bluuebunny/crossref_metadata_2025 huggingface dataset:
How Does Cataract Surgery Rate Affect Angle-closure Prevalence
Authors: Jin Guangming , Wang Lanhua , Scheetz Jane , Zhang Jian , He Mingguang
Published: October 2020

Précis:

A data simulation study suggests that prevalence of occludable angle will decrease when the cataract surgical rate increases in particularly when the surgery focuses on 70+ years old.

Purpose:

The purpose of this study was to estimate the effects of cataract surgical rates (CSR) on the prevalence of primary angle-closure glaucoma in the Chinese population.

Methods:

Participants aged 50 years and older from the Liwan Eye Study were included as the study sample. Occludable angle (OA) as a surrogate of primary angle-closure glaucoma was evaluated using static gonioscopy and anterior chamber depth was measured before dilation using A-mode ultrasound. Random sampling was used to generate 50 cohorts with a sample size of 200 for each predefined CSR at 2000, 4000, 6000, 8000, 10,000, 12,000, according to the multinomial distribution. The mean anterior chamber depth and OA rates of each cohort were calculated. Logistic function models of nonlinear least-squares estimation were used to predict the prevalence of OA.

Results:

Data of the right eye from 1280 participants were included. The prevalence of cataract surgery and OA was 2.27% and 11.3%, respectively. The projected prevalence of OA in the cohorts with CSR of 2000, 4000, 6000, 8000, 10,000 and 12,000 was 11.4% [95% confidence interval (CI), 10.8%-12.0%], 11.2% (95% CI, 10.6%-11.9%), 10.9% (95% CI, 10.3%-11.6%), 11.4% (95% CI, 10.8%-12.1%), 10.8% (95% CI, 10.2-11.4%), and 10.1% (95% CI, 9.46-10.7%), respectively. The OA rates decreased remarkably as CSR increased for those aged 70 years and older.

Conclusions:

Our study indicated that with CSR increased, the OA prevalence could decrease remarkably especially in the older population. It is advisable to perform cataract surgery at an appropriate time for patients in their late 60s to 70s with significant cataracts.
TTF-1 and c-MYC-defined Phenotypes of Large Cell Neuroendocrine Carcinoma and Delta-like Protein 3 Expression for Treatment Selection
Authors: Miyagawa-Hayashino Aya , Okada Satoru , Takeda-Miyata Naoko , Takashima Yasutoshi , Yamada Tadaaki , Takemura Yoshizumi , Uchino Junji , Inoue Masayoshi , Takayama Koichi , Konishi Eiichi
Published: October 2020

The standard treatment regimen has not yet been established for advanced pulmonary large cell neuroendocrine carcinoma (LCNEC) because of its rarity. LCNEC can be subdivided into 2 mutually exclusive molecular subgroups: STK11/KEAP1 and TP53 mutated with high neuroendocrine expression and transcriptional profile of ASCL1high/DLL3high/NOTCHlow (non–small cell lung carcinoma, NSCLC-like) or RB1 and TP53 mutated with reduced neuroendocrine markers and transcriptional pattern of ASCL1low/DLL3low/NOTCHhigh (small cell lung cancer, SCLC-like). Model-based clustering shows that SCLC has subdivided into 2 major proteomic subsets defined by either TTF-1high/c-MYClow or TTF-1low/c-MYChigh, which may correspond to 2 mutually exclusive molecular subgroups: NSCLC-like or SCLC-like, respectively. We herein investigated whether TTF-1 and c-MYC could be applied to LCNEC to identify distinct subsets immunohistochemically and assessed DLL3 expression in these subsets. The protein expression profile may be useful to select patients for potential efficacy of targeted therapies including aurora kinase inhibitors for MYC alterations or anti-DLL3 antibody-drug conjugates. TTF-1 and c-MYC expression was mutually exclusive in 25 of 27 (93%) cases; TTF-1+/c-MYC- in 10, TTF-1−/c-MYC+ in 15, and TTF-1+/c-MYC+ in 2. DLL3 expression was seen in 15 of 27 cases (56%). All 12 TTF-1+ LCNEC cases were positive for DLL3. Three of 15 (20%) TTF-1−/c-MYC+ cases showed DLL3 positivity. LCNEC could be separated into 2 subsets proteomically defined by TTF-1 and c-MYC expression, which may be suitable to guide treatment selection including aurora kinase inhibitors for c-MYC+ cases. TTF-1 positivity can serve as a surrogate marker for DLL3, but caution is necessary as 20% of TTF-1− cases showed DLL3 positivity.
Critical effect of Pol escape mutations associated with detrimental allele HLA-C∗15:05 on clinical outcome in HIV-1 subtype A/E infection
Authors: Murakoshi Hayato , Chikata Takayuki , Akahoshi Tomohiro , Zou Chengcheng , Borghan Mohamed Ali , Van Tran Giang , Nguyen Trung Vu , Van Nguyen Kinh , Kuse Nozomi , Takiguchi Masafumi
Published: October 2020

Objective:

The mechanism explaining the role of detrimental HLA alleles in HIV-1 infections has been investigated in very few studies. HLA-A∗29:01-B∗07:05-C∗15:05 is a detrimental haplotype in HIV-1 subtype A/E-infected Vietnamese individuals. The accumulation of mutations at Pol 653/657 is associated with a poor clinical outcome in these individuals. However, the detrimental HLA allele and the mechanism responsible for its detrimental effect remains unknown. Therefore, in this current study we identified the detrimental HLA allele and investigated the mechanism responsible for the detrimental effect.

