bluuebunny/crossref_metadata_2025_split huggingface dataset:
Machine learning in the identification of prognostic DNA methylation biomarkers among patients with cancer: a systematic review of epigenome-wide studies
Authors: Yuan Tanwei , Edelmann Dominic , Fan Ziwen , Alwers Elizabeth , Kather Jakob Nikolas , Brenner Hermann , Hoffmeister Michael 
Published: September 2022

SummaryBackgroundDNA methylation biomarkers have great potential in improving prognostic classification systems for patients with cancer. Machine learning (ML)-based analytic techniques might help overcome the challenges of analyzing high-dimensional data in relatively small sample sizes. This systematic review summarizes the current use of ML-based methods in epigenome-wide studies for the identification of DNA methylation signatures associated with cancer prognosis.MethodsWe searched three electronic databases including PubMed, EMBASE, and Web of Science for articles published until 8 June 2022. ML-based methods and workflows used to identify DNA methylation signatures associated with cancer prognosis were extracted and summarized. Two authors independently assessed the methodological quality of included studies by a seven-item checklist adapted from relevant guidelines.ResultsSeventy-six studies were included in this review. Three major types of ML-based workflows were identified: 1) unsupervised clustering, 2) supervised feature selection, and 3) deep learning-based feature transformation. For the three workflows, the most frequently used ML techniques were consensus clustering, least absolute shrinkage and selection operator (LASSO), and autoencoder, respectively. The systematic review revealed that the performance of these approaches has not been adequately evaluated yet and that methodological and reporting flaws were common in the identified studies using ML techniques.ConclusionsThere is great heterogeneity in ML-based methodological strategies used by epigenome-wide studies to identify DNA methylation markers associated with cancer prognosis. Benchmarking studies are needed to compare the relative performance of various approaches for specific cancer types. Adherence to relevant methodological and reporting guidelines is urgently needed.
Factors associated with viral load suppression and indicators of stigma among people living with HIV in Dar es Salaam tertiary hospitals, Tanzania
Authors: Kilapilo Mary Spicar , Mosha Idda Hubert , Bwire George Msema , Sambayi Godfrey Leonard , Sangeda Raphael Zozimus , Killewo Japhet 
Published: September 2022

AbstractPerception of stigma can contribute to virological failure among people living with HIV (PLHIV). This study was conducted to assess the effect of stigmatization and self-discrimination on viral load suppression among PLHIV at tertiary hospitals in Dar es Salaam, Tanzania. This was a hospital-based cross-sectional study conducted in Temeke Regional Referral Hospital (RRH) and Amana RRH at the Care and Treatment Clinic (CTC) between July and August 2020 using a structured questionnaire with open and close-ended questions. Factors for stigmatization and viral load suppression were compared using the Chi-square test, while factors for viral load suppression were analyzed using multinomial logistic regression analysis. Altogether, 406 PLHIV participated, with most female respondents, 298 (73.2%). The majority (50%) were aged between 25 and 44 years, whereas 171 (42.5%) respondents were married. Most of the participants, 382 (94.6%), were on a dolutegravir-based regimen, with the majority, 215 (52.8%), having a refilling interval of three months. The majority of the respondents, 379 (93.1%), disclosed their status. Most participants, 355 (87.4%), preferred having a separate HIV clinic, while 130 (32.1%) participants were not ready to be attended by the health care workers (HCWs) familiar to them. Male patients were 60% less likely to suppress their viral load as compared to female patients (adjusted odds ratio [aOR]: 0.4, 95% confidence interval [95%]: 0.19 – 0.77, p = 0.007). Refill interval was significantly associated with viral load suppression. For example, patients with a one-month refill interval had odds of 0.01 (95% CI: 0.003-0.42, p < 0.0001) compared to six-month refill intervals. Stigmatization elements appeared to influence the viral load suppression among PLHIV. Factors such as gender and refill time interval were significantly associated with viral load suppression among HIV patients in the Dar es Salaam region.
Performance Characteristics of Six Immunoglobulin M (IgM) ELISA Assays Used for Laboratory Confirmation of Measles
Authors: Sowers Sun B. , Anthony Kiana , Mercader Sara , Colley Heather , Crooke Stephen N. , Rota Paul A. , Latner Donald R. , Hickman Carole J. 
Published: September 2022

