bluuebunny/crossref_metadata_2025_split huggingface dataset:
AMELIE 3: Fully Automated Mendelian Patient Reanalysis at Under 1 Alert per Patient per Year
Authors: Birgmeier Johannes , Steinberg Ethan , Bodle Ethan E. , Deisseroth Cole A. , Jagadeesh Karthik A. , Kohler Jennefer N. , Bonner Devon , Marwaha Shruti , Martinez-Agosto Julian A. , Nelson Stan , Palmer Christina G. , Cogan Joy D. , Hamid Rizwan , Stoler Joan M. , Krier Joel B. , Rosenfeld Jill A. , Moretti Paolo , Adams David R. , Shashi Vandana , Worthey Elizabeth A. , Eng Christine M. , Ashley Euan A. , Wheeler Matthew T. ,   Undiagnosed Diseases Network, Stenson Peter D. , Cooper David N. , Bernstein Jonathan A. , Bejerano Gill 
Published: January 2021

AbstractBackgroundMany thousands of patients with a suspected Mendelian disease have their exomes/genomes sequenced every year, but only about 30% receive a definitive diagnosis. Since a novel Mendelian gene-disease association is published on average every business day, thousands of undiagnosed patient cases could receive a diagnosis each year if their genomes were regularly compared to the latest literature. With millions of genomes expected to be sequenced for rare disease analysis by 2025, and considering the current publication rate of 1.1 million new articles per annum in PubMed, manually reanalyzing the growing cases of undiagnosed patients is not sustainable.MethodsWe describe a fully automated reanalysis framework for patients with suspected, but undiagnosed, Mendelian disorders. The presented framework was tested by automatically parsing all ∼100,000 newly published peer reviewed papers every month and matching them on genotype and phenotype with all stored undiagnosed patients. If a new article contains a possible diagnosis for an undiagnosed patient, the system provides notification. We test the accuracy of the automatic reanalysis system on 110 patients, including 61 with available trio data.ResultsEven when trained only on older data, our system identifies 80% of reanalysis diagnoses, while sending only 0.5-1 alerts per patient per year, a 100-1,000-fold efficiency gain over manual literature surveillance of equivalent yield.ConclusionWe show that automatic reanalysis of patients with suspected Mendelian disease is feasible and has the potential to greatly streamline diagnosis. Our system is not intended to replace clinical judgment. Rather, clinical diagnostic services could greatly benefit from a modest re-allocation of time from manual literature exploration to review of automated reanalysis alerts. Our system additionally supports a new paradigm for medical IT systems: proactive, continuously learning and consequently able to autonomously identify valuable insights as they emerge in digital health records. We have launched automated patient reanalysis, trained on the latest data, with user accounts and daily literature updates at https://AMELIE.stanford.edu.
One Shot Model For The Prediction of COVID-19 and Lesions Segmentation In Chest CT Scans Through The Affinity Among Lesion Mask Features
Authors: Ter-Sarkisov Aram 
Published: January 2021

AbstractWe introduce a model that segments lesions and predicts COVID-19 from chest CT scans through the derivation of an affinity matrix between lesion masks. The novelty of the methodology is based on the computation of the affinity between the lesion masks’ features extracted from the image. First, a batch of vectorized lesion masks is constructed. Then, the model learns the parameters of the affinity matrix that captures the relationship between features in each vector. Finally, the affinity is expressed as a single vector of pre-defined length. Without any complicated data manipulation, class balancing tricks, and using only a fraction of the training data, we achieve a 91.74% COVID-19 sensitivity, 85.35% common pneumonia sensitivity, 97.26% true negative rate and 91.94% F1-score. Ablation studies show that the method can quickly generalize to new datasets. All source code, models and results are publicly available on https://github.com/AlexTS1980/COVID-Affinity-Model.
Respiratory and non-respiratory manifestations in children admitted with COVID 19 in Rio de Janeiro city, Brazil
Authors: Araujo da Silva AR , Fonseca CGB , Miranda JLPS , Travassos BV , Baião CR , Silva KD , dos Santos LBAE , de Britto MMR , Cerqueira PALS , Pereira SNB , Rios RBJ , Vieira CS , Leal IA , Martins NC , de Carvalho LMAC , Pereira AB , Teixeira CH 
Published: January 2021

