K-nearest neighbor, naive Bayes, discriminant analysis, and support vector machine algorithms to most accurately determine group membership. Results: A total of 50 individuals (9 women and 41 men; mean [SD] age, 33.6 [9.5] years; 14 neurosurgeons, 4 fellows, 10 senior residents, 10 junior residents, and 12 medical students) participated. Neurosurgeons were in practice between 1 and 25 years, with 9 (64%) involving a predominantly cranial practice. The K-nearest neighbor algorithm had an accuracy of 90% (45 of 50), the naive Bayes algorithm had an accuracy of 84% (42 of 50), the discriminant analysis algorithm had an accuracy of 78% (39 of 50), and the support vector machine algorithm had an accuracy of 76% (38 of 50). The K-nearest neighbor algorithm used 6 performance metrics to classify participants, the naive Bayes algorithm used 9 performance metrics, the discriminant analysis algorithm used 8 performance metrics, and the support vector machine algorithm used 8 performance metrics. Two neurosurgeons, 1 fellow or senior resident, 1 junior resident, and 1 medical student were misclassified. Conclusions and Relevance: In a virtual reality neurosurgical tumor resection study, a machine learning algorithm successfully classified participants into 4 levels of expertise with 90% accuracy. These findings suggest that algorithms may be capable of classifying surgical expertise with greater granularity and precision than has been previously demonstrated in surgery..",1
"The molecular landscape of glioma in patients with Neurofibromatosis 1. Neurofibromatosis type 1 (NF1) is a common tumor predisposition syndrome in which glioma is one of the prevalent tumors. Gliomagenesis in NF1 results in a heterogeneous spectrum of low- to high-grade neoplasms occurring during the entire lifespan of patients. The pattern of genetic and epigenetic alterations of glioma that develops in NF1 patients and the similarities with sporadic glioma remain unknown. Here, we present the molecular landscape of low- and high-grade gliomas in patients affected by NF1 (NF1-glioma). We found that the predisposing germline mutation of the NF1 gene was frequently converted to homozygosity and the somatic mutational load of NF1-glioma was influenced by age and grade. High-grade tumors harbored genetic alterations of TP53 and CDKN2A, frequent mutations of ATRX associated with Alternative Lengthening of Telomere, and were enriched in genetic alterations of transcription/chromatin regulation and PI3 kinase pathways. Low-grade tumors exhibited fewer mutations that were over-represented in genes of the MAP kinase pathway. Approximately 50% of low-grade NF1-gliomas displayed an immune signature, T lymphocyte infiltrates, and increased neo-antigen load. DNA methylation assigned NF1-glioma to LGm6, a poorly defined Isocitrate Dehydrogenase 1 wild-type subgroup enriched with ATRX mutations. Thus, the profiling of NF1-glioma defined a distinct landscape that recapitulates a subset of sporadic tumors.",0
"Three-dimensional spatially resolved geometrical and functional models of human liver tissue reveal new aspects of NAFLD progression. Early disease diagnosis is key to the effective treatment of diseases. Histopathological analysis of human biopsies is the gold standard to diagnose tissue alterations. However, this approach has low resolution and overlooks 3D (three-dimensional) structural changes resulting from functional alterations. Here, we applied multiphoton imaging, 3D digital reconstructions and computational simulations to generate spatially resolved geometrical and functional models of human liver tissue at different stages of non-alcoholic fatty liver disease (NAFLD). We identified a set of morphometric cellular and tissue parameters correlated with disease progression, and discover profound topological defects in the 3D bile canalicular (BC) network. Personalized biliary fluid dynamic simulations predicted an increased pericentral biliary pressure and micro-cholestasis, consistent with elevated cholestatic biomarkers in patients’ sera. Our spatially resolved models of human liver tissue can contribute to high-definition medicine by identifying quantitative multiparametric cellular and tissue signatures to define disease progression and provide new insights into NAFLD pathophysiology.",0
"Predict drug sensitivity of cancer cells with pathway activity inference. Background: Predicting cellular responses to drugs has been a major challenge for personalized drug therapy regimen. Recent pharmacogenomic studies measured the sensitivities of heterogeneous cell lines to numerous drugs, and provided valuable data resources to develop and validate computational approaches for the prediction of drug responses. Most of current approaches predict drug sensitivity by building prediction models with individual genes, which suffer from low reproducibility due to biologic variability and difficulty to interpret biological relevance of novel gene-drug associations. As an alternative, pathway activity scores derived from gene expression could predict drug response of cancer cells. Method: In this study, pathway-based prediction models were built with four approaches inferring pathway activity in unsupervised manner, including competitive scoring approaches (DiffRank and GSVA) and self-contained scoring approaches (PLAGE and Z-score). These unsupervised pathway activity inference approaches were applied to predict drug responses of cancer cells using data from Cancer Cell Line Encyclopedia (CCLE). Results: Our analysis on all the 24 drugs from CCLE demonstrated that pathway-based models achieved better predictions for 14 out of the 24 drugs, while taking fewer features as inputs. Further investigation on indicated that pathway-based models indeed captured pathways involving drug-related genes (targets, transporters and metabolic enzymes) for majority of drugs, whereas gene-models failed to identify these drug-related genes, in most cases. Among the four approaches, competitive scoring (DiffRank and GSVA) provided more accurate predictions and captured more pathways involving drug-related genes than self-contained scoring (PLAGE and Z-Score). Detailed interpretation of top pathways from the top method (DiffRank) highlights the merit of pathway-based approaches to predict drug response by identifying pathways relevant to drug mechanisms. Conclusion: Taken together, pathway-based modeling with inferred pathway activity is a promising alternative to predict drug response, with the ability to easily interpret results and provide biological insights into the mechanisms of drug actions.",0
"Structural bioinformatics insights into ATP binding mechanism in zebrafish (Danio rerio) cyclin-dependent kinase-like 5 (zCDKL5) protein. In mammalian systems, the conserved cyclin-dependent protein kinases (CDKs) control the process of cell division and curb the transcription mechanism in response to diverse signaling events that are essential for the catalytic activity. In zebrafish, zCDKL5 portrays differential expression profiling in several tissues and presumed to play a vital role in the neuronal development. In this present study, the sequence-structure relationship and mode of ATP binding in zCDKL5 was unveiled through theoretical modeling, molecular docking, and MD simulations. Like human CDKs, the modeled zCDKL5 was found to be bipartite in nature, where, ATP binds to the central cavity of the catalytic domain through a strong network of H-bonding, electrostatic, and hydrophobic interactions. MD simulation portrayed that conserved residues, viz, Ile10, Gly11, Glu12, Val18, Val64, Glu81, Cys143, and Asp144 were indispensable for tight anchoring of ATP and contribute to the stability of the zCDKL5-ATP complex. MM/PBSA binding free energy analysis displayed that van der Waal energy (ΔG vwd) and Electrostatic energy (ΔG ele) were the major contributors towards the overall binding free energy. Thus, the comparative structural bioinformatics approach has shed new insights into the dynamics and ATP binding mechanism of zCDKL5. The results from the study will help to undertake further research on the role of phosphorylated CDKL5 in the onset of neurodevelopmental disorders caused by mutations in higher eukaryotic systems.",0
"A parsimonious 3-gene signature predicts clinical outcomes in an acute myeloid leukemia multicohort study. Acute myeloid leukemia (AML) is a genetically heterogeneous hematological malignancy with variable responses to chemotherapy. Although recurring cytogenetic abnormalities and gene mutations are important predictors of outcome, 50% to 70% of AMLs harbor normal or risk-indeterminate karyotypes. Therefore, identifying more effective biomarkers predictive of treatment success and failure is essential for informing tailored therapeutic decisions. We applied an artificial neural network (ANN)-based machine learning approach to a publicly available data set for a discovery cohort of 593 adults with nonpromyelocytic AML. ANN analysis identified a parsimonious 3-gene expression signature comprising CALCRL, CD109, and LSP1, which was predictive of event-free survival (EFS) and overall survival (OS). We computed a prognostic index (PI) using normalized gene-expression levels and b-values from subsequently created Cox proportional hazards models, coupled with clinically established prognosticators. Our 3-gene PI separated the adult patients in each European LeukemiaNet cytogenetic risk category into subgroups with different survival probabilities and identified patients with very high-risk features, such as those with a high PI and either FLT3 internal tandem duplication or nonmutated nucleophosmin 1. The PI remained significantly associated with poor EFS and OS after adjusting for established prognosticators, and its ability to stratify survival was validated in 3 independent adult cohorts (n = 905 subjects) and 1 cohort of childhood AML (n = 145 subjects). Further in silico analyses established that AML was the only tumor type among 39 distinct malignancies for which the concomitant upregulation of CALCRL, CD109, and LSP1 predicted survival. Therefore, our ANN-derived 3-gene signature refines the accuracy of patient stratification and the potential to significantly improve outcome prediction.",1
"A robust fuzzy rule based integrative feature selection strategy for gene expression data in TCGA. Background: Lots of researches have been conducted in the selection of gene signatures that could distinguish the cancer patients from the normal. However, it is still an open question on how to extract the robust gene features. Methods: In this work, a gene signature selection strategy for TCGA data was proposed by integrating the gene expression data, the methylation data and the prior knowledge about cancer biomarkers. Different from the traditional integration method, the expanded 450 K methylation data were applied instead of the original 450 K array data, and the reported biomarkers were weighted in the feature selection. Fuzzy rule based classification method and cross validation strategy were applied in the model construction for performance evaluation. Results: Our selected gene features showed prediction accuracy close to 100% in the cross validation with fuzzy rule based classification model on 6 cancers from TCGA. The cross validation performance of our proposed model is similar to other integrative models or RNA-seq only model, while the prediction performance on independent data is obviously better than other 5 models. The gene signatures extracted with our fuzzy rule based integrative feature selection strategy were more robust, and had the potential to get better prediction results. Conclusion: The results indicated that the integration of expanded methylation data would cover more genes, and had greater capacity to retrieve the signature genes compared with the original 450 K methylation data. Also, the integration of the reported biomarkers was a promising way to improve the performance. PTCHD3 gene was selected as a discriminating gene in 3 out of the 6 cancers, which suggested that it might play important role in the cancer risk and would be worthy for the intensive investigation.",0
"ClearF: A supervised feature scoring method to find biomarkers using class-wise embedding and reconstruction. Background: Feature selection or scoring methods for the detection of biomarkers are essential in bioinformatics. Various feature selection methods have been developed for the detection of biomarkers, and several studies have employed information-theoretic approaches. However, most of these methods generally require a long processing time. In addition, information-theoretic methods discretize continuous features, which is a drawback that can lead to the loss of information. Results: In this paper, a novel supervised feature scoring method named ClearF is proposed. The proposed method is suitable for continuous-valued data, which is similar to the principle of feature selection using mutual information, with the added advantage of a reduced computation time. The proposed score calculation is motivated by the association between the reconstruction error and the information-theoretic measurement. Our method is based on class-wise low-dimensional embedding and the resulting reconstruction error. Given multi-class datasets such as a case-control study dataset, low-dimensional embedding is first applied to each class to obtain a compressed representation of the class, and also for the entire dataset. Reconstruction is then performed to calculate the error of each feature and the final score for each feature is defined in terms of the reconstruction errors. The correlation between the information theoretic measurement and the proposed method is demonstrated using a simulation. For performance validation, we compared the classification performance of the proposed method with those of various algorithms on benchmark datasets. Conclusions: The proposed method showed higher accuracy and lower execution time than the other established methods. Moreover, an experiment was conducted on the TCGA breast cancer dataset, and it was confirmed that the genes with the highest scores were highly associated with subtypes of breast cancer.",0
"Coronary artery centerline extraction in cardiac CT angiography using a CNN-based orientation classifier. Coronary artery centerline extraction in cardiac CT angiography (CCTA) images is a prerequisite for evaluation of stenoses and atherosclerotic plaque. In this work, we propose an algorithm that extracts coronary artery centerlines in CCTA using a convolutional neural network (CNN). In the proposed method, a 3D dilated CNN is trained to predict the most likely direction and radius of an artery at any given point in a CCTA image based on a local image patch. Starting from a single seed point placed manually or automatically anywhere in a coronary artery, a tracker follows the vessel centerline in two directions using the predictions of the CNN. Tracking is terminated when no direction can be identified with high certainty. The CNN is trained using manually annotated centerlines in training images. No image preprocessing is required, so that the process is guided solely by the local image values around the tracker's location. The CNN was trained using a training set consisting of 8 CCTA images with a total of 32 manually annotated centerlines provided in the MICCAI 2008 Coronary Artery Tracking Challenge (CAT08). Evaluation was performed within the CAT08 challenge using a test set consisting of 24 CCTA test images in which 96 centerlines were extracted. The extracted centerlines had an average overlap of 93.7% with manually annotated reference centerlines. Extracted centerline points were highly accurate, with an average distance of 0.21mm to reference centerline points. Based on these results the method ranks third among 25 publicly evaluated methods in CAT08. In a second test set consisting of 50 CCTA scans acquired at our institution (UMCU), an expert placed 5448 markers in the coronary arteries, along with radius measurements. Each marker was used as a seed point to extract a single centerline, which was compared to the other markers placed by the expert. This showed strong correspondence between extracted centerlines and manually placed markers. In a third test set containing 36 CCTA scans from the MICCAI 2014 Challenge on Automatic Coronary Calcium Scoring (orCaScore), fully automatic seeding and centerline extraction was evaluated using a segment-wise analysis. This showed that the algorithm is able to fully-automatically extract on average 92% of clinically relevant coronary artery segments. Finally, the limits of agreement between reference and automatic artery radius measurements were found to be below the size of one voxel in both the CAT08 dataset and the UMCU dataset. Extraction of a centerline based on a single seed point required on average 0.4+/-0.1 s and fully automatic coronary tree extraction required around 20 s. The proposed method is able to accurately and efficiently determine the direction and radius of coronary arteries based on information derived directly from the image data. The method can be trained with limited training data, and once trained allows fast automatic or interactive extraction of coronary artery trees from CCTA images.",1
"Matrine attenuates high-fat diet-induced in vivo and ox-LDL-induced in vitro vascular injury by regulating the PKCα/eNOS and PI3K/Akt/eNOS pathways. Lipid metabolism disorders lead to vascular endothelial injury. Matrine is an alkaloid that has been used to improve obesity and diabetes and for the treatment of hepatitis B. However, its effect on lipid metabolism disorders and vascular injury is unclear. Here, we investigated the effect of matrine on high-fat diet fed mice and oxidized low-density lipoprotein (ox-LDL)-induced human umbilical vein endothelial cells (HUVECs). Computational virtual docking analyses, phosphoinositide 3-kinase (PI3K) and protein kinase C-α (PKCα) inhibitors were used to localize matrine in vascular injuries. The results showed that matrine-treated mice were more resistant to abnormal lipid metabolism and inflammation than vehicle-treated mice and exhibited significantly alleviated ox-LDL-stimulated dysfunction of HUVECs, restored diminished nitric oxide release, decreased reactive oxygen species generation and increased expression phosphorylation of AKT-Ser473 and endothelial nitric oxide synthase (eNOS)-Ser1177. Matrine not only up-regulates eNOS-Ser1177 but also down-regulates eNOS-Thr495, a PKCα-controlled negative regulator of eNOS. Using computational virtual docking analyses and biochemical assays, matrine was also shown to influence eNOS/NO via PKCα inhibition. Moreover, the protective effects of matrine were significantly abolished by the simultaneous application of PKCα and the PI3K inhibitor. Matrine may thus be potentially employed as a novel therapeutic strategy against high-fat diet-induced vascular injury.",0
"Gastroenterologist-Level Identification of Small-Bowel Diseases and Normal Variants by Capsule Endoscopy Using a Deep-Learning Model. BACKGROUND & AIMS: Capsule endoscopy has revolutionized investigation of the small bowel. However, this technique produces a video that is 8-10 hours long, so analysis is time consuming for gastroenterologists. Deep convolutional neural networks (CNNs) can recognize specific images among a large variety. We aimed to develop a CNN-based algorithm to assist in the evaluation of small bowel capsule endoscopy (SB-CE) images. METHODS: We collected 113,426,569 images from 6970 patients who had SB-CE at 77 medical centers from July 2016 through July 2018. A CNN-based auxiliary reading model was trained to differentiate abnormal from normal images using 158,235 SB-CE images from 1970 patients. Images were categorized as normal, inflammation, ulcer, polyps, lymphangiectasia, bleeding, vascular disease, protruding lesion, lymphatic follicular hyperplasia, diverticulum, parasite, and other. The model was further validated in 5000 patients (no patient was overlap with the 1970 patients in the training set); the same patients were evaluated by conventional analysis and CNN-based auxiliary analysis by 20 gastroenterologists. If there was agreement in image categorization between the conventional analysis and CNN model, no further evaluation was performed. If there was disagreement between the conventional analysis and CNN model, the gastroenterologists re-evaluated the image to confirm or reject the CNN categorization. RESULTS: In the SB-CE images from the validation set, 4206 abnormalities in 3280 patients were identified after final consensus evaluation. The CNN-based auxiliary model identified abnormalities with 99.88% sensitivity in the per-patient analysis (95% CI, 99.67-99.96) and 99.90% sensitivity in the per-lesion analysis (95% CI, 99.74-99.97). Conventional reading by the gastroenterologists identified abnormalities with 74.57% sensitivity (95% CI, 73.05-76.03) in the per-patient analysis and 76.89% in the per-lesion analysis (95% CI, 75.58-78.15). The mean reading time per patient was 96.6 +/- 22.53 minutes by conventional reading and 5.9 +/- 2.23 minutes by CNN-based auxiliary reading (P < .001). CONCLUSIONS: We validated the ability of a CNN-based algorithm to identify abnormalities in SB-CE images. The CNN-based auxiliary model identified abnormalities with higher levels of sensitivity and significantly shorter reading times than conventional analysis by gastroenterologists. This algorithm provides an important tool to help gastroenterologists analyze SB-CE images more efficiently and more accurately.",1
"Automated segmentation of knee bone and cartilage combining statistical shape knowledge and convolutional neural networks: Data from the Osteoarthritis Initiative. We present a method for the automated segmentation of knee bones and cartilage from magnetic resonance imaging (MRI) that combines a priori knowledge of anatomical shape with Convolutional Neural Networks (CNNs). The proposed approach incorporates 3D Statistical Shape Models (SSMs) as well as 2D and 3D CNNs to achieve a robust and accurate segmentation of even highly pathological knee structures. The shape models and neural networks employed are trained using data from the Osteoarthritis Initiative (OAI) and the MICCAI grand challenge ""Segmentation of Knee Images 2010"" (SKI10), respectively. We evaluate our method on 40 validation and 50 submission datasets from the SKI10 challenge. For the first time, an accuracy equivalent to the inter-observer variability of human readers is achieved in this challenge. Moreover, the quality of the proposed method is thoroughly assessed using various measures for data from the OAI, i.e. 507 manual segmentations of bone and cartilage, and 88 additional manual segmentations of cartilage. Our method yields sub-voxel accuracy for both OAI datasets. We make the 507 manual segmentations as well as our experimental setup publicly available to further aid research in the field of medical image segmentation. In conclusion, combining localized classification via CNNs with statistical anatomical knowledge via SSMs results in a state-of-the-art segmentation method for knee bones and cartilage from MRI data.",1
"Genetic algorithm as an optimization tool for the development of sponge cell culture media. Sponges are rich sources of novel natural products. Production in cell cultures may be an option for supply of these compounds but there are currently no sponge cell lines. Because there is a lack of understanding about the precise conditions and nutritional requirements that are necessary to sustain sponge cells in vitro, there has yet to be a defined, sponge-specific nutrient medium. This study utilized a genetic algorithm approach to optimize the amino acid composition of a commercially available basal cell culture medium in order to increase the metabolic activity of cells of the marine sponge Dysidea etheria. Four generations of the algorithm were carried out in vitro in wet lab conditions and an optimal medium combination was selected for further evaluation. When compared to the basal medium control, there was a twofold increase in metabolic activity. The genetic algorithm approach can be used to optimize other components of culture media to efficiently optimize chosen parameters without the need for detailed knowledge on all possible interactions.",0
"Development of Chinese gastric cancer surgery: Opportunities and challenges. With the approaches of artificial intelligence and big data, the development of cancer genomics and updating of imaging technology, gastric cancer surgery is facing great challenges and opportunities. The main focus is on laparoscopic surgery technology, enhanced recovery after surgery, multidisciplinary comprehensive treatment, and precision medicine. Considering the common demand for reduced complication rate among doctors and patients, laparoscopic surgery has become widely popular owing to its advantages of small incision and rapid recovery. Furthermore, the development of artificial intelligence and big data has raised a new challenge in routine diagnosis and treatment. As a result, we encourage multicenter cooperation, and data standardization and sharing. At present, completion of the transition from empirical medicine to evidence-based medicine and promotion of the individualization and standardization of gastric cancer treatment are needed.",0
"Organ preservation in bladder cancer: an opportunity for truly personalized treatment. Radical treatment of many solid tumours has moved from surgery to multimodal organ preservation strategies combining systemic and local treatments. Trimodality bladder-preserving treatment (TMT) comprises maximal transurethral resection of the bladder tumour followed by radiotherapy and concurrent radiosensitizing treatment, thereby sparing the urinary bladder. From the patient's perspective, the choice of maintaining quality of life without a negative effect on the chances of cure and long-term survival is attractive. In muscle-invasive bladder cancer (MIBC), the evidence shows comparable clinical outcomes between patients undergoing radical cystectomy and TMT. Despite this evidence, many patients continue to be offered radical surgery as the standard-of-care treatment. Improvements in radiotherapy techniques with adaptive radiotherapy and advances in imaging translate to increases in the accuracy of treatment delivery and reductions in long-term toxicities. With the advent of novel biomarkers promising improved prediction of treatment response, stratification of patients for different treatments on the basis of tumour biology could soon be a reality. The future of oncological treatment lies in personalized medicine with the combination of technological and biological advances leading to truly bespoke management for patients with MIBC.",0
"Reversal effect of FW-04-806, a macrolide dilactone compound, on multidrug resistance mediated by ABCB1 and ABCG2 in vitro and in vivo. Background: Overexpression of ATP-binding cassette (ABC) transporters, such as ABCB1 and ABCG2, has been proved to be a major trigger for multidrug resistance (MDR) in certain types of cancer. A promising approach to reverse MDR is the combined use of nontoxic and potent ABC transporters inhibitor with conventional anticancer drugs. We previously reported that FW-04-806 (conglobatin) as a novel Hsp90 inhibitor with low toxicity, capable of attenuating Hsp90/Cdc37 /clients interactions and producing antitumor action in vitro and in vivo. Our early activity screening found that FW-04-806 at non-cytotoxic concentration was able to enhance the cytotoxicity of chemotherapeutic agents on the ABCB1 overexpressing cells. Therefore, we speculated that FW-04-806 might be a promising MDR reversal agent. In the present study we further investigated its reversal effect of MDR induced by ABC transporters in vitro and in vivo. Methods: MTT assay in vitro and xenograftes in vivo were used to investigate reversal effect of FW-04-806 on MDR in ABCB1 or ABCG2 overexpressing cancer cells. To understand the mechanisms for the MDR reversal, we examined the effects of FW-04-806 on intracellular accumulation of doxorubicin (DOX, adriamycin, adr)/Rhodamine 123 (Rho 123), efflux of doxorubicin, expression levels of gene and protein of ABCB1 or ABCG2 and ATPase activity of ABCB1, and carried out molecular docking between FW-04-806 and human ABCB1. Results: The results indicated that FW-04-806 significantly enhanced the cytotoxicity of substrate chemotherapeutic agents on the ABCB1 or ABCG2 overexpressing cells in vitro and in vivo suggesting its reversal MDR effects. FW-04-806 increased the intracellular accumulation of DOX or Rho123 by inhibiting the efflux function of ABC transporters in MDR cells rather than in their parental sensitive cells. However, unlike other ABC transporter inhibitors, FW-04-806 had no effect on the ATPase activity nor on the expression of ABCB1 or ABCG2 on either mRNA or protein level. Molecular docking suggested that FW-04-806 may have lower affinity to the ATPase site, which was consistent with its no significant effect on the ATPase activity of ABCB1; However FW-04-806 may bind to substrate binding site in TMDs more stably than substrate anticancer drugs therefore obstruct the anticancer drugs pumped out of the cell. Conclusions: FW-04-806 is a compound that has both anti-tumor and reversal MDR effects, and its antitumor clinical application is worth further study. Graphical abstract: [Figure not available: see fulltext.]",0
"Advances in clinical MRI technology. Advances in MRI technologies have the potential to detect, characterize, and monitor a wide variety of diseases.",0
"The Inhibitory Effect of GlmU Acetyltransferase Inhibitor TPSA on Mycobacterium tuberculosis May Be Affected Due to Its Methylation by Methyltransferase Rv0560c. Mycobacterium tuberculosis bifunctional enzyme GlmU is a novel target for anti-TB drugs and is involved in glycosyl donor UDP-N-acetylglucosamine biosynthesis. Here, we found that TPSA (2-[5-(2-{[4-(2-thienyl)-2-pyrimidinyl]sulfanyl}acetyl)-2-thienyl]acetic acid) was a novel inhibitor for GlmU acetyltransferase activity (IC50: 5.3 μM). The interaction sites of GlmU and TPSA by molecular docking were confirmed by site-directed mutagenesis. TPSA showed an inhibitory effect on Mtb H37Ra growth and intracellular H37Ra in macrophage cells (MIC: 66.5 μM). To investigate why TPSA at a higher concentration (66.5 μM) was able to inhibit H37Ra growth, proteome and transcriptome of H37Ra treated with TPSA were analyzed. The expression of two methyltransferases MRA_0565 (Rv0558) and MRA_0567 (Rv0560c) were markedly increased. TPSA was pre-incubated with purified Rv0558 and Rv0560c in the presence of S-adenosylmethionine (methyl donor) respectively, resulting in its decreased inhibitory effect of GlmU on acetyltransferase activity. The inhibition of TPSA on growth of H37Ra with overexpressed Rv0558 and Rv0560c was reduced. These implied that methyltransferases could modify TPSA. The methylation of TPSA catalyzed by Rv0560c was subsequently confirmed by LC-MS. Therefore, TPSA as a GlmU acetyltransferase activity inhibitor may offer a structural basis for new anti-tuberculosis drugs. TPSA needs to be modified further by some groups to prevent its methylation by methyltransferases.",0
"Contemporary Diagnosis and Management of Patients With Myocardial Infarction in the Absence of Obstructive Coronary Artery Disease: A Scientific Statement From the American Heart Association. Myocardial infarction in the absence of obstructive coronary artery disease is found in approximately 5% to 6% of all patients with acute infarction who are referred for coronary angiography. There are a variety of causes that can result in this clinical condition. As such, it is important that patients are appropriately diagnosed and an evaluation to uncover the correct cause is performed so that, when possible, specific therapies to treat the underlying cause can be prescribed. This statement provides a formal and updated definition for the broadly labelled term MINOCA (incorporating the definition of acute myocardial infarction from the newly released ""Fourth Universal Definition of Myocardial Infarction"") and provides a clinically useful framework and algorithms for the diagnostic evaluation and management of patients with myocardial infarction in the absence of obstructive coronary artery disease.",0
"Latent Dirichlet Allocation in predicting clinical trial terminations. BACKGROUND: This study used natural language processing (NLP) and machine learning (ML) techniques to identify reliable patterns from within research narrative documents to distinguish studies that complete successfully, from the ones that terminate. Recent research findings have reported that at least 10 % of all studies that are funded by major research funding agencies terminate without yielding useful results. Since it is well-known that scientific studies that receive funding from major funding agencies are carefully planned, and rigorously vetted through the peer-review process, it was somewhat daunting to us that study-terminations are this prevalent. Moreover, our review of the literature about study terminations suggested that the reasons for study terminations are not well understood. We therefore aimed to address that knowledge gap, by seeking to identify the factors that contribute to study failures. METHOD: We used data from the clinicialTrials.gov repository, from which we extracted both structured data (study characteristics), and unstructured data (the narrative description of the studies). We applied natural language processing techniques to the unstructured data to quantify the risk of termination by identifying distinctive topics that are more frequently associated with trials that are terminated and trials that are completed. We used the Latent Dirichlet Allocation (LDA) technique to derive 25 ""topics"" with corresponding sets of probabilities, which we then used to predict study-termination by utilizing random forest modeling. We fit two distinct models - one using only structured data as predictors and another model with both structured data and the 25 text topics derived from the unstructured data. RESULTS: In this paper, we demonstrate the interpretive and predictive value of LDA as it relates to predicting clinical trial failure. The results also demonstrate that the combined modeling approach yields robust predictive probabilities in terms of both sensitivity and specificity, relative to a model that utilizes the structured data alone. CONCLUSIONS: Our study demonstrated that the use of topic modeling using LDA significantly raises the utility of unstructured data in better predicating the completion vs. termination of studies. This study sets the direction for future research to evaluate the viability of the designs of health studies.",1
"Differential expression of the TwHMGS gene and its effect on triptolide biosynthesis in Tripterygium wilfordii. 3-Hydroxy-3-methylglutaryl-CoA synthase (HMGS) is the first committed enzyme in the MVA pathway and involved in the biosynthesis of terpenes in Tripterygium wilfordii. The full-length cDNA and a 515 bp RNAi target fragment of TwHMGS were ligated into the pH7WG2D and pK7GWIWG2D vectors to respectively overexpress and silence, TwHMGS was overexpressed and silenced in T. wilfordii suspension cells using biolistic-gun mediated transformation, which resulted in 2-fold increase and a drop to 70% in the expression level compared to cells with empty vector controls. During TwHMGS overexpression, the expression of TwHMGR, TwDXR and TwTPS7v2 was significantly upregulated to the control. In the RNAi group, the expression of TwHMGR, TwDXS, TwDXR and TwMCT visibly displayed downregulation to the control. The cells with TwHMGS overexpressed produced twice higher than the control value. These results proved that differential expression of TwHMGS determined the production of triptolide in T. wilfordii and laterally caused different trends of relative gene expression in the terpene biosynthetic pathway. Finally, the substrate acetyl-CoA was docked into the active site of TwHMGS, suggesting the key residues including His247, Lys256 and Arg296 undergo electrostatic or H-bond interactions with acetyl-CoA.",0
"Defining HLA-II Ligand Processing and Binding Rules with Mass Spectrometry Enhances Cancer Epitope Prediction. Increasing evidence indicates CD4(+) T cells can recognize cancer-specific antigens and control tumor growth. However, it remains difficult to predict the antigens that will be presented by human leukocyte antigen class II molecules (HLA-II), hindering efforts to optimally target them therapeutically. Obstacles include inaccurate peptide-binding prediction and unsolved complexities of the HLA-II pathway. To address these challenges, we developed an improved technology for discovering HLA-II binding motifs and conducted a comprehensive analysis of tumor ligandomes to learn processing rules relevant in the tumor microenvironment. We profiled >40 HLA-II alleles and showed that binding motifs were highly sensitive to HLA-DM, a peptide-loading chaperone. We also revealed that intratumoral HLA-II presentation was dominated by professional antigen-presenting cells (APCs) rather than cancer cells. Integrating these observations, we developed algorithms that accurately predicted APC ligandomes, including peptides from phagocytosed cancer cells. These tools and biological insights will enable improved HLA-II-directed cancer therapies.",1