AbstractLaboratory confirmation of infection is an essential component of measles surveillance. Detection of measles specific IgM in serum by enzyme linked immunosorbent assay (ELISA) is the most used method for confirming measles infection. ELISA formats vary as does the sensitivity and specificity of each assay. Specimens collected within 3 days of rash onset can yield a false negative result, which can delay confirmation of measles cases. Interfering substances can yield a false positive result, leading to unnecessary public health interventions. The IgM capture assay developed at the Centers for Disease Control (CDC) was compared against 5 commercially available ELISA kits for the ability to detect measles virus-specific IgM in a panel of 90 well-characterized specimens. Serum samples were tested in triplicate using each commercial kit as recommended by the manufacturer. Using the CDC measles IgM capture assay as the reference test; sensitivity and specificity for the commercial kits ranged from 50 to 83% and 86.9 to 98%, respectively. Discrepant results were observed for samples tested with all five commercial kits and ranged from 13.8 to 28.8% of the specimens tested. False positive results occurred in 2.0 to 13.1% of sera while negative results were observed in 16.7 to 50% of sera that were positive by the CDC measles IgM capture assay. Evaluation and interpretation of measles IgM serologic results can be complex, particularly in measles elimination settings. The performance characteristics of a measles IgM assay should be carefully considered when selecting an assay to achieve high quality measles surveillance.
Fine Motor Skill Decline after Brain RT – A Multivariate Normal Tissue Complication Probability Study of a Prospective Trial
Authors: Connor Michael , Salans Mia , Karunamuni Roshan , Unnikrishnan Soumya , Huynh-Le Minh-Phuong , Tibbs Michelle , Qian Alexander , Reyes Anny , Stasenko Alena , McDonald Carrie , Moiseenko Vitali , El-Naqa Issam , Hattangadi-Gluth Jona 
Published: September 2022

AbstractPurposeBrain radiotherapy can impair fine motor skills (FMS). FMS are essential for activities of daily living, enabling hand-eye coordination for manipulative movements. We developed normal tissue complication probability (NTCP) models for decline in FMS after fractionated brain RT.MethodsOn a prospective trial, 44 primary brain tumor patients received fractioned RT, underwent high-resolution volumetric MRI and diffusion tensor imaging, and comprehensive FMS assessments (Delis-Kaplan Executive Function System Trail Making Test Motor Speed [DKEFS-MS], and Grooved Pegboard Dominant/Non-Dominant Hands [PDH/PNDH]) at baseline and 6 months post-RT. Regions of interest subserving motor function (including cortex, superficial white matter, thalamus, basal ganglia, and white matter tracts) were autosegmented using validated methods and manually verified. Dosimetric and clinical variables were included in multivariate NTCP models, using automated bootstrapped logistic regression, least absolute shrinkage and selection operator (LASSO) logistic regression, and random forests with nested cross-validation.ResultsHalf of patients showed decline on PNDH, 17 of 42 (40.4%) on PDH, and 11 of 44 (25%) on DKEFS-TM. Automated bootstrapped logistic regression selected a one-term model including maximum dose to dominant postcentral white matter. LASSO selected this term and steroid use. The top five variables in random forest were all dosimetric: mean and maximum dose to dominant corticospinal tract; maximum dose to dominant thalamus; mean dose to dominant caudate; maximum dose to dominant postcentral white matter. This technique performed best with AUC 0.69 (95% CI 0.68 – 0.70) on nested cross-validation.ConclusionWe present the first NTCP models for FMS impairment after brain RT. Dose to several supratentorial motor-associated ROIs correlated with decline in dominant hand fine motor dexterity in primary brain tumor patients, outperforming clinical variables. These data can guide prospective fine motor-sparing strategies for brain RT.
Principled distillation of multidimensional UK Biobank data reveals insights into the correlated human phenome
Authors: Carey Caitlin E. , Shafee Rebecca , Elliott Amanda , Palmer Duncan S. , Compitello John , Kanai Masahiro , Abbott Liam , Schultz Patrick , Karczewski Konrad J. , Bryant Samuel C. , Cusick Caroline M. , Churchhouse Claire , Howrigan Daniel P. , King Daniel , Smith George Davey , Wedow Robbee , Neale Benjamin M. , Walters Raymond K. , Robinson Elise B. 
Published: September 2022