Design and methods:

A T-cell epitope including Pol 653/657 and its HLA restriction were identified by using overlapping HIV-1 peptides and cell lines expressing a single HLA. The effect of the mutations on the T-cell recognition of HIV-1-infected cells was investigated by using target cells infected with the mutant viruses. The effect of these mutations on the clinical outcome was analyzed in 74 HLA-C∗15:05+ Vietnamese infected with the subtype A/E virus.

Results:

We identified HLA-C∗15:05-restricted SL9 epitope including Pol 653/657. PolS653A/T/L mutations within this epitope critically impaired the T-cell recognition of HIV-1-infected cells, indicating that these mutations had escaped from the T cells. T-cell responders infected with these mutants showed significantly lower CD4+ T-cell counts than those with the wild-type virus or Pol S653K/Q mutants, which are not associated with HLA-C∗15:05.

Conclusion:

The accumulation of Pol S653A/T/L escape mutants critically affected the control of HIV-1 by SL9-specific T cells and led to a poor clinical outcome in the subtype A/E-infected individuals having the detrimental HLA-C∗15:05 allele.
Effect of Truvada lawsuit advertising on preexposure prophylaxis attitudes and decisions among sexual and gender minority youth and young adults at risk for HIV
Authors: Serrano Pedro A. , Daubert Elizabeth , Munoz Alejandro , Hosek Sybil G. , French Audrey L.
Published: October 2020

Objective:

In 2019, US advocates reported misleading language regarding the safety of TDF/FTC (Truvada) used by lawsuit advertisements against Gilead Sciences. We sought to ascertain the reach and effects of the advertisements on preexposure prophylaxis (PrEP) opinions and decisions in a cohort of youth and young adults at-risk for HIV.

Design:

An online survey was administered to participants enrolled in Keeping it LITE, a prospective US cohort study of ethnically diverse, sexually active, cisgender and transgender persons ages 13–37.

Methods:

Quantitative data were analyzed using descriptive and inferential analysis in SAS, and qualitative data via thematic analysis.

Results:

Survey response rate was 51.3% (n = 1485). Mean age at baseline was 24. Previous PrEP use was reported by 43% of respondents and 32.7% reported PrEP use in the past 6 months. Almost half (48.7%) were aware of the lawsuit. Most of these participants (81.3%) reported the advertisements did not impact their PrEP use, but 13.2% decided to not to begin a Truvada-based PrEP regimen and 5.5% decided to stop taking Truvada due to the advertisements claims. Predictors of changing PrEP behavior were lower education and no previous PrEP use. The qualitative analysis revealed the advertisements increased skepticism about safety and benefit of Truvada PrEP and led to greater distrust of the pharmaceutical industry.

Conclusion:

The advertisements reached a large, diverse US audience. Disturbingly, 18.7% of PrEP candidates who were aware of the lawsuit attributed not initiating or cessation of a Truvada-based PrEP regimen to exposure to the Truvada lawsuit advertisements.
Quality and Safety in Health Care, Part LXXX
Authors: Harolds Jay A. , Miller Laura B.
Published: October 2020

The National Database for Nursing Quality Indicators is an important source of information used to benchmark nursing by unit category in multiple areas related to not only structure and process but also outcome. It also provides some information regarding best practices and the cost to achieve certain results. The Practice Environment Scale of the Nursing Work Index is a frequently used way to measure the professional practice environment of nurses and the relationship of the latter to quality, safety, and other outcomes.
An Incidental Uptake of 18F-Choline at PET/CT in Gastric Neuroendocrine Tumor
Authors: Evangelista Laura , Cassarino Gianluca , Capobianco Decio , Povolato Michele , Battista Serena , Rensi Marco
Published: October 2020

We present a case of neuroendocrine tumor localized in the gastric mucosa, incidentally detected by 18F-choline PET/CT in a 69-year-old man with prostate cancer. 18F-choline PET/CT scan showed an increased activity in the stomach, later diagnosed as a well-differentiated neuroendocrine tumor, at biopsy. A careful attention of reading 18F-choline PET/CT images should be made, in order to avoid the missing of potential concomitant neoplasia in patients with prostate cancer.
Pituitary Metastases Discovered by 18F-FDG PET/CT During Other Cancers Monitoring
Authors: Piciu Andra , Mester Alexandru , Piciu Doina
Published: October 2020

18F-FDG PET/CT might discover incidental pituitary lesions. We present the case of a 46-year-old woman with breast cancer metastasis in her pituitary. We analyzed 10,347 FDG PET/CT examinations from a tertiary center, finding 4 cases (0.038%) of pituitary metastatic disease from other cancers. We analyzed the differences between SUVmax in cases of physiological high uptake, primary tumor, and hypophysis metastases from other cancers in our database compared with the literature.
Maximum Uptake and Hypermetabolic Volume of 18F-FDOPA PET Estimate Molecular Status and Overall Survival in Low-Grade Gliomas
Authors: Tatekawa Hiroyuki , Yao Jingwen , Oughourlian Talia C. , Hagiwara Akifumi , Wang Chencai , Raymond Catalina , Lai Albert , Cloughesy Timothy F. , Nghiemphu Phioanh L. , Liau Linda M. , Salamon Noriko , Ellingson Benjamin M.
Published: October 2020

Purpose

We evaluated18F-FDOPA PET and MRI characteristics in association with the molecular status and overall survival (OS) in a large number of low-grade gliomas (LGGs).