"Fully convolutional multi-scale residual DenseNets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers. Deep fully convolutional neural network (FCN) based architectures have shown great potential in medical image segmentation. However, such architectures usually have millions of parameters and inadequate number of training samples leading to over-fitting and poor generalization. In this paper, we present a novel DenseNet based FCN architecture for cardiac segmentation which is parameter and memory efficient. We propose a novel up-sampling path which incorporates long skip and short-cut connections to overcome the feature map explosion in conventional FCN based architectures. In order to process the input images at multiple scales and view points simultaneously, we propose to incorporate Inception module's parallel structures. We propose a novel dual loss function whose weighting scheme allows to combine advantages of cross-entropy and Dice loss leading to qualitative improvements in segmentation. We demonstrate computational efficacy of incorporating conventional computer vision techniques for region of interest detection in an end-to-end deep learning based segmentation framework. From the segmentation maps we extract clinically relevant cardiac parameters and hand-craft features which reflect the clinical diagnostic analysis and train an ensemble system for cardiac disease classification. We validate our proposed network architecture on three publicly available datasets, namely: (i) Automated Cardiac Diagnosis Challenge (ACDC-2017), (ii) Left Ventricular segmentation challenge (LV-2011), (iii) 2015 Kaggle Data Science Bowl cardiac challenge data. Our approach in ACDC-2017 challenge stood second place for segmentation and first place in automated cardiac disease diagnosis tasks with an accuracy of 100% on a limited testing set (n=50). In the LV-2011 challenge our approach attained 0.74 Jaccard index, which is so far the highest published result in fully automated algorithms. In the Kaggle challenge our approach for LV volume gave a Continuous Ranked Probability Score (CRPS) of 0.0127, which would have placed us tenth in the original challenge. Our approach combined both cardiac segmentation and disease diagnosis into a fully automated framework which is computationally efficient and hence has the potential to be incorporated in computer-aided diagnosis (CAD) tools for clinical application.",1
"A comparative study of deep learning architectures on melanoma detection. Melanoma is the most aggressive type of skin cancer, which significantly reduces the life expectancy. Early detection of melanoma can reduce the morbidity and mortality associated with skin cancer. Dermoscopic images acquired by dermoscopic instruments are used in computational analysis for skin cancer detection. However, some image quality limitations such as noises, shadows, artefacts exist that could compromise the robustness of the skin image analysis. Hence, developing an automatic intelligent system for skin cancer diagnosis with accurate detection rate is crucial. In this paper, we evaluate the performance of several state-of-the-art convolutional neural networks in dermoscopic images of skin lesions. Our experiment is conducted on a graphics processing unit (GPU)to speed up the training and deployment process. To enhance the quality of images, we employ different pre-processing steps. We also apply data augmentation methodology such as horizontal and vertical flipping techniques to address the class skewness problem. Both pre-processing and data augmentation could help to improve the final accuracy.",0
"Advances in protein structure prediction and design. The prediction of protein three-dimensional structure from amino acid sequence has been a grand challenge problem in computational biophysics for decades, owing to its intrinsic scientific interest and also to the many potential applications for robust protein structure prediction algorithms, from genome interpretation to protein function prediction. More recently, the inverse problem - designing an amino acid sequence that will fold into a specified three-dimensional structure - has attracted growing attention as a potential route to the rational engineering of proteins with functions useful in biotechnology and medicine. Methods for the prediction and design of protein structures have advanced dramatically in the past decade. Increases in computing power and the rapid growth in protein sequence and structure databases have fuelled the development of new data-intensive and computationally demanding approaches for structure prediction. New algorithms for designing protein folds and protein-protein interfaces have been used to engineer novel high-order assemblies and to design from scratch fluorescent proteins with novel or enhanced properties, as well as signalling proteins with therapeutic potential. In this Review, we describe current approaches for protein structure prediction and design and highlight a selection of the successful applications they have enabled.",0
"Co-occurring genomic alterations in non-small-cell lung cancer biology and therapy. The impressive clinical activity of small-molecule receptor tyrosine kinase inhibitors for oncogene-addicted subgroups of non-small-cell lung cancer (for example, those driven by activating mutations in the gene encoding epidermal growth factor receptor (EGFR) or rearrangements in the genes encoding the receptor tyrosine kinases anaplastic lymphoma kinase (ALK), ROS proto-oncogene 1 (ROS1) and rearranged during transfection (RET)) has established an oncogene-centric molecular classification paradigm in this disease. However, recent studies have revealed considerable phenotypic diversity downstream of tumour-initiating oncogenes. Co-occurring genomic alterations, particularly in tumour suppressor genes such as TP53 and LKB1 (also known as STK11), have emerged as core determinants of the molecular and clinical heterogeneity of oncogene-driven lung cancer subgroups through their effects on both tumour cell-intrinsic and non-cell-autonomous cancer hallmarks. In this Review, we discuss the impact of co-mutations on the pathogenesis, biology, microenvironmental interactions and therapeutic vulnerabilities of non-small-cell lung cancer and assess the challenges and opportunities that co-mutations present for personalized anticancer therapy, as well as the expanding field of precision immunotherapy.",0
"Predicting dark respiration rates of wheat leaves from hyperspectral reflectance. Greater availability of leaf dark respiration (Rdark ) data could facilitate breeding efforts to raise crop yield and improve global carbon cycle modelling. However, the availability of Rdark data is limited because it is cumbersome, time consuming, or destructive to measure. We report a non-destructive and high-throughput method of estimating Rdark from leaf hyperspectral reflectance data that was derived from leaf Rdark measured by a destructive high-throughput oxygen consumption technique. We generated a large dataset of leaf Rdark for wheat (1380 samples) from 90 genotypes, multiple growth stages, and growth conditions to generate models for Rdark . Leaf Rdark (per unit leaf area, fresh mass, dry mass or nitrogen, N) varied 7- to 15-fold among individual plants, whereas traits known to scale with Rdark , leaf N, and leaf mass per area (LMA) only varied twofold to fivefold. Our models predicted leaf Rdark , N, and LMA with r2 values of 0.50-0.63, 0.91, and 0.75, respectively, and relative bias of 17-18% for Rdark and 7-12% for N and LMA. Our results suggest that hyperspectral model prediction of wheat leaf Rdark is largely independent of leaf N and LMA. Potential drivers of hyperspectral signatures of Rdark are discussed.",0
"The implementation of natural language processing to extract index lesions from breast magnetic resonance imaging reports. BACKGROUND: There are often multiple lesions in breast magnetic resonance imaging (MRI) reports and radiologists usually focus on describing the index lesion that is most crucial to clinicians in determining the management and prognosis of patients. Natural language processing (NLP) has been used for information extraction from mammography reports. However, few studies have investigated NLP in breast MRI data based on free-form text. The objective of the current study was to assess the validity of our NLP program to accurately extract index lesions and their corresponding imaging features from free-form text of breast MRI reports. METHODS: This cross-sectional study examined 1633 free-form text reports of breast MRIs from 2014 to 2017. First, the NLP system was used to extract 9 features from all the lesions in the reports according to the Breast Imaging Reporting and Data System (BI-RADS) descriptors. Second, the index lesion was defined as the lesion with the largest number of imaging features. Third, we extracted the values of each imaging feature and the BI-RADS category from each index lesion. To evaluate the accuracy of our system, 478 reports were manually reviewed by two individuals. The time taken to extract data by NLP was compared with that by reviewers. RESULTS: The NLP system extracted 889 lesions from 478 reports. The mean number of imaging features per lesion was 6.5 ± 2.1 (range: 3-9; 95% CI: 6.362-6.638). The mean number of imaging features per index lesion was 8.0 ± 1.1 (range: 5-9; 95% CI: 7.901-8.099). The NLP system demonstrated a recall of 100.0% and a precision of 99.6% for correct identification of the index lesion. The recall and precision of NLP to correctly extract the value of imaging features from the index lesions were 91.0 and 92.6%, respectively. The recall and precision for the correct identification of the BI-RADS categories were 96.6 and 94.8%, respectively. NLP generated the total results in less than 1 s, whereas the manual reviewers averaged 4.47 min and 4.56 min per report. CONCLUSIONS: Our NLP method successfully extracted the index lesion and its corresponding information from free-form text.",1
"Interpretable deep neural network for cancer survival analysis by integrating genomic and clinical data. Background: Understanding the complex biological mechanisms of cancer patient survival using genomic and clinical data is vital, not only to develop new treatments for patients, but also to improve survival prediction. However, highly nonlinear and high-dimension, low-sample size (HDLSS) data cause computational challenges to applying conventional survival analysis. Results: We propose a novel biologically interpretable pathway-based sparse deep neural network, named Cox-PASNet, which integrates high-dimensional gene expression data and clinical data on a simple neural network architecture for survival analysis. Cox-PASNet is biologically interpretable where nodes in the neural network correspond to biological genes and pathways, while capturing the nonlinear and hierarchical effects of biological pathways associated with cancer patient survival. We also propose a heuristic optimization solution to train Cox-PASNet with HDLSS data. Cox-PASNet was intensively evaluated by comparing the predictive performance of current state-of-the-art methods on glioblastoma multiforme (GBM) and ovarian serous cystadenocarcinoma (OV) cancer. In the experiments, Cox-PASNet showed out-performance, compared to the benchmarking methods. Moreover, the neural network architecture of Cox-PASNet was biologically interpreted, and several significant prognostic factors of genes and biological pathways were identified. Conclusions: Cox-PASNet models biological mechanisms in the neural network by incorporating biological pathway databases and sparse coding. The neural network of Cox-PASNet can identify nonlinear and hierarchical associations of genomic and clinical data to cancer patient survival. The open-source code of Cox-PASNet in PyTorch implemented for training, evaluation, and model interpretation is available at: https://github.com/DataX-JieHao/Cox-PASNet.",0
"A Subset of Type I Conventional Dendritic Cells Controls Cutaneous Bacterial Infections through VEGFalpha-Mediated Recruitment of Neutrophils. Skin conventional dendritic cells (cDCs) exist as two distinct subsets, cDC1s and cDC2s, which maintain the balance of immunity to pathogens and tolerance to self and microbiota. Here, we examined the roles of dermal cDC1s and cDC2s during bacterial infection, notably Propionibacterium acnes (P. acnes). cDC1s, but not cDC2s, regulated the magnitude of the immune response to P. acnes in the murine dermis by controlling neutrophil recruitment to the inflamed site and survival and function therein. Single-cell mRNA sequencing revealed that this regulation relied on secretion of the cytokine vascular endothelial growth factor alpha (VEGF-alpha) by a minor subset of activated EpCAM(+)CD59(+)Ly-6D(+) cDC1s. Neutrophil recruitment by dermal cDC1s was also observed during S. aureus, bacillus Calmette-Guerin (BCG), or E. coli infection, as well as in a model of bacterial insult in human skin. Thus, skin cDC1s are essential regulators of the innate response in cutaneous immunity and have roles beyond classical antigen presentation.",0
"Natural language processing of Reddit data to evaluate dermatology patient experiences and therapeutics. BACKGROUND: There is a lack of research studying patient-generated data on Reddit, one of the world's most popular forums with active users interested in dermatology. Techniques within natural language processing, a field of artificial intelligence, can analyze large amounts of text information and extract insights. OBJECTIVE: To apply natural language processing to Reddit comments about dermatology topics to assess for feasibility and potential for insights and engagement. METHODS: A software pipeline preprocessed Reddit comments from 2005 to 2017 from 7 popular dermatology-related subforums on Reddit, applied latent Dirichlet allocation, and used spectral clustering to establish cohesive themes and the frequency of word representation and grouped terms within these topics. RESULTS: We created a corpus of 176,000 comments and identified trends in patient engagement in spaces such as eczema and acne, among others, with a focus on homeopathic treatments and isotretinoin. LIMITATIONS: Latent Dirichlet allocation is an unsupervised model, meaning there is no ground truth to which the model output can be compared. However, because these forums are anonymous, there seems little incentive for patients to be dishonest. CONCLUSIONS: Reddit data has viability and utility for dermatologic research and engagement with the public, especially for common dermatology topics such as tanning, acne, and psoriasis.",1
"Potent hERG channel inhibition by sarizotan, an investigative treatment for Rett Syndrome. Rett Syndrome (RTT) is an X-linked neurodevelopmental disorder associated with respiratory abnormalities and, in up to ~40% of patients, with prolongation of the cardiac QTc interval. QTc prolongation calls for cautious use of drugs with a propensity to inhibit hERG channels. The STARS trial has been undertaken to investigate the efficacy of sarizotan, a 5-HT1A receptor agonist, at correcting RTT respiratory abnormalities. The present study investigated whether sarizotan inhibits hERG potassium channels and prolongs ventricular repolarization. Whole-cell patch-clamp measurements were made at 37 °C from hERG-expressing HEK293 cells. Docking analysis was conducted using a recent cryo-EM structure of hERG. Sarizotan was a potent inhibitor of hERG current (IhERG; IC50 of 183 nM) and of native ventricular IKr from guinea-pig ventricular myocytes. 100 nM and 1 μM sarizotan prolonged ventricular action potential (AP) duration (APD90) by 14.1 ± 3.3% (n = 6) and 29.8 ± 3.1% (n = 5) respectively and promoted AP triangulation. High affinity IhERG inhibition by sarizotan was contingent upon channel gating and intact inactivation. Mutagenesis experiments and docking analysis implicated F557, S624 and Y652 residues in sarizotan binding, with weaker contribution from F656. In conclusion, sarizotan inhibits IKr/IhERG, accessing key binding residues on channel gating. This action and consequent ventricular AP prolongation occur at concentrations relevant to those proposed to treat breathing dysrhythmia in RTT. Sarizotan should only be used in RTT patients with careful evaluation of risk factors for QTc prolongation.",0
"An in silico pharmacological approach toward the discovery of potent inhibitors to combat drug resistance HIV-1 protease variants. Protease inhibitors (PIs) are crucial drugs in highly active antiretroviral therapy for human immunodeficiency virus-1 (HIV-1) infections. However, resistance owing to mutations challenge the long-term efficacy in the medication of HIV-1-infected individuals. Lopinavir (LPV) and darunavir (DRV), two second-generation drugs are the most potent among PIs, hustling the drug resistance when mutations occur in the active and nonactive site of the protease (PR). Herein, we strive for compounds that can stifle the function of wild-type (WT) HIV-1 PR along with four major single mutants (I54M, V82T, I84V, and L90M) instigating resistance to the PIs using in silico approach. Six common compounds are retrieved from six databases using combined pharmacophore-based and structure-based virtual screening methodology. LPV and DRV are docked and the binding free energy is calculated to set the cut-off value for selecting compounds. Further, to gain insight into the stability of the complexes the molecular dynamics simulation (MDS) is carried out, which uncovers two lead molecules namely NCI-524545 and ZINC12866729. Both the lead molecules connect with WT and mutant HIV-1 PRs through strong and stable hydrogen bond interactions when compared with LPV and DRV throughout the trajectory analysis. Interestingly, NCI-524545 and ZINC12866729 exhibit direct interactions with I50/50′ by replacing the conserved water molecule as evidenced by MDS, which indicates the credible potency of these compounds. Hence, we concluded that NCI-524545 and ZINC12866729 have great puissant to restrain the role of drug resistance HIV-1 PR variants, which can also show better activity through in vivo and in vitro conditions.",0
"Development and Validation of a Machine Learning Model to Aid Discharge Processes for Inpatient Surgical Care. Importance: Inpatient overcrowding is associated with delays in care, including the deferral of surgical care until beds are available to accommodate postoperative patients. Timely patient discharge is critical to address inpatient overcrowding and requires coordination among surgeons, nurses, case managers, and others. This is difficult to achieve without early identification and systemwide transparency of discharge candidates and their respective barriers to discharge. Objective: To validate the performance of a clinically interpretable feedforward neural network model that could improve the discharge process by predicting which patients would be discharged within 24 hours and their clinical and nonclinical barriers. Design, Setting, and Participants: This prognostic study included adult patients discharged from inpatient surgical care from May 1, 2016, to August 31, 2017, at a quaternary care teaching hospital. Model performance was assessed with standard cross-validation techniques. The model's performance was compared with a baseline model using historical procedure median length of stay to predict discharges. In prospective cohort analysis, the feedforward neural network model was used to make predictions on general surgical care floors with 63 beds. If patients were not discharged when predicted, the causes of delay were recorded. Main Outcomes and Measures: The primary outcome was the out-of-sample area under the receiver operating characteristic curve of the model. Secondary outcomes included the causes of discharge delay and the number of avoidable bed-days. Results: The model was trained on 15 201 patients (median [interquartile range] age, 60 [46-70] years; 7623 [50.1%] men) discharged from inpatient surgical care. The estimated out-of-sample area under the receiver operating characteristic curve of the model was 0.840 (SD, 0.008; 95% CI, 0.839-0.844). Compared with the baseline model, the neural network model had higher sensitivity (52.5% vs 56.6%) and specificity (51.7% vs 82.6%). The neural network model identified 65 barriers to discharge. In the prospective study of 605 patients, causes of delays included clinical barriers (41 patients [30.1%]), variation in clinical practice (30 patients [22.1%]), and nonclinical reasons (65 patients [47.8%]). Summing patients who were not discharged owing to variation in clinical practice and nonclinical reasons, 128 bed-days, or 1.2 beds per day, were classified as avoidable. Conclusions and Relevance: This cohort study found that a neural network model could predict daily inpatient surgical care discharges and their barriers. The model identified systemic causes of discharge delays. Such models should be studied for their ability to increase the timeliness of discharges.",1
"Using an artificial neural network to map cancer common data elements to the biomedical research integrated domain group model in a semi-automated manner. BACKGROUND: The medical community uses a variety of data standards for both clinical and research reporting needs. ISO 11179 Common Data Elements (CDEs) represent one such standard that provides robust data point definitions. Another standard is the Biomedical Research Integrated Domain Group (BRIDG) model, which is a domain analysis model that provides a contextual framework for biomedical and clinical research data. Mapping the CDEs to the BRIDG model is important; in particular, it can facilitate mapping the CDEs to other standards. Unfortunately, manual mapping, which is the current method for creating the CDE mappings, is error-prone and time-consuming; this creates a significant barrier for researchers who utilize CDEs. METHODS: In this work, we developed a semi-automated algorithm to map CDEs to likely BRIDG classes. First, we extended and improved our previously developed artificial neural network (ANN) alignment algorithm. We then used a collection of 1284 CDEs with robust mappings to BRIDG classes as the gold standard to train and obtain the appropriate weights of six attributes in CDEs. Afterward, we calculated the similarity between a CDE and each BRIDG class. Finally, the algorithm produces a list of candidate BRIDG classes to which the CDE of interest may belong. RESULTS: For CDEs semantically similar to those used in training, a match rate of over 90% was achieved. For those partially similar, a match rate of 80% was obtained and for those with drastically different semantics, a match rate of up to 70% was achieved. DISCUSSION: Our semi-automated mapping process reduces the burden of domain experts. The weights are all significant in six attributes. Experimental results indicate that the availability of training data is more important than the semantic similarity of the testing data to the training data. We address the overfitting problem by selecting CDEs randomly and adjusting the ratio of training and verification samples. CONCLUSIONS: Experimental results on real-world use cases have proven the effectiveness and efficiency of our proposed methodology in mapping CDEs with BRIDG classes, both those CDEs seen before as well as new, unseen CDEs. In addition, it reduces the mapping burden and improves the mapping quality.",1
"The physiological determinants of near-infrared spectroscopy-derived regional cerebral oxygenation in critically ill adults. Background: To maintain adequate oxygen delivery to tissue, resuscitation of critically ill patients is guided by assessing surrogate markers of perfusion. As there is no direct indicator of cerebral perfusion used in routine critical care, identifying an accurate strategy to monitor brain perfusion is paramount. Near-infrared spectroscopy (NIRS) is a non-invasive technique to quantify regional cerebral oxygenation (rSO2) that has been used for decades during cardiac surgery which has led to targeted algorithms to optimize rSO2 being developed. However, these targeted algorithms do not exist during critical care, as the physiological determinants of rSO2 during critical illness remain poorly understood. Materials and methods: This prospective observational study was an exploratory analysis of a nested cohort of patients within the CONFOCAL study (NCT02344043) who received high-fidelity vital sign monitoring. Adult patients (≥ 18 years) admitted < 24 h to a medical/surgical intensive care unit were eligible if they had shock and/or required mechanical ventilation. Patients underwent rSO2 monitoring with the FORESIGHT oximeter for 24 h, vital signs were concurrently recorded, and clinically ordered arterial blood gas samples and hemoglobin concentration were also documented. Simultaneous multiple linear regression was performed using all available predictors, followed by model selection using the corrected Akaike information criterion (AICc). Results: Our simultaneous multivariate model included age, heart rate, arterial oxygen saturation, mean arterial pressure, pH, partial pressure of oxygen, partial pressure of carbon dioxide (PaCO2), and hemoglobin concentration. This model accounted for a significant proportion of variance in rSO2 (R2 = 0.58, p < 0.01) and was significantly associated with PaCO2 (p < 0.05) and hemoglobin concentration (p < 0.01). Our selected regression model using AICc accounted for a significant proportion of variance in rSO2 (R2 = 0.54, p < 0.01) and was significantly related to age (p < 0.05), PaCO2 (p < 0.01), hemoglobin (p < 0.01), and heart rate (p < 0.05). Conclusions: Known and established physiological determinants of oxygen delivery accounted for a significant proportion of the rSO2 signal, which provides evidence that NIRS is a viable modality to assess cerebral oxygenation in critically ill adults. Further elucidation of the determinants of rSO2 has the potential to develop a NIRS-guided resuscitation algorithm during critical illness. Trial registration: This trial is registered on clinicaltrials.gov (Identifier: NCT02344043), retrospectively registered January 8, 2015.",0
"S100P is a molecular determinant of E-cadherin function in gastric cancer. Background: E-cadherin has been awarded a key role in the aetiology of both sporadic and hereditary forms of gastric cancer. In this study, we aimed to identify molecular interactors that influence the expression and function of E-cadherin associated to cancer. Methods: A data mining approach was used to predict stomach-specific candidate genes, uncovering S100P as a key candidate. The role of S100P was evaluated through in vitro functional assays and its expression was studied in a gastric cancer tissue microarray (TMA). Results: S100P was found to contribute to a cancer pathway dependent on the context of E-cadherin function. In particular, we demonstrated that S100P acts as an E-cadherin positive regulator in a wild-type E-cadherin context, and its inhibition results in decreased E-cadherin expression and function. In contrast, S100P is likely to be a pro-survival factor in gastric cancer cells with loss of functional E-cadherin, contributing to an oncogenic molecular program. Moreover, expression analysis in a gastric cancer TMA revealed that S100P expression impacts negatively among patients bearing Ecad- tumours, despite not being significantly associated with overall survival on its own. Conclusions: We propose that S100P has a dual role in gastric cancer, acting as an oncogenic factor in the context of E-cadherin loss and as a tumour suppressor in a functional E-cadherin setting. The discovery of antagonist effects of S100P in different E-cadherin contexts will aid in the stratification of gastric cancer patients who may benefit from S100P-targeted therapies. Graphical abstract: [Figure not available: see fulltext.]",0
"Identifying undetected dementia in UK primary care patients: a retrospective case-control study comparing machine-learning and standard epidemiological approaches. BACKGROUND: Identifying dementia early in time, using real world data, is a public health challenge. As only two-thirds of people with dementia now ultimately receive a formal diagnosis in United Kingdom health systems and many receive it late in the disease process, there is ample room for improvement. The policy of the UK government and National Health Service (NHS) is to increase rates of timely dementia diagnosis. We used data from general practice (GP) patient records to create a machine-learning model to identify patients who have or who are developing dementia, but are currently undetected as having the condition by the GP. METHODS: We used electronic patient records from Clinical Practice Research Datalink (CPRD). Using a case-control design, we selected patients aged >65y with a diagnosis of dementia (cases) and matched them 1:1 by sex and age to patients with no evidence of dementia (controls). We developed a list of 70 clinical entities related to the onset of dementia and recorded in the 5 years before diagnosis. After creating binary features, we trialled machine learning classifiers to discriminate between cases and controls (logistic regression, naïve Bayes, support vector machines, random forest and neural networks). We examined the most important features contributing to discrimination. RESULTS: The final analysis included data on 93,120 patients, with a median age of 82.6 years; 64.8% were female. The naïve Bayes model performed least well. The logistic regression, support vector machine, neural network and random forest performed very similarly with an AUROC of 0.74. The top features retained in the logistic regression model were disorientation and wandering, behaviour change, schizophrenia, self-neglect, and difficulty managing. CONCLUSIONS: Our model could aid GPs or health service planners with the early detection of dementia. Future work could improve the model by exploring the longitudinal nature of patient data and modelling decline in function over time.",1
"Applying deep matching networks to Chinese medical question answering: a study and a dataset. BACKGROUND: Medical and clinical question answering (QA) is highly concerned by researchers recently. Though there are remarkable advances in this field, the development in Chinese medical domain is relatively backward. It can be attributed to the difficulty of Chinese text processing and the lack of large-scale datasets. To bridge the gap, this paper introduces a Chinese medical QA dataset and proposes effective methods for the task. METHODS: We first construct a large scale Chinese medical QA dataset. Then we leverage deep matching neural networks to capture semantic interaction between words in questions and answers. Considering that Chinese Word Segmentation (CWS) tools may fail to identify clinical terms, we design a module to merge the word segments and produce a new representation. It learns the common compositions of words or segments by using convolutional kernels and selects the strongest signals by windowed pooling. RESULTS: The best performer among popular CWS tools on our dataset is found. In our experiments, deep matching models substantially outperform existing methods. Results also show that our proposed semantic clustered representation module improves the performance of models by up to 5.5% Precision at 1 and 4.9% Mean Average Precision. CONCLUSIONS: In this paper, we introduce a large scale Chinese medical QA dataset and cast the task into a semantic matching problem. We also compare different CWS tools and input units. Among the two state-of-the-art deep matching neural networks, MatchPyramid performs better. Results also show the effectiveness of the proposed semantic clustered representation module.",1
"Clinical validation of the FLIP algorithm and the SAF score in patients with non-alcoholic fatty liver disease. BACKGROUND & AIMS: Histological classifications used to diagnose/stage non-alcoholic fatty liver disease (NAFLD) are based on morphology, with undetermined clinical correlates and relevance. We assessed the clinical relevance of the fatty liver inhibition of progression (FLIP) algorithm and the steatosis, activity, and fibrosis (SAF) scoring system. METHODS: One hundred and forty consecutive patients with suspected NAFLD and a separate validation cohort of 78 patients enrolled in a therapeutic trial, all with central reading of liver biopsy, were included. FLIP and SAF were used to categorize patients with non-alcoholic steatohepatitis (NASH), non-NASH NAFLD (NAFL), or non-NAFLD. The SAF activity score assessed hepatocyte ballooning and lobular inflammation; a histologically severe disease was defined as a SAF activity score of >/=3 and/or bridging fibrosis or cirrhosis. Clinical, biochemical, and metabolic data were analyzed in relation to histology. RESULTS: Patients with NASH according to the FLIP algorithm had a clinical profile distinct from those with NAFL, with a higher prevalence of metabolic risk factors (increased body mass index [BMI], central obesity, serum glucose, and glycated hemoglobin), more severe insulin resistance (fasting insulin and homeostasis model assessment for insulin resistance [HOMA-IR] values), and higher levels of aminotransferases. Similar findings were documented for patients with severe disease vs. those without. Positive linear trends existed between NASH or severe disease and increasing BMI and HOMA-IR. There was a strong association between liver fibrosis and NASH or SAF-defined scores of activity. Patients with either significant or bridging fibrosis overwhelmingly had NASH, and bridging fibrosis most often coexisted with severe activity. CONCLUSIONS: The FLIP algorithm/SAF score, although based on purely morphological grounds, are clinically relevant, as they identify patients with distinct clinical and biological profiles of disease severity. Disease activity in NAFLD is associated with fibrosis severity. LAY SUMMARY: The examination of liver tissue under the microscope (histology) serves to define the type and severity of non-alcoholic fatty liver disease morphologically, and is also used to determine improvement in therapeutic or natural history clinical trials. The FLIP algorithm/SAF classification is a new histological classification well validated on morphological but not clinical grounds. Here, we demonstrate that different disease categories defined by the FLIP/SAF classification correspond to entities of different clinical and biological severity. We also show a strong association between the activity of steatohepatitis (defined histologically) and the amount of fibrotic scar.",0
"End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. With an estimated 160,000 deaths in 2018, lung cancer is the most common cause of cancer death in the United States(1). Lung cancer screening using low-dose computed tomography has been shown to reduce mortality by 20-43% and is now included in US screening guidelines(1-6). Existing challenges include inter-grader variability and high false-positive and false-negative rates(7-10). We propose a deep learning algorithm that uses a patient's current and prior computed tomography volumes to predict the risk of lung cancer. Our model achieves a state-of-the-art performance (94.4% area under the curve) on 6,716 National Lung Cancer Screening Trial cases, and performs similarly on an independent clinical validation set of 1,139 cases. We conducted two reader studies. When prior computed tomography imaging was not available, our model outperformed all six radiologists with absolute reductions of 11% in false positives and 5% in false negatives. Where prior computed tomography imaging was available, the model performance was on-par with the same radiologists. This creates an opportunity to optimize the screening process via computer assistance and automation. While the vast majority of patients remain unscreened, we show the potential for deep learning models to increase the accuracy, consistency and adoption of lung cancer screening worldwide.",0
"Urine organic acids as potential biomarkers for autism-spectrum disorder in chinese children. Autism spectrum disorder (ASD) is a neurodevelopmental disorder that lacks clear biological biomarkers. Existing diagnostic methods focus on behavioral and performance characteristics, which complicates the diagnosis of patients younger than 3 years-old. The purpose of this study is to characterize metabolic features of ASD that could be used to identify potential biomarkers for diagnosis and exploration of ASD etiology. We used gas chromatography-mass spectrometry (GC/MS) to evaluate major metabolic fluctuations in 76 organic acids present in urine from 156 children with ASD and from 64 non-autistic children. Three algorithms, Partial Least Squares-Discriminant Analysis (PLS-DA), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost), were used to develop models to distinguish ASD from typically developing (TD) children and to detect potential biomarkers. In an independent testing set, full model of XGBoost with all 76 acids achieved an AUR of 0.94, while reduced model with top 20 acids discovered by voting from these three algorithms achieved 0.93 and represent a good collection of potential ASD biomarkers. In summary, urine organic acids detection with GC/MS combined with XGBoost algorithm could represent a novel and accurate strategy for diagnosis of autism and the discovered potential biomarkers could be valuable for future research on the pathogenesis of autism and possible interventions.",1
