bluuebunny/crossref_metadata_2025_split huggingface dataset:
Identification of candidate biomarkers and therapeutic agents for heart failure by bioinformatics analysis
Authors: Vastrad Basavaraj , Tengli Anandkumar , Vastrad Chanabasayya 
Published: January 2021

AbstractHeart failure (HF) is a heterogeneous clinical syndrome and affects millions of people all over the world. HF occurs when the cardiac overload and injury, which is a worldwide complaint. The aim of this study was to screen and verify hub genes involved in developmental HF as well as to explore active drug molecules. The expression profiling by high throughput sequencing of GSE141910 dataset was downloaded from the Gene Expression Omnibus (GEO) database, which contained 366 samples, including 200 heart failure samples and 166 non heart failure samples. The raw data was integrated to find differentially expressed genes (DEGs) and were further analyzed with bioinformatics analysis. Gene ontology (GO) and REACTOME enrichment analyses were performed via ToppGene; protein-protein interaction (PPI) networks of the DEGs was constructed based on data from the HiPPIE interactome database; modules analysis was performed; target gene - miRNA regulatory network and target gene - TF regulatory network were constructed and analyzed; hub genes were validated; molecular docking studies was performed. A total of 881 DEGs, including 442 up regulated genes and 439 down regulated genes were observed. Most of the DEGs were significantly enriched in biological adhesion, extracellular matrix, signaling receptor binding, secretion, intrinsic component of plasma membrane, signaling receptor activity, extracellular matrix organization and neutrophil degranulation. The top hub genes ESR1, PYHIN1, PPP2R2B, LCK, TP63, PCLAF, CFTR, TK1, ECT2 and FKBP5 were identified from the PPI network. Module analysis revealed that HF was associated with adaptive immune system and neutrophil degranulation. The target genes, miRNAs and TFs were identified from the target gene - miRNA regulatory network and target gene - TF regulatory network. Furthermore, receiver operating characteristic (ROC) curve analysis and RT-PCR analysis revealed that ESR1, PYHIN1, PPP2R2B, LCK, TP63, PCLAF, CFTR, TK1, ECT2 and FKBP5 might serve as prognostic, diagnostic biomarkers and therapeutic target for HF. The predicted targets of these active molecules were then confirmed. The current investigation identified a series of key genes and pathways that might be involved in the progression of HF, providing a new understanding of the underlying molecular mechanisms of HF.
Impulse dispersion of aerosols during playing wind instruments
Authors: Gantner Sophia , Echternach Matthias , Veltrup Reinhard , Westphalen Caroline , Köberlein Marie Christine , Kuranova Liudmila , Peters Gregor , Jakubaß Bernhard , Benthaus Tobias , Döllinger Michael , Kniesburges Stefan 
Published: January 2021

AbstractMusical activities especially singing and playing wind instruments have been singled out as potentially high-risk activities for transmission of SARS CoV-2, because of a higher rate of aerosol production and emission. Playing wind instruments can produce condensation water, droplets of saliva, and aerosol particles, which hover and convectional spread in the environmental air and can be potentially infectious.The aim of this study is to investigate the primary impulse dispersion of aerosols during playing different wind instruments in comparison to breathing and speaking. Nine professional musicians (3 trumpeters, 3 cross flutists and 3 clarinetists) of the Bavarian Symphony Orchestra performed the main theme of Ludwig van Beethoven‘s 9th symphony, 4th movement in different pitches and loudness. Thereby, the inhaled air volume was marked with small aerosol particles produced with a commercial e-cigarette. The expelled aerosol cloud was recorded by cameras from different perspectives. Afterwards, the dimensions and dynamics of the aerosol cloud was measured by segmenting the video footage at every time point.Overall, the cross flutes produced the largest dispersion at the end of task of up to maximum distances of 1.88 m in front direction. Thereby it was observed an expulsion of aerosol in different directions: upwards and downwards at the mouthpiece, at the end of the instrument and along the cross flute at the key plane. In comparison, the maximum impulse dispersion generated by the trumpets and clarinets were lower in frontal and lateral direction (1.2 m and 1.0 m in front-direction). The expulsion to the sides was also lower. Consequently, a distance of 3 m to the front and to the sides of 2 m for the cross flutes in an orchestral formation is proposed, for trumpets and clarinets a safety distance of 2 m to the front and 1.5 m between instrumentalists are recommendable.
Phenotype Risk Scores: moving beyond ‘cases’ and ‘controls’ to classify psychiatric disease in hospital-based biobanks
Authors: Lebovitch Dannielle S. , Johnson Jessica S. , Dueñas Hillary R. , Huckins Laura M. 
Published: January 2021