Methods

Eighty-six patients who underwent18F-FDOPA PET and MRI and were diagnosed with new or recurrent LGGs were retrospectively evaluated with respect to their isocitrate dehydrogenase (IDH) and 1p19q status (10 IDH wild type, 57 mutant, 19 unknown; 1p19q status in IDH mutant: 20 noncodeleted, 37 codeleted). After segmentation of the hyperintense area on fluid-attenuated inversion recovery image (FLAIRROI), the following were calculated: normalized SUVmax (nSUVmax) of18F-FDOPA relative to the striatum,18F-FDOPA hypermetabolic volume (tumor-to-striatum ratios >1), FLAIRROIvolume, relative cerebral blood volume, and apparent diffusion coefficient within FLAIRROI. Receiver operating characteristic curve and Cox regression analyses were performed.

Results

PET and MRI metrics combined with age predicted the IDH mutation and 1p19q codeletion statuses with sensitivities of 73% and 76% and specificities of 100% and 94%, respectively. Significant correlations were found between OS and the IDH mutation status (hazard ratio [HR] = 4.939), nSUVmax (HR = 2.827),18F-FDOPA hypermetabolic volume (HR = 1.048), and FLAIRROIvolume (HR = 1.006). The nSUVmax (HR = 151.6) for newly diagnosed LGGs and the18F-FDOPA hypermetabolic volume (HR = 1.038) for recurrent LGGs demonstrated significant association with OS.

Conclusions

Combining18F-FDOPA PET and MRI with age proved useful for predicting the molecular status in patients with LGGs, whereas the nSUVmax and18F-FDOPA hypermetabolic volume may be useful for prognostication.
Prolonged (≥24 Hours) Normothermic (≥32 °C) Ex Vivo Organ Perfusion: Lessons From the Literature
Authors: Tchouta Lise N. , Alghanem Fares , Rojas-Pena Alvaro , Bartlett Robert H.
Published: October 2020

For 2 centuries, researchers have studied ex vivo perfusion intending to preserve the physiologic function of isolated organs. If it were indeed possible to maintain ex vivo organ viability for days, transplantation could become an elective operation with clinicians methodically surveilling and reconditioning allografts before surgery. To this day, experimental reports of successfully prolonged (≥24 hours) organ perfusion are rare and have not translated into clinical practice. To identify the crucial factors necessary for successful perfusion, this review summarizes the history of prolonged normothermic ex vivo organ perfusion. By examining successful techniques and protocols used, this review outlines the essential elements of successful perfusion, limitations of current perfusion systems, and areas where further research in preservation science is required.
Cognitive Event-Related Potentials–The P300 Wave Is a Prognostic Factor of Long-Term Disability Progression in Patients With Multiple Sclerosis
Authors: Szilasiová Jarmila , Rosenberger Jaroslav , Mikula Pavol , Vitková Marianna , Fedičová Miriam , Gdovinová Zuzana
Published: October 2020

Purpose:

Multiple sclerosis (MS) is a chronic disorder with a variable course. The aim of our study was to find out whether cognitive event-related potentials are prognostic for patient disability at the 15-year follow-up.

Methods:

In the observed cohort of patients with MS, we examined the event-related potentials at baseline (2003). Functional status (Expanded Disability Status Scale score) was then assessed 15 years later, and the prognostic model was developed using binary logistic regression analysis. The independent variables included demographic (age, sex, and education), clinical (disability in 2003), radiologic (MRI lesion load), and event-related potentials parameters. The prognostic accuracy of the proposed model was evaluated by calculating the area under the receiver-operating characteristics curve.

Results:

The study sample consisted of 85 patients with MS. The mean age was 35.5 (SD, 11.2) years, and the median disability score was 3.0 (1–7) in 2003 and 5.0 (1.5–9.5) in 2018. The significant prognostic factors of poor Expanded Disability Status Scale are higher baseline Expanded Disability Status Scale, longer MS duration, and prolonged P300 latency. The sensitivity and specificity of the cutoff at 5.0 for the disability score were 94% and 89%, respectively, with the area under the receiver-operating characteristics curve 0.94 (95% confidence interval, 0.889–0.984; P < 0.001).

Conclusions:

The results show that out of event-related potentials, the P300 wave latency is a prognostic of long-term disability progression in patients with MS.
What to do when everything happens at once: Analytic approaches to estimate the health effects of co-occurring social policies
Authors: Matthay Ellicott C. , Gottlieb Laura M. , Rehkopf David , Tan May Lynn , Vlahov David , Maria Glymour M.
Published: October 2020

Social policies have great potential to improve population health and reduce health disparities. Thus, increasing empirical research seeks to quantify the health effects of social policies by exploiting variation in the timing of policy changes across places. Multiple social policies are often adopted simultaneously or in close succession in the same locations, creating co-occurrence which must be handled analytically for valid inferences. Although this is a substantial methodological challenge for studies aiming to isolate social policy effects, limited prior work has systematically considered analytic solutions within a causal framework or assessed whether these solutions are being adopted. We designated seven analytic solutions to policy co-occurrence, including efforts to disentangle individual policy effects and efforts to estimate the combined effects of co-occurring policies. We leveraged an existing systematic review of social policies and health to evaluate how often policy co-occurrence is identified as a threat to validity and how often each analytic solution is applied in practice. Of the 55 studies, only 17 (31%) reported checking for any co-occurring policies, although 36 (67%) used at least one approach that helps address policy co-occurrence. The most common approaches were: adjusting for measures of co-occurring policies; defining the outcome on subpopulations likely to be affected by the policy of interest (but not other co-occurring policies); and selecting a less-correlated measure of policy exposure. As health research increasingly focuses on policy changes, we must systematically assess policy co-occurrence and apply analytic solutions to strengthen future studies on the health effects of social policies.
Fast and interpretable scRNA-seq data analysis
Authors: Çobanoğlu Murat Can
Published: October 2020