"Brain metabolism modulates neuronal excitability in a mouse model of pyruvate dehydrogenase deficiency. Glucose is the ultimate substrate for most brain activities that use carbon, including synthesis of the neurotransmitters glutamate and gamma-aminobutyric acid via mitochondrial tricarboxylic acid (TCA) cycle. Brain metabolism and neuronal excitability are thus interdependent. However, the principles that govern their relationship are not always intuitive because heritable defects of brain glucose metabolism are associated with the paradoxical coexistence, in the same individual, of episodic neuronal hyperexcitation (seizures) with reduced basal cerebral electrical activity. One such prototypic disorder is pyruvate dehydrogenase (PDH) deficiency (PDHD). PDH is central to metabolism because it steers most of the glucose-derived flux into the TCA cycle. To better understand the pathophysiology of PDHD, we generated mice with brain-specific reduced PDH activity that paralleled salient human disease features, including cerebral hypotrophy, decreased amplitude electroencephalogram (EEG), and epilepsy. The mice exhibited reductions in cerebral TCA cycle flux, glutamate content, spontaneous, and electrically evoked in vivo cortical field potentials and gamma EEG oscillation amplitude. Episodic decreases in gamma oscillations preceded most epileptiform discharges, facilitating their prediction. Fast-spiking neuron excitability was decreased in brain slices, contributing to in vivo action potential burst prolongation after whisker pad stimulation. These features were partially reversed after systemic administration of acetate, which augmented cerebral TCA cycle flux, glutamate-dependent synaptic transmission, inhibition and gamma oscillations, and reduced epileptiform discharge duration. Thus, our results suggest that dysfunctional excitability in PDHD is consequent to reduced oxidative flux, which leads to decreased neuronal activation and impaired inhibition, and can be mitigated by an alternative metabolic substrate.",0
"Homology modeling and site-directed mutagenesis identify amino acid residues underlying the substrate selection mechanism of human monocarboxylate transporters 1 (hMCT1) and 4 (hMCT4). Human monocarboxylate transporters (hMCTs/SLC16As) mediate the transport of monocarboxylic compounds across plasma membranes. Among the hMCTs, hMCT1 and hMCT4 are expressed in various tissues, and transport substrates involved in energy metabolism. Both transporters mediate l-lactate transport, but, although hMCT1 also transports l-5-oxoproline (l-OPro), this compound is minimally transported by hMCT4. Thus, we were interested in the molecular mechanism responsible for the difference in substrate specificity between hMCT1 and hMCT4. Therefore, we generated 3D structure models of hMCT1 and hMCT4 to identify amino acid residues involved in the substrate specificity of these transporters. We found that the substrate specificity of hMCT1 was regulated by residues involved in turnover number (M69) and substrate affinity (F367), and these residues were responsible for recognizing (directly or indirectly) the –NH– moiety of l-OPro. Furthermore, our homology model of hMCT1 predicted that M69 and F367 participate in hydrophobic interactions with another region of hMCT1, emphasizing its potentially important role in the binding and translocation cycle of l-OPro. Mutagenesis experiments supported this model, showing that efficient l-OPro transport required a hydrophobic, long linear structure at position 69 and a hydrophobic, γ-branched structure at position 367. Our work demonstrated that the amino acid residues, M69 and F367, are key molecular elements for the transport of l-OPro by hMCT1. These two residues may be involved in substrate recognition and/or substrate-induced conformational changes.",0
"ThalPred: a web-based prediction tool for discriminating thalassemia trait and iron deficiency anemia. BACKGROUND: The hypochromic microcytic anemia (HMA) commonly found in Thailand are iron deficiency anemia (IDA) and thalassemia trait (TT). Accurate discrimination between IDA and TT is an important issue and better methods are urgently needed. Although considerable RBC formulas and indices with various optimal cut-off values have been developed, distinguishing between IDA and TT is still a challenging problem due to the diversity of various anemic populations. To address this problem, it is desirable to develop an improved and automated prediction model for discriminating IDA from TT. METHODS: We retrospectively collected laboratory data of HMA found in Thai adults. Five machine learnings, including k-nearest neighbor (k-NN), decision tree, random forest (RF), artificial neural network (ANN) and support vector machine (SVM), were applied to construct a discriminant model. Performance was assessed and compared with thirteen existing discriminant formulas and indices. RESULTS: The data of 186 patients (146 patients with TT and 40 with IDA) were enrolled. The interpretable rules derived from the RF model were proposed to demonstrate the combination of RBC indices for discriminating IDA from TT. A web-based tool 'ThalPred' was implemented using an SVM model based on seven RBC parameters. ThalPred achieved prediction results with an external accuracy, MCC and AUC of 95.59, 0.87 and 0.98, respectively. CONCLUSION: ThalPred and an interpretable rule were provided for distinguishing IDA from TT. For the convenience of health care team experimental scientists, a web-based tool has been established at http://codes.bio/thalpred/ by which users can easily get their desired screening test result without the need to go through the underlying mathematical and computational details.",1
"Interpreting patient-Specific risk prediction using contextual decomposition of BiLSTMs: application to children with asthma. BACKGROUND: Predictive modeling with longitudinal electronic health record (EHR) data offers great promise for accelerating personalized medicine and better informs clinical decision-making. Recently, deep learning models have achieved state-of-the-art performance for many healthcare prediction tasks. However, deep models lack interpretability, which is integral to successful decision-making and can lead to better patient care. In this paper, we build upon the contextual decomposition (CD) method, an algorithm for producing importance scores from long short-term memory networks (LSTMs). We extend the method to bidirectional LSTMs (BiLSTMs) and use it in the context of predicting future clinical outcomes using patients' EHR historical visits. METHODS: We use a real EHR dataset comprising 11071 patients, to evaluate and compare CD interpretations from LSTM and BiLSTM models. First, we train LSTM and BiLSTM models for the task of predicting which pre-school children with respiratory system-related complications will have asthma at school-age. After that, we conduct quantitative and qualitative analysis to evaluate the CD interpretations produced by the contextual decomposition of the trained models. In addition, we develop an interactive visualization to demonstrate the utility of CD scores in explaining predicted outcomes. RESULTS: Our experimental evaluation demonstrate that whenever a clear visit-level pattern exists, the models learn that pattern and the contextual decomposition can appropriately attribute the prediction to the correct pattern. In addition, the results confirm that the CD scores agree to a large extent with the importance scores generated using logistic regression coefficients. Our main insight was that rather than interpreting the attribution of individual visits to the predicted outcome, we could instead attribute a model's prediction to a group of visits. CONCLUSION: We presented a quantitative and qualitative evidence that CD interpretations can explain patient-specific predictions using CD attributions of individual visits or a group of visits.",1
"Elementary motion sequence detectors in whisker somatosensory cortex. How the somatosensory cortex (S1) encodes complex patterns of touch, such as those that occur during tactile exploration, is poorly understood. In the mouse whisker S1, temporally dense stimulation of local whisker pairs revealed that most neurons are not classical single-whisker feature detectors, but instead are strongly tuned to two-whisker sequences that involve the columnar whisker (CW) and one specific surround whisker (SW), usually in a SW-leading-CW order. Tuning was spatiotemporally precise and diverse across cells, generating a rate code for local motion vectors defined by SW–CW combinations. Spatially asymmetric, sublinear suppression for suboptimal combinations and near-linearity for preferred combinations sharpened combination tuning relative to linearly predicted tuning. This resembles computation of motion direction selectivity in vision. SW-tuned neurons, misplaced in the classical whisker map, had the strongest combination tuning. Thus, each S1 column contains a rate code for local motion sequences involving the CW, thus providing a basis for higher-order feature extraction.",0
"A Rule-Out Strategy Based on High-Sensitivity Troponin and HEART Score Reduces Hospital Admissions. STUDY OBJECTIVE: We evaluate whether a combination of a 1-hour high-sensitivity cardiac troponin algorithm and History, ECG, Age, Risk Factors, and Troponin (HEART) score reduces admission rate (primary outcome) and affects time to discharge, health care-related costs, and 30-day outcome (secondary outcomes) in patients with symptoms suggestive of an acute coronary syndrome. METHODS: This prospective observational multicenter study was conducted before (2013 to 2014) and after (2015 to 2016) implementation of a strategy including level of high-sensitivity cardiac troponin T or I at 0 and 1 hour, combined with the HEART score. Patients with a nonelevated baseline high-sensitivity cardiac troponin level, a 1-hour change in high-sensitivity cardiac troponin T level less than 3 ng/L, or high-sensitivity cardiac troponin I level less than 6 ng/L and a HEART score less than or equal to 3 were considered to be ruled out of having acute coronary syndrome. A logistic regression analysis was performed to adjust for differences in baseline characteristics. RESULTS: A total of 1,233 patients were included at 6 centers. There were no differences in regard to median age (64 versus 63 years) and proportion of men (57% versus 54%) between the periods. After introduction of the new strategy, the admission rate decreased from 59% to 33% (risk ratio 0.55 [95% confidence interval {CI} 0.48 to 0.63]; odds ratio 0.33 [95% CI 0.26 to 0.42]; adjusted odds ratio 0.33 [95% CI 0.25 to 0.42]). The median hospital stay was reduced from 23.2 to 4.7 hours (95% CI of difference -20.4 to -11.4); median health care-related costs, from $1,748 to $1,079 (95% CI of difference -$953 to -$391). The number of clinical events was very low. CONCLUSION: In this before-after study, clinical implementation of a 1-hour high-sensitivity cardiac troponin algorithm combined with the HEART score was associated with a reduction in admission rate and health care burden, with very low rates of adverse clinical events.",0
"A novel Rhein derivative: Activation of Rac1/NADPH pathway enhances sensitivity of nasopharyngeal carcinoma cells to radiotherapy. Radiation resistance and recurrent have become the major factors resulting in poor prognosis in the clinical treatment of patients with nasopharyngeal carcinoma (NPC). New strategies to enhance the efficacy of radiotherapy have been focused on the development of radiosensitizers and searching for directly targets that modulated tumor radiosensitivity. A novel potential radiosensitizer 1,8-Dihydroxy −3-(2′-(4″-methylpiperazin-1″-yl) ethyl-9,10-anthraquinone −3-carboxylate (RP-4) was designed and synthesized based on molecular docking technology, which was expected to regulate the radiosensitivity of tumor cells through targeting Rac1. In order to assess the radiosensitization activity of RP-4 on NPC cells, the highly differentiated CNE1 and poorly differentiated CNE2 cells NPC lines were employed. According to the results, RP-4 showed higher binding affinity toward the interaction with Rac1 than lead compounds. We found that RP-4 could inhibit cell viability and proliferation in CNE1 and CNE2 cells and significantly induced apoptosis after non-toxic concentration of RP-4 combined with 2Gy irradiation. RP-4 could effectively modulated the radiosensitivity both CNE1 cells and CNE2 cells through activating Rac1/NADPH signaling pathway and its downstream JNK/AP-1 pathway. What's more, Rac1/NADPH signaling pathway were significantly activated in Rac1-overexpressed CNE1 and CNE2 cells after treated with RP-4. Taken together, Rac1 and its downstream pathway may probably be the direct targets of RP-4 in regulating radiosensitivity of NPC cells, our finding provided a novel strategy for the development of therapeutic agents in response to tumorous radiation resistance.",0
"Virtual screening identifies a PIN1 inhibitor with possible antiovarian cancer effects. Peptidyl-prolyl cis–trans isomerase, NIMA-interacting 1 (PIN1) is a peptidyl-prolyl isomerase that binds phospho-Ser/Thr-Pro motifs in proteins and catalyzes the cis–trans isomerization of proline peptide bonds. PIN1 is overexpressed in several cancers including high-grade serous ovarian cancer. Since few therapies are effective against this cancer, PIN1 could be a therapeutic target but effective PIN1 inhibitors are lacking. To identify molecules with in vivo inhibitory effects on PIN1, we used consensus docking to model existing PIN1-ligand X-ray structures and to screen a chemical database for candidate inhibitors. Ten molecules were selected and tested in cellular assays, leading to the identification of VS10 that bound and inhibited PIN1. VS10 treatment reduced the viability of ovarian cancer cell lines by inducing proteasomal PIN1 degradation, without effects on PIN1 transcription, and also reduced the levels of downstream targets β-catenin, cyclin D1, and pSer473-Akt. VS10 is a selective PIN1 inhibitor that may offer new opportunities for treating PIN1-overexpressing tumors.",0
"Systematic profiling identifies PDLIM2 as a novel prognostic predictor for oesophageal squamous cell carcinoma (ESCC). Till now, no appropriate biomarkers for high-risk population screening and prognosis prediction have been identified for patients with oesophageal squamous cell carcinoma (ESCC). In this study, by the combined use of data from the Gene Expression Omnibus (GEO) datasets and The Cancer Genome Atlas (TCGA)-oesophageal carcinoma (ESCA), we aimed to screen dysregulated genes with prognostic value in ESCC and the genetic and epigenetic alterations underlying the dysregulation. About 222 genes that had at least fourfold change in ESCC compared with adjacent normal tissues were identified using the microarray data in GDS3838. Among these genes, only PDLIM2 was associated with nodal invasion and overall survival (OS) at the same time. The high PDLIM2 expression group had significantly longer OS and its expression was independently associated with better OS (HR: 0.64, 95% CI: 0.43-0.95, P = 0.03), after adjustment for gender and pathologic stages. The expression of its exon 7/8/9/10 had the highest AUC value (0.724) and better prognostic value (HR: 0.43, 95% CI: 0.22-0.83, P = 0.01) than total PDLIM2 expression. PDLIM2 DNA copy deletion was common in ESCC and was associated with decreased gene expression. The methylation status of two CpG sites (cg23696886 and cg20449614) in the proximal promoter region of PDLIM2 showed a moderate negative correlation with the gene expression in PDLIM2 copy neutral/amplification group. In conclusion, we infer that PDLIM2 expression might be a novel prognostic indicator for ESCC patients. Its exon 7/8/9/10 expression had the best prognostic value. Its down-regulation might be associated with gene-level copy deletion and promoter hypermethylation.",0
"Selecting precise reference normal tissue samples for cancer research using a deep learning approach. Background: Normal tissue samples are often employed as a control for understanding disease mechanisms, however, collecting matched normal tissues from patients is difficult in many instances. In cancer research, for example, the open cancer resources such as TCGA and TARGET do not provide matched tissue samples for every cancer or cancer subtype. The recent GTEx project has profiled samples from healthy individuals, providing an excellent resource for this field, yet the feasibility of using GTEx samples as the reference remains unanswered. Methods: We analyze RNA-Seq data processed from the same computational pipeline and systematically evaluate GTEx as a potential reference resource. We use those cancers that have adjacent normal tissues in TCGA as a benchmark for the evaluation. To correlate tumor samples and normal samples, we explore top varying genes, reduced features from principal component analysis, and encoded features from an autoencoder neural network. We first evaluate whether these methods can identify the correct tissue of origin from GTEx for a given cancer and then seek to answer whether disease expression signatures are consistent between those derived from TCGA and from GTEx. Results: Among 32 TCGA cancers, 18 cancers have less than 10 matched adjacent normal tissue samples. Among three methods, autoencoder performed the best in predicting tissue of origin, with 12 of 14 cancers correctly predicted. The reason for misclassification of two cancers is that none of normal samples from GTEx correlate well with any tumor samples in these cancers. This suggests that GTEx has matched tissues for the majority cancers, but not all. While using autoencoder to select proper normal samples for disease signature creation, we found that disease signatures derived from normal samples selected via an autoencoder from GTEx are consistent with those derived from adjacent samples from TCGA in many cases. Interestingly, choosing top 50 mostly correlated samples regardless of tissue type performed reasonably well or even better in some cancers. Conclusions: Our findings demonstrate that samples from GTEx can serve as reference normal samples for cancers, especially those do not have available adjacent tissue samples. A deep-learning based approach holds promise to select proper normal samples.",0
"GTX.Digest.VCF: An online NGS data interpretation system based on intelligent gene ranking and large-scale text mining. Background: An important task in the interpretation of sequencing data is to highlight pathogenic genes (or detrimental variants) in the field of Mendelian diseases. It is still challenging despite the recent rapid development of genomics and bioinformatics. A typical interpretation workflow includes annotation, filtration, manual inspection and literature review. Those steps are time-consuming and error-prone in the absence of systematic support. Therefore, we developed GTX.Digest.VCF, an online DNA sequencing interpretation system, which prioritizes genes and variants for novel disease-gene relation discovery and integrates text mining results to provide literature evidence for the discovery. Its phenotype-driven ranking and biological data mining approach significantly speed up the whole interpretation process. Results: The GTX.Digest.VCF system is freely available as a web portal at http://vcf.gtxlab.com for academic research. Evaluation on the DDD project dataset demonstrates an accuracy of 77% (235 out of 305 cases) for top-50 genes and an accuracy of 41.6% (127 out of 305 cases) for top-5 genes. Conclusions: GTX.Digest.VCF provides an intelligent web portal for genomics data interpretation via the integration of bioinformatics tools, distributed parallel computing, biomedical text mining. It can facilitate the application of genomic analytics in clinical research and practices.",0
"Polycomb Repressive Complex 2 Proteins EZH1 and EZH2 Regulate Timing of Postnatal Hepatocyte Maturation and Fibrosis by Repressing Genes With Euchromatic Promoters in Mice. BACKGROUND & AIMS: Little is known about mechanisms that underlie postnatal hepatocyte maturation and fibrosis at the chromatin level. We investigated the transcription of genes involved in maturation and fibrosis in postnatal hepatocytes of mice, focusing on the chromatin compaction the roles of the Polycomb repressive complex 2 histone methyltransferases EZH1 and EZH2. METHODS: Hepatocytes were isolated from mixed background C57BL/6J-C3H mice, as well as mice with liver-specific disruption of Ezh1 and/or Ezh2, at postnatal day 14 and 2 months after birth. Liver tissues were collected and analyzed by RNA sequencing, H3K27me3 chromatin immunoprecipitation sequencing, and sonication-resistant heterochromatin sequencing (a method to map heterochromatin and euchromatin). Liver damage was characterized by histologic analysis. RESULTS: We found more than 3000 genes differentially expressed in hepatocytes during liver maturation from postnatal day 14 to month 2 after birth. Disruption of Ezh1 and Ezh2 in livers caused perinatal hepatocytes to differentiate prematurely and to express genes at postnatal day 14 that would normally be induced by month 2 and differentiate prematurely. Loss of Ezh1 and Ezh2 also resulted in liver fibrosis. Genes with H3K27me3-postive and H3K4me3-positive euchromatic promoters were prematurely induced in hepatocytes with loss of Ezh1 and Ezh2-these genes included those that regulate hepatocyte maturation, fibrosis, and genes not specifically associated with the liver lineage. CONCLUSIONS: The Polycomb repressive complex 2 proteins EZH1 and EZH2 regulate genes that control hepatocyte maturation and fibrogenesis and genes not specifically associated with the liver lineage by acting at euchromatic promoter regions. EZH1 and EZH2 thereby promote liver homeostasis and prevent liver damage. Strategies to manipulate Polycomb proteins might be used to improve hepatocyte derivation protocols or developed for treatment of patients with liver fibrosis.",0
"Identification of potential drugs targeting L,L-diaminopimelate aminotransferase of Chlamydia trachomatis: An integrative pharmacoinformatics approach. Chlamydia trachomatis (C.t) is a gram-negative obligate intracellular bacteria, which is a major causative of infectious blindness and sexually transmitted diseases. A surge in multidrug resistance among chlamydial species has posed a challenge to adopt alternative drug targeting strategies. Recently, in C.t, L,L-diaminopimelate aminotransferase (CtDAP-AT) is proven to be a potential drug target due its essential role in cell survival and host nonspecificity. Hence, in this study, a multilevel precision-based virtual screening of CtDAP-AT was performed to identify potential inhibitors, wherein, an integrative stringent scoring and filtration were performed by coupling, glide docking score, binding free energy, ADMET (absorption, distribution, metabolism, and excretion, toxicity) prediction, density functional theory (quantum mechanics), and molecular dynamics simulation (molecular mechanics). On cumulative analysis, NSC_5485 (1,3-bis((7-chloro-4-quinolinyl)amino)-2-propanol) was found to be the most potential lead, as it showed higher order significance in terms of binding affinity, bonded interactions, favorable ADMET, chemical reactivity, and greater stabilization during complex formation. This is the first report on prioritization of small molecules from National Cancer Institute (NCI) and Maybridge data sets (341 519 compounds) towards targeting CtDAP-AT. Thus, the proposed compound shall aid in effective combating of a broad spectrum of C.t infections as it surpassed all the levels of prioritization.",0
"Integrated network analysis and machine learning approach for the identification of key genes of triple-negative breast cancer. Triple-negative breast cancer (TNBC) has attracted more attention compared with other breast cancer subtypes due to its aggressive nature, poor prognosis, and chemotherapy remains the mainstay of treatment with no other approved targeted therapy. Therefore, the study aimed to discover more promising therapeutic targets and investigating new insights of biological mechanism of TNBC. Six microarray data sets consisting of 463 non-TNBC and 405 TNBC samples were mined from Gene Expression Omnibus. The data sets were integrated by meta-analysis and identified 1075 differentially expressed genes. Protein-protein interaction network was constructed which consists of 486 nodes and 1932 edges, where 29 hub genes were obtained with high topological measures. Further, 16 features (hub genes), 12 upregulated (AURKB, CCNB2, CDC20, DDX18, EGFR, ENO1, MYC, NUP88, PLK1, PML, POLR2F, and SKP2) and four downregulated (CCND1, GLI3, SKP1, and TGFB3) were selected through machine learning correlation based feature selection method on training data set. A naïve Bayes based classifier built using the expression profiles of 16 features (hub genes) accurately and reliably classify TNBC from non-TNBC samples in the validation test data set with a receiver operating curve of 0.93 to 0.98. Subsequently, Gene Ontology analysis revealed that the hub genes were enriched in mitotic cell cycle processes and Kyoto Encyclopedia of Genes and Genomes pathway analysis showed that they were enriched in cell cycle pathways. Thus, the identified key hub genes and pathways highlighted in the study would enhance the understanding of molecular mechanism of TNBC which may serve as potential therapeutic target.",0
"Characterization of proteome variation during modern maize breeding. The success of modern maize breeding has been demonstrated by remarkable increases in productivity with tremendous modification of agricultural phenotypes over the last century. Although the underlying genetic changes of the maize adaptation from tropical to temperate regions have been extensively studied, our knowledge is limited regarding the accordance of protein and mRNA expression levels accompanying such adaptation. Here we conducted an integrative analysis of proteomic and transcriptomic changes in a maize association panel. The minimum extent of correlation between protein and RNA levels suggests that variation in mRNA expression is often not indicative of protein expression at a population scale. This is corroborated by the observation that mRNA- and protein-based coexpression networks are relatively independent of each other, and many pQTLs arise without the presence of corresponding eQTLs. Importantly, compared with transcriptome, the subtypes categorized by the proteome show a markedly high accuracy to resemble the genomic subpopulation. These findings suggest that proteome evolved under a greater evolutionary constraint than transcriptome during maize adaptation from tropical to temperate regions. Overall, the integrated multi-omics analysis provides a functional context to interpret gene expression variation during modern maize breeding.",0
"Application of machine learning methodology to assess the performance of DIABETIMSS program for patients with type 2 diabetes in family medicine clinics in Mexico. BACKGROUND: The study aimed to assess the performance of a multidisciplinary-team diabetes care program called DIABETIMSS on glycemic control of type 2 diabetes (T2D) patients, by using available observational patient data and machine-learning-based targeted learning methods. METHODS: We analyzed electronic health records and laboratory databases from the year 2012 to 2016 of T2D patients from six family medicine clinics (FMCs) delivering the DIABETIMSS program, and five FMCs providing routine care. All FMCs belong to the Mexican Institute of Social Security and are in Mexico City and the State of Mexico. The primary outcome was glycemic control. The study covariates included: patient sex, age, anthropometric data, history of glycemic control, diabetic complications and comorbidity. We measured the effects of DIABETIMSS program through 1) simple unadjusted mean differences; 2) adjusted via standard logistic regression and 3) adjusted via targeted machine learning. We treated the data as a serial cross-sectional study, conducted a standard principal components analysis to explore the distribution of covariates among clinics, and performed regression tree on data transformed to use the prediction model to identify patient sub-groups in whom the program was most successful. To explore the robustness of the machine learning approaches, we conducted a set of simulations and the sensitivity analysis with process-of-care indicators as possible confounders. RESULTS: The study included 78,894 T2D patients, from which 37,767patients received care through DIABETIMSS. The impact of DIABETIMSS ranged, among clinics, from 2 to 8% improvement in glycemic control, with an overall (pooled) estimate of 5% improvement. T2D patients with fewer complications have more significant benefit from DIABETIMSS than those with more complications. At the FMC's delivering the conventional model the predicted impacts were like what was observed empirically in the DIABETIMSS clinics. The sensitivity analysis did not change the overall estimate average across clinics. CONCLUSIONS: DIABETIMSS program had a small, but significant increase in glycemic control. The use of machine learning methods yields both population-level effects and pinpoints the sub-groups of patients the program benefits the most. These methods exploit the potential of routine observational patient data within complex healthcare systems to inform decision-makers.",1
"Using machine learning models to improve stroke risk level classification methods of China national stroke screening. BACKGROUND: With the character of high incidence, high prevalence and high mortality, stroke has brought a heavy burden to families and society in China. In 2009, the Ministry of Health of China launched the China national stroke screening and intervention program, which screens stroke and its risk factors and conducts high-risk population interventions for people aged above 40 years old all over China. In this program, stroke risk factors include hypertension, diabetes, dyslipidemia, smoking, lack of exercise, apparently overweight and family history of stroke. People with more than two risk factors or history of stroke or transient ischemic attack (TIA) are considered as high-risk. However, it is impossible for this criterion to classify stroke risk levels for people with unknown values in fields of risk factors. The missing of stroke risk levels results in reduced efficiency of stroke interventions and inaccuracies in statistical results at the national level. In this paper, we use 2017 national stroke screening data to develop stroke risk classification models based on machine learning algorithms to improve the classification efficiency. METHOD: Firstly, we construct training set and test sets and process the imbalance training set based on oversampling and undersampling method. Then, we develop logistic regression model, Naïve Bayesian model, Bayesian network model, decision tree model, neural network model, random forest model, bagged decision tree model, voting model and boosting model with decision trees to classify stroke risk levels. RESULT: The recall of the boosting model with decision trees is the highest (99.94%), and the precision of the model based on the random forest is highest (97.33%). Using the random forest model (recall: 98.44%), the recall will be increased by about 2.8% compared with the method currently used, and several thousands more people with high risk of stroke can be identified each year. CONCLUSION: Models developed in this paper can improve the current screening method in the way that it can avoid the impact of unknown values, and avoid unnecessary rescreening and intervention expenditures. The national stroke screening program can choose classification models according to the practice need.",1
"Radiomics versus Visual and Histogram-based Assessment to Identify Atheromatous Lesions at Coronary CT Angiography: An ex Vivo Study. Background Visual and histogram-based assessments of coronary CT angiography have limited accuracy in the identification of advanced lesions. Radiomics-based machine learning (ML) could provide a more accurate tool. Purpose To compare the diagnostic performance of radiomics-based ML with that of visual and histogram-based assessment of ex vivo coronary CT angiography cross sections to identify advanced atherosclerotic lesions defined with histologic examination. Materials and Methods In this prospective study, 21 coronary arteries from seven hearts obtained from male donors (mean age, 52.3 years +/- 5.3) were imaged ex vivo with coronary CT angiography between February 23, 2009, and July 31, 2010. From 95 coronary plaques, 611 histologic cross sections were coregistered with coronary CT cross sections. Lesions were considered advanced if early fibroatheroma, late fibroatheroma, or thin-cap atheroma was present. CT cross sections were classified as showing homogeneous, heterogeneous, or napkin-ring sign plaques on the basis of visual assessment. The area of low attenuation (<30 HU) and the average Hounsfield unit were quantified. Radiomic parameters were extracted and used as inputs to ML algorithms. Eight radiomics-based ML models were trained on randomly selected cross sections (training set, 75% of the cross sections) to identify advanced lesions. Visual assessment, histogram-based assessment, and the best ML model were compared on the remaining 25% of the data (validation set) by using the area under the receiver operating characteristic curve (AUC) to identify advanced lesions. Results After excluding sections with no visible plaque (n = 134) and with heavy calcium (n = 32), 445 cross sections were analyzed. Of those 445 cross sections, 134 (30.1%) were advanced lesions. Visual assessment of the 445 cross sections indicated that 207 (46.5%) were homogeneous, 200 (44.9%) were heterogeneous, and 38 (8.5%) demonstrated the napkin-ring sign. A radiomics-based ML model incorporating 13 parameters outperformed visual assessment (AUC = 0.73 with 95% confidence interval [CI] of 0.63, 0.84 vs 0.65 with 95% CI of 0.56, 0.73, respectively; P = .04), area of low attenuation (AUC = 0.55 with 95% CI of 0.42, 0.68; P = .01), and average Hounsfield unit (AUC = 0.53 with 95% CI of 0.42, 0.65; P = .004) in the identification of advanced atheromatous lesions. Conclusion Radiomics-based machine learning analysis improves the discriminatory power of coronary CT angiography in the identification of advanced atherosclerotic lesions. Published under a CC BY 4.0 license.",1
"Machine learning in medicine: a practical introduction. BACKGROUND: Following visible successes on a wide range of predictive tasks, machine learning techniques are attracting substantial interest from medical researchers and clinicians. We address the need for capacity development in this area by providing a conceptual introduction to machine learning alongside a practical guide to developing and evaluating predictive algorithms using freely-available open source software and public domain data. METHODS: We demonstrate the use of machine learning techniques by developing three predictive models for cancer diagnosis using descriptions of nuclei sampled from breast masses. These algorithms include regularized General Linear Model regression (GLMs), Support Vector Machines (SVMs) with a radial basis function kernel, and single-layer Artificial Neural Networks. The publicly-available dataset describing the breast mass samples (N=683) was randomly split into evaluation (n=456) and validation (n=227) samples. We trained algorithms on data from the evaluation sample before they were used to predict the diagnostic outcome in the validation dataset. We compared the predictions made on the validation datasets with the real-world diagnostic decisions to calculate the accuracy, sensitivity, and specificity of the three models. We explored the use of averaging and voting ensembles to improve predictive performance. We provide a step-by-step guide to developing algorithms using the open-source R statistical programming environment. RESULTS: The trained algorithms were able to classify cell nuclei with high accuracy (.94 -.96), sensitivity (.97 -.99), and specificity (.85 -.94). Maximum accuracy (.96) and area under the curve (.97) was achieved using the SVM algorithm. Prediction performance increased marginally (accuracy =.97, sensitivity =.99, specificity =.95) when algorithms were arranged into a voting ensemble. CONCLUSIONS: We use a straightforward example to demonstrate the theory and practice of machine learning for clinicians and medical researchers. The principals which we demonstrate here can be readily applied to other complex tasks including natural language processing and image recognition.",1
"Alisol A attenuates high-fat-diet-induced obesity and metabolic disorders via the AMPK/ACC/SREBP-1c pathway. Obesity and its associated metabolic disorders such as diabetes, hepatic steatosis and chronic heart diseases are affecting billions of individuals. However there is no satisfactory drug to treat such diseases. In this study, we found that alisol A, a major active triterpene isolated from the Chinese traditional medicine Rhizoma Alismatis, could significantly attenuate high-fat-diet-induced obesity. Our biochemical detection demonstrated that alisol A remarkably decreased lipid levels, alleviated glucose metabolism disorders and insulin resistance in high-fat-diet-induced obese mice. We also found that alisol A reduced hepatic steatosis and improved liver function in the obese mice model.In addition, protein expression investigation revealed that alisol A had an active effect on AMPK/ACC/SREBP-1c pathway. As suggested by the molecular docking study, such bioactivity of alisol A may result from its selective binding to the catalytic region of AMPK.Therefore, we believe that Alisol A could serve as a promising agent for treatment of obesity and its related metabolic diseases.",0