AbstractCurrent phenotype classifiers for large biobanks with coupled electronic health records EHR and multi-omic data rely on ICD-10 codes for definition. However, ICD-10 codes are primarily designed for billing purposes, and may be insufficient for research. Nuanced phenotypes composed of a patients’ experience in the EHR will allow us to create precision psychiatry to predict disease risk, severity, and trajectories in EHR and clinical populations. Here, we create a phenotype risk score (PheRS) for major depressive disorder (MDD) using 2,086 cases and 31,000 individuals from Mount Sinai’s biobank BioMe ™. Rather than classifying individuals as ‘cases’ and ‘controls’, PheRS provide a whole-phenome estimate of each individual’s likelihood of having a given complex trait. These quantitative scores substantially increase power in EHR analyses and may identify individuals with likely ‘missing’ diagnoses (for example, those with large numbers of comorbid diagnoses and risk factors, but who lack explicit MDD diagnoses).Our approach applied ten-fold cross validation and elastic net regression to select comorbid ICD-10 codes for inclusion in our PheRS. We identified 158 ICD-10 codes significantly associated with Moderate MDD (F33.1). Phenotype Risk Score were significantly higher among individuals with ICD-10 MDD diagnoses compared to the rest of the population (Kolgorov-Smirnov p<2.2e-16), and were significantly correlated with MDD polygenic risk scores (R2>0.182). Accurate classifiers are imperative for identification of genetic associations with psychiatric disease; therefore, moving forward research should focus on algorithms that can better encompass a patient’s phenome.
Home-based management of COVID-19 by identification of low-risk features
Authors: Cabanillas Fernando , Morales Javier , Conde José G. , Bertrán-Pasarell Jorge , Fernández Ricardo , Hernandez-Silva Yaimara , Liboy Idalia 
Published: January 2021

AbstractBackgroundCovid-19 is a triphasic disorder characterized by a viral phase lasting 7-10 days from first onset of symptoms. In approximately 20% it is followed by a second stage heralded by elevation of pro-inflammatory markers such as ferritin, IL-6, CRP, LDH and D-dimers. We hypothesized that those with few abnormalities would have a low risk for progression to respiratory insufficiency and could be monitored at home without treatment.MethodsInclusion criteria included age >21, O2 saturation >90%. To be observed without treatment patients could not have >1 of the following: CRP > 10 mg/dL, high LDH, ferritin > 500 ng/ml, D-dimer > 1 mg/L, IL-6 > 10 pg/ml, absolute lymphocyte count <1,000, O2 sat <94%, or CT chest evidence of pneumonia. Primary endpoint: progression to respiratory failure. Secondary endpoint: 28-day survival.ResultsOf 208 entered, 132 were monitored without therapy. None progressed to respiratory failure or died.ConclusionsWe have shown that our approach can identify cases who can safely be observed without treatment, thus avoiding expensive, potentially toxic therapies, and circumventing unnecessary, costly hospitalizations. These results support our hypothesis that after applying our criteria, 64% of Covid-19 cases can be monitored as outpatients without therapy.
ARE GEOGRAPHIC FACTORS ASSOCIATED WITH POORER OUTCOMES IN PATIENTS DIAGNOSED WITH COVID-19?
Authors: Magallón-Botaya Rosa , Oliván-Blázquez Bárbara , Ramirez-Cervantes Karen Lizzette , Mendez-Lopez de la Mazanara Fatima , Casajuana-Closas Marc , Esteban-Andrés Eva 
Published: January 2021