AbstractIntroductionCOVID 19 is still a challenge in pediatrics due to variety of symptoms and different presentationsAimTo describe clinical, laboratorial and treatment of confirmed COVID-19 pediatric admitted in hospitals.MethodsA retrospective study was conducted in children (0-18 years), admitted between March and November 15, 2020, with confirmed COVID-19 by reverse transcription polymerase chain reaction or serological tests. Clinical data about symptoms, laboratorial exams and treatments were analysed. Patients were evaluated according predominant (PRS) or non-predominant respiratory symptoms (non-PRS)ResultsSixty-four patients were evaluated, being the median age 5.6 years. Forty-seven (73.4%) children were admitted with PRS and 17 (26.4%) with non-PRS. The main symptoms in the PRS group were fever in 74.5% of children and cough in 66%; and fever in 76.5% and edema/cavitary effusion in 29.4% in the non-PRS group. The median of C-reactive protein (in mg/dl) was 2.5 in the PRS group and 6.1 in the non-PRS group. Antibiotics were used in 85.1% of the PRS group and 94.1% of non-group. Comorbidity was present in 30/47 (63.8%) of PRS group and 8/17 (47.1%) of non-PRS group (p=0.22). Length of stay until 7 days in patients with comorbidity was present in 27/64 (42.1%) and more than 7 days in 11/64 (17.1%) (p= 0.2)ConclusionNon-PRS represented more than one quarter of admitted patients. Fever was the main symptom detected, elevated CRP was frequent and antibiotics were commonly prescribed. Comorbidity was found in both groups and his presence was not associated with a longer length of stay.
Antemortem vs. postmortem histopathological and ultrastructural findings in paired transbronchial biopsies and lung autopsy samples from three patients with confirmed SARS-CoV-2 infection
Authors: Gagiannis D , Umathum VG , Bloch W , Rother C , Stahl M , Witte HM , Djudjaj S , Boor P , Steinestel K 
Published: January 2021

ABSTRACTBackgroundAcute respiratory distress syndrome (ARDS) is the major cause of death in coronavirus disease 2019 (COVID-19). Multiple autopsy-based reports of COVID-19 lung pathology describe diffuse alveolar damage (DAD), organizing pneumonia (OP) and fibrotic change, but data on early pathological changes as well as during progression of the disease are rare.Research questionComparison of histopathological and ultrastructural findings in paired transbronchial biopsies (TBBs) and autopsy material from three patients with confirmed SARS-CoV-2-infection.MethodsWe prospectively enrolled 3 patients with confirmed SARS-CoV-2 infection. Full clinical evaluation was performed including high-resolution computed tomography (HR-CT). We took TBBs at different time points during the disease and autopsy tissue samples after the patients’ death.ResultsSARS-CoV-2 was detected by RT-PCR and/or FISH in all TBBs. Lung histology revealed pneumocyte hyperplasia and capillary congestion in one patient who died short after hospital admission with detectable virus in 1/2 autopsy samples from the lung. SARS-CoV-2 was detected in 2/2 autopsy samples from a patient with a fulminant course of the disease and very short latency between biopsy and autopsy, both showing widespread DAD. In a third patient with a prolonged course, i.e. five weeks of ICU treatment with ECMO, autopsy samples showed extensive interstitial fibrosis without detectable virus by RT-PCR and/or FISH.InterpretationWe report the course of COVID-19 in paired TBB and autopsy samples from three patients at an early stage, in rapidly progressive and in a prolonged disease course. Our findings illustrate vascular, organizing and fibrotic patterns of COVID-19-induced lung injury and suggest an early spread of SARS-CoV-2 from the upper airways to the lung periphery with diminishing viral load during disease.
Does the Pareto Principle describe parasite counts in humans? The case of hookworm in pregnant women
Authors: Orobio Yenifer , Alexander Neal 
Published: January 2021

