bluuebunny/crossref_metadata_2025_split huggingface dataset:
Classification of 12-lead ECGs: the PhysioNet/Computing in Cardiology Challenge 2020
Authors: Alday Erick Andres Perez , Gu Annie , Shah Amit , Robichaux Chad , Wong An-Kwok Ian , Liu Chengyu , Liu Feifei , Rad Ali Bahrami , Elola Andoni , Seyedi Salman , Li Qiao , Sharma Ashish , Clifford Gari D. , Reyna Matthew A. 
Published: August 2020

AbstractThe subject of the PhysioNet/Computing in Cardiology Challenge 2020 was the identification of cardiac abnormalities in 12-lead electrocardiogram (ECG) recordings. A total of 66,405 recordings were sourced from hospital systems from four distinct countries and annotated with clinical diagnoses, including 43,101 annotated recordings that were posted publicly.For this Challenge, we asked participants to design working, open-source algorithms for identifying cardiac abnormalities in 12-lead ECG recordings. This Challenge provided several innovations. First, we sourced data from multiple institutions from around the world with different demographics, allowing us to assess the generalizability of the algorithms. Second, we required participants to submit both their trained models and the code for reproducing their trained models from the training data, which aids the generalizability and reproducibility of the algorithms. Third, we proposed a novel evaluation metric that considers different misclassification errors for different cardiac abnormalities, reflecting the clinical reality that some diagnoses have similar outcomes and varying risks.Over 200 teams submitted 850 algorithms (432 of which successfully ran) during the unofficial and official phases of the Challenge, representing a diversity of approaches from both academia and industry for identifying cardiac abnormalities. The official phase of the Challenge is ongoing.
Is Blood Type Associated with COVID-19 Severity?
Authors: Mendy Angelico , Keller Jason L. , Apewokin Senu , Morrow Ardythe L. 
Published: August 2020

ABSTRACTBlood type purportedly influences susceptibility to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection, but whether it affects severity of coronavirus disease 2019 (COVID-19) is unclear. Therefore, we examined the association of blood type and rhesus with hospitalization and disease severity among 428 COVID-19 patients diagnosed at the University of Cincinnati health system. In the sample, 50.2% of participants had the blood type O, 38.7% had the blood type A, 17.5% had the blood type B, and 3.5% had the blood type AB. In analysis adjusted for sociodemographic characteristics and comorbidities, the blood types A (OR: 0.90, 95% CI: 0.54, 1.50), B (OR: 0.93, 95% CI: 0.51, 1.69), AB (OR: 0.69, 95% CI: 0.20, 2.41), and O (OR: 1.18, 95%: 0.74, 1.98) were not associated with hospitalization for COVID-19. Similarly, the blood types A (OR: 0.93, 95% CI: 0.52, 1.65), B (OR: 0.92, 95% CI: 0.46, 1.84), AB (OR: 0.30, 95% CI: 0.04, 2.44), and O (OR: 1.25, 95%: 0.73, 2.14) were not associated with admission to intensive care unit or death in COVID-19. In conclusion, blood type is not associated with hospitalization or disease severity in COVID-19; therefore, it may not be useful marker for identifying patients at risk for severe COVID-19.
Clinical, Laboratory, and Imaging Features of 148 Patients with COVID-19 in Bushehr: A Report from the South of Iran
Authors: Keshavarz Mohsen , Tavakoli Ahmad , Zanganeh Sareh , Mousavi Mohammad Javad , Vahdat Katayoun , Mahmudpour Mehdi , Nabipour Iraj , Darabi Amir Hossein , Keshmiri Saeid 
Published: August 2020

AbstractAimTo investigate clinical characteristics, laboratory findings, and imaging features of patients confirmed with COVID-19 in Bushehr, a southern province of Iran.MethodDuring April 29th to May 30th 2020, a total of 148 patients confirmed with COVID-19 infection were admitted to three hospitals in Bushehr province, assigned by the Iranian Ministry of Health.ResultsThe most common coexisting disease was type 2 diabetes. Levels of ESR, CRP, LDH, and AST among inpatients were higher than the outpatients (P<0.05). There were significant differences in the levels of creatinine and BUN between elderly and non-elderly patients (P<0.05).ConclusionPatients with comorbidities and elderly patients are at increased risk of severe progression of COVID-19.
Federated Learning of Electronic Health Records Improves Mortality Prediction in Patients Hospitalized with COVID-19
Authors: Vaid Akhil , Jaladanki Suraj K , Xu Jie , Teng Shelly , Kumar Arvind , Lee Samuel , Somani Sulaiman , Paranjpe Ishan , Freitas Jessica K De , Wanyan Tingyi , Johnson Kipp W , Bicak Mesude , Klang Eyal , Kwon Young Joon , Costa Anthony , Zhao Shan , Miotto Riccardo , Charney Alexander W , Böttinger Erwin , Fayad Zahi A , Nadkarni Girish N , Wang Fei , Glicksberg Benjamin S 
Published: August 2020