ABSTRACTBackgroundThe prognosis of patients with COVID-19, with older age and comorbidities, is associated with a more severe course and higher fatality rates but no analysis has yet included factors related to the geographical area/municipality in which the affected patients live. So the objective of this study is to analyse the prognosis of patients with COVID-19 in terms of sex, age, comorbidities, and geographic variables.MethodsA retrospective cohort of 6286 patients diagnosed with COVID-19 was analysed, considering demographic data, previous comorbidities and geographic variables. The main study variables were hospital admission, Intensive Care Unit (ICU) admission and death due to worsening symptoms; and the secondary variables were sex, age, comorbidities and geographic variables (size of the area of residence, distance to the hospital and the driving time to the hospital). A comparison analysis and a multivariate Cox model were performed.ResultsThe multivariate Cox model showed that women had a better prognosis in any type of analysed prognosis. Most of the comorbidities studied were related to a poorer prognosis except for dementia, which is related to lower admissions and higher mortality. Suburban areas were associated with greater mortality and with less hospital or ICU admission. Distance to the hospital was also associated with hospital admission.ConclusionsFactors such as type of municipality and distance to hospital act as social health determinants. This fact must be taken account in order to stablish specifics prevention measures and treatment protocols.
Implementation of predictive risk stratification to reduce emergency admissions to hospital: experiences of general practitioners and practice managers
Authors: Evans Bridie Angela , Davies Jan , Dale Jeremy , Hutchings Hayley , Kingston Mark , Porter Alison , Russell Ian , Williams Victoria , Snooks Helen 
Published: January 2021

AbstractAimIn a trial evaluating the introduction of a predictive risk stratification model (PRISM) into primary care, we reported statistically significant increases in emergency hospital admissions and use of other NHS services without evidence of benefits to patients or the NHS. The aim of this study was to explore the views and experiences of general practitioners (GPs) and practice managers on incorporating PRISM into routine practice.MethodsWe interviewed 22 GPs and practice managers in 18 participating practices at two timepoints: 3-6 months after PRISM was available in their practice; and at study end, up to 18 months later. We recorded and transcribed interviews and analysed data thematically using Normalisation Process Theory.ResultsRespondents reported that the decision to use PRISM was based mainly on fulfilling reporting requirements for Quality and Outcome Framework (QOF) incentives. Most applied it to a very small number of patients for a short period. Using PRISM entailed technical tasks, information sharing within practice meetings and changes to patient care. These were diverse and generally small scale. Use was inhibited by PRISM not being integrated with practice systems. Respondents’ evaluation of PRISM was mixed: most doubted it had any large scale impact, but many cited examples of impact on individual patient care. They reported increased awareness of patients in high risk groups.ConclusionsQualitative results suggest mixed views of predictive risk stratification in primary care and raised awareness of highest-risk patient groups, potentially affecting unplanned hospital attendance and admissions. To inform future policy, decision-makers need more information about implementation and effects of emergency admissions risk stratification tools in primary and community settings.Trial registrationControlled Clinical Trials no. ISRCTN55538212.
Molecular epidemiology of SARS-CoV-2 - a regional to global perspective
Authors: Brandt Christian , Spott Riccardo , Hölzer Martin , Kühnert Denise , Fuchs Stephan , Lohde Mara , Marquet Mike , Viehweger Adrian , Rimek Dagmar , Pletz Mathias W. 
Published: January 2021

AbstractBackgroundAfter a year of the global SARS-CoV-2 pandemic, a highly dynamic genetic diversity is surfacing. Among nearly 1000 reported virus lineages, dominant lineages such as B.1.1.7 or B.1.351 attract media attention with questions regarding vaccine efficiency and transmission potential. In response to the pandemic, the Jena University Hospital began sequencing SARS-CoV-2 samples in Thuringia in early 2020.MethodsViral RNA was sequenced in tiled amplicons using Nanopore sequencing. Subsequently, bioinformatic workflows were used to process the generated data. As a genomic background, 9,642 representative SARS-CoV-2 genomes (1,917 of German origin) were extracted from more than 300.000 genomes.ResultsIn a comprehensive bioinformatics analysis, we have set Thuringian isolates in the German, European and global context. In Thuringia, a largely rural German region without an international airport and a population density below the German average, we discovered many of the common “EU lineages”. German samples are scattered across eight major clades, and Thuringian samples occupy four of them.ConclusionThe rapid emergence and spread of novel variants are of great concern as these lineages could transmit more efficiently, evade current vaccine efforts or undermine diagnostic test accuracy. To anticipate and mitigate these threats, a continuous molecular surveillance is essential.Key messagesBioinformatics analysis of 1,917, 4,251, and 3,474 SARS-CoV-2 genomes from Germany, the EU (except Germany), and non-EU, respectively, subsampled from more than 300,000 public genomes and placed in the context of Thuringian sequencesConstant antigenic drift for SARS-CoV-2 and no clear pattern or clustering is visible in Thuringia based on the current number of samplesCurrently over 100 described lineages are identified in Germany and only a subset (9) are detected in Thuringia so far, most likely due to genetic undersamplingFrom a national perspective, it is likely that high-frequency lineages, which are currently spreading throughout Europe, will eventually also reach ThuringiaSystematic and dense molecular surveillance via whole-genome sequencing is needed to detect concerning new lineages early, limit spread and adjust vaccines if necessary
REM: An Integrative Rule Extraction Methodology for Explainable Data Analysis in Healthcare
Authors: Shams Zohreh , Dimanov Botty , Kola Sumaiyah , Simidjievski Nikola , Terre Helena Andres , Scherer Paul , Matjašec Urška , Abraham Jean , Jamnik Mateja , Liò Pietro 
Published: January 2021