One of the key challenges in single-cell data analysis is the annotation of cells with their cell types. This task is divided into two different sub-tasks: identifying known cell types and identifying novel cell types. In the former case, we can benefit from being able to transfer annotations from bulk RNA-seq because there are many more types profiled with that more established technology. In the latter case, we would benefit from interpretable models that can describe the reasons for grouping a number of cells together. We propose that both of these problems can be solved by generative Bayesian Dirichlet-multinomial models. In the supervised learning context, we propose a generative Bayesian Dirichlet-multinomial classifier. We show that such a classifier can effectively transfer cell labels from bulk to single-cell RNA-sequencing data. We also show that alternative well-established machine learning models have difficulty with this transition, even if they are effective within the same regime (i.e. single cell to single cell). In the unsupervised learning context, we propose a Bayesian Dirichlet-multinomial mixture model. We show that the proposed model learns meaningful clusters where the automatically learned relationships between cell types and genes overlap with ground truth associations. Furthermore, there are no density or connectivity based clustering assumptions in this model, which differs with almost every approach in this field. Consequently the clustering results from the generative method can effectively represent nuanced differences among cells.

Contact

murat.cobanoglu@utsouthwestern.edu
Haisu: Hierarchical Supervised Nonlinear Dimensionality Reduction
Authors: VanHorn Kevin C. , Çobanoğlu Murat Can
Published: October 2020

We propose a novel strategy for incorporating hierarchical supervised label information into nonlinear dimensionality reduction techniques. Specifically, we extend t-SNE, UMAP, and PHATE to include known or predicted class labels and demonstrate the efficacy of our approach on multiple single-cell RNA sequencing datasets. Our approach, “Haisu,” is applicable across domains and methods of nonlinear dimensionality reduction. In general, the mathematical effect of Haisu can be summarized as a variable perturbation of the high dimensional space in which the original data is observed. We thereby preserve the core characteristics of the visualization method and only change the manifold to respect known or assumed class labels when provided. Our strategy is designed to aid in the discovery and understanding of underlying patterns in a dataset that is heavily influenced by parent-child relationships. We show that using our approach can also help in semi-supervised settings where labels are known for only some datapoints (for instance when only a fraction of the cells is labeled). In summary, Haisu extends existing popular visualization methods to enable a user to incorporate known, relevant relationships via a user-defined hierarchical distancing factor.

Availability

github.com/Cobanoglu-Lab/Haisu
Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks
Authors: Li Yang , Zhang Chengxin , Bell Eric W. , Zheng Wei , Zhou Xiaogen , Yu Dong-Jun , Zhang Yang
Published: October 2020

The topology of protein folds can be specified by the inter-residue contact-maps and accurate contact-map prediction can helpab initiostructure folding. We developed TripletRes to deduce protein contact-maps from discretized distance profiles by end-to-end training of deep residual neural-networks. Compared to previous approaches, the major advantage of TripletRes is in its ability to learn and directly fuse a triplet of coevolutionary matrices extracted from the whole-genome and metagenome databases and therefore minimize the information loss during the course of contact model training. TripletRes was tested on a large set of 245 non-homologous proteins from CASP and CAMEO experiments, and outperformed other state-of-the-art methods by at least 58.4% for the CASP 11&12 and 44.4% for the CAMEO targets in the top-Llong-range contact precision. On the 31 FM targets from the latest CASP13 challenge, TripletRes achieved the highest precision (71.6%) for the top-L/5 long-range contact predictions. These results demonstrate a novel efficient approach to extend the power of deep convolutional networks for high-accuracy medium- and long-range protein contact-map predictions starting from primary sequences, which are critical for constructing 3D structure of proteins that lack homologous templates in the PDB library.

Availability

The training and testing data, standalone package, and the online server for TripletRes are available athttps://zhanglab.ccmb.med.umich.edu/TripletRes/.

Author Summary

Ab initioprotein folding has been a major unsolved problem in computational biology for more than half a century. Recent community-wide Critical Assessment of Structure Prediction (CASP) experiments have witnessed exciting progress onab initiostructure prediction, which was mainly powered by the boosting of contact-map prediction as the latter can be used as constraints to guideab initiofolding simulations. In this work, we proposed a new open-source deep-learning architecture, TripletRes, built on the residual convolutional neural networks for high-accuracy contact prediction. The large-scale benchmark and blind test results demonstrate significant advancement of the proposed methods over other approaches in predicting medium- and long-range contact-maps that are critical for guiding protein folding simulations. Detailed data analyses showed that the major advantage of TripletRes lies in the unique protocol to fuse multiple evolutionary feature matrices which are directly extracted from whole-genome and metagenome databases and therefore minimize the information loss during the contact model training.
Lexical parafoveal previewing predicts reading speed
Authors: Pan Yali , Frisson Steven , Jensen Ole
Published: October 2020