AbstractBroad yet detailed data collected in biobanks captures variation reflective of human health and behavior, but insights are hard to extract given their complexity and scale. In the largest factor analysis to date, we distill hundreds of medical record codes, physical assays, and survey items from UK Biobank into 35 understandable latent constructs. The identified factors recapitulate known disease classifications, highlight the relevance of psychiatric constructs, improve measurement of health-related behavior, and disentangle elements of socioeconomic status. We demonstrate the power of this principled data reduction approach to clarify genetic signal, enhance discovery, and identify associations between underlying phenotypic structure and health outcomes such as mortality. We emphasize the importance of considering the interwoven nature of the human phenome when evaluating large-scale patterns relevant to public health.
Segregation of dynamic resting-state reward, default mode and attentional networks after remitted patients transition into a recurrent depressive episode
Authors: Martinez Sonsoles Alonso , Tyborowska Anna , Ikani Nessa , Mocking Roel J. , Figueroa Caroline A. , Schene Aart H. , Deco Gustavo , Kringelbach Morten L. , Cabral Joana , Ruhé Henricus G. 
Published: September 2022

AbstractIntroductionRecurrence in major depression disorder (MDD) is common, but neurobiological models capturing vulnerability for recurrences are scarce. Disturbances in multiple resting-state networks have been linked to MDD, but most approaches focus on stable (vs. dynamic) network characteristics. We investigated how the brain’s dynamical repertoire changes after patients transition from remission to recurrence of a new depressive episode.MethodsSixty drug-free, MDD-patients with ≥2 episodes underwent a baseline resting-state fMRI scan when in remission. Over 30-months follow-up, 11 patients with a recurrence and 17 matched-remitted MDD-patients without a recurrence underwent a second fMRI scan. Recurrent patterns of functional connectivity were characterized by applying leading eigenvector dynamics analysis (LEiDA). Differences between baseline and follow-up were identified for the 11 non-remitted patients, while data from the 17 matched-remitted patients was used as a validation dataset.ResultsAfter the transition into a depressive state, the reward and a visuo-attentional networks were detected significantly more often, whereas default mode network activity was found to have a longer duration. Additionally, the fMRI signal in the areas underlying the reward network were significantly less synchronized with the rest of the brain after recurrence (compared to a state of remission). These changes were not observed in the matched-remitted patients who were scanned twice while in remission.ConclusionThese findings characterize the changes that are specifically associated with the transition from remission to recurrence and provide first evidence of increased segregation in the brain’s dynamical repertoire when a recurrent depressive episode occurs.
Default Mode Network Hypoalignment of Function to Structure Correlates with Depression and Rumination
Authors: Thomas Paul J. , Leow Alex , Klumpp Heide , Phan K. Luan , Ajilore Olusola 
Published: September 2022

ABSTRACTRecent studies have begun to examine the extent to which signals in the brain correspond to the underlying white matter structure by using tools from the field of graph signal processing to quantify brain function ‘alignment’ to brain network topology. Here, we apply this framework for the first time towards a transdiagnostic cohort of internalizing psychopathologies, including mood and anxiety disorders, to uncover how such alignment within the default mode network (DMN) is related to depression and rumination symptoms. We found that signal alignment within the posterior DMN is greater in IP patients than healthy controls and is anticorrelated with baseline depression and rumination scales. Signal alignment within the posterior DMN was also found to correlate with the ratio of total within-DMN to extra-DMN functional connectivity for these regions. These findings are consistent with previous literature regarding pathologic promiscuity of posterior DMN connectivity and provide the first GSP-based analyses in a transdiagnostic IP cohort.
Effects of nasogastric tube on oral microbiome among long-term care patients
Authors: Wang Ding-Han , Tsai Fa-Tzu , Tu Hsi-Feng , Yang Cheng-Chieh , Hsu Ming-Lun , Huang Lin-Jack , Lin Chiu-Tzu , Hsu Wun-Eng , Lin Yu-Cheng 
Published: September 2022