ABSTRACTMachine learning (ML) models require large datasets which may be siloed across different healthcare institutions. Using federated learning, a ML technique that avoids locally aggregating raw clinical data across multiple institutions, we predict mortality within seven days in hospitalized COVID-19 patients. Patient data was collected from Electronic Health Records (EHRs) from five hospitals within the Mount Sinai Health System (MSHS). Logistic Regression with L1 regularization (LASSO) and Multilayer Perceptron (MLP) models were trained using local data at each site, a pooled model with combined data from all five sites, and a federated model that only shared parameters with a central aggregator. Both the federated LASSO and federated MLP models performed better than their local model counterparts at four hospitals. The federated MLP model also outperformed the federated LASSO model at all hospitals. Federated learning shows promise in COVID-19 EHR data to develop robust predictive models without compromising patient privacy.
Drawing transmission graphs for COVID-19 in the perspective of network science
Authors: Gürsakal N. , Batmaz B. , Aktuna G. 
Published: August 2020

ABSTRACTWhen we consider a probability distribution about how many COVID-19 infected people will transmit the disease, two points become important. First, there should be super-spreaders in these distributions/networks and secondly, the Pareto principle should be valid in these distributions/networks. When we accept that these two points are valid, the distribution of transmission becomes a discrete Pareto distribution, which is a kind of power law. Having such a transmission distribution, then we can simulate COVID-19 networks and find super-spreaders using the centricity measurements in these networks. In this research, in the first we transformed a transmission distribution of statistics and epidemiology into a transmission network of network science and secondly we try to determine who the super-spreaders are by using this network and eigenvalue centrality measure. We underline that determination of transmission probability distribution is a very important point in the analysis of the epidemic and determining the precautions to be taken.
Development of a duplex real-time PCR assay for the differentiation ofBlastomyces dermatitidisandB. gilchristiiand a retrospective study of blastomycosis in New York (2005-2019)
Authors: Kaplan Mitchell , Zhu YanChun , Kus Julianne V , McTaggart Lisa , Chaturvedi Vishnu , Chaturvedi Sudha 
Published: August 2020

ABSTRACTBlastomycosis due toBlastomyces dermatitidisandB. gilchristiiis a notable cause of respiratory mycoses in North America with recurrent outbreaks. We developed a highly sensitive, specific, and reproducible Taqman duplex real-time PCR assay for the differentiation ofB. dermatitidisandB. gilchristii. The new assay permitted retrospective analysis ofBlastomycescultures (2005 to 2019), and primary clinical specimens (2013-2019) from NY patients.Blastomyces dermatitidiswas the causal agent for the majority of 38 cases whileB. gilchristiiwas implicated in five cases; a rare finding reported from New York. The duplex real-time PCR assay will be useful for further understanding of ecology and epidemiology of blastomycosis caused byB. dermatitidisandB. gilchristii.
Clinical Explainability Failure (CEF) & Explainability Failure Ratio (EFR) – changing the way we validate classification algorithms?
Authors: Venugopal V. , Takhar R. , Gupta S. , Saboo A. , Mahajan V. 
Published: August 2020

AbstractAdoption of Artificial Intelligence (AI) algorithms into the clinical realm will depend on their inherent trustworthiness, which is built not only by robust validation studies but is also deeply linked to the explainability and interpretability of the algorithms. Most validation studies for medical imaging AI report performance of algorithms on study-level labels and lay little emphasis on measuring the accuracy of explanations generated by these algorithms in the form of heat maps or bounding boxes, especially in true positive cases. We propose a new metric – Explainability Failure Ratio (EFR) – derived from Clinical Explainability Failure (CEF) to address this gap in AI evaluation. We define an Explainability Failure as a case where the classification generated by an AI algorithm matches with study-level ground truth but the explanation output generated by the algorithm is inadequate to explain the algorithms output. We measured EFR for two algorithms that automatically detect consolidation on chest X-rays to determine the applicability of the metric and observed a lower EFR for the model that had lower sensitivity for identifying consolidation on chest X-rays, implying that trustworthiness of a model should be determined not only by routine statistical metrics but also by novel ‘clinically-oriented’ models.
Observational Study on Clinical Features, Treatment and Outcome of COVID 19 in a tertiary care Centre in India- a retrospective case series
Authors: Bhattacharya Raja , Ghosh Rohini , Kulshrestha Manish , Chowdhury Sampurna , Mukherjee Rishav , Ray Indranil 
Published: August 2020