While reading is an essential human skill, many of the neuronal mechanisms supporting reading are not well understood. In spite of the reduced visual acuity, parafoveal information plays a critical role in natural reading1; however, it is strongly debated whether words are previewed parafoveally at the lexical level2–9. This is a key dispute for competing models on reading and of great significance since lexical retrieval is important for guiding eye movements. We found evidence for parafoveal previewing at the lexical level by combining a novelrapid invisible frequency tagging(RIFT)10, 11approach with magnetoencephalography (MEG) and eye-tracking. In a silent reading task, target words of either low or high lexical frequency were tagged (flickered) subliminally at 60 Hz. The tagging response measured when fixating on the pre-target word reflected parafoveal previewing of the target word. We observed a stronger tagging response as early as 100 ms following fixations on pre-target words that were followed by target words of low compared with high lexical frequency. Moreover, the difference in tagging response with respect to target lexicality predicted individual reading speed. Our findings demonstrate that reading unfolds in the fovea and parafovea simultaneously to support fluent reading.
A statistical 3D model of the human cortical vasculature to compute the hemodynamic fingerprint of the BOLD fMRI signal
Authors: Báez-Yáñez Mario Gilberto , Siero Jeroen C.W. , Petridou Natalia
Published: October 2020

BOLD fMRI is a commonly used technique to map brain activity; nevertheless, BOLD fMRI is an indirect measurement of brain function triggered by neurometabolic and neurovascular coupling. Hence, the origin of the BOLD fMRI signal is quite complex, and the signal formation depends, among others, on the geometry of the cortical vasculature and the associated hemodynamic behavior. To characterize and quantify the hemodynamic contributions to the BOLD signal response in humans, it is necessary to adopt a computational model that resembles the human cortical vascular architecture and mimics realistic hemodynamic changes. To this end, we have developed a statistically defined 3D vascular model that resembles the human cortical vasculature. Using this model, we simulated hemodynamic changes triggered by a neuronal activation and local magnetic field disturbances created by the vascular topology and the blood oxygenation changes. The proposed model considers also the biophysical interactions and the intrinsic magnetic properties of the nearby tissue in order to compute adynamicBOLD fMRI signal response. This computational pipeline results in an integrated biophysical model that can provide a better insight on the understanding and quantification of the hemodynamic fingerprint of the BOLD fMRI signal evolution.
LABRAT reveals association of alternative polyadenylation with transcript localization, RNA binding protein expression, transcription speed, and cancer survival
Authors: Goering Raeann , Engel Krysta L. , Gillen Austin E. , Fong Nova , Bentley David L. , Taliaferro J. Matthew
Published: October 2020

The sequence content of the 3′ UTRs of many mRNA transcripts is regulated through alternative polyadenylation (APA). The study of this process using RNAseq data, though, has been historically challenging. To combat this problem, we developed LABRAT, an APA quantification method. LABRAT takes advantage of newly developed transcriptome quantification techniques to accurately determine relative APA site usage and how it varies across conditions. Using LABRAT, we found consistent relationships between gene-distal APA and subcellular RNA localization in multiple cell types. We also observed connections between transcription speed and APA site choice as well as tumor-specific transcriptome-wide shifts in APA in hundreds of patient-derived tumor samples that were associated with patient prognosis. We investigated the effects of APA on transcript expression and found a weak overall relationship, although many individual genes showed strong correlations between APA and expression. We interrogated the roles of 191 RNA-binding proteins in the regulation of APA, finding that dozens promote broad, directional shifts in relative APA isoform abundance bothin vitroand in patient-derived samples. Finally, we find that APA site shifts in the two classes of APA, tandem UTRs and alternative last exons, are strongly correlated across many contexts, suggesting that they are coregulated.
Single-Cell Analysis of Hyperthermic Intraperitoneal Chemotherapy Treated Tumors Reveals Distinct Cellular and Molecular Responses
Authors: Horowitz Max P. , Alali Zahraa , Alban Tyler , Hong Changjin , Esakov Emily L. , Hwang Tae Hyun , Lathia Justin D. , Michener Chad M. , DeBernardo Robert , Reizes Ofer
Published: October 2020

Hyperthermic intraperitoneal chemotherapy (HIPEC) has emerged as a clinical regimen that prolongs overall survival for patients with advanced Epithelial Ovarian Cancer (EOC). However, the mechanism of action of HIPEC remains poorly understood. To provide insights into the rapid changes that accompany HIPEC, tumors from five patients with high grade serous ovarian cancer were harvested from the omentum at time of debulking and after 90 minutes of HIPEC treatment. Specimens were rapidly dissociated into single cells and processed for single cell RNA-seq. Unbiased clustering identified 19 cell clusters that were annotated based on cellular transcriptome signatures to identify the epithelial, stromal, T and B immune cells, macrophages, and natural killer cell populations. Hallmark pathway analysis revealed heat shock, metabolic reprogramming, inflammatory, and EMT pathway enrichment in distinct cell populations upon HIPEC treatment. Collectively, our findings provide the foundation for mechanistic studies focused on how HIPEC orchestrates the ovarian cancer tissue response.
PlasForest: a homology-based random forest classifier for plasmid detection in genomic datasets
Authors: Pradier Léa , Tissot Tazzio , Fiston-Lavier Anna-Sophie , Bedhomme Stéphanie
Published: October 2020