ABSTRACTDeep learning models are receiving increasing attention in clinical decision-making, however the lack of explainability impedes their deployment in day-to-day clinical practice. We propose REM, an explainable methodology for extracting rules from deep neural networks and combining them with rules from non-deep learning models. This allows integrating machine learning and reasoning for investigating basic and applied biological research questions. We evaluate the utility of REM in two case studies for the predictive tasks of classifying histological and immunohistochemical breast cancer subtypes from genotype and phenotype data. We demonstrate that REM efficiently extracts accurate, comprehensible rulesets from deep neural networks that can be readily integrated with rulesets obtained from tree-based approaches. REM provides explanation facilities for predictions and enables the clinicians to validate and calibrate the extracted rulesets with their domain knowledge. With these functionalities, REM caters for a novel and direct human-in-the-loop approach in clinical decision-making.
AngioNet: A Convolutional Neural Network for Vessel Segmentation in X-ray Angiography
Authors: Iyer Kritika , Najarian Cyrus P. , Fattah Aya A. , Arthurs Christopher J. , Soroushmehr S.M. Reza , Subban Vijayakumar , Sankardas Mullasari A. , Nadakuditi Raj R. , Nallamothu Brahmajee K. , Figueroa C. Alberto 
Published: January 2021

AbstractCoronary Artery Disease (CAD) is commonly diagnosed using X-ray angiography, in which images are taken as radio-opaque dye is flushed through the coronary vessels to visualize stenosis severity. Cardiologists typically use visual estimation to approximate the percent diameter reduction of the stenosis, and this directs therapies like stent placement. A fully automatic method to segment the vessels would eliminate potential subjectivity and provide a quantitative and systematic measurement of diameter reduction. Here, we have designed a convolutional neural network, AngioNet, for vessel segmentation in X-ray angiography images. The main innovation in this network is the introduction of an Angiographic Processing Network which significantly improves segmentation performance on multiple network backbones, with the best performance using Deeplabv3+ (Dice score 0.864, sensitivity 0.918, specificity 0.987). We have also demonstrated the interchangeability of our network in measuring vessel diameter with Quantitative Coronary Angiography. Our results indicate that AngioNet is a powerful tool for automatic angiographic vessel segmentation that could facilitate systematic anatomical assessment of coronary stenosis in the clinical workflow.
Community structured model for vaccine strategies to control COVID19 spread: a mathematical study
Authors: Aruffo Elena , Yuan Pei , Tan Yi , Gatov Evgenia , Gournis Effie , Collier Sarah , Ogden Nick , Bélair Jacques , Zhu Huaiping 
Published: January 2021