"Clinicopathological Features of Triple-Negative Breast Cancer Epigenetic Subtypes. Background/Objective: Triple-negative breast cancer (TNBC) is a heterogeneous collection of breast tumors with numerous differences including morphological characteristics, genetic makeup, immune-cell infiltration, and response to systemic therapy. DNA methylation profiling is a robust tool to accurately identify disease-specific subtypes. We aimed to generate an epigenetic subclassification of TNBC tumors (epitypes) with utility for clinical decision-making. Methods: Genome-wide DNA methylation profiles from TNBC patients generated in the Cancer Genome Atlas project were used to build machine learning-based epigenetic classifiers. Clinical and demographic variables, as well as gene expression and gene mutation data from the same cohort, were integrated to further refine the TNBC epitypes. Results: This analysis indicated the existence of four TNBC epitypes, named as Epi-CL-A, Epi-CL-B, Epi-CL-C, and Epi-CL-D. Patients with Epi-CL-B tumors showed significantly shorter disease-free survival and overall survival [log rank; P = 0.01; hazard ratio (HR) 3.89, 95% confidence interval (CI) 1.3–11.63 and P = 0.003; HR 5.29, 95% CI 1.55–18.18, respectively]. Significant gene expression and mutation differences among the TNBC epitypes suggested alternative pathway activation that could be used as ancillary therapeutic targets. These epigenetic subtypes showed complementarity with the recently described TNBC transcriptomic subtypes. Conclusions: TNBC epigenetic subtypes exhibit significant clinical and molecular differences. The links between genetic make-up, gene expression programs, and epigenetic subtypes open new avenues in the development of laboratory tests to more efficiently stratify TNBC patients, helping optimize tailored treatment approaches.",1
"Using Artificial Intelligence to Revise ACR TI-RADS Risk Stratification of Thyroid Nodules: Diagnostic Accuracy and Utility. Background Risk stratification systems for thyroid nodules are often complicated and affected by low specificity. Continual improvement of these systems is necessary to reduce the number of unnecessary thyroid biopsies. Purpose To use artificial intelligence (AI) to optimize the American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS). Materials and Methods A total of 1425 biopsy-proven thyroid nodules from 1264 consecutive patients (1026 women; mean age, 52.9 years [range, 18-93 years]) were evaluated retrospectively. Expert readers assigned points based on five ACR TI-RADS categories (composition, echogenicity, shape, margin, echogenic foci), and a genetic AI algorithm was applied to a training set (1325 nodules). Point and pathologic data were used to create an optimized scoring system (hereafter, AI TI-RADS). Performance of the systems was compared by using a test set of the final 100 nodules with interpretations from the expert reader, eight nonexpert readers, and an expert panel. Initial performance of AI TI-RADS was calculated by using a test for differences between binomial proportions. Additional comparisons across readers were conducted by using bootstrapping; diagnostic performance was assessed by using area under the receiver operating curve. Results AI TI-RADS assigned new point values for eight ACR TI-RADS features. Six features were assigned zero points, which simplified categorization. By using expert reader data, the diagnostic performance of ACR TI-RADS and AI TI-RADS was area under the receiver operating curve of 0.91 and 0.93, respectively. For the same expert, specificity of AI TI-RADS (65%, 55 of 85) was higher (P < .001) than that of ACR TI-RADS (47%, 40 of 85). For the eight nonexpert radiologists, mean specificity for AI TI-RADS (55%) was also higher (P < .001) than that of ACR TI-RADS (48%). An interactive AI TI-RADS calculator can be viewed at http://deckard.duhs.duke.edu/ approximately ai-ti-rads . Conclusion An artificial intelligence-optimized Thyroid Imaging Reporting and Data System (TI-RADS) validates the American College of Radiology TI-RADS while slightly improving specificity and maintaining sensitivity. Additionally, it simplifies feature assignments, which may improve ease of use. (c) RSNA, 2019 Online supplemental material is available for this article.",1
"Small molecule nAS-E targeting cAMP response element binding protein (CREB) and CREB-binding protein interaction inhibits breast cancer bone metastasis. Bone is the most common metastatic site for breast cancer. The excessive osteoclast activity in the metastatic bone lesions often produces osteolysis. The cyclic-AMP (cAMP)-response element binding protein (CREB) serves a variety of biological functions including the transformation and immortalization of breast cancer cells. In addition, evidence has shown that CREB plays a key role in osteoclastgenesis and bone resorption. Small organic molecules with good pharmacokinetic properties and specificity, targeting CREB-CBP (CREB-binding protein) interaction to inhibit CREB-mediated gene transcription have attracted more considerations as cancer therapeutics. We recently identified naphthol AS-E (nAS-E) as a cell-permeable inhibitor of CREB-mediated gene transcription through inhibiting CREB-CBP interaction. In this study, we tested the effect of nAS-E on breast cancer cell proliferation, survival, migration as well as osteoclast formation and bone resorption in vitro for the first time. Our results demonstrated that nAS-E inhibited breast cancer cell proliferation, migration, survival and suppressed osteoclast differentiation as well as bone resorption through inhibiting CREB-CBP interaction. In addition, the in vivo effect of nAS-E in protecting against breast cancer-induced osteolysis was evaluated. Our results indicated that nAS-E could reverse bone loss induced by MDA-MB-231 tumour. These results suggest that small molecules targeting CREB-CBP interaction to inhibit CREB-mediated gene transcription might be a potential approach for the treatment of breast cancer bone metastasis.",0
"Single-cell, high-throughput analysis of cell docking to vessel wall. Therapeutic potential of mesenchymal stem cells (MSCs) has been reported consistently in animal models of stroke, with mechanism mainly through immunomodulation and paracrine activity. Intravenous injection has been a prevailing route for MSCs administration, but cell quantities needed when scaling-up from mouse to human are extremely high putting into question feasibility of that approach. Intra-arterial delivery directly routes the cells to the brain thus lowering the required dose. Cell engineering may additionally improve cell homing, further potentiating the value of intra-arterial route. Therefore, our goal was to create microfluidic platform for screening and fast selection of molecules that enhance the docking of stem cells to vessel wall. We hypothesized that our software will be capable of detecting distinct docking properties of naïve and ITGA4-engineered MSCs. Indeed, the cell flow tracker analysis revealed positive effect of cell engineering on docking frequency of MSCs (42% vs. 9%, engineered vs. control cells, p < 0.001). These observations were then confirmed in an animal model of focal brain injury where cell engineering resulted in improved homing to the brain. To conclude, we developed a platform to study the docking of cells to the vessel wall which is highly relevant for intraarterial cell targeting or studies on neuroinflammation.",0
"Safety and Efficacy of Antithrombotic Strategies in Patients with Atrial Fibrillation Undergoing Percutaneous Coronary Intervention: A Network Meta-analysis of Randomized Controlled Trials. Importance: The antithrombotic treatment of patients with atrial fibrillation (AF) and coronary artery disease, in particular with acute coronary syndrome (ACS) and/or percutaneous coronary intervention (PCI), poses a significant treatment dilemma in clinical practice. Objective: To study the safety and efficacy of different antithrombotic regimens using a network meta-analysis of randomized controlled trials in this population. Data Sources: PubMed, EMBASE, EBSCO, and Cochrane databases were searched to identify randomized controlled trials comparing antithrombotic regimens. Study Selection: Four randomized studies were included (n = 10026; WOEST, PIONEER AF-PCI, RE-DUAL PCI, and AUGUSTUS). Data Extraction and Synthesis: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were used in this systematic review and network meta-analysis between 4 regimens using a Bayesian random-effects model. A pre hoc statistical analysis plan was written, and the review protocol was registered at PROSPERO. Data were analyzed between November 2018 and February 2019. Main Outcomes and Measures: The primary safety outcome was Thrombolysis in Myocardial Infarction (TIMI) major bleeding; secondary safety outcomes were combined TIMI major and minor bleeding, trial-defined primary bleeding events, intracranial hemorrhage, and hospitalization. The primary efficacy outcome was trial-defined major adverse cardiovascular events (MACE); secondary efficacy outcomes were individual components of MACE. Results: The overall prevalence of ACS varied from 28% to 61%. The mean age ranged from 70 to 72 years; 20% to 29% of the trial population were women; and most patients were at high risk for thromboembolic and bleeding events. Compared with a regimen of vitamin K antagonist (VKA) plus dual antiplatelet therapy (DAPT; P2Y12 inhibitor plus aspirin), the odds ratios (ORs) for TIMI major bleeding were 0.58 (95% CI, 0.31-1.08) for VKA plus P2Y12 inhibitor, 0.49 (95% CI, 0.30-0.82) for non-VKA oral anticoagulant (NOAC) plus P2Y12 inhibitor, and 0.70 (95% CI, 0.38-1.23) for NOAC plus DAPT. Compared with VKA plus DAPT, the ORs for MACE were 0.96 (95% CI, 0.60-1.46) for VKA plus P2Y12 inhibitor, 1.02 (95% CI, 0.71-1.47) for NOAC plus P2Y12 inhibitor, and 0.94 (95% CI, 0.60-1.45) for NOAC plus DAPT. Conclusions and Relevance: A regimen of NOACs plus P2Y12 inhibitor was associated with less bleeding compared with VKAs plus DAPT. Strategies omitting aspirin caused less bleeding, including intracranial bleeding, without significant difference in MACE, compared with strategies including aspirin. Our results support the use of NOAC plus P2Y12 inhibitor as the preferred regimen post-percutaneous coronary intervention for these high-risk patients with AF. A regimen of VKA plus DAPT should generally be avoided.",0
"Validation of the Decipher Test for Predicting Distant Metastatic Recurrence in Men with High-risk Nonmetastatic Prostate Cancer 10 Years After Surgery. We investigated the prognostic role of Decipher among patients with high-risk prostate cancer. In European and US cohorts, each 10% increase in the Decipher score translated to a 53% and 58% increase in the risk of distant metastases, respectively, within 10 yr.",0
"A De Novo Shape Motif Discovery Algorithm Reveals Preferences of Transcription Factors for DNA Shape Beyond Sequence Motifs. DNA shape adds specificity to sequence motifs but has not been explored systematically outside this context. We hypothesized that DNA-binding proteins (DBPs) preferentially occupy DNA with specific structures (“shape motifs”) regardless of whether or not these correspond to high information content sequence motifs. We present ShapeMF, a Gibbs sampling algorithm that identifies de novo shape motifs. Using binding data from hundreds of in vivo and in vitro experiments, we show that most DBPs have shape motifs and can occupy these in the absence of sequence motifs. This “shape-only binding” is common for many DBPs and in regions co-bound by multiple DBPs. When shape and sequence motifs co-occur, they can be overlapping, flanking, or separated by consistent spacing. Finally, DBPs within the same protein family have different shape motifs, explaining their distinct genome-wide occupancy despite having similar sequence motifs. These results suggest that shape motifs not only complement sequence motifs but also facilitate recognition of DNA beyond conventionally defined sequence motifs.",0
"Peptide selected by phage display increases survival of SH-SY5Y neurons comparable to brain-derived neurotrophic factor. Brain-derived neurotrophic factor (BDNF) is a well-known neuroprotectant and a potent therapeutic candidate for neurodegenerative diseases. However, there are several clinical concerns about its therapeutic applications. In the current study, we designed and developed BDNF-mimicking small peptides as an alternative to circumvent these problems. A phage-displayed peptide library was screened using BDNF receptor (neurotrophic tyrosine kinase receptor type2 [NTRK2]) and evaluated by ELISA. The peptide sequences showed similarity to loop2 of BDNF, they were recognized as discontinuous epitopes though. Interestingly, in silico molecular docking showed strong interactions between the peptide three-dimensional models and the surface residues of the NTRK2 protein at the IgC2 domain. A consensus peptide sequence was then synthesized to generate a mimetic construct (named as RNYK). The affinity binding and function of this construct was confirmed by testing against the native structure of NTRK2 in SH-SY5Y cells in vitro using flow-cytometry and MTT assays, respectively. RNYK at 5 ng/mL prevented neuronal degeneration of all- trans-retinoic acid-treated SH-SY5Y with equal efficacy to or even better than BDNF at 50 ng/mL.",0
"Structural determinants of the APOBEC3G N-terminal domain for HIV-1 RNA association. APOBEC3G (A3G) is a cellular protein that inhibits HIV-1 infection through virion incorporation. The interaction of the A3G N-terminal domain (NTD) with RNA is essential for A3G incorporation in the HIV-1 virion. The interaction between A3G-NTD and RNA is not completely understood. The A3G-NTD is also recognized by HIV-1 Viral infectivity factor (Vif) and A3G-Vif binding leads to A3G degradation. Therefore, the A3G-Vif interaction is a target for the development of antiviral therapies that block HIV-1 replication. However, targeting the A3G-Vif interactions could disrupt the A3G-RNA interactions that are required for A3G's antiviral activity. To better understand A3G-RNA binding, we generated in silico docking models to simulate the RNA-binding propensity of A3G-NTD. We simulated the A3G-NTD residues with high RNA-binding propensity, experimentally validated our prediction by testing A3G-NTD mutations, and identified structural determinants of A3G-RNA binding. In addition, we found a novel amino acid residue, I26 responsible for RNA interaction. The new structural insights provided here will facilitate the design of pharmaceuticals that inhibit A3G-Vif interactions without negatively impacting A3G-RNA interactions.",0
"Development and Performance of the Pulmonary Embolism Result Forecast Model (PERFORM) for Computed Tomography Clinical Decision Support. Importance: Pulmonary embolism (PE) is a life-threatening clinical problem, and computed tomographic imaging is the standard for diagnosis. Clinical decision support rules based on PE risk-scoring models have been developed to compute pretest probability but are underused and tend to underperform in practice, leading to persistent overuse of CT imaging for PE. Objective: To develop a machine learning model to generate a patient-specific risk score for PE by analyzing longitudinal clinical data as clinical decision support for patients referred for CT imaging for PE. Design, Setting, and Participants: In this diagnostic study, the proposed workflow for the machine learning model, the Pulmonary Embolism Result Forecast Model (PERFORM), transforms raw electronic medical record (EMR) data into temporal feature vectors and develops a decision analytical model targeted toward adult patients referred for CT imaging for PE. The model was tested on holdout patient EMR data from 2 large, academic medical practices. A total of 3397 annotated CT imaging examinations for PE from 3214 unique patients seen at Stanford University hospitals and clinics were used for training and validation. The models were externally validated on 240 unique patients seen at Duke University Medical Center. The comparison with clinical scoring systems was done on randomly selected 100 outpatient samples from Stanford University hospitals and clinics and 101 outpatient samples from Duke University Medical Center. Main Outcomes and Measures: Prediction performance of diagnosing acute PE was evaluated using ElasticNet, artificial neural networks, and other machine learning approaches on holdout data sets from both institutions, and performance of models was measured by area under the receiver operating characteristic curve (AUROC). Results: Of the 3214 patients included in the study, 1704 (53.0%) were women from Stanford University hospitals and clinics; mean (SD) age was 60.53 (19.43) years. The 240 patients from Duke University Medical Center used for validation included 132 women (55.0%); mean (SD) age was 70.2 (14.2) years. In the samples for clinical scoring system comparisons, the 100 outpatients from Stanford University hospitals and clinics included 67 women (67.0%); mean (SD) age was 57.74 (19.87) years, and the 101 patients from Duke University Medical Center included 59 women (58.4%); mean (SD) age was 73.06 (15.3) years. The best-performing model achieved an AUROC performance of predicting a positive PE study of 0.90 (95% CI, 0.87-0.91) on intrainstitutional holdout data with an AUROC of 0.71 (95% CI, 0.69-0.72) on an external data set from Duke University Medical Center; superior AUROC performance and cross-institutional generalization of the model of 0.81 (95% CI, 0.77-0.87) and 0.81 (95% CI, 0.73-0.82), respectively, were noted on holdout outpatient populations from both intrainstitutional and extrainstitutional data. Conclusions and Relevance: The machine learning model, PERFORM, may consider multitudes of applicable patient-specific risk factors and dependencies to arrive at a PE risk prediction that generalizes to new population distributions. This approach might be used as an automated clinical decision-support tool for patients referred for CT PE imaging to improve CT use.",1
"Recognizing ion ligand binding sites by SMO algorithm. Background: In many important life activities, the execution of protein function depends on the interaction between proteins and ligands. As an important protein binding ligand, the identification of the binding site of the ion ligands plays an important role in the study of the protein function. Results: In this study, four acid radical ion ligands (NO2-,CO32-,SO42-,PO43-) and ten metal ion ligands (Zn2+,Cu2+,Fe2+,Fe3+,Ca2+,Mg2+,Mn2+,Na+,K+,Co2+) are selected as the research object, and the Sequential minimal optimization (SMO) algorithm based on sequence information was proposed, better prediction results were obtained by 5-fold cross validation. Conclusions: An efficient method for predicting ion ligand binding sites was presented.",0
"The alexipharmic mechanisms of five licorice ingredients involved in CYP450 and Nrf2 pathways in paraquat-induced mice acute lung injury. Oxidative stress is an important mechanism in acute lung injury (ALI) induced by paraquat (PQ), one of the most widely used herbicides in developing countries. In clinical prophylaxis and treatment, licorice is a widely used herbal medicine in China due to its strong alexipharmic characteristics. However, the corresponding biochemical mechanism of antioxidation and detoxification enzymes induced by licorice's ingredients is still not fully demonstrated. In this study, the detoxification effect of licorice was evaluated in vivo and in vitro. The detoxification and antioxidation effect of its active ingredients involved in the treatment was screened systematically according to Absorption, Distribution, Metabolism, and Excretion (ADME): Predictions and evidence-based literature mining methods in silico approach. Data shows that licorice alleviate pulmonary edema and fibrosis, decrease Malondialdehyde (MDA) contents and increase Superoxide Dismutase (SOD) activity in PQ-induced ALI mice, protect the morphologic appearance of lung tissues, induce cytochrome 3A4 (CYA3A4) and Nuclear factor erythroid 2-related factor 2 (Nrf2) expression to active detoxification pathways, reduce the accumulation of PQ in vivo, protect or improve the liver and renal function of mice, and increase the survival rate. The 104 genes of PPI network contained all targets of licorice ingredients and PQ, which displayed the two redox regulatory enzymatic group modules cytochrome P450 (CYP450) and Nrf2 via a score-related graphic theoretic clustering algorithm in silico. According to ADME properties, glycyrol, isolicoflavonol, licochalcone A, 18beta-glycyrrhetinic acid, and licoisoflavone A were employed due to their oral bioavailability OB ≤ 30%, drug-likeness DL ≤ 0 1, and being highly associated with CYP450 and Nrf2 pathways, as potential activators to halt PQ-induced cells death in vitro. Both 3A4 inhibitor and silenced Nrf2 gene decreased the alexipharmic effects of those ingredients significantly. All these disclosed the detoxification and antioxidation effects of licorice on acute lung injury induced by PQ, and glycyrol, isolicoflavonol, licochalcone A, 18beta-glycyrrhetinic acid, and licoisoflavone A upregulated CYP450 and Nrf2 pathways underlying the alexipharmic mechanisms of licorice.",0
"Discovery of N-phenyl-(2,4-dihydroxypyrimidine-5-sulfonamido) phenylurea-based thymidylate synthase (TS) inhibitor as a novel multi-effects antitumor drugs with minimal toxicity. Thymidylate synthase (TS) is a hot target for tumor chemotherapy, and its inhibitors are an essential direction for anti-tumor drug research. To our knowledge, currently, there are no reported thymidylate synthase inhibitors that could inhibit cancer cell migration. Therefore, for optimal therapeutic purposes, combines our previous reports and findings, we hope to obtain a multi-effects inhibitor. This study according to the principle of flattening we designed and synthesized 18 of N-phenyl-(2,4-dihydroxypyrimidine-5-sulfonamido)phenyl urea derivatives as multi-effects inhibitors. The biological evaluation results showed that target compounds could significantly inhibit the hTS enzyme, BRaf kinase and EGFR kinase activity in vitro, and most of the compounds had excellent anti-cell viability for six cancer cell lines. Notably, the candidate compound L14e (IC50 = 0.67 μM) had the superior anti-cell viability and safety to A549 and H460 cells compared with pemetrexed. Further studies had shown that L14e could cause G1/S phase arrest then induce intrinsic apoptosis. Transwell, western blot, and tube formation results proved that L14e could inhibit the activation of the EGFR signaling pathway, then ultimately achieve the purpose of inhibiting cancer cell migration and angiogenesis in cancer tissues. Furthermore, in vivo pharmacology evaluations of L14e showed significant antitumor activity in A549 cells xenografts with minimal toxicity. All of these results demonstrated that the L14e has the potential for drug discovery as a multi-effects inhibitor and provides a new reference for clinical treatment of non-small cell lung cancer.",0
"Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective study. BACKGROUND: The Response Assessment in Neuro-Oncology (RANO) criteria and requirements for a uniform protocol have been introduced to standardise assessment of MRI scans in both clinical trials and clinical practice. However, these criteria mainly rely on manual two-dimensional measurements of contrast-enhancing (CE) target lesions and thus restrict both reliability and accurate assessment of tumour burden and treatment response. We aimed to develop a framework relying on artificial neural networks (ANNs) for fully automated quantitative analysis of MRI in neuro-oncology to overcome the inherent limitations of manual assessment of tumour burden. METHODS: In this retrospective study, we compiled a single-institution dataset of MRI data from patients with brain tumours being treated at Heidelberg University Hospital (Heidelberg, Germany; Heidelberg training dataset) to develop and train an ANN for automated identification and volumetric segmentation of CE tumours and non-enhancing T2-signal abnormalities (NEs) on MRI. Independent testing and large-scale application of the ANN for tumour segmentation was done in a single-institution longitudinal testing dataset from the Heidelberg University Hospital and in a multi-institutional longitudinal testing dataset from the prospective randomised phase 2 and 3 European Organisation for Research and Treatment of Cancer (EORTC)-26101 trial (NCT01290939), acquired at 38 institutions across Europe. In both longitudinal datasets, spatial and temporal tumour volume dynamics were automatically quantified to calculate time to progression, which was compared with time to progression determined by RANO, both in terms of reliability and as a surrogate endpoint for predicting overall survival. We integrated this approach for fully automated quantitative analysis of MRI in neuro-oncology within an application-ready software infrastructure and applied it in a simulated clinical environment of patients with brain tumours from the Heidelberg University Hospital (Heidelberg simulation dataset). FINDINGS: For training of the ANN, MRI data were collected from 455 patients with brain tumours (one MRI per patient) being treated at Heidelberg hospital between July 29, 2009, and March 17, 2017 (Heidelberg training dataset). For independent testing of the ANN, an independent longitudinal dataset of 40 patients, with data from 239 MRI scans, was collected at Heidelberg University Hospital in parallel with the training dataset (Heidelberg test dataset), and 2034 MRI scans from 532 patients at 34 institutions collected between Oct 26, 2011, and Dec 3, 2015, in the EORTC-26101 study were of sufficient quality to be included in the EORTC-26101 test dataset. The ANN yielded excellent performance for accurate detection and segmentation of CE tumours and NE volumes in both longitudinal test datasets (median DICE coefficient for CE tumours 0.89 [95% CI 0.86-0.90], and for NEs 0.93 [0.92-0.94] in the Heidelberg test dataset; CE tumours 0.91 [0.90-0.92], NEs 0.93 [0.93-0.94] in the EORTC-26101 test dataset). Time to progression from quantitative ANN-based assessment of tumour response was a significantly better surrogate endpoint than central RANO assessment for predicting overall survival in the EORTC-26101 test dataset (hazard ratios ANN 2.59 [95% CI 1.86-3.60] vs central RANO 2.07 [1.46-2.92]; p<0.0001) and also yielded a 36% margin over RANO (p<0.0001) when comparing reliability values (ie, agreement in the quantitative volumetrically defined time to progression [based on radiologist ground truth vs automated assessment with ANN] of 87% [266 of 306 with sufficient data] compared with 51% [155 of 306] with local vs independent central RANO assessment). In the Heidelberg simulation dataset, which comprised 466 patients with brain tumours, with 595 MRI scans obtained between April 27, and Sept 17, 2018, automated on-demand processing of MRI scans and quantitative tumour response assessment within the simulated clinical environment required 10 min of computation time (average per scan). INTERPRETATION: Overall, we found that ANN enabled objective and automated assessment of tumour response in neuro-oncology at high throughput and could ultimately serve as a blueprint for the application of ANN in radiology to improve clinical decision making. Future research should focus on prospective validation within clinical trials and application for automated high-throughput imaging biomarker discovery and extension to other diseases. FUNDING: Medical Faculty Heidelberg Postdoc-Program, Else Kroner-Fresenius Foundation.",1
"Zinc ions increase GH signaling ability through regulation of available plasma membrane-localized GHR. It is well known that zinc ion (Zn2+) can regulate the biological activity of growth hormone (GH). However, until now, the mechanism by which Zn2+ regulates GH biological activity remains unclear. In the current study, we first performed molecular docking between Zn2+ and porcine GH (pGH) using computational biology. We then explored the effect of Zn2+ on the GH signaling ability in the cell model expressing porcine growth hormone receptor (GHR). It was found that the phosphorylation levels of Janus kinase 2, signal transducers and activators of transcription 5/3/1, and GHR increased significantly under Zn2+ treatment, indicating that Zn2+ can enhance the signaling ability of GH/GHR. On this basis, we further explored how Zn2+ regulates the biological activity of GH/GHR. The results showed that downregulation and turnover of GHR changed under Zn2+/pGH treatment. Zn2+ enhanced the membrane residence time of pGH/GHR and delayed GHR downregulation. Further investigation showed that the internalization dynamic of pGH/GHR was changed by Zn2+, which prolonged the residence time of pGH/GHR in the cell membrane. These factors acted together to upregulate the signaling of GH/GHR. This study lays a foundation for further exploration of the biological effects of Zn2+ on GH. (Figure presented.).",0
"Synthesis and DNase I inhibitory properties of some 4-thiazolidinone derivatives. Twelve new thiazolidinones were synthesized and, together with 41 previously synthesized thiazolidinones, evaluated for inhibitory activity against deoxyribonuclease I (DNase I) in vitro. Ten compounds inhibited commercial bovine pancreatic DNase I with an IC50 below 200 μM and showed to be more potent DNase I inhibitors than crystal violet (IC50 = 365.90 ± 47.33 μM), used as a positive control. Moreover, three compounds were active against DNase I in rat liver homogenate, having an IC50 below 200 μM. (3-Methyl-1,4-dioxothiazolidin-2-ylidene)-N-(2-phenylethyl)ethanamide (41) exhibited the most potent DNase I inhibition against both commercial and rat liver DNase I with IC50 values of 115.96 ± 11.70 and 151.36 ± 15.85 μM, respectively. Site Finder and molecular docking defined the thiazolidinones interactions with the most important catalytic residues of DNase I, including the H-acceptor interaction with residues His 134 and His 252 and/or H-donor interaction with residues Glu 39 and Asp 168. The three most active compounds against both commercial and rat liver DNase I (31, 38, and 41) exhibited favorable physico-chemical, pharmacokinetic, and toxicological properties. These observations could be utilized to guide the rational design and optimization of novel thiazolidinone inhibitors. Thiazolidinones as novel DNase I inhibitors could have potential therapeutic applications due to the significant involvement of DNase I in the pathophysiology of many disease conditions.",0
"Assessment of Automated Identification of Phases in Videos of Cataract Surgery Using Machine Learning and Deep Learning Techniques. Importance: Competence in cataract surgery is a public health necessity, and videos of cataract surgery are routinely available to educators and trainees but currently are of limited use in training. Machine learning and deep learning techniques can yield tools that efficiently segment videos of cataract surgery into constituent phases for subsequent automated skill assessment and feedback. Objective: To evaluate machine learning and deep learning algorithms for automated phase classification of manually presegmented phases in videos of cataract surgery. Design, Setting, and Participants: This was a cross-sectional study using a data set of videos from a convenience sample of 100 cataract procedures performed by faculty and trainee surgeons in an ophthalmology residency program from July 2011 to December 2017. Demographic characteristics for surgeons and patients were not captured. Ten standard labels in the procedure and 14 instruments used during surgery were manually annotated, which served as the ground truth. Exposures: Five algorithms with different input data: (1) a support vector machine input with cross-sectional instrument label data; (2) a recurrent neural network (RNN) input with a time series of instrument labels; (3) a convolutional neural network (CNN) input with cross-sectional image data; (4) a CNN-RNN input with a time series of images; and (5) a CNN-RNN input with time series of images and instrument labels. Each algorithm was evaluated with 5-fold cross-validation. Main Outcomes and Measures: Accuracy, area under the receiver operating characteristic curve, sensitivity, specificity, and precision. Results: Unweighted accuracy for the 5 algorithms ranged between 0.915 and 0.959. Area under the receiver operating characteristic curve for the 5 algorithms ranged between 0.712 and 0.773, with small differences among them. The area under the receiver operating characteristic curve for the image-only CNN-RNN (0.752) was significantly greater than that of the CNN with cross-sectional image data (0.712) (difference, -0.040; 95% CI, -0.049 to -0.033) and the CNN-RNN with images and instrument labels (0.737) (difference, 0.016; 95% CI, 0.014 to 0.018). While specificity was uniformly high for all phases with all 5 algorithms (range, 0.877 to 0.999), sensitivity ranged between 0.005 (95% CI, 0.000 to 0.015) for the support vector machine for wound closure (corneal hydration) and 0.974 (95% CI, 0.957 to 0.991) for the RNN for main incision. Precision ranged between 0.283 and 0.963. Conclusions and Relevance: Time series modeling of instrument labels and video images using deep learning techniques may yield potentially useful tools for the automated detection of phases in cataract surgery procedures.",1
"iTRAQ-Based Global Phosphoproteomics Reveals Novel Molecular Differences Between Toxoplasma gondii Strains of Different Genotypes. To gain insights into differences in the virulence among T. gondii strains at the post-translational level, we conducted a quantitative analysis of the phosphoproteome profile of T. gondii strains belonging to three different genotypes. Phosphopeptides from three strains, type I (RH strain), type II (PRU strain) and ToxoDB#9 (PYS strain), were enriched by titanium dioxide (TiO2) affinity chromatography and quantified using iTRAQ technology. A total of 1,441 phosphopeptides, 1,250 phosphorylation sites and 759 phosphoproteins were detected. In addition, 392, 298, and 436 differentially expressed phosphoproteins (DEPs) were identified in RH strain when comparing RH/PRU strains, in PRU strain when comparing PRU/PYS strains, and in PYS strain when comparing PYS/RH strains, respectively. Functional characterization of the DEPs using GO, KEGG, and STRING analyses revealed marked differences between the three strains. In silico kinase substrate motif analysis of the DEPs revealed three (RxxS, SxxE, and SxxxE), three (RxxS, SxxE, and SP), and five (SxxE, SP, SxE, LxRxxS, and RxxS) motifs in RH strain when comparing RH/PRU strains, in PRU strain when comparing PRU/PYS, and in PYS strain when comparing PYS/RH strains, respectively. This suggests that multiple overrepresented protein kinases including PKA, PKG, CKII, IKK, and MAPK could be involved in such a difference between T. gondii strains. Kinase associated network analysis showed that ROP5, ROP16, and cell-cycle-associated protein kinase CDK were the most connected kinase peptides. Our data reveal significant changes in the abundance of phosphoproteins between T. gondii genotypes, which explain some of the mechanisms that contribute to the virulence heterogeneity of this parasite.",0
"Artificial intelligence and computer-aided diagnosis in colonoscopy: current evidence and future directions. Computer-aided diagnosis offers a promising solution to reduce variation in colonoscopy performance. Pooled miss rates for polyps are as high as 22%, and associated interval colorectal cancers after colonoscopy are of concern. Optical biopsy, whereby in-vivo classification of polyps based on enhanced imaging replaces histopathology, has not been incorporated into routine practice because it is limited by interobserver variability and generally only meets accepted standards in expert settings. Real-time decision-support software has been developed to detect and characterise polyps, and also to offer feedback on the technical quality of inspection. Some of the current algorithms, particularly with recent advances in artificial intelligence techniques, match human expert performance for optical biopsy. In this Review, we summarise the evidence for clinical applications of computer-aided diagnosis and artificial intelligence in colonoscopy.",0