Plasmids are mobile genetic elements that often carry accessory genes, and are vectors for horizontal transfer between bacterial genomes. The detection of plasmids in large sets of genomes is crucial to analyze their spread and quantify their role in bacteria adaptation and particularly in antibiotic resistance genes propagation. Several bioinformatics methods have been developed to detect plasmids. However, they suffer from low sensitivity (i.e., most plasmids remain undetected) or low precision (i.e., these methods identify chromosomes as plasmids), and are overall not adapted to identify plasmids in whole genomes that are not fully assembled (contigs and scaffolds). Here, we present PlasForest, a homology-based random forest classifier identifying bacterial plasmid sequences in unassembled genomes. This tool is based on the determination of homologies against a database of plasmid sequences, which allow a random forest classifier to discriminate plasmid contigs. Without knowing the taxonomical origin of the samples, PlasForest identifies contigs as plasmids or chromosomes with an accuracy of 98%. Notably, it can detect 96% of plasmid contigs over 50kb with 3.3% of false positives. PlasForest outperforms other currently available tools on test datasets by being both sensitive and precise. We implemented this tool in a user-friendly pipeline that can identify plasmids in large datasets in a reasonable amount of time.
Deep transcriptome profiling of multiple myeloma with quantitative measures using the SPECTRA approach
Authors: Waller Rosalie Griffin , Hanson Heidi A. , Avery Brian J. , Madsen Michael J. , Sborov Douglas W. , Camp Nicola J.
Published: October 2020

SPECTRA is a new data framework to describe variation in a transcriptome as a set of unsupervised quantitative variables. Spectra variables provide a deep dive into the transcriptome, representing both large and small sources of variance, and are ideal for modeling alongside other variables for any outcome of interest. Each spectrum can also be considered a phenotypic trait, providing new avenues for disease characterization or to explore disease risk. We applied the SPECTRA approach to multiple myeloma (MM), the second most common blood cancer. Using RNA sequencing from malignant CD138+ cells, we derived 39 spectra in 767 patients from the MMRF CoMMpass study. We included spectra in prediction models for clinical endpoints, compared to established expression-based risk scores, and used descriptive modeling to identify associations with patient characteristics. Spectra-based risk scores added predictive value beyond established clinical risk factors and other expression-based risk scores for overall survival, progression-free survival, and time to first-line treatment failure. Significant spectra in models may provide mechanistic insight via gene set enrichment based on their gene weights. Gene set enrichment in CD138+ spectrum S5, which was significant for all prognostic endpoints, indicated enrichment for genes in the unfolded protein response, a mechanism targeted by proteasome inhibitors, common first line agents in MM treatment. We also identified significant associations between CD138+ spectra and tumor cytogenetics, race, gender, and age at diagnosis. The SPECTRA approach provides measures of transcriptome variation to deeply profile tumors with greater flexibility to model clinical outcomes and characteristics.

AUTHOR SUMMARY

Complex diseases, including cancer, are highly heterogeneous, and large molecular datasets are increasingly part of describing an individual’s unique experience. Gene expression is particularly attractive because it captures genetic, epigenetic, and environmental consequences. Transcriptome studies are gaining momentum in genomic epidemiology, and the need to incorporate these data in multivariable models alongside other risk factors brings demands for new approaches. The SPECTRA approach is a new intrinsic quantitative data framework for transcriptomes. A tissue is described by a set of quantitative measures (or ‘spectra’ variables) to deeply profile gene expression in a tissue. Spectra variables are independent and offer flexibility for use in predictive or descriptive modeling. We applied the SPECTRA approach to multiple myeloma, the second most common blood cancer. A set of 39 spectra variables were derived to represent the myeloma tumors. Outcome modeling provided SPECTRA-based risk scores that added predictive value for clinical outcomes beyond established risk factors.
A Real-life Study of the Positive Response to DAA-based Therapies for Hepatitis C in Brazil
Authors: Vivaldini Simone Monzani , Abraão Ribeiro Rachel , Júnior Gláucio Mosimann , Tonini Karen Cristine , Pereira Gerson Fernando Mendes , de Araújo Wildo Navegantes
Published: October 2020

Treating hepatitis C virus (HCV) infections reduces overall mortality and the risk of multiple extrahepatic complications. Direct-acting antivirals (DAAs) are molecules that target specific non-structural proteins of the virus, resulting in the disruption of viral replication and infection. This report describes the activities undertaken to assess the cure of HCV in patients of different age groups, supported by the Viral Hepatitis National Program of the Unified Health System in Brazil.

Around 11,000 patients wereevaluated in an electronic retrospective cohort, which used therapeutic schemes with Sofosbuvir (SOF), Daclatasvir (DCV), Simeprevir (SMV), and an association of Ombitasvir, Veruprevir/Ritonavir and Dasabuvir (3D) with or without Ribavirin (RBV), with sustained virologic response (SVR) or viral cure after a 12-week treatment. We conducted logistic regressions to identify factors independently associated with positive response to DAA-based therapies.