AbstractEfforts to mitigate the COVID-19 pandemic have relied heavily on non-pharmaceutical interventions (NPIs), including physical distancing, hand hygiene, and mask-wearing. However, an effective vaccine is essential to containing the spread of the virus. The first doses were distributed at the end of 2020, but the efficacy, period of immunity it will provide, and percentage of coverage still remain unclear. We developed a compartment model to examine different vaccine strategies for controlling the spread of COVID-19. Our framework accounts for testing rates, test-turnaround times, and vaccination waning immunity. Using reported case data from the city of Toronto, Canada between Mar-Dec, 2020 we defined epidemic phases of infection using contact rates, which depend on individuals’ duration of time spent within the household, workplace/school, or community settings, as well as the probability of transmission upon contact. We investigated the impact of vaccine distribution by comparing different permutations of waning immunity, vaccine coverage and efficacy throughout various stages of NPI’s relaxation in terms of cases, deaths, and household transmission, as measured using the basic reproduction number (R0). We observed that widespread vaccine coverage substantially reduced the number of cases and deaths. In order for NPIs to be relaxed 8 months after vaccine distribution, infection spread can be kept under control with either 60% vaccine coverage, no waning immunity, and 70% efficacy, or with 60% coverage with a 12-month waning immunity and 90% vaccine efficacy. Widespread virus resurgence can result when the immunity wanes under 3 months and/or when NPI’s are relaxed in concomitance with vaccine distribution. In addition to vaccination, our analysis of R0 showed that the basic reproduction number is reduced by decreasing the tests turnaround time and transmission in the household. While we found that household transmission can decrease following the introduction of a vaccine, public health efforts to reduce test turnaround times remain important for virus containment. Our findings suggest that vaccinating two-thirds of the population with a vaccine that is at least 70% effective may be sufficient for controlling COVID-19 spread, as long as NPI’s are not immediately relaxed.
Logistic advantage of two-step screening strategy for SARS-CoV-2 at airport quarantine
Authors: Yokota Isao , Shane Peter Y , Teshima Takanori 
Published: January 2021

SummaryBackgroundAirport quarantine is required to reduce the risk of entry of travelers infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, it is challenging for both high accuracy and rapid turn-around time to coexist in testing; polymerase chain reaction (PCR) is time-consuming with high accuracy, while antigen testing is rapid with less accuracy.Methods88,924 (93.2%) of 95,457 arrivals at three international airports in Japan were tested for SARS-CoV-2 using self-collected saliva by a screening strategy with initial chemiluminescent enzyme immunoassay (CLEIA) followed by confirmatory nucleic acid amplification tests (NAAT) only for intermediate range antigen concentrations.Results254 (0.27%) persons were found to be SARS-CoV-2 antigen positive (≥ 4.0 pg/mL) by CLEIA. NAAT was required for confirmatory testing in 513 (0.54%) persons with intermediate antigen concentrations (0.67-4.0 pg/mL) whereby the virus was detected in 34 (6.6%) persons. This two-step strategy dramatically reduced the utilization of NAAT to approximately one out of every 200 test subjects.Estimated performance of this strategy did not show significant increase in false negatives as compared to performing NAAT in all subjects. Further reduction in imported cases may be achieved by post-screening quarantine.ConclusionsPoint of care testing by quantitative CLEIA using self-collected saliva is less labor-intensive and yields results rapidly, thus suitable as an initial screening test. Reserving NAAT for CLEIA indeterminate cases may prevent compromising accuracy while significantly improving the logistics of administering mass-screening at large venues.
A Catalog of the Diversity and Ubiquity of Metabolic Organelles in Bacteria
Authors: Sutter Markus , Melnicki Matthew R. , Schulz Frederik , Woyke Tanja , Kerfeld Cheryl A. 
Published: January 2021

AbstractBacterial microcompartments (BMCs) are organelles that segregate segments of metabolic pathways, which are incompatible with surrounding metabolism. In contrast to their eukaryotic counterparts, the selectively permeable membrane of BMCs, the shell, is composed of protein. While the sequestered enzymes vary among functionally distinct BMCs, the proteins that form diverse BMC shells are structurally homologous; this enables the bioinformatic identification of the organelles by locating genes encoding shell proteins, which are typically proximal to those for the encapsulated enzymes. With recent advances in genome‐resolved metagenomics and the emphasis on “microbial dark matter”, many new genome sequences from diverse and obscure bacterial clades have become available. We find the number of identifiable BMC loci has increased twenty‐fold since the last comprehensive census of 2014. Moreover, the addition of new types we uncovered doubles the number of distinct BMC types known. These expand the range of catalysis encapsulated in BMCs, underscoring that there is dark biochemistry that is compartmentalized in bacterial organelles yet to be discovered through genome sequencing. Our comprehensive catalog of BMCs provides a framework for their identification, correlation with bacterial niche adaptation, and experimental characterization, and broadens the foundation for the development of BMC‐based nanoarchitectures for biomedical and bioengineering applications.