"Platelet protein disulfide isomerase promotes glycoprotein ib&-mediated platelet-neutrophil interactions under thromboinflammatory conditions. Background: Platelet-neutrophil interactions contribute to vascular occlusion and tissue damage in thromboinflammatory disease. Platelet glycoprotein Ib& (GPIb&), a key receptor for the cell-cell interaction, is believed to be constitutively active for ligand binding. Here, we established the role of platelet-derived protein disulfide isomerase (PDI) in reducing the allosteric disulfide bonds in GPIb& and enhancing the ligand-binding activity under thromboinflammatory conditions. Methods: Bioinformatic analysis identified 2 potential allosteric disulfide bonds in GPIb&. Agglutination assays, flow cytometry, surface plasmon resonance analysis, a protein-protein docking model, proximity ligation assays, and mass spectrometry were used to demonstrate a direct interaction between PDI and GPIb& and to determine a role for PDI in regulating GPIb& function and platelet-neutrophil interactions. Also, real-Time microscopy and animal disease models were used to study the pathophysiological role of PDI-GPIb& signaling under thromboinflammatory conditions. Results: Deletion or inhibition of platelet PDI significantly reduced GPIb&-mediated platelet agglutination. Studies using PDI-null platelets and recombinant PDI or Anfibatide, a clinical-stage GPIb& inhibitor, revealed that the oxidoreductase activity of platelet surface-bound PDI was required for the ligand-binding function of GPIb&. PDI directly bound to the extracellular domain of GPIb& on the platelet surface and reduced the Cys4-Cys17 and Cys209-Cys248 disulfide bonds. Real-Time microscopy with platelet-specific PDI conditional knockout and sickle cell disease mice demonstrated that PDI-regulated GPIb& function was essential for platelet-neutrophil interactions and vascular occlusion under thromboinflammatory conditions. Studies using a mouse model of ischemia/reperfusion-induced stroke indicated that PDI-GPIb& signaling played a crucial role in tissue damage. Conclusions: Our results demonstrate that PDI-facilitated cleavage of the allosteric disulfide bonds tightly regulates GPIb& function, promoting platelet-neutrophil interactions, vascular occlusion, and tissue damage under thromboinflammatory conditions.",0
"Dual-Energy CT in Children: Imaging Algorithms and Clinical Applications. Dual-energy CT enables the simultaneous acquisition of CT images at two different x-ray energy spectra. By acquiring high- and low-energy spectral data, dual-energy CT can provide unique qualitative and quantitative information about tissue composition, allowing differentiation of multiple materials including iodinated contrast agents. The two dual-energy CT postprocessing techniques that best exploit the advantages of dual-energy CT in children are the material-decomposition images (which include virtual nonenhanced, iodine, perfused lung blood volume, lung vessel, automated bone removal, and renal stone characterization images) and virtual monoenergetic images. Clinical applications include assessment of the arterial system, lung perfusion, neoplasm, bowel diseases, renal calculi, tumor response to treatment, and metal implants. Of importance, the radiation exposure level of dual-energy CT is equivalent to or less than that of conventional single-energy CT. In this review, the authors discuss the basic principles of the dual-energy CT technologies and postprocessing techniques and review current clinical applications in the pediatric chest and abdomen.",0
"Structural insights of resveratrol with its binding partners in the toll-like receptor 4 pathway. The benefits associated with resveratrol (Resv; 3,4′,5-trihydroxy-trans-stilbene) are known for a long time. The therapeutic properties of Resv are observed in diseases like cancer, neurological disorders, atherosclerosis, aging, inflammation, etc. Multiple studies suggest that the beneficial properties of Resv are due to its binding to targets in multiple pathways. The same has been reflected in inflammation, where Resv has been shown to inhibit nuclear factor κ light-chain enhancer of activated B cells in the toll-like receptor 4 (TLR4) pathway. There are multiple cellular targets which bind to Resv, however the mode and the key interactions involved remain elusive for many of them. In the current work, we have investigated the structural insights of Resv with three of its binding partners involved in the inflammatory TLR4 signaling pathway. Through a structure-based modelling and molecular dynamics study, we have unraveled the molecular and atomic interactions involved in the Resv-binary complexes of inhibitor of κB kinase, cyclooxygeanse-2, and tank-binding kinase I, all three of which are key players in TLR4 inflammatory signaling. This study is the latest addition to the investigations of the structural partners of Resv and its molecular interactions.",0
"Effects of flow changes on radiotracer binding: Simultaneous measurement of neuroreceptor binding and cerebral blood flow modulation. The potential effects of changes in blood flow on the delivery and washout of radiotracers has been an ongoing question in PET bolus injection studies. This study provides practical insight into this topic by experimentally measuring cerebral blood flow (CBF) and neuroreceptor binding using simultaneous PET/MRI. Hypercapnic challenges (7% CO2) were administered to non-human primates in order to induce controlled increases in CBF, measured with pseudo-continuous arterial spin labeling. Simultaneously, dopamine D2/D3 receptor binding of [11C]raclopride or [18F]fallypride was monitored with dynamic PET. Experiments showed that neither time activity curves nor quantification of binding through binding potentials (BPND) were measurably affected by CBF increases, which were larger than two-fold. Simulations of experimental procedures showed that even large changes in CBF should have little effect on the time activity curves of radiotracers, given a set of realistic assumptions. The proposed method can be applied to experimentally assess the flow sensitivity of other radiotracers. Results demonstrate that CBF changes, which often occur due to behavioral tasks or pharmacological challenges, do not affect PET [11C]raclopride or [18F]fallypride binding studies and their quantification. The results from this study suggest flow effects may have limited impact on many PET neuroreceptor tracers with similar properties.",0
"Multi-institutional Clinical Tool for Predicting High-risk Lesions on 3 Tesla Multiparametric Prostate Magnetic Resonance Imaging. Background: Multiparametric magnetic resonance imaging (mpMRI) for prostate cancer detection without careful patient selection may lead to excessive resource utilization and costs. Objective: To develop and validate a clinical tool for predicting the presence of high-risk lesions on mpMRI. Design, setting, and participants: Four tertiary care centers were included in this retrospective and prospective study (BiRCH Study Collaborative). Statistical models were generated using 1269 biopsy-naive, prior negative biopsy, and active surveillance patients who underwent mpMRI. Using age, prostate-specific antigen, and prostate volume, a support vector machine model was developed for predicting the probability of harboring Prostate Imaging Reporting and Data System 4 or 5 lesions. The accuracy of future predictions was then prospectively assessed in 214 consecutive patients. Outcome measurements and statistical analysis: Receiver operating characteristic, calibration, and decision curves were generated to assess model performance. Results and limitations: For biopsy-naïve and prior negative biopsy patients (n = 811), the area under the curve (AUC) was 0.730 on internal validation. Excellent calibration and high net clinical benefit were observed. On prospective external validation at two separate institutions (n = 88 and n = 126), the machine learning model discriminated with AUCs of 0.740 and 0.744, respectively. The final model was developed on the Microsoft Azure Machine Learning platform (birch.azurewebsites.net). This model requires a prostate volume measurement as input. Conclusions: In patients who are naïve to biopsy or those with a prior negative biopsy, BiRCH models can be used to select patients for mpMRI. Patient summary: In this multicenter study, we developed and prospectively validated a calculator that can be used to predict prostate magnetic resonance imaging (MRI) results using patient age, prostate-specific antigen, and prostate volume as input. This tool can aid health care professionals and patients to make an informed decision regarding whether to get an MRI. Previously, there were no clinical tools to predict Prostate Imaging Reporting and Data System 4–5 before magnetic resonance imaging (MRI). BiRCH models were developed to predict the MRI result using logistic regression and machine learning, and can be used to select patients for multiparametric MRI.",1
"The PI(4)P phosphatase Sac2 controls insulin granule docking and release. Insulin granule biogenesis involves transport to, and stable docking at, the plasma membrane before priming and fusion. Defects in this pathway result in impaired insulin secretion and are a hallmark of type 2 diabetes. We now show that the phosphatidylinositol 4-phosphate phosphatase Sac2 localizes to insulin granules in a substrate-dependent manner and that loss of Sac2 results in impaired insulin secretion. Sac2 operates upstream of granule docking, since loss of Sac2 prevented granule tethering to the plasma membrane and resulted in both reduced granule density and number of exocytic events. Sac2 levels correlated positively with the number of docked granules and exocytic events in clonal â cells and with insulin secretion in human pancreatic islets, and Sac2 expression was reduced in islets from type 2 diabetic subjects. Taken together, we identified a phosphoinositide switch on the surface on insulin granules that is required for stable granule docking at the plasma membrane and impaired in human type 2 diabetes.",0
"Single-Cell RNA-Seq Reveals AML Hierarchies Relevant to Disease Progression and Immunity. Acute myeloid leukemia (AML) is a heterogeneous disease that resides within a complex microenvironment, complicating efforts to understand how different cell types contribute to disease progression. We combined single-cell RNA sequencing and genotyping to profile 38,410 cells from 40 bone marrow aspirates, including 16 AML patients and five healthy donors. We then applied a machine learning classifier to distinguish a spectrum of malignant cell types whose abundances varied between patients and between subclones in the same tumor. Cell type compositions correlated with prototypic genetic lesions, including an association of FLT3-ITD with abundant progenitor-like cells. Primitive AML cells exhibited dysregulated transcriptional programs with co-expression of stemness and myeloid priming genes and had prognostic significance. Differentiated monocyte-like AML cells expressed diverse immunomodulatory genes and suppressed T cell activity in vitro. In conclusion, we provide single-cell technologies and an atlas of AML cell states, regulators, and markers with implications for precision medicine and immune therapies. VIDEO ABSTRACT.",0
"Casticin inhibits nasopharyngeal carcinoma growth by targeting phosphoinositide 3-kinase. Background: Casticin, an isoflavone compound extracted from the herb Fructus Viticis, has demonstrated anti-inflammatory and anticancer activities and properties. The aim of this study was to investigate the effects and mechanisms of casticin in nasopharyngeal carcinoma (NPC) cells and to determine its potential for targeted use as a medicine. Methods: NPC cells were used to perform the experiments. The CCK 8 assay and colony formation assays were used to assess cell viability. Flow cytometry was used to measure the cell cycle and apoptosis analysis (annexin V/PI assay). A three-dimensional (3D) tumour sphere culture system was used to characterize the effect of casticin on NPC stem cells. In silico molecular docking prediction and high-throughput KINOME scan assays were used to evaluate the binding of casticin to phosphoinositide 3-kinase (PI3K), including wild-type and most of mutants variants. We also used the SelectScreen assay to detect the IC50 of ATP activity in the active site of the target kinase. Western blotting was used to evaluate the changes in key proteins involved cell cycle, apoptosis, stemness, and PI3K/protein kinase B (AKT) signalling. The effect of casticin treatment in vivo was determined by using a xenograft mouse model. Results: Our results indicate that casticin is a new and novel selective PI3K inhibitor that can significantly inhibit NPC proliferation and that it induces G2/GM arrest and apoptosis by upregulating Bax/BCL2 expression. Moreover, casticin was observed to affect the self-renewal ability of the nasopharyngeal carcinoma cell lines, and a combination of casticin with BYL719 was observed to induce a decrease in the level of the phosphorylation of mTORC1 downstream targets in BYL719-insensitive NPC cell lines. Conclusion: Casticin is a newly emerging selective PI3K inhibitor with potential for use as a targeted therapeutic treatment for nasopharyngeal carcinoma. Accordingly, casticin might represent a novel and effective agent against NPC and likely has high potential for combined use with pharmacological agents targeting PI3K/AKT.",0
"Activation of prolyl hydroxylase-2 for stabilization of mitochondrial stress along with simultaneous downregulation of HIF-1α/FASN in ER + breast cancer subtype. The present study was undertaken to inquest the chemical activation of prolyl hydroxylase-2 for the curtailment of hypoxia-inducible factor-1α and fatty acid synthase. It was well documented that hypoxia-inducible factor-1α and fatty acid synthase were overexpressed in mammary gland carcinomas. After screening a battery of compounds, BBAP-2 was retrieved as a potential prolyl hydroxylase-2 activator and validates its activity using ER + MCF-7 cell line and n-methyl-n-nitrosourea-induced rat in vivo model, respectively. BBAP-2 was palpable for the morphological characteristics of apoptosis along with changes in the mitochondrial intergrity as visualized by acridine orange/ethidium bromide and JC-1 staining against ER + MCF-7 cells. BBAP-2 also arrest the cell cycle of ER + MCF-7 cells at G2/M phase. Afterward, BBAP-2 has scrutinized against n-methyl-n-nitrosourea-induced mammary gland carcinoma in albino Wistar rats. BBAP-2 restored the morphological architecture when screened through carmine staining, haematoxylin and eosin staining, and scanning electron microscopy. BBAP-2 also delineated the markers of oxidative stress favourably. The immunoblotting and mRNA expression analysis validated that BBAP-2 has a potentialty activate the prolyl hydroxylase-2 with sequential downregulating effect on hypoxia-inducible factor-1α and its downstream checkpoint. BBAP-2 also fostered apoptosis through mitochondrial-mediated death pathway. The present study elaborates the chemical activation of prolyl hydroxylase-2 by which the increased expression of HIF-1α and FASN can be reduced in mammary gland carcinoma.",0
"A Review of and Algorithmic Approach to Soft Palate Reconstruction. Importance: The soft palate contributes to deglutition, articulation, and respiration. Current reconstructive techniques focus on restoration of both form and function. The unique challenges of soft palate reconstruction include maintenance of complex upper aerodigestive tract function, with minimal local or donor site morbidity. Objective: To review the literature on soft palate reconstruction and present an algorithm on how to approach soft palate defects based on this review. Evidence Review: A review of the literature for articles reporting studies on and that described concepts related to soft palate reconstruction was conducted in March 2017. In all, 1804 candidate titles and abstracts were independently reviewed. English-language articles that discussed acquired soft palate defect reconstruction were included. Non-English language studies without available translations, studies on primary soft palate defect reconstruction (ie, cleft palate repair) and primary cleft palate repair, studies in which the soft palate was not the focus of the article, and studies involving animals were excluded. Findings: The following observations were made from the review of 92 included articles. Soft palate anatomy is a complex interplay of multiple structures working in a 3-dimensional area. Three of the authors created an initial algorithmic framework based on the selected studies. After this, a round table discussion among 3 authors considered experts was used to refine the algorithm based on their expert opinion. The 4 most important factors were determined to be defect size, defect extension to other subsites, defect thickness, and history of radiotherapy or planned radiotherapy. This algorithm includes both surgical and nonsurgical options. Defects in the soft palate not only affect the size and shape of the organ but, more critically, the function. The reconstructive ladder is used to help maximize the remaining soft palate functional tissue and minimize the effect of nonfunctional implanted tissue. Partial-thickness defects or defects less than one-fourth of the soft palate may not require locoregional tissue transfer. Patients with a history of radiotherapy or defects of up to 75% of the soft palate may require locoregional tissue transfer. Defects greater than 75% of the soft palate, defects that include exposure of the neck vasculature, or defects that include significant portions of the hard palate or adjacent oropharyngeal subsites may require free tissue transfer. Obturation should be considered a second-line option in most cases. Conclusions and Relevance: Ideal reconstruction of the soft palate relies on a comprehensive understanding of soft palate anatomy, a full consideration of the armamentarium of surgical techniques, consideration for adjacent subsite deficits, and a detailed knowledge of various intrinsic and extrinsic patient factors to optimize speech, swallowing, and airway outcomes. The included algorithm may serve as a useful starting point for the surgeon when considering reconstruction.",0
"Gene expression profiling in blood from cerebral malaria patients and mild malaria patients living in Senegal. Background: Plasmodium falciparum malaria remains a major health problem in Africa. The mechanisms of pathogenesis are not fully understood. Transcriptomic studies may provide new insights into molecular pathways involved in the severe form of the disease. Methods: Blood transcriptional levels were assessed in patients with cerebral malaria, non-cerebral malaria, or mild malaria by using microarray technology to look for gene expression profiles associated with clinical status. Multi-way ANOVA was used to extract differentially expressed genes. Network and pathways analyses were used to detect enrichment for biological pathways. Results: We identified a set of 443 genes that were differentially expressed in the three patient groups after applying a false discovery rate of 10%. Since the cerebral patients displayed a particular transcriptional pattern, we focused our analysis on the differences between cerebral malaria patients and mild malaria patients. We further found 842 differentially expressed genes after applying a false discovery rate of 10%. Unsupervised hierarchical clustering of cerebral malaria-informative genes led to clustering of the cerebral malaria patients. The support vector machine method allowed us to correctly classify five out of six cerebral malaria patients and six of six mild malaria patients. Furthermore, the products of the differentially expressed genes were mapped onto a human protein-protein network. This led to the identification of the proteins with the highest number of interactions, including GSK3B, RELA, and APP. The enrichment analysis of the gene functional annotation indicates that genes involved in immune signalling pathways play a role in the occurrence of cerebral malaria. These include BCR-, TCR-, TLR-, cytokine-, FcεRI-, and FCGR-signalling pathways and natural killer cell cytotoxicity pathways, which are involved in the activation of immune cells. In addition, our results revealed an enrichment of genes involved in Alzheimer's disease. Conclusions: In the present study, we examine a set of genes whose expression differed in cerebral malaria patients and mild malaria patients. Moreover, our results provide new insights into the potential effect of the dysregulation of gene expression in immune pathways. Host genetic variation may partly explain such alteration of gene expression. Further studies are required to investigate this in African populations.",0
"Urinary Metabolomic Markers of Protein Glycation, Oxidation, and Nitration in Early-Stage Decline in Metabolic, Vascular, and Renal Health. Glycation, oxidation, nitration, and crosslinking of proteins are implicated in the pathogenic mechanisms of type 2 diabetes, cardiovascular disease, and chronic kidney disease. Related modified amino acids formed by proteolysis are excreted in urine. We quantified urinary levels of these metabolites and branched-chain amino acids (BCAAs) in healthy subjects and assessed changes in early-stage decline in metabolic, vascular, and renal health and explored their diagnostic utility for a noninvasive health screen. We recruited 200 human subjects with early-stage health decline and healthy controls. Urinary amino acid metabolites were determined by stable isotopic dilution analysis liquid chromatography-tandem mass spectrometry. Machine learning was applied to optimise and validate algorithms to discriminate between study groups for potential diagnostic utility. Urinary analyte changes were as follows: impaired metabolic health - increased Nϵ-carboxymethyl-lysine, glucosepane, glutamic semialdehyde, and pyrraline; impaired vascular health - increased glucosepane; and impaired renal health - increased BCAAs and decreased Nϵ-(γ-glutamyl)lysine. Algorithms combining subject age, BMI, and BCAAs discriminated between healthy controls and impaired metabolic, vascular, and renal health study groups with accuracy of 84%, 72%, and 90%, respectively. In 2-step analysis, algorithms combining subject age, BMI, and urinary Nϵ-fructosyl-lysine and valine discriminated between healthy controls and impaired health (any type), accuracy of 78%, and then between types of health impairment with accuracy of 69%-78% (cf. random selection 33%). From likelihood ratios, this provided small, moderate, and conclusive evidence of early-stage cardiovascular, metabolic, and renal disease with diagnostic odds ratios of 6 - 7, 26 - 28, and 34 - 79, respectively. We conclude that measurement of urinary glycated, oxidized, crosslinked, and branched-chain amino acids provides the basis for a noninvasive health screen for early-stage health decline in metabolic, vascular, and renal health.",1
"Re-formulating Gehan's design as a flexible two-stage single-arm trial. BACKGROUND: Gehan's two-stage design was historically the design of choice for phase II oncology trials. One of the reasons it is less frequently used today is that it does not allow for a formal test of treatment efficacy, and therefore does not control conventional type-I and type-II error-rates. METHODS: We describe how recently developed methodology for flexible two-stage single-arm trials can be used to incorporate the hypothesis test commonly associated with phase II trials in to Gehan's design. We additionally detail how this hypothesis test can be optimised in order to maximise its power, and describe how the second stage sample sizes can be chosen to more readily provide the operating characteristics that were originally envisioned by Gehan. Finally, we contrast our modified Gehan designs to Simon's designs, based on two examples motivated by real clinical trials. RESULTS: Gehan's original designs are often greatly under- or over-powered when compared to type-II error-rates typically used in phase II. However, we demonstrate that the control parameters of his design can be chosen to resolve this problem. With this, though, the modified Gehan designs have operating characteristics similar to the more familiar Simon designs. CONCLUSIONS: The trial design settings in which Gehan's design will be preferable over Simon's designs are likely limited. Provided the second stage sample sizes are chosen carefully, however, one scenario of potential utility is when the trial's primary goal is to ascertain the treatment response rate to a certain precision.",0
"DeepFHR: intelligent prediction of fetal Acidemia using fetal heart rate signals based on convolutional neural network. BACKGROUND: Fetal heart rate (FHR) monitoring is a screening tool used by obstetricians to evaluate the fetal state. Because of the complexity and non-linearity, a visual interpretation of FHR signals using common guidelines usually results in significant subjective inter-observer and intra-observer variability. OBJECTIVE: Therefore, computer aided diagnosis (CAD) systems based on advanced artificial intelligence (AI) technology have recently been developed to assist obstetricians in making objective medical decisions. METHODS: In this work, we present an 8-layer deep convolutional neural network (CNN) framework to automatically predict fetal acidemia. After signal preprocessing, the input 2-dimensional (2D) images are obtained using the continuous wavelet transform (CWT), which provides a better way to observe and capture the hidden characteristic information of the FHR signals in both the time and frequency domains. Unlike the conventional machine learning (ML) approaches, this work does not require the execution of complex feature engineering, i.e., feature extraction and selection. In fact, 2D CNN model can self-learn useful features from the input data with the prerequisite of not losing informative features, representing the tremendous advantage of deep learning (DL) over ML. RESULTS: Based on the test open-access database (CTU-UHB), after comprehensive experimentation, we achieved better classification performance using the optimal CNN configuration compared to other state-of-the-art methods: the averaged ten-fold cross-validation of the accuracy, sensitivity, specificity, quality index defined as the geometric mean of the sensitivity and specificity, and the area under the curve yielded results of 98.34, 98.22, 94.87, 96.53 and 97.82%, respectively CONCLUSIONS: Once the proposed CNN model is successfully trained, the corresponding CAD system can be served as an effective tool to predict fetal asphyxia objectively and accurately.",1
"Discovery of stroke-related blood biomarkers from gene expression network models. Background: Identifying molecular biomarkers characteristic of ischemic stroke has the potential to aid in distinguishing stroke cases from stroke mimicking symptoms, as well as advancing the understanding of the physiological changes that underlie the body's response to stroke. This study uses machine learning-based analysis of gene co-expression to identify transcription patterns characteristic of patients with acute ischemic stroke. Methods: Mutual information values for the expression levels among 13,243 quantified transcripts were computed for blood samples from 82 stroke patients and 68 controls to construct a co-expression network of genes (separately) for stroke and control samples. Page rank centrality scores were computed for every gene; a gene's significance in the network was assessed according to the differences in their network's pagerank centrality between stroke and control expression patterns. A hybrid genetic algorithm - support vector machine learning tool was used to classify samples based on gene centrality in order to identify an optimal set of predictor genes for stroke while minimizing the number of genes in the model. Results: A predictive model with 89.6% accuracy was identified using 6 network-central and differentially expressed genes (ID3, MBTPS1, NOG, SFXN2, BMX, SLC22A1), characterized by large differences in association network connectivity between stroke and control samples. In contrast, classification models based solely on individual genes identified by significant fold-changes in expression level provided lower predictive accuracies: < 71% for any single gene, and even models with larger (10-25) numbers of gene transcript biomarkers gave lower predictive accuracies (≤ 82%) than the 6 network-based gene signature classification. miRNA:mRNA target prediction computational analysis revealed 8 differentially expressed micro-RNAs (miRNAs) that are significantly associated with at least 2 of the 6 network-central genes. Conclusions: Network-based models have the potential to identify a more statistically robust pattern of gene expression typical of acute ischemic stroke and to generate hypotheses about possible interactions among functionally relevant genes, leading to the identification of more informative biomarkers.",0
"Development and Assessment of a Machine Learning Model to Help Predict Survival among Patients with Oral Squamous Cell Carcinoma. Importance: Predicting survival of oral squamous cell carcinoma through the use of prediction modeling has been underused, and the development of prediction models would augment clinicians' ability to provide absolute risk estimates for individual patients. Objectives: To develop a prediction model using machine learning for 5-year overall survival among patients with oral squamous cell carcinoma and compare this model with a prediction model created from the TNM (Tumor, Node, Metastasis) clinical and pathologic stage. Design, Setting, and Participants: A retrospective cohort study was conducted of 33065 patients with oral squamous cell carcinoma from the National Cancer Data Base between January 1, 2004, and December 31, 2011. Patients were excluded if the treatment was considered palliative, staging demonstrated T0 or Tis, or survival or staging data were missing. Patient, tumor, treatment, and outcome information were obtained from the National Cancer Data Base. The data were split into a distribution of 80% for training and 20% for testing. The model was created using 2-class decision forest architecture. Permutation feature importance scores were used to determine the variables that were used in the model's prediction and their order of significance. Statistical analysis was conducted from August 1, 2018, to January 10, 2019. Main Outcomes and Measures: Ability to predict 5-year overall survival assessed through area under the curve, accuracy, precision, and recall. Results: Among the 33065 patients in the study, the mean (SD) age was 64.6 (14.0) years, 19791 were men (59.9%), 13 274 were women (40.1%), and 29783 (90.1%) were white. At 60 months, there were 16745 deaths (50.6%). The median time of follow-up was 56.8 months (range, 0-155.6 months). Age, pathologic T stage, positive margins at the time of surgery, lymph node size, and institutional identification were identified among the most significant variables. The calculated area under the curve for this machine learning model was 0.80 (95% CI, 0.79-0.81), accuracy was 71%, precision was 71%, and recall was 68%. In comparison, the calculated area under the curve of the TNM staging system was 0.68 (95% CI, 0.67-0.70), accuracy was 65%, precision was 69%, and recall was 52%. Conclusions and Relevance: Using machine learning algorithms, a prediction model was created based on patient social, demographic, clinical, and pathologic features. The developed prediction model proved to be better than a prediction model that exclusively used TNM pathologic and clinical stage according to all performance metrics. This study highlights the role that machine learning may play in individual patient risk estimation in the era of big data.",1
"Viral Infections Exacerbate FUS-ALS Phenotypes in iPSC-Derived Spinal Neurons in a Virus Species-Specific Manner. Amyotrophic lateral sclerosis (ALS) arises from an interplay of genetic mutations and environmental factors. ssRNA viruses are possible ALS risk factors, but testing their interaction with mutations such as in FUS, which encodes an RNA-binding protein, has been difficult due to the lack of a human disease model. Here, we use isogenic induced pluripotent stem cell (iPSC)-derived spinal neurons (SNs) to investigate the interaction between ssRNA viruses and mutant FUS. We find that rabies virus (RABV) spreads ALS phenotypes, including the formation of stress granules (SGs) with aberrant composition due to increased levels of FUS protein, as well as neurodegeneration and reduced restriction activity by FUS mutations. Consistent with this, iPSC-derived SNs harboring mutant FUS are more sensitive to human immunodeficiency virus (HIV-1) and Zika viruses (ZIKV). We demonstrate that RABV and HIV-1 exacerbate cytoplasmic mislocalization of FUS. Our results demonstrate that viral infections worsen ALS pathology in SNs with genetic risk factors, suggesting a novel role for viruses in modulating patient phenotypes.",0
"The road map of cancer precision medicine with the innovation of advanced cancer detection technology and personalized immunotherapy. The advancement of cancer genomics research due to the development of next generation sequencing technologies is going to bring the promise of cancer precision medicine, in turn revolutionizing cancer detection and treatment. In this review, we will discuss the possible road map for implementation of cancer precision medicine into the clinical practice by mainly focusing on the role of liquid biopsy, particularly circulating tumor DNA, as a potential tool for cancer screening, selection of an appropriate drug(s), surveillance of minimal residual diseases, and early detection of recurrence. We will also review the current status of genome-driven oncology and emerging field of immunotherapies that could be provided to patients to improve their clinical outcome and quality of life. Lastly, we will discuss the usefulness of artificial intelligence that facilitate complex data integration in our health care/medical care system.",0
A Deep Learning Framework for Predicting Response to Therapy in Cancer. Sakellaropoulos et al. designed a machine learning workflow to predict drug response and survival of cancer patients. All pipelines are trained on a large panel of cancer cell lines and tested in clinical cohorts. DNN outperforms other machine learning algorithms by capturing pathways that link gene expression with drug response.,0
"Anchoring of actin to the plasma membrane enables tension production in the fission yeast cytokinetic ring. The cytokinetic ring generates tensile force that drives cell division, but how tension emerges from the relatively disordered ring organization remains unclear. Long ago, a musclelike sliding filament mechanism was proposed, but evidence for sarcomeric order is lacking. Here we present quantitative evidence that in fission yeast, ring tension originates from barbed-end anchoring of actin filaments to the plasma membrane, providing resistance to myosin forces that enables filaments to develop tension. The role of anchoring was highlighted by experiments on isolated fission yeast rings, where sections of ring became unanchored from the membrane and shortened ∼30-fold faster than normal. The dramatically elevated constriction rates are unexplained. Here we present a molecularly explicit simulation of constricting partially anchored rings as studied in these experiments. Simulations accurately reproduced the experimental constriction rates and showed that following anchor release, a segment becomes tensionless and shortens via a novel noncontractile reeling-in mechanism at about the velocity of load-free myosin II. The ends are reeled in by barbed end–anchored actin filaments in adjacent segments. Other actin anchoring schemes failed to constrict rings. Our results quantitatively support a specific organization and anchoring scheme that generate tension in the cytokinetic ring.",0
"Development and evaluation of a computable phenotype to identify pediatric patients with leukemia and lymphoma treated with chemotherapy using electronic health record data. Background: Widespread implementation of electronic health records (EHR) has created new opportunities for pediatric oncology observational research. Little attention has been given to using EHR data to identify patients with pediatric hematologic malignancies. Methods: This study used EHR-derived data in a pediatric clinical data research network, PEDSnet, to develop and evaluate a computable phenotype algorithm to identify pediatric patients with leukemia and lymphoma who received treatment with chemotherapy. To guide early development, multiple computable phenotype-defined cohorts were compared to one institution's tumor registry. The most promising algorithm was chosen for formal evaluation and consisted of at least two leukemia/lymphoma diagnoses (Systematized Nomenclature of Medicine codes) within a 90-day period, two chemotherapy exposures, and three hematology-oncology provider encounters. During evaluation, the computable phenotype was executed against EHR data from 2011 to 2016 at three large institutions. Classification accuracy was assessed by masked medical record review with phenotype-identified patients compared to a control group with at least three hematology-oncology encounters. Results: The computable phenotype had sensitivity of 100% (confidence interval [CI] 99%, 100%), specificity of 99% (CI 99%, 100%), positive predictive value (PPV) and negative predictive value (NPV) of 100%, and C-statistic of 1 at the development institution. The computable phenotype performance was similar at the two test institutions with sensitivity of 100% (CI 99%, 100%), specificity of 99% (CI 99%, 100%), PPV of 96%, NPV of 100%, and C-statistic of 0.99. Conclusion: The EHR-based computable phenotype is an accurate cohort identification tool for pediatric patients with leukemia and lymphoma who have been treated with chemotherapy and is ready for use in clinical studies.",1