Among evaluated patients, 57.1% were male; 48.3% identified as white; 78.3% were over 50 years old; 44.1% were from the Southeast region; 47.7% had genotype 1b; and 84.5% were treated for 12 weeks. The SVR rates with DAAs ranged from 87% to 100%. Genotypes 1 and 4 had a better response (96.3 to 100% of SVR) to therapy with DAAs, and genotypes 2 and 3 had SVR of 90.6% to 92.2%. Different treatment periods showed SVR with an average of 95.0% and 95.9% for 12 and 24 weeks, respectively. In the odds ratio assessment, we verified that females were half as likely (OR 0.5; CI 95% 0.4-0.6) to have a negative response to therapy, when compared to males, and that genotypes 2 and 3 were around twice more likely (OR 1.5-2.2; CI 95% 0.7-2.9; 1.2-3.6 and OR 2.7-2.8; CI 95% 2.0-3.8, respectively) to not have SVR after therapy when compared to genotype 1. Patients aged between 50 and 69 years old were 1.2 times (OR 1,2; CI95% 0,7-1,9) more likely to not have SVR when treated with DAAs, when compared to other age groups.

This study is the first of this magnitude to be held in a Latin-American country with high SVR results, supported by a free-of-charge universal and public health system. We confirm the high performance of DAA-based therapies which support the Brazilian public health policy decision of adopting them as an important part of the country’s strategy to eliminate HCV by 2030.
Plasma ACE2 activity is persistently elevated following SARS-CoV-2 infection: implications for COVID-19 pathogenesis and consequences
Authors: Patel Sheila K , Juno Jennifer A , Lee Wen Shi , Wragg Kathleen M. , Hogarth P. Mark , Kent Stephen J , Burrell Louise M
Published: October 2020

COVID-19 causes persistent endothelial inflammation, lung and cardiovascular complications. SARS-CoV-2 utilises the catalytic site of full-length membrane-bound angiotensin converting enzyme 2 (ACE2) for cell entry causing downregulation of tissue ACE2. We reported downregulation of cardiac ACE2 is associated with increased plasma ACE2 activity. In this prospective observational study in recovered COVID-19 patients, we hypothesised that SARS-CoV-2 infection would be associated with shedding of ACE2 from cell membranes and increased plasma ACE2 activity.

Methods

We measured plasma ACE2 catalytic activity using a validated, sensitive quenched fluorescent substrate-based assay in a cohort of Australians aged ≥18 years (n=66) who had recovered from mild, moderate or severe SARS-CoV-2 infection (positive result by PCR testing) and age and gender matched uninfected controls (n=70). Serial samples were available in 23 recovered SARS-CoV-2 patients.

Results

Plasma ACE2 activity at a median of 35 days post-infection [interquartile range 30-38 days] was 97-fold higher in recovered SARS-CoV-2 patients compared to controls (5.8 [2-11.3] vs. 0.06 [0.02-2.2] pmol/min/ml, p<0.0001). There was a significant difference in plasma ACE2 activity according to disease severity (p=0.033), with severe COVID-19 associated with higher ACE2 activity compared to mild disease (p=0.027). Men (n=39) who were SARS-CoV-2 positive had higher median plasma ACE2 levels compared to women (n=27) (p<0.0001). We next analysed whether an elevated plasma ACE2 activity level persisted following SARS-CoV-2 infection in subjects with blood samples at 63 [56-65] and 114 [111-125] days post infection. Plasma ACE2 activity remained persistently elevated in almost all subjects, with no significant differences between timepoints in post-hoc comparisons (p>0.05).

Discussion

This is the first description that plasma ACE2 activity is elevated after COVID-19 infection, and the first with longitudinal data indicating plasma ACE2 activity remains elevated out to a median of 114 days post-infection. Larger studies are now needed to determine if persistent elevated plasma ACE2 activity identifies people at risk of prolonged illness following COVID-19.
Applying Machine Learning to Increase Efficiency and Accuracy of Meta-Analytic Review
Authors: Gorelik Aaron J. , Gorelik Mark G. , Ridout Kathryn K. , Nimarko Akua F. , Peisch Virginia , Kuramkote Shamanth R. , Low Michelle , Pan Tracy , Singh Simirthi , Nrusimha Ananya , Singh Manpreet K.
Published: October 2020

The rapidly burgeoning quantity and complexity of publications makes curating and synthesizing information for meta-analyses ever more challenging. Meta-analyses require manual review of abstracts for study inclusion, which is time consuming, and variation among reviewer interpretation of inclusion/exclusion criteria for selecting a paper to be included in a review can impact a study’s outcome. To address these challenges in efficiency and accuracy, we propose and evaluate a machine learning approach to capture the definition of inclusion/exclusion criteria using a machine learning model to automate the selection process. We trained machine learning models on a manually reviewed dataset from a meta-analysis of resilience factors influencing psychopathology development. Then, the trained models were applied to an oncology dataset and evaluated for efficiency and accuracy against trained human reviewers. The results suggest that machine learning models can be used to automate the paper selection process and reduce the abstract review time while maintaining accuracy comparable to trained human reviewers. We propose a novel approach which uses model confidence to propose a subset of abstracts for manual review, thereby increasing the accuracy of the automated review while reducing the total number of abstracts requiring manual review. Furthermore, we delineate how leveraging these models more broadly may facilitate the sharing and synthesis of research expertise across disciplines.
jFuzzyMachine – An Open–source Fuzzy Logic–based Regulatory Inference Engine for High–throughput Biological Data
Authors: Aiyetan Paul
Published: October 2020