AbstractThe Pareto Principle (PP) — that 80% of an attribute are found in 20% of individuals — is one way to characterize heterogeneity in infectious diseases. An alternative is the dispersion parameter (k) of the negative binomial distribution (NBD). The NBD has two parameters, while the PP is a single property which may hold for any distribution family. The objectives of the current work are: a) to obtain a relation between the PP and NBD, i.e. to specify which combinations of NBD parameters comply with the PP; b) for hookworm, a soil-transmitted intestinal parasite of humans, to identify whether the PP or the NBD is a more parsimonious description of heterogeneity of infection load. For objective a), an empirical relation is found in the form of a saw-toothed line on a plot ofkagainst the mean, reaching an asymptote of approximately 0.24. For objective b), we estimatekand the mean from nine studies from a systematic review of hookworm in pregnant women. Seven studies had higher heterogeneity than the PP, ranging from 83:20 to 100:20: we call these super-Pareto. One study was sub-Pareto (74:20), and one was Pareto (80:20). This suggests that at least two parameters, as supplied by the NBD, are necessary to describe the heterogeneity of hookworm. The probability of reaching a target reduction in prevalence is less when there is greater aggregation, which suggests that estimating aggregation via a subsample could be worthwhile, in order to set a target coverage threshold before starting mass drug administration.
Gut Microbiome Predicts Clinically Important Improvement in Patients with Rheumatoid Arthritis
Authors: Gupta Vinod K. , Cunningham Kevin Y. , Bakshi Utpal , Hur Benjamin , Huang Harvey , Warrington Kenneth J. , Taneja Veena , Myasoedova Elena , Davis John M. , Sung Jaeyun 
Published: January 2021

AbstractBackgroundRapid advances in the past decade have shown that dysbiosis of the gut microbiome is a key hallmark of rheumatoid arthritis (RA). Yet, the relationship between gut microbiome and clinical improvement in RA disease activity remains unclear. In this study, we explored the gut microbiome of patients with RA to identify features that are associated with, as well as predictive of, minimum clinically important improvement (MCII) in disease activity.MethodsWhole metagenome shotgun sequencing was performed on 64 stool samples, which were collected from 32 patients with RA at two separate time-points. The Clinical Disease Activity Index (CDAI) of each patient was measured at both time-points to assess achievement of MCII; depending on this clinical status, patients were distinguished into two groups. Multiple linear regression models were used to identify microbial taxa and biochemical pathways associated with MCII while controlling for potentially confounding factors. Lastly, a deep-learning neural network was trained upon gut microbiome, clinical, and demographic data at baseline to classify patients according to MCII status, thereby enabling the prediction of whether a patient will achieve MCII at follow-up.ResultsWe determined that MCII status can explain a significant proportion of the overall compositional variance in the gut microbiome (R2 = 3.8%, P = 0.005, PERMANOVA). Additionally, by looking at patients’ baseline gut microbiome profiles, we observed significantly different microbiome traits between patients who eventually showed MCII and those who did not. Taxonomic features include alpha- and beta-diversity measures, as well as several microbial taxa, such as Coprococcus, Bilophila sp. 4_1_30, and Ruminococcus sp. Functional profiling identified thirteen biochemical pathways, most of which were involved in the biosynthesis of L-arginine and L-methionine, to be differentially abundant between the MCII patient groups. In addition to these observations at baseline, we found microbiome features that vary differently in fold-change (from baseline to follow-up) between the two patient groups. These results could suggest that, depending on the clinical course, gut microbiomes not only start at different ecological states, but also are on separate trajectories. Finally, the neural network proved to be highly effective in predicting which patient will achieve MCII (balanced accuracy = 90.0%), demonstrating potential clinical utility of gut microbiome profiles.ConclusionsOur findings confirm the presence of taxonomic and functional signatures of the gut microbiome associated with MCII in RA patients. Ultimately, the gut microbiome may aid in the development of non-invasive tools for predicting future prognosis in RA.Trial registrationN/A
A MULTI-MINERAL INTERVENTION TO MODULATE COLONIC MUCOSAL PROTEIN PROFILE: Results from a 90-day trial in healthy human subjects
Authors: Aslam Muhammad N. , McClintock Shannon D. , Jawad-Makki Mohamed Ali H. , Knuver Karsten , Ahmad Haris M. , Basrur Venkatesha , Bergin Ingrid L. , Zick Suzanna M. , Sen Ananda , Turgeon D. Kim , Varani James 
Published: January 2021