"Assessment of Deep Natural Language Processing in Ascertaining Oncologic Outcomes From Radiology Reports. Importance: A rapid learning health care system for oncology will require scalable methods for extracting clinical end points from electronic health records (EHRs). Outside of clinical trials, end points such as cancer progression and response are not routinely encoded into structured data. Objective: To determine whether deep natural language processing can extract relevant cancer outcomes from radiologic reports, a ubiquitous but unstructured EHR data source. Design, Setting, and Participants: A retrospective cohort study evaluated 1112 patients who underwent tumor genotyping for a diagnosis of lung cancer and participated in the Dana-Farber Cancer Institute PROFILE study from June 26, 2013, to July 2, 2018. Exposures: Patients were divided into curation and reserve sets. Human abstractors applied a structured framework to radiologic reports for the curation set to ascertain the presence of cancer and changes in cancer status over time (ie, worsening/progressing vs improving/responding). Deep learning models were then trained to capture these outcomes from report text and subsequently evaluated in a 10% held-out test subset of curation patients. Cox proportional hazards regression models compared human and machine curations of disease-free survival, progression-free survival, and time to improvement/response in the curation set, and measured associations between report classification and overall survival in the curation and reserve sets. Main Outcomes and Measures: The primary outcome was area under the receiver operating characteristic curve (AUC) for deep learning models; secondary outcomes were time to improvement/response, disease-free survival, progression-free survival, and overall survival. Results: A total of 2406 patients were included (mean [SD] age, 66.5 [10.8] years; 1428 female [59.7%]; 2170 [90.2%] white). Radiologic reports (n = 14230) were manually reviewed for 1112 patients in the curation set. In the test subset (n = 109), deep learning models identified the presence of cancer, improvement/response, and worsening/progression with accurate discrimination (AUC >0.90). Machine and human curation yielded similar measurements of disease-free survival (hazard ratio [HR] for machine vs human curation, 1.18; 95% CI, 0.71-1.95); progression-free survival (HR, 1.11; 95% CI, 0.71-1.71); and time to improvement/response (HR, 1.03; 95% CI, 0.65-1.64). Among 15000 additional reports for 1294 reserve set patients, algorithm-detected cancer worsening/progression was associated with decreased overall survival (HR for mortality, 4.04; 95% CI, 2.78-5.85), and improvement/response was associated with increased overall survival (HR, 0.41; 95% CI, 0.22-0.77). Conclusions and Relevance: Deep natural language processing appears to speed curation of relevant cancer outcomes and facilitate rapid learning from EHR data.",1
"A temporal visualization of chronic obstructive pulmonary disease progression using deep learning and unstructured clinical notes. BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a progressive lung disease that is classified into stages based on disease severity. We aimed to characterize the time to progression prior to death in patients with COPD and to generate a temporal visualization that describes signs and symptoms during different stages of COPD progression. METHODS: We present a two-step approach for visualizing COPD progression at the level of unstructured clinical notes. We included 15,500 COPD patients who both received care within Partners Healthcare's network and died between 2011 and 2017. We first propose a four-layer deep learning model that utilizes a specially configured recurrent neural network to capture irregular time lapse segments. Using those irregular time lapse segments, we created a temporal visualization (the COPD atlas) to demonstrate COPD progression, which consisted of representative sentences at each time window prior to death based on a fraction of theme words produced by a latent Dirichlet allocation model. We evaluated our approach on an annotated corpus of COPD patients' unstructured pulmonary, radiology, and cardiology notes. RESULTS: Experiments compared to the baselines showed that our proposed approach improved interpretability as well as the accuracy of estimating COPD progression. CONCLUSIONS: Our experiments demonstrated that the proposed deep-learning approach to handling temporal variation in COPD progression is feasible and can be used to generate a graphical representation of disease progression using information extracted from clinical notes.",1
"miR-22-5p and miR-29a-5p Are Reliable Reference Genes for Analyzing Extracellular Vesicle-Associated miRNAs in Adipose-Derived Mesenchymal Stem Cells and Are Stable under Inflammatory Priming Mimicking Osteoarthritis Condition. During the last two decades, mesenchymal stem cells (MSCs) gained a place of privilege in the field of regenerative medicine. Recently, extracellular vesicles (EVs) have been identified as major mediators of MSCs immunosuppressive as well as pro-regenerative activities in many disease models, including inflammatory/degenerative conditions as joint diseases and osteoarthritis. In order to shed light on EVs potential, a rigorous profiling of embedded proteins, lipids and nucleic acids (mRNA/miRNA) is mandatory. Nevertheless, reliable strategies to efficiently score miRNA cargo and modulation under diverse experimental conditions or treatments are missing. The aim of this work was to identify reliable reference genes (RGs) to analyze miRNA content in EVs secreted by adipose-derived MSCs (ASCs) and verify their consistency under inflammatory conditions that were proposed to enhance ASC-EVs immunomodulatory and regenerative potential. RefFinder algorithm, that integrates the currently available major computational programs (geNorm, NormFinder, BestKeeper, and Delta Ct method), allowed to identify miR-22-5p and miR-29a-5p as the most stable RGs. Notably, both miRNAs maintained the highest stability when EVs isolated from IFNg-treated ASCs were included in the analysis. In addition, considerable effects of suboptimal RGs choice on the reliable quantification of miRNAs involved at different levels (tissue homeostasis or macrophage polarization) in the osteoarthritis phenotype, and thus considered as promising therapeutic molecule, have clearly been demonstrated. In conclusion, a proper normalization method is not only needed for research purposes but also mandatory to characterize clinical products and predict their therapeutic potential, especially in the emerging field of MSCs derived-EVs as new tools for regenerative medicine.",0
"Laparoscopic supracervical hysterectomy versus endometrial ablation for women with heavy menstrual bleeding (HEALTH): a parallel-group, open-label, randomised controlled trial. Background: Heavy menstrual bleeding affects 25% of women in the UK, many of whom require surgery to treat it. Hysterectomy is effective but has more complications than endometrial ablation, which is less invasive but ultimately leads to hysterectomy in 20% of women. We compared laparoscopic supracervical hysterectomy with endometrial ablation in women seeking surgical treatment for heavy menstrual bleeding. Methods: In this parallel-group, multicentre, open-label, randomised controlled trial in 31 hospitals in the UK, women younger than 50 years who were referred to a gynaecologist for surgical treatment of heavy menstrual bleeding and who were eligible for endometrial ablation were randomly allocated (1:1) to either laparoscopic supracervical hysterectomy or second generation endometrial ablation. Women were randomly assigned by either an interactive voice response telephone system or an internet-based application with a minimisation algorithm based on centre and age group (<40 years vs ≥40 years). Laparoscopic supracervical hysterectomy involves laparoscopic (keyhole) surgery to remove the upper part of the uterus (the body) containing the endometrium. Endometrial ablation aims to treat heavy menstrual bleeding by destroying the endometrium, which is responsible for heavy periods. The co-primary clinical outcomes were patient satisfaction and condition-specific quality of life, measured with the menorrhagia multi-attribute quality of life scale (MMAS), assessed at 15 months after randomisation. Our analysis was based on the intention-to-treat principle. The trial was registered with the ISRCTN registry, number ISRCTN49013893. Findings: Between May 21, 2014, and March 28, 2017, we enrolled and randomly assigned 660 women (330 in each group). 616 (93%) of 660 women were operated on within the study period, 588 (95%) of whom received the allocated procedure and 28 (5%) of whom had an alternative surgery. At 15 months after randomisation, more women allocated to laparoscopic supracervical hysterectomy were satisfied with their operation compared with those in the endometrial ablation group (270 [97%] of 278 women vs 244 [87%] of 280 women; adjusted percentage difference 9·8, 95% CI 5·1–14·5; adjusted odds ratio [OR] 2·53, 95% CI 1·83–3·48; p<0·0001). Women randomly assigned to laparoscopic supracervical hysterectomy were also more likely to have the best possible MMAS score of 100 than women assigned to endometrial ablation (180 [69%] of 262 women vs 146 [54%] of 268 women; adjusted percentage difference 13·3, 95% CI 3·8–22·8; adjusted OR 1·87, 95% CI 1·31–2·67; p=0·00058). 14 (5%) of 309 women in the laparoscopic supracervical hysterectomy group and 11 (4%) of 307 women in the endometrial ablation group had at least one serious adverse event (adjusted OR 1·30, 95% CI 0·56–3·02; p=0·54). Interpretation: Laparoscopic supracervical hysterectomy is superior to endometrial ablation in terms of clinical effectiveness and has a similar proportion of complications, but takes longer to perform and is associated with a longer recovery. Funding: UK National Institute for Health Research Health Technology Assessment Programme.",0
"Automated classification of dense calcium tissues in gray-scale intravascular ultrasound images using a deep belief network. BACKGROUND: IVUS is widely used to quantitatively assess coronary artery disease. The purpose of this study was to automatically characterize dense calcium (DC) tissue in the gray scale intravascular ultrasound (IVUS) images using the image textural features. METHODS: A total of 316 Gy-scale IVUS and corresponding virtual histology images from 26 patients with acute coronary syndrome who underwent IVUS along with X-ray angiography between October 2009 to September 2014 were retrospectively acquired and analyzed. One expert performed all procedures and assessed their IVUS scans. After image acquisition, the DC candidate and corresponding acoustic shadow regions were automatically determined. Then, nine image-base feature groups were extracted from the DC candidates. In order to reduce the dimensionalities, principal component analysis (PCA) was performed, and selected feature sets were utilized as an input for a deep belief network. Classification results were validated using 10-fold cross validation. RESULTS: The dimensionality of the feature map was efficiently reduced by 50% (from 66 to 33) without any performance decrease using PCA method. Sensitivity, specificity, and accuracy of the proposed method were 92.8 ± 0.1%, 85.1 ± 0.1%, and 88.4 ± 0.1%, respectively (p < 0.05). We found that the window size could largely influence the characterization results, and selected the 5 × 5 size as the best condition. We also validated the performance superiority of the proposed method with traditional classification methods. CONCLUSIONS: These experimental results suggest that the proposed method has significant clinical applicability for IVUS-based cardiovascular diagnosis.",1
"Meta-Analysis Reveals Reproducible Gut Microbiome Alterations in Response to a High-Fat Diet. Multiple research groups have shown that diet impacts the gut microbiome; however, variability in experimental design and quantitative assessment have made it challenging to assess the degree to which similar diets have reproducible effects across studies. Through an unbiased subject-level meta-analysis framework, we re-analyzed 27 dietary studies including 1,101 samples from rodents and humans. We demonstrate that a high-fat diet (HFD) reproducibly changes gut microbial community structure. Finer taxonomic analysis revealed that the most reproducible signals of a HFD are Lactococcus species, which we experimentally demonstrate to be common dietary contaminants. Additionally, a machine-learning approach defined a signature that predicts the dietary intake of mice and demonstrated that phylogenetic and gene-centric transformations of this model can be translated to humans. Together, these results demonstrate the utility of microbiome meta-analyses in identifying robust and reproducible features for mechanistic studies in preclinical models.",0
"Flavokawain B targets protein neddylation for enhancing the anti-prostate cancer effect of Bortezomib via Skp2 degradation. Background: Flavokawain B (FKB) has been identified from kava root extracts as a potent apoptosis inducer for inhibiting the growth of various cancer cell lines, including prostate cancer. However, the molecular targets of FKB in prostate cancer cells remain unknown. Methods: An in vitro NEDD8 Initiation Conjugation Assay was used to evaluate the neddylation inhibitory activity of FKB. Molecular docking and a cellular thermal shift assay were performed to assess the direct interaction between FKB and the NEDD8 activating enzyme (NAE) complex. Protein neddylation, ubiqutination, stability and expression in cells were assessed with immunoprecipitation and Western blotting methods using specific antibodies. Deletion and site specific mutants and siRNAs were used to evaluate deep mechanisms by which FKB induces Skp2 degradation. Cell growth inhibition and apoptosis induction were measured by MTT, ELISA and Western blotting methods. Results: FKB inhibits NEDD8 conjugations to both Cullin1 and Ubc12 in prostate cancer cell lines and Ubc12 neddylation in an in vitro assay. Molecular docking study and a cellular thermal shift assay reveal that FKB interacts with the regulatory subunit (i.e. APP-BP1) of the NAE. In addition, FKB causes Skp2 degradation in an ubiquitin and proteasome dependent manner. Overexpression of dominant-negative cullin1 (1-452), K720R mutant (the neddylation site) Cullin1 or the F-box deleted Skp2 that losses its binding to the Skp1/Cullin1 complex causes the resistance to FKB-induced Skp2 degradation, whereas siRNA knock-down of Cdh1, a known E3 ligase of Skp2 for targeted degradation, didn't attenuate the effect of FKB on Skp2 degradation. These results suggest that degradation of Skp2 by FKB is involved in a functional Cullin1. Furthermore, proteasome inhibitors Bortezomib and MG132 transcriptionally down-regulate the expression of Skp2, and their combinations with FKB result in enhanced inhibitory effects on the growth of prostate cancer cell lines via synergistic down-regulation of Skp2 and up-regulation of p27/Kip1 and p21/WAF1 protein expression. FKB also selectively inhibits the growth of RB deficient cells with high expression of Skp2. Conclusion: These findings provide a rationale for further investigating combination of FKB and Bortezomib for treatment of RB deficient, castration-resistant prostate cancer.",0
"Parsing clinical text using the state-of-the-art deep learning based parsers: a systematic comparison. BACKGROUND: A shareable repository of clinical notes is critical for advancing natural language processing (NLP) research, and therefore a goal of many NLP researchers is to create a shareable repository of clinical notes, that has breadth (from multiple institutions) as well as depth (as much individual data as possible). METHODS: We aimed to assess the degree to which individuals would be willing to contribute their health data to such a repository. A compact e-survey probed willingness to share demographic and clinical data categories. Participants were faculty, staff, and students in two geographically diverse major medical centers (Utah and New York). Such a sample could be expected to respond like a typical potential participant from the general public who is given complete and fully informed consent about the pros and cons of participating in a research study. RESULTS: 2140 respondents completed the surveys. 56% of respondents were ""somewhat/definitely willing"" to share clinical data with identifiers, while 89% of respondents were ""somewhat (17%) /definitely willing (72%)"" to share without identifiers. Results were consistent across gender, age, and education, but there were some differences by geographical region. Individuals were most reluctant (50-74%) sharing mental health, substance abuse, and domestic violence data. CONCLUSIONS: We conclude that a substantial fraction of potential patient participants, once educated about risks and benefits, would be willing to donate de-identified clinical data to a shared research repository. A slight majority even would be willing to share absent de-identification, suggesting that perceptions about data misuse are not a major concern. Such a repository of clinical notes should be invaluable for clinical NLP research and advancement.",0
"Identification of Cancer-associated metabolic vulnerabilities by modeling multi-objective optimality in metabolism. Background: Cancer cells undergo global reprogramming of cellular metabolism to satisfy demands of energy and biomass during proliferation and metastasis. Computational modeling of genome-scale metabolic models is an effective approach for designing new therapeutics targeting dysregulated cancer metabolism by identifying metabolic enzymes crucial for satisfying metabolic goals of cancer cells, but nearly all previous studies neglect the existence of metabolic demands other than biomass synthesis and trade-offs between these contradicting metabolic demands. It is thus necessary to develop computational models covering multiple metabolic objectives to study cancer metabolism and identify novel metabolic targets. Methods: We developed a multi-objective optimization model for cancer cell metabolism at genome-scale and an integrated, data-driven workflow for analyzing the Pareto optimality of this model in achieving multiple metabolic goals and identifying metabolic enzymes crucial for maintaining cancer-associated metabolic phenotypes. Using this workflow, we constructed cell line-specific models for a panel of cancer cell lines and identified lists of metabolic targets promoting or suppressing cancer cell proliferation or the Warburg Effect. The targets were then validated using knockdown and over-expression experiments in cultured cancer cell lines. Results: We found that the multi-objective optimization model correctly predicted phenotypes including cell growth rates, essentiality of metabolic genes and cell line specific sensitivities to metabolic perturbations. To our surprise, metabolic enzymes promoting proliferation substantially overlapped with those suppressing the Warburg Effect, suggesting that simply targeting the overlapping enzymes may lead to complicated outcomes. We also identified lists of metabolic enzymes important for maintaining rapid proliferation or high Warburg Effect while having little effect on the other. The importance of these enzymes in cancer metabolism predicted by the model was validated by their association with cancer patient survival and knockdown and overexpression experiments in a variety of cancer cell lines. Conclusions: These results confirm this multi-objective optimization model as a novel and effective approach for studying trade-off between metabolic demands of cancer cells and identifying cancer-associated metabolic vulnerabilities, and suggest novel metabolic targets for cancer treatment. Graphical abstract: [Figure not available: See fulltext.]",0
"Virtual screening of naphthoquinone analogs for potent inhibitors against the cancer-signaling PI3K/AKT/mTOR pathway. The PI3K/AKT/mTOR pathway is one of the most commonly disrupted signaling pathways that plays a role in the development and pathogenicity of multiple cancers. Therefore, the critical proteins of this pathway have been targeted for anticancer therapy. The scientific community has increasingly been realizing the anti-cancer therapeutic potential of naphthoquinone analogs. These compounds constitute a major class of diverse sets of plant metabolites, which include various natural products and synthetic compounds with proven anticancer activity. The current study involved structural computational biology approaches to explore compounds from a diverse pool of naphthoquinone analogs that can inhibit key cancer-signaling proteins phosphoinositide 3-kinase (PI3K), protein kinase B, PKB (AKT), and mammalian target of rapamycin (mTOR). The novel compound identified commonly among the top 10 dock score lists of PI3K, AKT, and mTOR was selected for further study and proposed as a potential inhibitor of the 3 cancer-signaling proteins and an anticancer agent. Further, to check the docking accuracy and potential of the compound, post docking analyses, namely, binding comparison with the native ligand, the role of the interacting residue role in binding, predicted binding energy and dissociation constant calculations, etc., were performed. All these measures showed good-quality binding, and thus provide weight to our prediction of the novel compound as a pan PI3K/AKT/mTOR inhibitor and an anticancer agent. Finally, to compare the binding and similarity in the active sites of the 3 protein kinases, a ligand-based active site alignment was performed and analyzed. Thus, the study proposed a novel naphthoquinone analog as a potential anticancer drug, and provided comparative structural insight into its binding to the 3 protein kinases.",0
"In silico evidence of de novo interactions between ribosomal and Epstein - Barr virus proteins. Background: Association of Epstein-Barr virus (EBV) encoded latent gene products with host ribosomal proteins (RPs) has not been fully explored, despite their involvement in the aetiology of several human cancers. To gain an insight into their plausible interactions, we employed a computational approach that encompasses structural alignment, gene ontology analysis, pathway analysis, and molecular docking. Results: In this study, the alignment analysis based on structural similarity allows the prediction of 48 potential interactions between 27 human RPs and the EBV proteins EBNA1, LMP1, LMP2A, and LMP2B. Gene ontology analysis of the putative protein-protein interactions (PPIs) reveals their probable involvement in RNA binding, ribosome biogenesis, metabolic and biosynthetic processes, and gene regulation. Pathway analysis shows their possible participation in viral infection strategies (viral translation), as well as oncogenesis (Wnt and EGFR signalling pathways). Finally, our molecular docking assay predicts the functional interactions of EBNA1 with four RPs individually: EBNA1-eS10, EBNA1-eS25, EBNA1-uL10 and EBNA1-uL11. Conclusion: These interactions have never been revealed previously via either experimental or in silico approach. We envisage that the calculated interactions between the ribosomal and EBV proteins herein would provide a hypothetical model for future experimental studies on the functional relationship between ribosomal proteins and EBV infection.",0
"Tetrahydroxy stilbene glucoside alleviates palmitic acid-induced inflammation and apoptosis in cardiomyocytes by regulating miR-129-3p/Smad3 signaling. Objective: Tetrahydroxy stilbene glucoside (TSG) has been reported to exert a cytoprotective effect against various toxicants. However, the function and mechanism of TSG in palmitic acid (PA)-induced inflammation and apoptosis in cardiomyocytes are still unknown. The present study was designed to investigate the post-transcriptional mechanism in TSG-treated cardiomyocytes’ inflammation and apoptosis induced by PA. Methods: The mRNA and protein levels were assayed by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and western blotting, respectively. The targeted genes were predicted by a bioinformatics algorithm and confirmed by a dual luciferase reporter assay. Cell proliferation was analyzed by CCK-8 assay. Annexin V-fluorescein isothiocyanate/polyimide (annexin V-FITC/PI) staining was used to evaluate apoptosis using flow cytometry. Results: TSG restricted the detrimental effects, including the activated inflammatory response and apoptosis, of PA in cardiomyocytes, as well as the up-regulation of miR-129-3p and down-regulation of p-Smad3 expression. In addition, bioinformatics and experimental analysis suggested that Smad3 was a direct target of miR-129-3p, which could inhibit or enhance the expression of p-Smad by transfection with miR-129-3p mimics or inhibitors, respectively. Furthermore, our results demonstrated that overexpression of Smad3 reversed the inhibition of inflammation and apoptosis by overexpression of miR-129-3p in PA-stimulated cardiomyocytes. Conclusion: TSG targeted to miR-129-3p/Smad3 signaling inhibited PA-induced inflammation and apoptosis in cardiomyocytes.",0
"Investigation of deleterious effects of nsSNPs in the POT1 gene: a structural genomics-based approach to understand the mechanism of cancer development. Protection of telomere 1 (POT1) is one of the key components of shelterin complex, implicated in maintaining the telomere homeostasis, and thus stability of the eukaryotic genome. A large number of non-synonymous single nucleotide polymorphisms (nsSNPs) in the POT1 gene have been reported to cause varieties of human diseases, including cancer. In recent years, a number of mutations in POT1 has been markedly increased, and interpreting the effect of these large numbers of mutations to understand the mechanism of associated diseases seems impossible using experimental approaches. Herein, we employ varieties of computational methods such as PROVEAN, PolyPhen-2, SIFT, PoPMuSiC, SDM2, STRUM, and MAESTRO to identify the effects of 387 nsSNPs on the structure and function of POT1 protein. We have identified about 183 nsSNPs as deleterious and termed them as “high-confidence nsSNPs.” Distribution of these high-confidence nsSNPs demonstrates that the mutation in oligonucleotide binding domain 1 is highly deleterious (one in every three nsSNPs), and high-confidence nsSNPs show a strong correlation with residue conservation. The structure analysis provides a detailed insights into the structural changes occurred in consequence of conserved mutations which lead to the cancer progression. This study, for the first time, offers a newer prospective on the role of POT1 mutations on the structure, function, and their relation to associated diseases.",0
"Theaflavin-3,3´-digallate increases the antibacterial activity of β-lactam antibiotics by inhibiting metallo-β-lactamase activity. Metallo-β-lactamases (MBLs) are some of the best known β-lactamases produced by common Gram-positive and Gram-negative pathogens and are crucial factors in the rise of bacterial resistance against β-lactam antibiotics. Although many types of β-lactamase inhibitors have been successfully developed and used in clinical settings, no MBL inhibitors have been identified to date. Nitrocefin, checkerboard and time-kill assays were used to examine the enzyme behaviour in vitro. Molecular docking calculation, molecular dynamics simulation, calculation of the binding free energy and ligand-residue interaction decomposition were used for mechanistic research. The behaviour of the enzymes in vivo was investigated by a mouse infection experiment. We showed that theaflavin-3,3´-digallate (TFDG), a natural compound lacking antibacterial activities, can inhibit the hydrolysis of MBLs. In the checkerboard and time-kill assays, we observed a synergistic effect of TFDG with β-lactam antibiotics against methicillin-resistant Staphylococcus aureus BAA1717. Molecular dynamics simulations were used to identify the mechanism of the inhibition of MBLs by TFDG, and we observed that the hydrolysis activity of the MBLs was restricted by the binding of TFDG to Gln242 and Ser369. Furthermore, the combination of TFDG with β-lactam antibiotics showed effective protection in a mouse Staphylococcus aureus pneumonia model. These findings suggest that TFDG can effectively inhibit the hydrolysis activity of MBLs and enhance the antibacterial activity of β-lactam antibiotics against pathogens in vitro and in vivo.",0
"Phthalides serve as potent modulators to boost fetal hemoglobin induction therapy for β-hemoglobinopathies. Fetal hemoglobin (HbF) induction therapy has become the most promising strategy for treating β-hemoglobinopathies, including sickle-cell diseases and β-thalassemia. However, subtle but critical structural difference exists between HbF and normal adult hemoglobin (HbA), which inevitably leads to reduced binding of the endogenous modulator 2,3-bisphosphoglycerate (2,3-BPG) to HbF and thus increased oxygen affinity and decreased oxygen transport efficiency of HbF. We combined the oxygen equilibrium experiments, resonance Raman (RR) spectroscopy, and molecular docking modeling, and we discuss 2 phthalides, z-butylidenephthalide and z-ligustilide, that can effectively lower the oxygen affinity of HbF. They adjust it to a level closer to that of HbA and make it a more satisfactory oxygen carrier for adults. From the oxygen equilibrium curve measurements, we show that the 2 phthalides are more effective than 2,3-BPG for modulating HbF. The RR spectra show that phthalides allosterically stabilize the oxygenated HbF in the low oxygen affinity conformation, and the molecular docking modeling reveals that the 2 chosen phthalides interact with HbF via the cleft around the γ1/γ2 interface with a binding strength ;1.6 times stronger than that of 2,3-BPG. We discuss the implications of z-butylidenephthalide and z-ligustilide in boosting the efficacy of HbF induction therapy to mitigate the clinical severities of β-hemoglobinopathies.",0
"Reproducibility and intercorrelation of graph theoretical measures in structural brain connectivity networks. Diffusion-weighted magnetic resonance imaging can be used to non-invasively probe the brain microstructure. In addition, recent advances have enabled the identification of complex fiber configurations present in most of the white matter. This has improved the investigation of structural connectivity with tractography methods. Whole-brain structural connectivity networks, or connectomes, are reconstructed by parcellating the gray matter and performing tractography to determine connectivity between these regions. These complex networks can be analyzed with graph theoretical methods, which measure their global and local properties. However, as these tools have only recently been applied to structural brain networks, there is little information about the reproducibility and intercorrelation of network properties, connectivity weights and fiber tractography reconstruction parameters in the brain. We studied the reproducibility and correlation in structural brain connectivity networks reconstructed with constrained spherical deconvolution based probabilistic streamlines tractography. Diffusion-weighted data from 19 subjects were acquired with b = 2800s/mm(2) and 75 gradient orientations. Intrasubject variability was computed with residual bootstrapping. Our findings indicate that the reproducibility of graph theoretical metrics is generally excellent with the exception of betweenness centrality. A reconstruction density of approximately one million streamlines is necessary for excellent reproducibility, but the reproducibility increases further with higher densities. The reproducibility decreases, but only slightly, when switching to a higher order in constrained spherical deconvolution. Moreover, in binary networks, using sufficiently high threshold values improves the reproducibility. We show that multiple network properties and connectivity weights are highly intercorrelated. The experiments were replicated by using a test-retest dataset of 44 healthy subjects provided by the Human Connectome Project. In conclusion, our results provide guidelines for reproducible investigation of structural brain networks.",0
"Molecular docking studies of chloroquine and its derivatives against P23pro-zbd domain of chikungunya virus: Implication in designing of novel therapeutic strategies. The arthropod-transmitted chikungunya virus has emerged as an epidemic menace that causes debilitating polyarthritis. With this life-threatening impact on humans, the possible treatment requires to cure the viral infectivity. But, devoid of any vaccine against the chikungunya virus (CHIKV), there is a need to develop a novel chemotherapeutic strategy to treat this noxious infection. CHIKV carries highly compact P23pro-zbd structure that possesses potential RNA-binding surface domains which extremely influences the use of RNA template during genome replication at the time of infection and pathogenesis. Therefore, computational approaches were used to explore the novel small molecule inhibitors targeting P23pro-zbd domain. The tertiary structure was modeled and optimized using in silico approaches. The results obtained from PROCHECK (93.1% residues in favored regions), ERRAT (87.480 overall model quality) and ProSA (Z-score: −11.72) revealed the reliability of the proposed model. Interestingly, a previously reported inhibitor, chloroquine possesses good binding affinities with the target domain. In-depth analysis revealed that chloroquine derivatives such as didesethyl chloroquine hydroxyacetamide, cletoquine, hydroxychloroquine exhibited a better binding affinity. Notably, MD simulation analysis exhibited that Thr1312, Ala1355, Ala1356, Asn1357, Asp1364, Val1366, Cys1367, Ala1401, Gly1403, Ser1443, Tyr1444, Gly1445, Asn1459, and Thr1463 residues are the key amino acid responsible for stable ligand-protein interaction. The results obtained from this study provide new insights and advances the understanding to develop a new approach to consider effective and novel drug against chikungunya. However, a detailed in vivo study is required to explore its drug likeliness against this life-threatening disease.",0
"Sequencing-based methods and resources to study antimicrobial resistance. Antimicrobial resistance extracts high morbidity, mortality and economic costs yearly by rendering bacteria immune to antibiotics. Identifying and understanding antimicrobial resistance are imperative for clinical practice to treat resistant infections and for public health efforts to limit the spread of resistance. Technologies such as next-generation sequencing are expanding our abilities to detect and study antimicrobial resistance. This Review provides a detailed overview of antimicrobial resistance identification and characterization methods, from traditional antimicrobial susceptibility testing to recent deep-learning methods. We focus on sequencing-based resistance discovery and discuss tools and databases used in antimicrobial resistance studies.",0
"Validation of the United Kingdom copy-number alteration classifier in 3239 children with B-cell precursor ALL. Genetic abnormalities provide vital diagnostic and prognostic information in pediatric acute lymphoblastic leukemia (ALL) and are increasingly used to assign patients to risk groups. We recently proposed a novel classifier based on the copy-number alteration (CNA) profile of the 8 most commonly deleted genes in B-cell precursor ALL. This classifier defined 3 CNA subgroups in consecutive UK trials and was able to discriminate patients with intermediate-risk cytogenetics. In this study, we sought to validate the United Kingdom ALL (UKALL)–CNA classifier and reevaluate the interaction with cytogenetic risk groups using individual patient data from 3239 cases collected from 12 groups within the International BFM Study Group. The classifier was validated and defined 3 risk groups with distinct event-free survival (EFS) rates: good (88%), intermediate (76%), and poor (68%) (P, .001). There was no evidence of heterogeneity, even within trials that used minimal residual disease to guide therapy. By integrating CNA and cytogenetic data, we replicated our original key observation that patients with intermediate-risk cytogenetics can be stratified into 2 prognostic subgroups. Group A had an EFS rate of 86% (similar to patients with good-risk cytogenetics), while group B patients had a significantly inferior rate (73%, P, .001). Finally, we revised the overall genetic classification by defining 4 risk groups with distinct EFS rates: very good (91%), good (81%), intermediate (73%), and poor (54%), P, .001. In conclusion, the UKALL-CNA classifier is a robust prognostic tool that can be deployed in different trial settings and used to refine established cytogenetic risk groups.",0
"Identification of a gene set associated with colorectal cancer in microarray data using the entropy method. Objective: We sought to apply Shannon’s entropy to determine colorectal cancer genes in a microarray dataset. Materials and Methods: In the retrospective study, 36 samples were analysed, 18 colorectal carcinoma and 18 paired normal tissue samples. After identification of the gene fold-changes, we used the entropy theory to identify an effective gene set. These genes were subsequently categorised into homogenous clusters. Results: We assessed 36 tissue samples. The entropy theory was used to select a set of 29 genes from 3128 genes that had fold-changes greater than one, which provided the most information on colorectal cancer. This study shows that all genes fall into a cluster, except for the R08183 gene. Conclusion: This study has identified several genes associated with colon cancer using the entropy method, which were not detected by custom methods. Therefore, we suggest that the entropy theory should be used to identify genes associated with cancers in a microarray dataset.",0