AbstractDysbiosis of oral microbiome causes chronic diseases including dental caries and periodontitis, which frequently affects elderly, frail patients receiving long-term care. Severely disabled patients may require nutritional supply via nasogastric (NG) tube, which impacts patients’ oral condition and possibly microbial composition. However, little is known about the effect of NG tube on oral microbes and its potential ramification. Here, by using 16S rRNA next-generation sequencing, we characterized the tongue microbiome of 27 patients fed with NG tubes and 26 others fed orally. The microbial compositions of NG-tube and oral-feeding patients were substantially different, with more Gram-negative aerobes enriched in the presence of NG tube. Specifically, NG-tube patients presented more opportunistic pathogens like Corynebacterium and Pseudomonas associated with pneumonia, and lower levels of commensal Streptococcus and Veillonella. Together, we present a systematic, high-throughput profiling of oral microbiome with regards to NG tube indwelling, providing empirical evidence for better clinical practice.ImportanceLong-term use of NG tubes on elderly patients often leads to poor oral hygiene and chronic infectious diseases, e.g. periodontitis and tooth decay. More importantly, because patients fed with NG tubes usually have swallowing dysfunctions, they are more likely to suffer from aspiration pneumonia, a life-threatening lung infection caused by inhalation of oral bacteria. Together, clinical implications of chronic NG-tube indwelling are significantly related to oral microbes. Understanding the effects of NG tubes on oral microbiome would generally inform how clinical care should be given, particularly antimicrobial therapy.
A living biobank of canine mammary tumor organoids as a comparative model for human breast cancer
Authors: Inglebert Marine , Dettwiler Martina , Hahn Kerstin , Letko Anna , Drögemüller Cord , Doench John , Brown Adam , Memari Yasin , Davies Helen , Degasperi Andrea , Nik-Zainal Serena , Rottenberg Sven 
Published: September 2022

AbstractMammary tumors in dogs hold great potential as naturally occurring breast cancer models in translational oncology, as they share the same environmental risk factors, key histological features, hormone receptor expression patterns, prognostic factors, and genetic characteristics as their human counterparts. We aimed to develop in vitro tools that allow functional analysis of canine mammary tumors (CMT), as we have a poor understanding of the underlying biology that drives the growth of these heterogeneous tumors. We established the long-term culture of 24 organoid lines from 16 patients, including organoids derived from normal mammary epithelium or benign lesions. CMT organoids recapitulated key morphological and immunohistological features of the primary tissue from which they were derived, including hormone receptor status. Furthermore, genetic characteristics (driver gene mutations, DNA copy number variations, and single-nucleotide variants) were conserved within tumororganoid pairs. We show how CMT organoids are a suitable model for in vitro drug assays and can be used to investigate whether specific mutations predict therapy outcomes. In addition, we could genetically modify the CMT organoids and use them to perform pooled CRISPR/Cas9 screening, where library representation was accurately maintained. In summary, we present a robust 3D in vitro preclinical model that can be used in translational research, where organoids from normal, benign as well as malignant mammary tissues can be propagated from the same patient to study tumorigenesis.
Developmental Basis of SHH Medulloblastoma Heterogeneity
Authors: Gold Maxwell P. , Ong Winnie , Masteller Andrew M. , Galindo Julie Anne , Park Noel R. , Saurez Raul A. , Vladoiu Maria C. , Donovan Laura K. , Walker Adam D. , Benetatos Joseph , Garzia Livia , Wechsler-Reya Robert J. , Mesirov Jill P. , Korshunov Andrey , Pomeroy Scott L. , Davidson Shawn M. , Cotter Jennifer A. , Taylor Michael D. , Fraenkel Ernest 
Published: September 2022