ABSTRACTThe overall goal of this study was to determine if Aquamin®, a calcium- and magnesium-rich natural product, would alter the expression of proteins involved in growth-regulation, differentiation and barrier formation in the colon. Thirty healthy human subjects were enrolled in a three-arm, 90-day interventional trial in which Aquamin®(provided daily to deliver 800-mg of calcium per day) was compared to calcium alone and placebo. Before and after the 90-day interventional period, colonic biopsies were obtained. Biopsies were evaluated by immunohistology for expression of Ki67 (a proliferation marker) and for CK20 and p21 (differentiation markers). Tandem mass tag-mass spectrometry-based detection was used to assess levels of multiple proteins. As compared to placebo or calcium, Aquamin®reduced the level of Ki67 expression (20%). Neither intervention altered CK20 expression, while a trend toward increased p21 was observed with calcium and Aquamin®(117% and 99% respectively). In the proteomic screen, Aquamin®treatment resulted in many more proteins being upregulated or downregulated (1.5 fold-change with ≤2% false-discovery rate) than placebo. Included among the upregulated proteins were cytokeratins, cell-cell adhesion molecules and components of the basement membrane. Many of the downregulated proteins were those involved in proliferation and nucleic acid metabolism. Calcium alone also altered the expression of many of the same proteins but not to the same extent as Aquamin®. We conclude that daily Aquamin®ingestion alters protein expression profile in the colon that could be beneficial to colonic health. These data warrant additional studies with a larger sample size to validate these findings.Prevention RelevanceA multi-mineral approach reduced proliferation and induced differentiation inex vivosettings and has been shown to decrease colon polyp incidence in mouse (polyp-prevention) studies. The findings from a 90-day trial in human subjects (presented here) demonstrated improved biomarker-modulation efficacy, warranting to conduct the polyp-prevention trial in at-risk human subjects.
SANS serif: alignment-free, whole-genome based phylogenetic reconstruction
Authors: Rempel Andreas , Wittler Roland 
Published: January 2021

AbstractSummarySANS serif is a novel software for alignment-free, whole-genome based phylogeny estimation that follows a pangenomic approach to efficiently calculate a set of splits in a phylogenetic tree or network.Availability and ImplementationImplemented in C++ and supported on Linux, MacOS, and Windows. The source code is freely available for download athttps://gitlab.ub.uni-bielefeld.de/gi/sans.Contactandreas.rempel@uni-bielefeld.de
Cellsnp-lite: an efficient tool for genotyping single cells
Authors: Huang Xianjie , Huang Yuanhua 
Published: January 2021

AbstractSummarySingle-cell sequencing is an increasingly used technology and has promising applications in basic research and clinical translations. However, genotyping methods developed for bulk sequencing data have not been well adapted for single-cell data, in terms of both computational parallelization and simplified user interface. Here we introduce a software, cellsnp-lite, implemented in C/C++ and based on well supported package htslib, for genotyping in single-cell sequencing data for both droplet and well based platforms. On various experimental data sets, it shows substantial improvement in computational speed and memory efficiency with retaining highly concordant results compared to existing methods. Cellsnp-lite therefore lightens the genetic analysis for increasingly large single-cell data.AvailabilityThe source code is freely available athttps://github.com/single-cell-genetics/cellsnp-lite.Contactyuanhua@hku.hk
Distribution modeling ofLantana camaraand identification of hotspot for future management of the species in the lower Shivalik region of Uttarakhand, India
Authors: Mondal Tamali , Bhatt Dinesh , Ramesh K. 
Published: January 2021

AbstractInvasive alien species are one of the most significant threats to biodiversity of the earth ecosystem. Spatial modeling has played a vital role in predicting and mapping the suitable areas of the species habitat for management implementation.Lantana camarais one of the ten worst invasive weed enlisted by IUCN., and has its distribution in more than 154000 sq. km area in India. We predicted the distribution ofLantana camarausing Ecological Niche Modeling (ENM) in MaxEnt software for Rajaji- Corbett landscape in the lower Shivalik region of the state of Uttarakhand, India. The predicted area for the invasion was classified into four categories high, good, medium, and low potential areas. The distribution hotspots were identified using Getis Ord G* method in ArcGIS 10.5 by calculating plot-wise species occurrence. Major hotspot areas under threat of high infestation are Dholkhand, Gohri, Ramgarh, Motichur, and Chilla from Rajaji Tiger Reserve, Palain, Mandal and Maidavan from Kalagarh Tiger Reserve and Bijrani, Dheela, Dhikala, Jhirna and Kalagarh from Corbett Tiger Reserve. This study provides a current status of the invasive species in the landscape with identified areas that need immediate actions to control the spread of the species. The study results can help the policymakers and stakeholders build an efficient and strategically weed management plan.
Identification of Common Molecular Signatures Shared between Alzheimer’s and Parkinson’s Diseases and Therapeutic Agents Exploration: An Integrated Genomics Approach
Authors: Faruqui Nairita Ahsan , Prium Durdana Hossain , Mowna Sadrina Afrin , Rahaman Tanjim Ishraq , Dutta Arundhati Roy , Akter Mst. Farjana 
Published: January 2021