Elucidating mechanistic relationships between and among intracellular macromolecules is fundamental to understanding the molecular basis of normal and diseased processes. Here, we introducejFuzzyMachine– a fuzzy logic-based regulatory network inference engine for high-throughput biological data. We describe its design and implementation. We demonstrate its functions on a sampled expression profile of the vorinostat-resistant HCT116 cell line. We comparedjFuzzyMachine’s inferred regulatory network to that inferred by theARACNe(an Algorithm for the Reconstruction of Gene Regulatory Networks) tool. Potentially more sensitive,jFuzzyMachineshowed a slight increase in identified regulatory edges compared toARACNe. A significant overlap was also observed in the identified edges between the two inference methods. Over 70 percent of edges identified byARACNewere identified byjFuzzyMachine. Beyond identifying edges,jFuzzyMachineshows direction of interactions, including bidirectional interactions – specifying regulatory inputs and outputs of inferred relationships.jFuzzyMachineaddresses an apparent lack of freely available community tool implementing a fuzzy logic regulatory network inference method – mitigating a limitation to applying and extending benefits of the fuzzy inference system to understanding biological data.jFuzzyMachine’s source codes and precompiled binaries are freely available at the Github repository locations:

https://github.com/paiyetan/jfuzzymachineand

https://github.com/paiyetan/jfuzzymachine/releases/tag/v1.7.21.
CancerVar: an Artificial Intelligence empowered platform for clinical interpretation of somatic mutations in cancer
Authors: Li Quan , Ren Zilin , Cao Kajia , Li Marilyn M. , Wang Kai , Zhou Yunyun
Published: October 2020

Several knowledgebases, such as CIViC and OncoKB, have been manually curated to support clinical interpretations of a limited number of “hotspot” somatic mutations in cancer, yet discrepancies or even conflicting interpretations have been observed among these knowledgebases. Additionally, while these knowledgebases have been extremely useful, they typically cannot interpret novel mutations, which may also have functional and clinical impacts in cancer. To address these challenges, we developed an automated interpretation tool called CancerVar (Cancer Variants interpretation) to score more than 12.9 million somatic mutations and classify them into four tiers: strong clinical significance, potential clinical significance, uncertain clinical significance, and benign/likely benign, based on the AMP/ASCO/CAP 2017 guideline. Considering that the AMP/ASCO/CAP rule-based scoring system may have inherent limitations, such as lack of a clear guidance on weighing different pieces of functional evidence or unclear definition for certain clinical evidence, it may cause misinterpretation for certain variants that have functional impacts but no proven clinical significance. To address this issue, we further introduced a deep learning-based scoring system to predict oncogenicity of mutations by semi-supervised generative adversarial network (SGAN) method using both functional and clinical evidence. We trained and validated the SGAN model on 5,234 somatic mutations from an in-house database of clinical reports on cancer patients, and achieved a good performance when testing on 6,226 variants that were curated by us through literature search. We also compared the prediction with several independent datasets and showed great utility in classifying variants with previously unknown interpretations. CancerVar is also incorporated into a web server that can generate automated texts with summarized descriptive interpretations, such as diagnostic, prognostic, targeted drug responses and clinical trial information for many hotspot mutations. In summary, CancerVar can facilitate clinical interpretation and hypothesis generation for somatic mutations, and greatly reduce manual workload for retrieving relevant evidence and implementing existing guidelines.
Targeting hypoxic habitats with hypoxia pro-drug evofosfamide in preclinical models of sarcoma
Authors: Jardim-Perassi Bruna V. , Mu Wei , Huang Suning , Tomaszewski Michal R. , Poleszczuk Jan , Abdalah Mahmoud A. , Budzevich Mikalai M. , Dominguez-Viqueira William , Reed Damon R. , Bui Marilyn M. , Johnson Joseph O. , Martinez Gary V. , Gillies Robert J.
Published: October 2020

Hypoxic regions (habitats) within tumors are heterogeneously distributed and can be widely variant. Hypoxic habitats are generally pan-therapy resistant. For this reason, hypoxia-activated prodrugs (HAPs) have been developed to target these resistant volumes. The HAP evofosfamide (TH-302) has shown promise in preclinical and early clinical trials of sarcoma. However, in a phase III clinical trial, TH-302 did not improve survival in combination with doxorubicin (dox), most likely due to a lack of patient stratification based on hypoxic status. Herein, our goal was to develop deep-learning (DL) models to identify hypoxic habitats, using multiparametric (mp) MRI and co-registered histology, and to non-invasively monitor response to TH-302 in a patient-derived xenograft (PDX) of rhabdomyosarcoma and a syngeneic model of fibrosarcoma (RIF-1). A DL convolutional neural network showed strong correlations (>0.81) between the true hypoxic portion in histology and the predicted hypoxic portion in multiparametric MRI. TH-302 monotherapy or in combination with Dox delayed tumor growth and increased survival in the hypoxic PDX model (p<0.05), but not in the RIF-1 model, which had lower volume of hypoxic habitats. Control studies showed that RIF-1 resistance was due to hypoxia and not to other causes. Notably, PDX tumors developed resistance to TH-302 under prolonged treatment. In conclusion, response to TH-302 can be attributed to differences in hypoxia status prior therapy. Development of non-invasive MR imaging to assess hypoxia is crucial in determining the effectiveness of TH-302 therapy and to follow response. In further studies, our approach can be used to better plan therapeutic schedules to avoid resistance.

One Sentence Summary

Development of non-invasive MR imaging to assess hypoxia is crucial in determining the effectiveness of TH-302 therapy and to follow response.