AbstractMedulloblastoma (MB) is one of the most common malignant pediatric brain tumors. The sonic hedgehog (SHH) subtype accounts for 30% of MB cases and likely arises from mutations in granule cell precursors (GCPs), neuronal progenitors of the cerebellar cortex that differentiate into granule neurons. SHH MB is extremely heterogeneous, but it is unknown whether this heterogeneity relates to the tumors’ developmental origins. To investigate this question, we performed single-nucleus RNA-Sequencing on seven highly differentiated SHH MB with extensively nodular histology and observed malignant cells resembling each stage of granule neuron development. Using novel computational approaches, we connected these results to published datasets and found that established molecular subtypes of SHH MB are enriched for specific developmental cell types. Additionally, some genomic copy number variations are associated with certain developmental stages, and we observed distinct metabolic and histological profiles for tumors containing cells resembling late-stage granule neurons. This work details computational and experimental approaches that can be repurposed for analysis of tumor cell differentiation in other cancers.
Modelling perception as a hierarchical competition differentiates imagined, veridical, and hallucinated percepts
Authors: Sulfaro Alexander A , Robinson Amanda K , Carlson Thomas A 
Published: September 2022

AbstractMental imagery is a process by which thoughts become experienced with sensory characteristics. Yet, it is not clear why mental images appear diminished compared to veridical images, nor how mental images are phenomenologically distinct from hallucinations, another type of non-veridical sensory experience. Current evidence suggests that imagination and veridical perception share neural resources. If so, we argue that considering how neural representations of externally-generated stimuli (i.e. sensory input) and internally-generated stimuli (i.e. thoughts) might interfere with one another can sufficiently differentiate veridical, imaginary, and hallucinatory perception. We here use a simple computational model of a serially-connected, hierarchical network with bidirectional information flow to emulate the primate visual system. We show that modelling even first-approximations of neural competition can more coherently explain imagery phenomenology than non-competitive models. Our simulations predict that, without competing sensory input, imagined stimuli should ubiquitously dominate hierarchical representations. However, with competition, imagination should dominate high-level representations but largely fail to outcompete sensory inputs at lower processing levels. To interpret our findings, we assume low-level stimulus information (e.g. in early visual cortex) contributes most to the sensory aspects of perceptual experience, while high-level stimulus information (e.g. towards temporal regions) contributes most to its abstract aspects. Our findings therefore suggest that ongoing bottom-up inputs during waking life may prevent imagination from overriding veridical sensory experience. In contrast, internally-generated stimuli may be hallucinated when sensory input is dampened or eradicated. Our approach can explain individual differences in imagery, along with aspects of daydreaming, hallucinations, and non-visual mental imagery.
LncDLSM: Identification of Long Non-coding RNAs with Deep Learning-based Sequence Model
Authors: Wang Ying , Zhao Pengfei , Du Hongkai , Cao Yingxin , Peng Qinke , Fu Laiyi 
Published: September 2022

AbstractLong non-coding RNAs (LncRNAs) serve a vital role in regulating gene expressions and other biological processes. Differentiation of lncRNAs from protein-coding transcripts helps researchers dig into the mechanism of lncRNA formation and its downstream regulations related to various diseases. Previous works have been proposed to identify lncRNAs, including traditional bio-sequencing and machine learning approaches. Considering the tedious work of biological characteristic-based feature extraction procedures and inevitable artifacts during bio-sequencing processes, those lncRNA detection methods are not always satisfactory. Hence, in this work, we presented lncDLSM, a deep learning-based framework differentiating lncRNA from other protein-coding transcripts without dependencies on prior biological knowledge. lncDLSM is a helpful tool for identifying lncRNAs compared with other biological feature-based machine learning methods and can be applied to other species by transfer learning achieving satisfactory results. Further experiments showed that different species display distinct boundaries among distributions corresponding to the homology and the specificity among species, respectively. An online web server is provided to the community for easy use and efficient identification of lncRNA, available athttp://39.106.16.168/lncDLSM.