AbstractAlzheimer’s disease (AD) and Parkinson’s disease (PD) are two most prevalent age-related dementias that severely affect a large number of elderly people around the globe. Poor understanding of pathogenesis of these neurological diseases imposes challenge to discover therapeutic measures and effective diagnosis methods. In this study, a network-based approach was utilized to identify potential common molecular signatures and therapeutic agents for AD and PD. Protein-protein interaction analysis revealed NCK1, UBC, CDH1, CDC20, ACTB, PSMA7, PRPF8, RPL7, XRCC6 and HSP90AB1 as the best proteome signatures. Different regulatory transcriptional signatures i.e., YY1, NFKB1, BRCA1, TP53, GATA2, SREBF2, E2F1, FOXC1, RELA and NFIC and post-transcriptional signatures i.e., hsa-mir-186-5p, hsamir-92a-3p, hsa-mir-615-3p, hsa-let-7c-5p, hsa-mir-100-5p, hsa-mir-93-3p, hsa-mir-5681a, hsamir-484, hsa-mir-193b-3p and hsa-mir-16p-5p were identified from other interaction network. Drug-gene interaction study revealed possible therapeutic agents which may reverse the AD and PD condition. The scientific approach of this study should contribute to identify potential biomarkers, drug targets and therapeutic agents against AD and PD which should in turn advance the present efforts of scientists to secure effective diagnosis and therapeutic options. However, furtherin vivoandin vitroexperiments might be required to validate the outcomes of this study.
Machine Learning to Summarize and Provide Context for Sleep and Eating Schedules
Authors: Chen Tianyi , Chen Yiwen , Gao Jingyi , Gao Peiheng , Moon Jeong Hyun , Ren Jingyi , Zhu Ranran , Song Shanshan , Clark Jeanne M. , Bennett Wendy , Lehman Harold , Budavari Tamas , Woolf Thomas B. 
Published: January 2021

AbstractThe relative timing of sleep and of eating within the circadian day is important for human health. Despite much data on sleep, and a growing data set for eating, there remains a need for an interpretative framework for the understanding of this data for health decisions. This study provides a new statistical and machine learning analysis of more than 500 participants in the Daily24 project. From their data, and the analysis, we propose a framework for determining the classification of participants into different chronotypes and with that the ability to realize the potential impact of daily circadian habits on health. We propose that our resulting distribution curves could be used, similar to NHANES (National Health and Nutrition Examination Survey) data for pediatric growth, as a measure for circadian misalignment and used to help guide re-entrainment schedules.Author summaryDaily habits can be positive, negative or neutral for human health. Generally sleep and eating schedules are assumed without thought for their potential to help or interfere with health. In this study we propose a framework, based on data from more than 500 participants, for evaluating the relative timing of meals and sleep schedules. This evaluation, similar to pediatric growth charts, can guide clinical suggestions for those at the extremes, while helping others to realize that they are unusual relative to the population average
Mechanistic Insight from Full Quantum Mechanical Modeling: Laccase as a Detoxifier of Aflatoxins
Authors: Zaccaria Marco , Dawson William , Kish Darius Russell , Reverberi Massimo , di Patti Maria Carmela Bonaccorsi , Domin Marek , Cristiglio Viviana , Dellafiora Luca , Gabel Frank , Nakajima Takahito , Genovese Luigi , Momeni Babak 
Published: January 2021

AbstractWe demonstrate a path towards full Quantum Mechanics (QM) characterization of enzymatic activity. As a case-study, we investigate the detoxification of aflatoxin, a carcinogenic food contaminant, by laccase, a versatile oxidase capable of—but not efficient for—degrading aflatoxin. We use a combination of quantitative experimentation and QM modeling to show that low enzymatic steric affinity for aflatoxin is the main bottleneck, rather that the oxidative activity of laccase. To identify the structural elements responsible for low reaction rates, we perform a density functional theory (DFT) based modeling of both the substrate and the enzyme in a full QM simulation of more than 7,000 atoms. Thanks to our approach we point to amino acid residues that determine the affinity of laccase for aflatoxin. We show that these residues are substrate-dependent, making a full QM approach necessary for enzyme optimization. Altogether, we establish a roadmap for rational enzyme engineering applicable beyond our case study.