"Bioprospection of anti-inflammatory phytochemicals suggests rutaecarpine and quinine as promising 15-lipoxygenase inhibitors. 15-Lipoxygenase (15-LOX) belongs to the family of nonheme iron containing enzymes that catalyzes the peroxidation of polyunsaturated fatty acids (PUFAs) to generate eicosanoids that play an important role in signaling pathways. The role of 15-LOX has been demonstrated in atherosclerosis as well as other inflammatory diseases. In the present study, drug-like compounds were first screened from a set of anti-inflammatory phytochemicals based on Lipinski's rule of five (ROF) and in silico toxicity filters. Two lead compounds-quinine (QUIN) and rutaecarpine (RUT) were shortlisted by analyzing molecular interactions and binding energies of the filtered compounds with the target using molecular docking. Molecular dynamics simulation studies indicate stable trajectories of apo_15-LOX and docked complexes (15-LOX_QUIN and 15-LOX_RUT). In vitro 15-LOX inhibition studies shows that both QUIN and RUT have lower inhibitory concentration (IC50) value than the control (quercetin). Both QUIN and RUT exhibit moderate antioxidant activities. The cell viability study of these compounds suggests no significant toxicity in HEK-293 cell lines. Further, QUIN and RUT both did not show any inhibition against selected Gram-positive and Gram-negative bacterial species. Thus, based on our present findings, rutaecarpine and quinine may be suggested as promising 15-LOX inhibitor for the prevention of the atherosclerosis development.",0
"Quality of Reporting on Guideline, Protocol, or Algorithm Implementation in Adult Trauma Centers: A Systematic Review. OBJECTIVE: To appraise the quality of reporting on guideline, protocol, and algorithm implementations in adult trauma settings according to the Revised Standards for Quality Improvement Reporting Excellence (SQUIRE 2.0). BACKGROUND: At present we do not know if published reports of guideline implementations in trauma settings are of sufficient quality to facilitate replication by other centers wishing to implement the same or similar guidelines. METHODS: A systematic review of the literature was conducted. Articles were identified through electronic databases and hand searching relevant trauma journals. Studies meeting inclusion criteria focused on a guideline, protocol, or algorithm that targeted adult trauma patients >/=18 years and/or trauma patient care providers, and evaluated the effectiveness of guideline, protocol, or algorithm implementation in terms of change in clinical practice or patient outcomes. Each included study was assessed in duplicate for adherence to the 18-item SQUIRE 2.0 criteria. The primary endpoint was the proportion of studies meeting at least 80% (score >/=15) of SQUIRE 2.0. RESULTS: Of 7368 screened studies, 74 met inclusion criteria. Thirty-nine percent of studies scored >/=80% on SQUIRE 2.0. Criteria that were met most frequently were abstract (93%), problem description (93%), and specific aims (89%). The lowest scores appeared in the funding (28%), context (47%), and results (54%) criteria. No study indicated using SQUIRE 2.0 as a guideline to writing the report. CONCLUSIONS: Significant opportunity exists to improve the utility of guideline implementation reports in adult trauma settings, particularly in the domains of study context and the implications of context for study outcomes.",0
"Development and validation of an automated HIV prediction algorithm to identify candidates for pre-exposure prophylaxis: a modelling study. BACKGROUND: HIV pre-exposure prophylaxis (PrEP) is effective but underused, in part because clinicians do not have the tools to identify PrEP candidates. We developed and validated an automated prediction algorithm that uses electronic health record (EHR) data to identify individuals at increased risk for HIV acquisition. METHODS: We used machine learning algorithms to predict incident HIV infections with 180 potential predictors of HIV risk drawn from EHR data from 2007-15 at Atrius Health, an ambulatory group practice in Massachusetts, USA. We included EHRs of all patients aged 15 years or older with at least one clinical encounter during 2007-15. We used ten-fold cross-validated area under the receiver operating characteristic curve (cv-AUC) with 95% CIs to assess the model's performance at identifying individuals with incident HIV and patients independently prescribed PrEP by clinicians. The best-performing model was validated prospectively with 2016 data from Atrius Health and externally with 2011-16 data from Fenway Health, a community health centre specialising in sexual health care in Boston (MA, USA). We calculated HIV risk scores (ie, probability of an incident HIV diagnosis) for every HIV-uninfected patient not on PrEP during 2007-15 at Atrius Health and assessed the distribution of scores for thresholds to determine possible candidates for PrEP in the three study cohorts. FINDINGS: We included 1 155 966 Atrius Health patients from 2007-15 (150 [<0.1%] patients with incident HIV) in our development cohort, 537 257 Atrius Health patients in 2016 (16 [<0.1%] with incident HIV) in our prospective validation cohort, and 33 404 Fenway Health patients from 2011-16 (423 [1.3%] with incident HIV) in our external validation cohort. The best-performing algorithm was obtained with least absolute shrinkage and selection operator (LASSO) and had a cv-AUC of 0.86 (95% CI 0.82-0.90) for identification of incident HIV infections in the development cohort, 0.91 (0.81-1.00) on prospective validation, and 0.77 (0.74-0.79) on external validation. The LASSO model successfully identified patients independently prescribed PrEP by clinicians at Atrius Health in 2016 (cv-AUC 0.93, 95% CI 0.90-0.96) or Fenway Health (0.79, 0.78-0.80). HIV risk scores increased steeply at the 98th percentile. Using this score as a threshold, we prospectively identified 9515 (1.8%) of 536 384 patients at Atrius Health in 2016 and 4385 (15.3%) of 28 702 Fenway Health patients as potential PrEP candidates. INTERPRETATION: Automated algorithms can efficiently identify patients at increased risk for HIV acquisition. Integrating these models into EHRs to alert providers about patients who might benefit from PrEP could improve prescribing and prevent new HIV infections. FUNDING: Harvard University Center for AIDS Research, Providence/Boston Center for AIDS Research, Rhode Island IDeA-CTR, the National Institute of Mental Health, and the US Centers for Disease Control and Prevention.",1
"Protein tyrosine phosphatase 1B inhibitory activities of ursane-type triterpenes from Chinese raspberry, fruits of Rubus chingii. Protein tyrosine phosphatase 1B (PTP1B) has led to an intense interest in developing its inhibitors as anti-diabetes, anti-obesity and anti-cancer agents. The fruits of Rubus chingii (Chinese raspberry) were used as a kind of dietary traditional Chinese medicine. The methanolic extract of R. chingii fruits exhibited significant PTP1B inhibitory activity. Further bioactivity-guided fractionation resulted in the isolation of three PTP1B inhibitory ursane-type triterpenes: ursolic acid (1), 2-oxopomolic acid (2), and 2α 19α-dihydroxy-3-oxo-urs-12-en-28-oic acid (3). Kinetics analyses revealed that 1 was a non-competitive PTP1B inhibitor, and 2 and 3 were mixed type PTP1B inhibitors. Compounds 1–3 and structurally related triterpenes (4–8) were further analyzed the structure-activity relationship, and were evaluated the inhibitory selectivity against four homologous protein tyrosine phosphatases (TCPTP, VHR, SHP-1 and SHP-2). Molecular docking simulations were also carried out, and the result indicated that 1, 3-acetoxy-urs-12-ene-28-oic acid (5), and pomolic acid-3β-acetate (6) bound at the allosteric site including α3, α6, and α7 helix of PTP1B.",0
"The practical implementation of artificial intelligence technologies in medicine. The development of artificial intelligence (AI)-based technologies in medicine is advancing rapidly, but real-world clinical implementation has not yet become a reality. Here we review some of the key practical issues surrounding the implementation of AI into existing clinical workflows, including data sharing and privacy, transparency of algorithms, data standardization, and interoperability across multiple platforms, and concern for patient safety. We summarize the current regulatory environment in the United States and highlight comparisons with other regions in the world, notably Europe and China.",0
"Medical image classification using synergic deep learning. The classification of medical images is an essential task in computer-aided diagnosis, medical image retrieval and mining. Although deep learning has shown proven advantages over traditional methods that rely on the handcrafted features, it remains challenging due to the significant intra-class variation and inter-class similarity caused by the diversity of imaging modalities and clinical pathologies. In this paper, we propose a synergic deep learning (SDL) model to address this issue by using multiple deep convolutional neural networks (DCNNs) simultaneously and enabling them to mutually learn from each other. Each pair of DCNNs has their learned image representation concatenated as the input of a synergic network, which has a fully connected structure that predicts whether the pair of input images belong to the same class. Thus, if one DCNN makes a correct classification, a mistake made by the other DCNN leads to a synergic error that serves as an extra force to update the model. This model can be trained end-to-end under the supervision of classification errors from DCNNs and synergic errors from each pair of DCNNs. Our experimental results on the ImageCLEF-2015, ImageCLEF-2016, ISIC-2016, and ISIC-2017 datasets indicate that the proposed SDL model achieves the state-of-the-art performance in these medical image classification tasks.",1
"Targeted protein degradation: expanding the toolbox. Proteolysis-targeting chimeras (PROTACs) and related molecules that induce targeted protein degradation by the ubiquitin–proteasome system represent a new therapeutic modality and are the focus of great interest, owing to potential advantages over traditional occupancy-based inhibitors with respect to dosing, side effects, drug resistance and modulating ‘undruggable’ targets. However, the technology is still maturing, and the design elements for successful PROTAC-based drugs are currently being elucidated. Importantly, fewer than 10 of the more than 600 E3 ubiquitin ligases have so far been exploited for targeted protein degradation, and expansion of knowledge in this area is a key opportunity. Here, we briefly discuss lessons learned about targeted protein degradation in chemical biology and drug discovery and systematically review the expression profile, domain architecture and chemical tractability of human E3 ligases that could expand the toolbox for PROTAC discovery.",0
"Metabolic pathways associated with right ventricular adaptation to pulmonary hypertension: 3D analysis of cardiac magnetic resonance imaging. AIMS: We sought to identify metabolic pathways associated with right ventricular (RV) adaptation to pulmonary hypertension (PH). We evaluated candidate metabolites, previously associated with survival in pulmonary arterial hypertension, and used automated image segmentation and parametric mapping to model their relationship to adverse patterns of remodelling and wall stress. METHODS AND RESULTS: In 312 PH subjects (47.1% female, mean age 60.8 ± 15.9 years), of which 182 (50.5% female, mean age 58.6 ± 16.8 years) had metabolomics, we modelled the relationship between the RV phenotype, haemodynamic state, and metabolite levels. Atlas-based segmentation and co-registration of cardiac magnetic resonance imaging was used to create a quantitative 3D model of RV geometry and function-including maps of regional wall stress. Increasing mean pulmonary artery pressure was associated with hypertrophy of the basal free wall (β = 0.29) and reduced relative wall thickness (β = -0.38), indicative of eccentric remodelling. Wall stress was an independent predictor of all-cause mortality (hazard ratio = 1.27, P = 0.04). Six metabolites were significantly associated with elevated wall stress (β = 0.28-0.34) including increased levels of tRNA-specific modified nucleosides and fatty acid acylcarnitines, and decreased levels (β = -0.40) of sulfated androgen. CONCLUSION: Using computational image phenotyping, we identify metabolic profiles, reporting on energy metabolism and cellular stress-response, which are associated with adaptive RV mechanisms to PH.",0
"An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. BACKGROUND: Atrial fibrillation is frequently asymptomatic and thus underdetected but is associated with stroke, heart failure, and death. Existing screening methods require prolonged monitoring and are limited by cost and low yield. We aimed to develop a rapid, inexpensive, point-of-care means of identifying patients with atrial fibrillation using machine learning. METHODS: We developed an artificial intelligence (AI)-enabled electrocardiograph (ECG) using a convolutional neural network to detect the electrocardiographic signature of atrial fibrillation present during normal sinus rhythm using standard 10-second, 12-lead ECGs. We included all patients aged 18 years or older with at least one digital, normal sinus rhythm, standard 10-second, 12-lead ECG acquired in the supine position at the Mayo Clinic ECG laboratory between Dec 31, 1993, and July 21, 2017, with rhythm labels validated by trained personnel under cardiologist supervision. We classified patients with at least one ECG with a rhythm of atrial fibrillation or atrial flutter as positive for atrial fibrillation. We allocated ECGs to the training, internal validation, and testing datasets in a 7:1:2 ratio. We calculated the area under the curve (AUC) of the receiver operatoring characteristic curve for the internal validation dataset to select a probability threshold, which we applied to the testing dataset. We evaluated model performance on the testing dataset by calculating the AUC and the accuracy, sensitivity, specificity, and F1 score with two-sided 95% CIs. FINDINGS: We included 180 922 patients with 649 931 normal sinus rhythm ECGs for analysis: 454 789 ECGs recorded from 126 526 patients in the training dataset, 64 340 ECGs from 18 116 patients in the internal validation dataset, and 130 802 ECGs from 36 280 patients in the testing dataset. 3051 (8.4%) patients in the testing dataset had verified atrial fibrillation before the normal sinus rhythm ECG tested by the model. A single AI-enabled ECG identified atrial fibrillation with an AUC of 0.87 (95% CI 0.86-0.88), sensitivity of 79.0% (77.5-80.4), specificity of 79.5% (79.0-79.9), F1 score of 39.2% (38.1-40.3), and overall accuracy of 79.4% (79.0-79.9). Including all ECGs acquired during the first month of each patient's window of interest (ie, the study start date or 31 days before the first recorded atrial fibrillation ECG) increased the AUC to 0.90 (0.90-0.91), sensitivity to 82.3% (80.9-83.6), specificity to 83.4% (83.0-83.8), F1 score to 45.4% (44.2-46.5), and overall accuracy to 83.3% (83.0-83.7). INTERPRETATION: An AI-enabled ECG acquired during normal sinus rhythm permits identification at point of care of individuals with atrial fibrillation. FUNDING: None.",1
"Agreement and Predictors of Discordance of 6 Visual Field Progression Algorithms. PURPOSE: To determine the agreement of 6 established visual field (VF) progression algorithms in a large dataset of VFs from multiple institutions and to determine predictors of discordance among these algorithms. DESIGN: Retrospective longitudinal cohort study. PARTICIPANTS: Visual fields from 5 major eye care institutions in the United States were analyzed, including a subset of eyes with at least 5 Swedish interactive threshold algorithm standard 24-2 VFs that met our reliability criteria. Of a total of 831 240 VFs, a subset of 90 713 VFs from 13 156 eyes of 8499 patients met the inclusion criteria. METHODS: Six commonly used VF progression algorithms (mean deviation [MD] slope, VF index slope, Advanced Glaucoma Intervention Study, Collaborative Initial Glaucoma Treatment Study, pointwise linear regression, and permutation of pointwise linear regression) were applied to this cohort, and each eye was determined to be stable or progressing using each measure. Agreement between individual algorithms was tested using Cohen's kappa coefficient. Bivariate and multivariate analyses were used to determine predictors of discordance (3 algorithms progressing and 3 algorithms stable). MAIN OUTCOME MEASURES: Agreement and discordance between algorithms. RESULTS: Individual algorithms showed poor to moderate agreement with each other when compared directly (kappa range, 0.12-0.52). Based on at least 4 algorithms, 11.7% of eyes progressed. Major predictors of discordance or lack of agreement among algorithms were more depressed initial MD (P < 0.01) and older age at first available VF (P < 0.01). A greater number of VFs (P < 0.01), more years of follow-up (P < 0.01), and eye care institution (P = 0.03) also were associated with discordance. CONCLUSIONS: This extremely large comparative series demonstrated that existing algorithms have limited agreement and that agreement varies with clinical parameters, including institution. These issues underscore the challenges to the clinical use and application of progression algorithms and of applying big-data results to individual practices.",0
"A collaborative computer aided diagnosis (C-CAD) system with eye-tracking, sparse attentional model, and deep learning. Computer aided diagnosis (CAD) tools help radiologists to reduce diagnostic errors such as missing tumors and misdiagnosis. Vision researchers have been analyzing behaviors of radiologists during screening to understand how and why they miss tumors or misdiagnose. In this regard, eye-trackers have been instrumental in understanding visual search processes of radiologists. However, most relevant studies in this aspect are not compatible with realistic radiology reading rooms. In this study, we aim to develop a paradigm shifting CAD system, called collaborative CAD (C-CAD), that unifies CAD and eye-tracking systems in realistic radiology room settings. We first developed an eye-tracking interface providing radiologists with a real radiology reading room experience. Second, we propose a novel algorithm that unifies eye-tracking data and a CAD system. Specifically, we present a new graph based clustering and sparsification algorithm to transform eye-tracking data (gaze) into a graph model to interpret gaze patterns quantitatively and qualitatively. The proposed C-CAD collaborates with radiologists via eye-tracking technology and helps them to improve their diagnostic decisions. The C-CAD uses radiologists' search efficiency by processing their gaze patterns. Furthermore, the C-CAD incorporates a deep learning algorithm in a newly designed multi-task learning platform to segment and diagnose suspicious areas simultaneously. The proposed C-CAD system has been tested in a lung cancer screening experiment with multiple radiologists, reading low dose chest CTs. Promising results support the efficiency, accuracy and applicability of the proposed C-CAD system in a real radiology room setting. We have also shown that our framework is generalizable to more complex applications such as prostate cancer screening with multi-parametric magnetic resonance imaging (mp-MRI).",1
"RCorp: a resource for chemical disease semantic extraction in Chinese. BACKGROUND: To robustly identify synergistic combinations of drugs, high-throughput screenings are desirable. It will be of great help to automatically identify the relations in the published papers with machine learning based tools. To support the chemical disease semantic relation extraction especially for chronic diseases, a chronic disease specific corpus for combination therapy discovery in Chinese (RCorp) is manually annotated. METHODS: In this study, we extracted abstracts from a Chinese medical literature server and followed the annotation framework of the BioCreative CDR corpus, with the guidelines modified to make the combination therapy related relations available. An annotation tool was incorporated to the standard annotation process. RESULTS: The resulting RCorp consists of 339 Chinese biomedical articles with 2367 annotated chemicals, 2113 diseases, 237 symptoms, 164 chemical-induce-disease relations, 163 chemical-induce-symptom relations, and 805 chemical-treat-disease relations. Each annotation includes both the mention text spans and normalized concept identifiers. The corpus gets an inter-annotator agreement score of 0.883 for chemical entities, 0.791 for disease entities which are measured by F score. And the F score for chemical-treat-disease relations gets 0.788 after unifying the entity mentions. CONCLUSIONS: We extracted and manually annotated a chronic disease specific corpus for combination therapy discovery in Chinese. The result analysis of the corpus proves its quality for the combination therapy related knowledge discovery task. Our annotated corpus would be a useful resource for the modelling of entity recognition and relation extraction tools. In the future, an evaluation based on the corpus will be held.",0
"Automated detection of nonmelanoma skin cancer using digital images: a systematic review. BACKGROUND: Computer-aided diagnosis of skin lesions is a growing area of research, but its application to nonmelanoma skin cancer (NMSC) is relatively under-studied. The purpose of this review is to synthesize the research that has been conducted on automated detection of NMSC using digital images and to assess the quality of evidence for the diagnostic accuracy of these technologies. METHODS: Eight databases (PubMed, Google Scholar, Embase, IEEE Xplore, Web of Science, SpringerLink, ScienceDirect, and the ACM Digital Library) were searched to identify diagnostic studies of NMSC using image-based machine learning models. Two reviewers independently screened eligible articles. The level of evidence of each study was evaluated using a five tier rating system, and the applicability and risk of bias of each study was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool. RESULTS: Thirty-nine studies were reviewed. Twenty-four models were designed to detect basal cell carcinoma, two were designed to detect squamous cell carcinoma, and thirteen were designed to detect both. All studies were conducted in silico. The overall diagnostic accuracy of the classifiers, defined as concordance with histopathologic diagnosis, was high, with reported accuracies ranging from 72 to 100% and areas under the receiver operating characteristic curve ranging from 0.832 to 1. Most studies had substantial methodological limitations, but several were robustly designed and presented a high level of evidence. CONCLUSION: Most studies of image-based NMSC classifiers report performance greater than or equal to the reported diagnostic accuracy of the average dermatologist, but relatively few studies have presented a high level of evidence. Clinical studies are needed to assess whether these technologies can feasibly be implemented as a real-time aid for clinical diagnosis of NMSC.",0
"The modulation of gamma oscillations by methamphetamine in rat hippocampal slices. Gamma frequency oscillations (γ, 30–100 Hz) have been suggested to underlie various cognitive and motor functions. The psychotomimetic drug methamphetamine (MA) enhances brain γ oscillations associated with changes in psychomotor state. Little is known about the cellular mechanisms of MA modulation on γ oscillations. We explored the effects of multiple intracellular kinases on MA modulation of γ induced by kainate in area CA3 of rat ventral hippocampal slices. We found that dopamine receptor type 1 and 2 (DR1 and DR2) antagonists, the serine/threonine kinase PKB/Akt inhibitor and N-methyl-D-aspartate receptor (NMDAR) antagonists prevented the enhancing effect of MA on γ oscillations, whereas none of them affected baseline γ strength. Protein kinase A, phosphoinositide 3-kinase and extracellular signal-related kinases inhibitors had no effect on MA. We propose that the DR1/DR2-Akt-NMDAR pathway plays a critical role for the MA enhancement of γ oscillations. Our study provides an new insight into the mechanisms of acute MA on MA-induced psychosis.",0
"In vitro and in silico molecular interaction of multiphase nanoparticles containing inositol hexaphosphate and jacalin: Therapeutic potential against colon cancer cells (HCT-15). Inositol hexaphosphate (IP6) is a natural constituent found in almost all cereals and legumes. It is known to cause numerous antiangiogenic manifestations. Notwithstanding its great potential, it is underutilized due to the chelation and rapid excretion from the body. Jacalin is another natural constituent obtained from seeds of jackfruit and can target disaccharides overexpressed in tumor cells. The current study was in-quested to develop and evaluate a surface-modified gold nanoparticulate system containing IP6 and jacalin which may maximize the apoptotic effect of IP6 against HCT-15 cell lines. IP6 loaded jacalin-pectin-gold nanoparticles (IJP-GNPs) were developed through reduction followed by incubation method. The developed formulation was tested for various in vitro and in silico studies to investigate its potential. HCT-15 cells when exposed to IJP-GNP resulted in significant apoptotic effects in dose as well as time-dependent manner, as measured using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide, micronucleus, and reactive oxygen species assay. IJP-GNP displayed cell cycle arrest at the G0/G1 phase. To further explore the mechanism of chemoprevention, in silico studies were performed. The docking results revealed that the interactive behavior of IP6, P-GNP, and jacalin could target and inhibit the tumor formation activity, supported by in vitro studies. Taken together, all the findings suggested that IP6 loaded nanoparticles may increase the hope of future drug delivery strategy for targeting colon cancer.",0
"Clinical and molecular recursive partitioning analysis of high-grade glioma treated with IMRT. Introduction: Despite multimodal treatment for high-grade gliomas, prognosis remains grim. Prior Radiation Therapy Oncology Group-Recursive Partitioning Analysis (RTOG-RPA) reports indicate based on pretreatment and treatment-related factors, a subset of patients experience a significantly improved survival. Since the development of the RTOG-RPA, high-grade gliomas have seen the widespread introduction of temozolomide and tumor oncogenetics. Here we aimed to determine whether the RTOG-RPA retained prognostic significance in the context of modern treatment, as well as generate an updated RPA incorporating both clinical and genetic variables. Methods: Patients with histologically proven glioblastoma, gliosarcoma, anaplastic astrocytoma, and anaplastic oligodendroglioma treated with intensity-modulated radiation therapy (IMRT) between 2004 and 2017 were reviewed. The primary endpoint was overall survival from date of diagnosis. Primary analysis compared actual survival rates to that expected of corresponding RTOG-RPA class. Secondary analysis utilized the rpart function to recursively partition overall survival by numerous clinical and genetic pretreatment and treatment-related variables. A tertiary analysis recursively partitioned a subset of patients in which the status of all genetic markers were known. Results: We identified 878 patients with histologically proven high-grade glioma treated with IMRT and 291 patients in our genetic subset. Median overall survival for the entire cohort was 14.2 months (95% confidence interval, 13.1-15.3). Applying the RTOG-RPA to our cohort validated the relative prognostic ordering of the survival classes except class II. Generating our new RPA created 7 significantly different survival classes (P<0.001, χ 2 =584) with median survival ranging from 96.4 to 2.9 months based on age, histology, O6-methylguanine-DNA methyltransferase methylation status, radiation fractions, tumor location, radiation dose, temozolomide status, and resection status. Our second RPA of our genetic subset generated 5 significantly different survival classes (P<0.001, χ 2 =166) with survival ranging from 65.3 to 5.6 months based on age, isocitrate dehydrogenase 1 mutation status, O6-methylguanine-DNA methyltransferase methylation status, neurological functional classification, hospitalization during IMRT, temozolomide status, and Karnofsky performance status. Conclusions: The RTOG-RPA retains partial prognostic significance, however, should be updated to reflect recent advancements. This series represents a large RPA analyzing both clinical and genetic factors and generated 7 distinct survival classes. Further assessment of patients with fully available genetic markers generated 5 distinct survival classes. These survival classifications need to be validated by a prospective data set and compared against the RTOG-RPA to determine whether they provide improved prognostic power.",0
"A study of deep learning methods for de-identification of clinical notes in cross-institute settings. BACKGROUND: De-identification is a critical technology to facilitate the use of unstructured clinical text while protecting patient privacy and confidentiality. The clinical natural language processing (NLP) community has invested great efforts in developing methods and corpora for de-identification of clinical notes. These annotated corpora are valuable resources for developing automated systems to de-identify clinical text at local hospitals. However, existing studies often utilized training and test data collected from the same institution. There are few studies to explore automated de-identification under cross-institute settings. The goal of this study is to examine deep learning-based de-identification methods at a cross-institute setting, identify the bottlenecks, and provide potential solutions. METHODS: We created a de-identification corpus using a total 500 clinical notes from the University of Florida (UF) Health, developed deep learning-based de-identification models using 2014 i2b2/UTHealth corpus, and evaluated the performance using UF corpus. We compared five different word embeddings trained from the general English text, clinical text, and biomedical literature, explored lexical and linguistic features, and compared two strategies to customize the deep learning models using UF notes and resources. RESULTS: Pre-trained word embeddings using a general English corpus achieved better performance than embeddings from de-identified clinical text and biomedical literature. The performance of deep learning models trained using only i2b2 corpus significantly dropped (strict and relax F1 scores dropped from 0.9547 and 0.9646 to 0.8568 and 0.8958) when applied to another corpus annotated at UF Health. Linguistic features could further improve the performance of de-identification in cross-institute settings. After customizing the models using UF notes and resource, the best model achieved the strict and relaxed F1 scores of 0.9288 and 0.9584, respectively. CONCLUSIONS: It is necessary to customize de-identification models using local clinical text and other resources when applied in cross-institute settings. Fine-tuning is a potential solution to re-use pre-trained parameters and reduce the training time to customize deep learning-based de-identification models trained using clinical corpus from a different institution.",1
"Pyridine derivatives as anticancer lead compounds with Fatty Acid Synthase as the target: An in silico-guided in vitro study. For the past few decades, structure-based drug discovery (SBDD) has become an inevitable technique in the drug development process for screening hit compounds against therapeutic targets. Here, we have successfully used the SBDD approach viz. virtual high-throughput screening to identify potential inhibitors against the Ketoacyl synthase (KS) domain of Fatty acid synthase (FASN). Overexpression of FASN, and subsequent enhancement of de novo lipogenesis is a key survival strategy of cancer cells. Hence, targeting lipid metabolism using FASN inhibitors has been considered as a promising method to induce metabolic stress, thereby posing a survival disadvantage to cancer cells. In the present study, we have successfully identified eight FASN inhibitors from Asinex Elite database by implementing in silico tools. Five of the hit compounds share a common ring structure, which enables characteristic binding interactions with FASN-KS. Among them, in vitro validation showed that SFA 22637550 possesses significant FASN inhibitory activity and antiproliferative effect in human cancer cells of various origins. The maximum sensitivity was exhibited towards HepG2 hepatocellular carcinoma cells (IC50 = 28 µM). The mode of cell death was found to be apoptosis with a significant increase in SubG0 population without affecting any other phases of the cell cycle. The current study puts forward an excellent core structure for the development of potent FASN inhibitors for successfully targeting cancer cell metabolism, thereby causing selective cell death.",0
"Effect of Electroencephalography-Guided Anesthetic Administration on Postoperative Delirium Among Older Adults Undergoing Major Surgery: The ENGAGES Randomized Clinical Trial. Importance: Intraoperative electroencephalogram (EEG) waveform suppression, often suggesting excessive general anesthesia, has been associated with postoperative delirium. Objective: To assess whether EEG-guided anesthetic administration decreases the incidence of postoperative delirium. Design, Setting, and Participants: Randomized clinical trial of 1232 adults aged 60 years and older undergoing major surgery and receiving general anesthesia at Barnes-Jewish Hospital in St Louis. Recruitment was from January 2015 to May 2018, with follow-up until July 2018. Interventions: Patients were randomized 1:1 (stratified by cardiac vs noncardiac surgery and positive vs negative recent fall history) to receive EEG-guided anesthetic administration (n = 614) or usual anesthetic care (n = 618). Main Outcomes and Measures: The primary outcome was incident delirium during postoperative days 1 through 5. Intraoperative measures included anesthetic concentration, EEG suppression, and hypotension. Adverse events included undesirable intraoperative movement, intraoperative awareness with recall, postoperative nausea and vomiting, medical complications, and death. Results: Of the 1232 randomized patients (median age, 69 years [range, 60 to 95]; 563 women [45.7%]), 1213 (98.5%) were assessed for the primary outcome. Delirium during postoperative days 1 to 5 occurred in 157 of 604 patients (26.0%) in the guided group and 140 of 609 patients (23.0%) in the usual care group (difference, 3.0% [95% CI, -2.0% to 8.0%]; P = .22). Median end-tidal volatile anesthetic concentration was significantly lower in the guided group than the usual care group (0.69 vs 0.80 minimum alveolar concentration; difference, -0.11 [95% CI, -0.13 to -0.10), and median cumulative time with EEG suppression was significantly less (7 vs 13 minutes; difference, -6.0 [95% CI, -9.9 to -2.1]). There was no significant difference between groups in the median cumulative time with mean arterial pressure below 60 mm Hg (7 vs 7 minutes; difference, 0.0 [95% CI, -1.7 to 1.7]). Undesirable movement occurred in 137 patients (22.3%) in the guided and 95 (15.4%) in the usual care group. No patients reported intraoperative awareness. Postoperative nausea and vomiting was reported in 48 patients (7.8%) in the guided and 55 patients (8.9%) in the usual care group. Serious adverse events were reported in 124 patients (20.2%) in the guided and 130 (21.0%) in the usual care group. Within 30 days of surgery, 4 patients (0.65%) in the guided group and 19 (3.07%) in the usual care group died. Conclusions and Relevance: Among older adults undergoing major surgery, EEG-guided anesthetic administration, compared with usual care, did not decrease the incidence of postoperative delirium. This finding does not support the use of EEG-guided anesthetic administration for this indication. Trial Registration: ClinicalTrials.gov Identifier: NCT02241655.",0
"The DNA methylation profile of non-coding RNAs improves prognosis prediction for pancreatic adenocarcinoma. Background: Compelling lines of evidence indicate that DNA methylation of non-coding RNAs (ncRNAs) plays critical roles in various tumour progression. In addition, the differential methylation of ncRNAs can predict prognosis of patients. However, little is known about the clear relationship between DNA methylation profile of ncRNAs and the prognosis of pancreatic adenocarcinoma (PAC) patients. Methods: The data of DNA methylation, RNA-seq, miRNA-seq and clinical features of PAC patients were collected from TCGA database. The DNA methylation profile was obtained using the Infinium HumanMethylation450 BeadChip array. LASSO regression was performed to construct two methylation-based classifiers. The risk score of methylation-based classifiers was calculated for each patient, and the accuracy of the classifiers in predicting overall survival (OS) was examined by ROC curve analysis. In addition, Cox regression models were utilized to assess whether clinical variables and the classifiers were independent prognostic factors for OS. The targets of miRNA and the genes co-expressed with lncRNA were identified with DIANA microT-CDS and the Multi-Experiment Matrix (MEM), respectively. Moreover, DAVID Bioinformatics Resources were applied to analyse the functional enrichment of these targets and co-expressed genes. Results: A total of 4004 CpG sites of miRNA and 11,259 CpG sites of lncRNA were screened. Among these CpG sites, 8 CpG sites of miRNA and 7 CpG sites of lncRNA were found with regression coefficients. By multiplying the sum of methylation degrees of the selected CpGs with these coefficients, two methylation-based classifiers were constructed. The classifiers have shown good performance in predicting the survival rate of PAC patients at varying follow-up times. Interestingly, both of these two classifiers were predominant and independent factors for OS. Furthermore, functional enrichment analysis demonstrated that aberrantly methylated miRNAs and lncRNAs are related to calcium ion transmembrane transport and MAPK, Ras and calcium signalling pathways. Conclusion: In the present study, we identified two methylation-based classifiers of ncRNA associated with OS in PAC patients through a comprehensive analysis of miRNA and lncRNA profiles. We are the first group to demonstrate a relationship between the aberrant DNA methylation of ncRNAs and the prognosis of PAC, and this relationship would contribute to individualized PAC therapy.",0
"Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram. Asymptomatic left ventricular dysfunction (ALVD) is present in 3-6% of the general population, is associated with reduced quality of life and longevity, and is treatable when found(1-4). An inexpensive, noninvasive screening tool for ALVD in the doctor's office is not available. We tested the hypothesis that application of artificial intelligence (AI) to the electrocardiogram (ECG), a routine method of measuring the heart's electrical activity, could identify ALVD. Using paired 12-lead ECG and echocardiogram data, including the left ventricular ejection fraction (a measure of contractile function), from 44,959 patients at the Mayo Clinic, we trained a convolutional neural network to identify patients with ventricular dysfunction, defined as ejection fraction </=35%, using the ECG data alone. When tested on an independent set of 52,870 patients, the network model yielded values for the area under the curve, sensitivity, specificity, and accuracy of 0.93, 86.3%, 85.7%, and 85.7%, respectively. In patients without ventricular dysfunction, those with a positive AI screen were at 4 times the risk (hazard ratio, 4.1; 95% confidence interval, 3.3 to 5.0) of developing future ventricular dysfunction compared with those with a negative screen. Application of AI to the ECG-a ubiquitous, low-cost test-permits the ECG to serve as a powerful screening tool in asymptomatic individuals to identify ALVD.",1
"Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology. Stain variation is a phenomenon observed when distinct pathology laboratories stain tissue slides that exhibit similar but not identical color appearance. Due to this color shift between laboratories, convolutional neural networks (CNNs) trained with images from one lab often underperform on unseen images from the other lab. Several techniques have been proposed to reduce the generalization error, mainly grouped into two categories: stain color augmentation and stain color normalization. The former simulates a wide variety of realistic stain variations during training, producing stain-invariant CNNs. The latter aims to match training and test color distributions in order to reduce stain variation. For the first time, we compared some of these techniques and quantified their effect on CNN classification performance using a heterogeneous dataset of hematoxylin and eosin histopathology images from 4 organs and 9 pathology laboratories. Additionally, we propose a novel unsupervised method to perform stain color normalization using a neural network. Based on our experimental results, we provide practical guidelines on how to use stain color augmentation and stain color normalization in future computational pathology applications.",1
"Combining entity co-occurrence with specialized word embeddings to measure entity relation in Alzheimer's disease. BACKGROUND: Extracting useful information from biomedical literature plays an important role in the development of modern medicine. In natural language processing, there have been rigorous attempts to find meaningful relationships between entities automatically by co-occurrence-based methods. It has been increasingly important to understand whether relationships exist, and if so how strong, between any two entities extracted from a large number of texts. One of the defining methods is to measure semantic similarity and relatedness between two entities. METHODS: We propose a hybrid ranking method that combines a co-occurrence approach considering both direct and indirect entity pair relationship with specialized word embeddings for measuring the relatedness of two entities. RESULTS: We evaluate the proposed ranking method comparatively with other well-known methods such as co-occurrence, Word2Vec, COALS (Correlated Occurrence Analog to Lexical Semantics), and random indexing by calculating top-ranked entities related to Alzheimer's disease. In addition, we analyze gene, pathway, and gene-phenotype relationships. Overall, the proposed method tends to find more hidden relationships than the other methods. CONCLUSION: Our proposed method is able to select more useful related entities that not only highly co-occur but also have more indirect relations for the target entity. In pathway analysis, our proposed method shows superior performance at identifying (functional) cross clustering and higher-level pathways. Our proposed method, resulting from phenotype analysis, has an advantage in identifying the common genotype relating to phenotypes from biological literature.",1
"Robust motion correction for cardiac T1 and ECV mapping using a T1 relaxation model approach. T1 and ECV mapping are quantitative methods for myocardial tissue characterization using cardiac MRI, and are highly relevant for the diagnosis of diffuse myocardial diseases. Since the maps are calculated pixel-by-pixel from a set of MRI images with different T1-weighting, it is critical to assure exact spatial correspondence between these images. However, in practice, different sources of motion e.g. cardiac motion, respiratory motion or patient motion, hamper accurate T1 and ECV calculation such that retrospective motion correction is required. We propose a new robust non-rigid registration framework combining a data-driven initialization with a model-based registration approach, which uses a model for T1 relaxation to avoid direct registration of images with highly varying contrast. The registration between native T1 and enhanced T1 to obtain a motion free ECV map is also calculated using information from T1 model-fitting. The method was validated on three datasets recorded with two substantially different acquisition protocols (MOLLI (dataset 1 (n=15) and dataset 2 (n=29)) and STONE (dataset 3 (n = 210))), one in breath-hold condition and one free-breathing. The average Dice coefficient increased from 72.6+/-12.1% to 82.3+/-7.4% (P<0.05) and mean boundary error decreased from 2.91+/-1.51mm to 1.62+/-0.80mm (P<0.05) for motion correction in a single T1-weighted image sequence (3 datasets) while average Dice coefficient increased from 63.4+/-22.5% to 79.2+/-8.5% (P<0.05) and mean boundary error decreased from 3.26+/-2.64mm to 1.77+/-0.86mm (P<0.05) between native and enhanced sequences (dataset 1 and 2). Overall, the native T1 SD error decreased from 67.32+/-32.57ms to 58.11+/-21.59ms (P<0.05), enhanced SD error from 30.15+/-25ms to 22.74+/-8.94ms (P<0.05) and ECV SD error from 10.08+/-9.59% to 5.42+/-3.21% (P<0.05) (dataset 1 and 2).",0
"Enhanced phosphorylation of AMPK by lutein and oxidised lutein that lead to mitochondrial biogenesis in hyperglycemic HepG2 cells. The stimulation of adenosine monophosphate-activated protein kinase (AMPK) is a prime target to decrease the hyperglycemic condition, hence it is a lutein (L) and oxidised lutein (OXL) is a target molecule for the treatment of type II diabetes. In the current study, a plausible interaction of L and OXL with AMPK was investigated by molecular docking. In addition, the effect of L and OXL for the activation of AMPK that triggers the downstream regulator peroxisome proliferator-activated receptor γ coactivator 1α (PGC-1α), TFAM expression, mitochondrial DNA (mtDNA), mitochondrial biogenesis and superoxide dismutase 2 (SOD2) in high glucose treated HepG2 cells were investigated by quantitative polymerase chain reaction and Western blot analysis. Molecular docking reveals higher binding affinity of L (ΔG = −6.3 kcal/mol) and OXL (ΔG = −15.5 kcal/mol) with AMPK, compared with metformin (ΔG = −5.0 kcal/mol). The phosphorylation of AMPK increased by 1.3- and 1.5-fold with L and OXL treatment, respectively, in high glucose induced HepG2 cells. The activation of PGC-1α is significant (P < 0.05) in OXL group than L. Similarly, TFAM expression is increased with L and OXL compared with the high glucose group. Further increase in SOD2 and mtDNA, confirms the efficacy of L and OXL in restoring the mitochondrial biogenesis in high glucose induced cells through AMPK, PGC-1α, and TFAM.",0
"Chest Radiographs in Congestive Heart Failure: Visualizing Neural Network Learning. Purpose To examine Generative Visual Rationales (GVRs) as a tool for visualizing neural network learning of chest radiograph features in congestive heart failure (CHF). Materials and Methods A total of 103 489 frontal chest radiographs in 46 712 patients acquired from January 1, 2007, to December 31, 2016, were divided into a labeled data set (with B-type natriuretic peptide [BNP] result as a marker of CHF) and unlabeled data set (without BNP result). A generative model was trained on the unlabeled data set, and a neural network was trained on the encoded representations of the labeled data set to estimate BNP. The model was used to visualize how a radiograph with high estimated BNP would look without disease (a ""healthy"" radiograph). An overfitted model was developed for comparison, and 100 GVRs were blindly assessed by two experts for features of CHF. Area under the receiver operating characteristic curve (AUC), kappa coefficient, and mixed-effects logistic regression were used for statistical analyses. Results At a cutoff BNP of 100 ng/L as a marker of CHF, the correctly trained model achieved an AUC of 0.82. Assessment of GVRs revealed that the correctly trained model highlighted conventional radiographic features of CHF as reasons for an elevated BNP prediction more frequently than the overfitted model, including cardiomegaly (153 [76.5%] of 200 vs 64 [32%] of 200, respectively; P < .001) and pleural effusions (47 [23.5%] of 200 vs 16 [8%] of 200, respectively; P = .003). Conclusion Features of congestive heart failure on chest radiographs learned by neural networks can be identified using Generative Visual Rationales, enabling detection of bias and overfitted models. (c) RSNA, 2018 See also the editorial by Ngo in this issue.",1
"TFAP2C- and p63-Dependent Networks Sequentially Rearrange Chromatin Landscapes to Drive Human Epidermal Lineage Commitment. Tissue development results from lineage-specific transcription factors (TFs) programming a dynamic chromatin landscape through progressive cell fate transitions. Here, we define epigenomic landscape during epidermal differentiation of human pluripotent stem cells (PSCs) and create inference networks that integrate gene expression, chromatin accessibility, and TF binding to define regulatory mechanisms during keratinocyte specification. We found two critical chromatin networks during surface ectoderm initiation and keratinocyte maturation, which are driven by TFAP2C and p63, respectively. Consistently, TFAP2C, but not p63, is sufficient to initiate surface ectoderm differentiation, and TFAP2C-initiated progenitor cells are capable of maturing into functional keratinocytes. Mechanistically, TFAP2C primes the surface ectoderm chromatin landscape and induces p63 expression and binding sites, thus allowing maturation factor p63 to positively autoregulate its own expression and close a subset of the TFAP2C-initiated surface ectoderm program. Our work provides a general framework to infer TF networks controlling chromatin transitions that will facilitate future regenerative medicine advances.",0
"An automatic restoration framework based on GPU-accelerated collateral filtering in brain MR images. BACKGROUND: Image restoration is one of the fundamental and essential tasks within image processing. In medical imaging, developing an effective algorithm that can automatically remove random noise in brain magnetic resonance (MR) images is challenging. The collateral filter has been shown a more powerful algorithm than many existing methods. However, the computation of the collateral filter is more time-consuming and the selection of the filter parameters is also laborious. This paper proposes an automatic noise removal system based on the accelerated collateral filter for brain MR images. METHODS: To solve these problems, we first accelerated the collateral filter with parallel computing using the graphics processing unit (GPU) architecture. We adopted the compute unified device architecture (CUDA), an application programming interface for the GPU by NVIDIA, to hasten the computation. Subsequently, the optimal filter parameters were selected and the automation was achieved by artificial neural networks. Specifically, an artificial neural network system associated with image feature analysis was adopted to establish the automatic image restoration framework. The best feature combination was selected by the paired t-test and the sequential forward floating selection (SFFS) methods. RESULTS: Experimental results indicated that not only did the proposed automatic image restoration algorithm perform dramatically faster than the traditional collateral filter, but it also effectively removed the noise in a wide variety of brain MR images. A speed up gain of 34 was attained to process an MR image, which completed within 0.1 s. Representative illustrations of brain tumor images demonstrated the capability of identifying lesion boundaries, which outperformed many existing methods. CONCLUSIONS: We believe that our accelerated and automated restoration framework is promising for achieving robust filtering in many brain MR image restoration applications.",1
"Computer Vision Analysis of Intraoperative Video: Automated Recognition of Operative Steps in Laparoscopic Sleeve Gastrectomy. OBJECTIVE(S): To develop and assess AI algorithms to identify operative steps in laparoscopic sleeve gastrectomy (LSG). BACKGROUND: Computer vision, a form of artificial intelligence (AI), allows for quantitative analysis of video by computers for identification of objects and patterns, such as in autonomous driving. METHODS: Intraoperative video from LSG from an academic institution was annotated by 2 fellowship-trained, board-certified bariatric surgeons. Videos were segmented into the following steps: 1) port placement, 2) liver retraction, 3) liver biopsy, 4) gastrocolic ligament dissection, 5) stapling of the stomach, 6) bagging specimen, and 7) final inspection of staple line. Deep neural networks were used to analyze videos. Accuracy of operative step identification by the AI was determined by comparing to surgeon annotations. RESULTS: Eighty-eight cases of LSG were analyzed. A random 70% sample of these clips was used to train the AI and 30% to test the AI's performance. Mean concordance correlation coefficient for human annotators was 0.862, suggesting excellent agreement. Mean (+/-SD) accuracy of the AI in identifying operative steps in the test set was 82% +/- 4% with a maximum of 85.6%. CONCLUSIONS: AI can extract quantitative surgical data from video with 85.6% accuracy. This suggests operative video could be used as a quantitative data source for research in intraoperative clinical decision support, risk prediction, or outcomes studies.",1
"Proteasome-associated cysteine deubiquitinases are molecular targets of environmental optical brightener compounds. The levels of organic pollutants, such as optical brightener (OB) compounds, in the global environment have been increasing in recent years. The toxicological effects and signal transduction systems associated with OB toxicity have not been thoroughly studied. The ubiquitin-proteasome system (UPS) plays a crucial role in regulating multiple essential cellular processes, and proteasome-associated cysteine deubiquitinases (DUBs), ubiquitin C-terminal hydrolase L5 (UCHL5) and USP14, are two major regulators for (de)ubiquitination and stability of many important target proteins. Therefore, potential inhibition of UCHL5 and USP14 activities by some environmental chemicals might cause in vivo toxicity. In the current study we hypothesize that electrophilic OB compounds, such as 4,4′-diamino-2,2′-stilbenedisulfonic acid(DAST), fluorescent brightener 28 (FB-28) and FB-71, can interact with the catalytic triads (CYS, HIS, and ASP) of UCHL5 and USP14 and inhibit their enzymatic activities, leading to cell growth suppression. This hypothesis is supported by our findings presented in this study. Results from in silico computational docking and ubiquitin vinyl sulfone assay confirmed the UCHL5/USP14-inhibitory activities of these OB compounds that have potencies in an order of: FB-71 > FB-28 > DAST. Furthermore, inhibition of these two proteasomal DUBs by OBs resulted in cell growth inhibition and apoptosis induction in two human breast cancer cell models. In addition, we found that OB-mediated DUB inhibition triggers a feedback reaction in which expression of UCHL5 and USP14 proteins is increased to compromise the suppressed activities. Our study suggests that these commonly used OB compounds may target and inhibit proteasomal cysteine DUBs, which should contribute to their toxicological effects in vivo.",0
"Next-generation sequencing with comprehensive bioinformatics analysis facilitates somatic mosaic APC gene mutation detection in patients with familial adenomatous polyposis. Background: Familial adenomatous polyposis (FAP) is an autosomal dominant colorectal tumor characterized by numerous adenomatous colonic polyps that often lead to colon cancer. Although most patients with FAP harbored germline mutations in APC gene, it was recently recognized that patients with clinical FAP, but without detectable pathogenic mutations, could be associated with somatic mosaic APC mutation. Methods: We reanalyzed the nest-generation sequencing (NGS) gene panel testing results of patients who were diagnosed with FAP, but did not have APC mutations, at Yonsei Cancer Prevention Center between July 2016 and March 2018. We tested several variant calling algorithms to identify low level mosaic variants. In one patient with a low frequency APC mutation, NGS analysis was performed together with endoscopic biopsy. Variant calling tools HaplotypeCaller, MuTect2, VarScan2, and Pindel were used. We also used 3′-Modified Oligonucleotides (MEMO)-PCR or conventional PCR for confirmation. Results: Among 28 patients with clinical suspicion of FAP but no detectable pathogenic variants of colonic polyposis associated genes, somatic mosaic pathogenic variants were identified in seven patients. The variant allele frequency ranged from 0.3 to 7.7%. These variants were mostly detected through variant caller MuTect2 and Pindel, and were further confirmed using mutant enrichment with MEMO-PCR. Conclusions: The NGS with an adequate combination of bioinformatics tools is effective to detect low level somatic variants in a single assay. Because mosaic APC mutations are more frequent than previously thought, the presence of mosaic mutations must be considered when analyzing genetic tests of patients with FAP.",0
"Convolutional neural networks for automated fracture detection and localization on wrist radiographs. Purpose: To demonstrate the feasibility and performance of an object detection convolutional neural network (CNN) for fracture detection and localization on wrist radiographs. Materials and Methods: Institutional review board approval was obtained with waiver of consent for this retrospective study. A total of 7356 wrist radiographic studies were extracted from a hospital picture archiving and communication system. Radiologists annotated all radius and ulna fractures with bounding boxes. The dataset was split into training (90%) and validation (10%) sets and used to train fracture localization models for frontal and lateral images. Inception-ResNet Faster R-CNN architecture was implemented as a deep learning model. The models were tested on an unseen test set of 524 consecutive emergency department wrist radiographic studies with two radiologists in consensus as the reference standard. Per-fracture, per-image (ie, per-view), and per-study sensitivity and specificity were determined. Area under the receiver operating characteristic curve (AUC) analysis was performed. Results: The model detected and correctly localized 310 (91.2%) of 340 and 236 (96.3%) of 245 of all radius and ulna fractures on the frontal and lateral views, respectively. The per-image sensitivity, specificity, and AUC were 95.7% (95% confidence interval [CI]: 92.4%, 97.8%), 82.5% (95% CI: 77.4%, 86.8%), and 0.918 (95% CI: 0.894, 0.941), respectively, for the frontal view and 96.7% (95% CI: 93.6%, 98.6%), 86.4% (95% CI: 81.9%, 90.2%), and 0.933 (95% CI: 0.912, 0.954), respectively, for the lateral view. The per-study sensitivity, specificity, and AUC were 98.1% (95% CI: 95.6%, 99.4%), 72.9% (95% CI: 67.1%, 78.2%), and 0.895 (95% CI: 0.870, 0.920), respectively. Conclusion: The ability of an object detection CNN to detect and localize radius and ulna fractures on wrist radiographs with high sensitivity and specificity was demonstrated.",1
"Studies of the Anti-amnesic Effects and Mechanisms of Single and Combined Use of Donepezil and Ginkgo Ketoester Tablet on Scopolamine-Induced Memory Impairment in Mice. Ginkgo ketoester tablets (GT) and donepezil were a clinically used combination for the treatment of Alzheimer's disease (AD). The aim of the study was undertaken to investigate the antiamnesic effects of the two drugs alone and in combination through in vivo models of the Morris water maze along with in vitro antioxidants, acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE). The potential mechanisms were speculated by the activities of acetylcholine (ACh), AChE, superoxide dismutase (SOD), and malondialdehyde (MDA) and the protein expression of brain-derived neurotrophic factor (BDNF) and tyrosine protein kinase B (TrkB). The combination group showed a concentration-dependent inhibition of cholinesterase and antioxidation. As far as its mechanism was concerned, the combination of two drugs exerted excellent effects on oxidative stress, cholinergic pathway damage, and inactivation of the BDNF-TrkB signaling pathway. Additionally, to elucidate the binding mechanism of GT active ingredients into the structure of AChE, the results of molecular docking studies indicated that hydrogen and/or hydrophobic bonds might play an important role in their binding process. Thus, the combination of drugs could treat AD perfectly and further verify the scientific rationality of clinical medication.",0
"Identification of Factors Associated With Variation in US County-Level Obesity Prevalence Rates Using Epidemiologic vs Machine Learning Models. Importance: Obesity is a leading cause of high health care expenditures, disability, and premature mortality. Previous studies have documented geographic disparities in obesity prevalence. Objective: To identify county-level factors associated with obesity using traditional epidemiologic and machine learning methods. Design, Setting, and Participants: Cross-sectional study using linear regression models and machine learning models to evaluate the associations between county-level obesity and county-level demographic, socioeconomic, health care, and environmental factors from summarized statistical data extracted from the 2018 Robert Wood Johnson Foundation County Health Rankings and merged with US Census data from each of 3138 US counties. The explanatory power of the linear multivariate regression and the top performing machine learning model were compared using mean R2 measured in 30-fold cross validation. Exposures: County-level demographic factors (population; rural status; census region; and race/ethnicity, sex, and age composition), socioeconomic factors (median income, unemployment rate, and percentage of population with some college education), health care factors (rate of uninsured adults and primary care physicians), and environmental factors (access to healthy foods and access to exercise opportunities). Main Outcomes and Measures: County-level obesity prevalence in 2018, its association with each county-level factor, and the percentage of variation in county-level obesity prevalence explained by linear multivariate and gradient boosting machine regression measured with R2. Results: Among the 3138 counties studied, the mean (range) obesity prevalence was 31.5% (12.8%-47.8%). In multivariate regressions, demographic factors explained 44.9% of variation in obesity prevalence; socioeconomic factors, 33.0%; environmental factors, 15.5%; and health care factors, 9.1%. The county-level factors with the strongest association with obesity were census region, median household income, and percentage of population with some college education. R2 values of univariate regressions of obesity prevalence were 0.238 for census region, 0.218 for median household income, and 0.160 for percentage of population with some college education. Multivariate linear regression and gradient boosting machine regression (the best-performing machine learning model) of obesity prevalence using all county-level demographic, socioeconomic, health care, and environmental factors had R2 values of 0.58 and 0.66, respectively (P < .001). Conclusions and Relevance: Obesity prevalence varies significantly between counties. County-level demographic, socioeconomic, health care, and environmental factors explain the majority of variation in county-level obesity prevalence. Using machine learning models may explain significantly more of the variation in obesity prevalence..",1
"Guideline-Based Clinical Assessment Versus Procalcitonin-Guided Antibiotic Use in Pneumonia: A Pragmatic Randomized Trial. STUDY OBJECTIVE: Efforts to reduce unnecessary and unnecessarily long antibiotic treatment for community-acquired pneumonia have been attempted through use of procalcitonin and through guidelines based on serial clinical assessment. Our aim is to compare guideline-based clinical assessment- and procalcitonin algorithm-guided antibiotic use among patients with community-acquired pneumonia. METHODS: We performed a pragmatic, randomized, multicenter trial from November 2012 to April 2015 at 12 French hospitals. We included emergency department (ED) patients older than 18 years with community-acquired pneumonia. Patients were randomly assigned to either the procalcitonin-guided or clinical assessment group. In accordance with past studies, we hypothesized that serial clinical assessment would be superior to procalcitonin-guided care. The primary outcome was antibiotic duration, and secondary outcomes included rates of antibiotic duration less than or equal to 5 days, and clinical success and combined serious adverse outcomes at 30 days in the intention-to-treat population. RESULTS: Of 370 eligible patients, 285 (77%) were randomly assigned to either clinical assessment- (n=143) or procalcitonin-guided care (n=142). Median age was 67 years (range 18 to 93 years) and 40% of patients were deemed to have Pneumonia Severity Index class IV or V. Procalcitonin algorithm adherence was 76%. Antibiotic duration was not significantly different between clinical assessment- and procalcitonin-guided groups (median 9 versus 10 days, respectively). Clinical success rate was 92% in each group and serious adverse outcome rates were similar (15% versus 20%, respectively). CONCLUSION: Guideline-based serial clinical assessment did not reduce antibiotic exposure compared with procalcitonin-guided care among ED patients with community-acquired pneumonia. The strategies were similar in terms of duration of antibiotic use and clinical outcomes.",0
"Towards early detection of adverse drug reactions: combining pre-clinical drug structures and post-market safety reports. BACKGROUND: Adverse drug reaction (ADR) is a major burden for patients and healthcare industry. Early and accurate detection of potential ADRs can help to improve drug safety and reduce financial costs. Post-market spontaneous reports of ADRs remain a cornerstone of pharmacovigilance and a series of drug safety signal detection methods play an important role in providing drug safety insights. However, existing methods require sufficient case reports to generate signals, limiting their usages for newly approved drugs with few (or even no) reports. METHODS: In this study, we propose a label propagation framework to enhance drug safety signals by combining drug chemical structures with FDA Adverse Event Reporting System (FAERS). First, we compute original drug safety signals via common signal detection algorithms. Then, we construct a drug similarity network based on chemical structures. Finally, we generate enhanced drug safety signals by propagating original signals on the drug similarity network. Our proposed framework enriches post-market safety reports with pre-clinical drug similarity network, effectively alleviating issues of insufficient cases for newly approved drugs. RESULTS: We apply the label propagation framework to four popular signal detection algorithms (PRR, ROR, MGPS, BCPNN) and find that our proposed framework generates more accurate drug safety signals than the corresponding baselines. In addition, our framework identifies potential ADRs for newly approved drugs, thus paving the way for early detection of ADRs. CONCLUSIONS: The proposed label propagation framework combines pre-clinical drug structures with post-market safety reports, generates enhanced drug safety signals, and can potentially help to accurately detect ADRs ahead of time. AVAILABILITY: The source code for this paper is available at: https://github.com/ruoqi-liu/LP-SDA.",0
"End-to-End Differentiable Learning of Protein Structure. Predicting protein structure from sequence is a central challenge of biochemistry. Co-evolution methods show promise, but an explicit sequence-to-structure map remains elusive. Advances in deep learning that replace complex, human-designed pipelines with differentiable models optimized end to end suggest the potential benefits of similarly reformulating structure prediction. Here, we introduce an end-to-end differentiable model for protein structure learning. The model couples local and global protein structure via geometric units that optimize global geometry without violating local covalent chemistry. We test our model using two challenging tasks: predicting novel folds without co-evolutionary data and predicting known folds without structural templates. In the first task, the model achieves state-of-the-art accuracy, and in the second, it comes within 1–2 Å; competing methods using co-evolution and experimental templates have been refined over many years, and it is likely that the differentiable approach has substantial room for further improvement, with applications ranging from drug discovery to protein design.",0
"Automated detection and classification of thyroid nodules in ultrasound images using clinical-knowledge-guided convolutional neural networks. Accurate diagnosis of thyroid nodules using ultrasonography is a valuable but tough task even for experienced radiologists, considering both benign and malignant nodules have heterogeneous appearances. Computer-aided diagnosis (CAD) methods could potentially provide objective suggestions to assist radiologists. However, the performance of existing learning-based approaches is still limited, for direct application of general learning models often ignores critical domain knowledge related to the specific nodule diagnosis. In this study, we propose a novel deep-learning-based CAD system, guided by task-specific prior knowledge, for automated nodule detection and classification in ultrasound images. Our proposed CAD system consists of two stages. First, a multi-scale region-based detection network is designed to learn pyramidal features for detecting nodules at different feature scales. The region proposals are constrained by the prior knowledge about size and shape distributions of real nodules. Then, a multi-branch classification network is proposed to integrate multi-view diagnosis-oriented features, in which each network branch captures and enhances one specific group of characteristics that were generally used by radiologists. We evaluated and compared our method with the state-of-the-art CAD methods and experienced radiologists on two datasets, i.e. Dataset I and Dataset II. The detection and diagnostic accuracy on Dataset I were 97.5% and 97.1%, respectively. Besides, our CAD system also achieved better performance than experienced radiologists on Dataset II, with improvements of accuracy for 8%. The experimental results demonstrate that our proposed method is effective in the discrimination of thyroid nodules.",1
"Genome-wide de novo L1 Retrotransposition Connects Endonuclease Activity with Replication. L1 retrotransposon-derived sequences comprise approximately 17% of the human genome. Darwinian selective pressures alter L1 genomic distributions during evolution, confounding the ability to determine initial L1 integration preferences. Here, we generated high-confidence datasets of greater than 88,000 engineered L1 insertions in human cell lines that act as proxies for cells that accommodate retrotransposition in vivo. Comparing these insertions to a null model, in which L1 endonuclease activity is the sole determinant dictating L1 integration preferences, demonstrated that L1 insertions are not significantly enriched in genes, transcribed regions, or open chromatin. By comparison, we provide compelling evidence that the L1 endonuclease disproportionately cleaves predominant lagging strand DNA replication templates, while lagging strand 3'-hydroxyl groups may prime endonuclease-independent L1 retrotransposition in a Fanconi anemia cell line. Thus, acquisition of an endonuclease domain, in conjunction with the ability to integrate into replicating DNA, allowed L1 to become an autonomous, interspersed retrotransposon. The examination of de novo engineered L1 retrotransposition events in cultured human cells reveals that L1 endonuclease activity and DNA replication dictate L1 insertion preferences and promote its widespread integration throughout the human genome.",0
"A comparison of machine learning techniques for classification of HIV patients with antiretroviral therapy-induced mitochondrial toxicity from those without mitochondrial toxicity. BACKGROUND: Antiretroviral therapy (ART) has significantly reduced HIV-related morbidity and mortality. However, therapeutic benefit of ART is often limited by delayed drug-associated toxicity. Nucleoside reverse transcriptase inhibitors (NRTIs) are the backbone of ART regimens. NRTIs compete with endogenous deoxyribonucleotide triphosphates (dNTPs) in incorporation into elongating DNA chain resulting in their cytotoxic or antiviral effect. Thus, the efficacy of NRTIs could be affected by direct competition with endogenous dNTPs and/or feedback inhibition of their metabolic enzymes. In this paper, we assessed whether the levels of ribonucleotides (RN) and dNTP pool sizes can be used as biomarkers in distinguishing between HIV-infected patients with ART-induced mitochondrial toxicity and HIV-infected patients without toxicity. METHODS: We used data collected through a case-control study from 50 subjects. Cases were defined as HIV-infected individuals with clinical and/or laboratory evidence of mitochondrial toxicity. Each case was age, gender, and race matched with an HIV-positive without evidence of toxicity. We used a range of machine learning procedures to distinguish between patients with and without toxicity. Using resampling methods like Monte Carlo k-fold cross validation, we compared the accuracy of several machine learning algorithms applied to our data. We used the algorithm with highest classification accuracy rate in evaluating the diagnostic performance of 12 RN and 14 dNTP pool sizes as biomarkers of mitochondrial toxicity. RESULTS: We used eight classification algorithms to assess the diagnostic performance of RN and dNTP pool sizes distinguishing HIV patients with and without NRTI-associated mitochondrial toxicity. The algorithms resulted in cross-validated classification rates of 0.65-0.76 for dNTP and 0.72-0.83 for RN, following reduction of the dimensionality of the input data. The reduction of input variables improved the classification performance of the algorithms, with the most pronounced improvement for RN. Complex tree-based methods worked the best for both the deoxyribose dataset (Random Forest) and the ribose dataset (Classification Tree and AdaBoost), but it is worth noting that simple methods such as Linear Discriminant Analysis and Logistic Regression were very competitive in terms of classification performance. CONCLUSIONS: Our finding of changes in RN and dNTP pools in participants with mitochondrial toxicity validates the importance of dNTP pools in mitochondrial function. Hence, levels of RN and dNTP pools can be used as biomarkers of ART-induced mitochondrial toxicity.",1