ABSTRACTObjectiveThis study will attempt to explore the demographic profile and outcome in the patients receiving multidisciplinary, personalised approach including use of Broad Spectrum Antivirals - Ivermectin, anti-inflammatory and antioxidants roles of Statins and N-acetyl-cysteine along with Standard of Care (SOC) in hospitalised COVID19 patients in a tertiary care centre.SettingInpatient departmentParticipants191 COVID-19 patients with laboratory confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections in the year 2020 between June 14-28, 2020Main outcome measuresThe outcome of Interests are:Studying the demographic profile of COVID 19 casesStudy the treatment outcomes in terms of death or discharge in patients receiving Ivermectin+N-acetyl-cysteine+Statin along with Standard of care.Results148 patients were included in the study. All of them had confirmed COVID19 infection by the rtPCR method. Average age of the patients was 57.57 years (Range = 17 - 88), 49% were male, 51% female. 81% of the patients had at least one or more comorbidities. Most common comorbidities included diabetes (32%), Hypertension (27%),Ischaemic Heart Disease (8%). More comorbidities. The in hospital, Case Fatality Rate was therefore, 1.35 %. The remaining 144 were discharged from the facility after an average 12 days duration of stay.ConclusionsTriple therapy with ivermectin + atorvastatin + N-acetylcysteine can be an useful adjunct to standard of care.
Integrating psychosocial variables and societal diversity in epidemic models for predicting COVID-19 transmission dynamics
Authors: Jirsa VK , Petkoski S , Wang H , Woodman M , Fousek J , Betsch C , Felgendreff L , Böhm R , Lilleholt L , Zettler I , Faber SM , Shen K , McIntosh AR 
Published: August 2020

SummaryDuring the current COVID-19 pandemic, governments must make decisions based on a variety of information including estimations of infection spread, health care capacity, economic and psychosocial considerations. The disparate validity of current short-term forecasts of these factors is a major challenge to governments. By causally linking an established epidemiological spread model with dynamically evolving psychosocial variables, using Bayesian inference we estimate the strength and direction of these interactions for German and Danish data of disease spread, human mobility, and psychosocial factors based on the serial cross-sectional COVID-19 Snapshot Monitoring (COSMO;N= 16,981). We demonstrate that the strength of cumulative influence of psychosocial variables on infection rates is of a similar magnitude as the influence of physical distancing. We further show that the efficacy of political interventions to contain the disease strongly depends on societal diversity, in particular group-specific sensitivity to affective risk perception. As a consequence, the model may assist in quantifying the effect and timing of interventions, forecasting future scenarios, and differentiating the impact on diverse groups as a function of their societal organization. Importantly, the careful handling of societal factors, including support to the more vulnerable groups, adds another direct instrument to the battery of political interventions fighting epidemic spread.
Patient outcomes after hospitalisation with COVID-19 and implications for follow-up; results from a prospective UK cohort
Authors: Arnold DT , Hamilton FW , Milne A , Morley A , Viner J , Attwood M , Noel A , Gunning S , Hatrick J , Hamilton S , Elvers KT , Hyams C , Bibby A , Moran E , Adamali H , Dodd J , Maskell NA , Barratt S 
Published: August 2020

ABSTRACTBackgroundCOVID-19 causes a wide spectrum of disease. The incidence and severity of sequelae after the acute infection is uncertain. Data measuring the longer-term impact of COVID-19 on symptoms, radiology and pulmonary function are urgently needed to plan follow-up services.MethodsConsecutive patients hospitalised with COVID-19 were prospectively recruited to this observational study with outcomes recorded at 28-days. All were invited to a systematic follow up at 8-12 weeks, including chest radiograph, spirometry, exercise test, bloods, and health-related quality of life (HRQoL) questionnaires.FindingsBetween 30th March and 3rd June 2020, 163 patients with COVID-19 were recruited. Median hospital length of stay was 5 days (IQR 2-8) and 19 patients died. At 8-12 weeks post admission, 134 patients were available for follow up and 110 attended. Most (74%) had persistent symptoms (notably breathlessness and excessive fatigue) with reduced HRQoL.Only patients who required oxygen therapy in hospital had abnormal radiology, clinical examination or spirometry at follow up. Thirteen (12%) patients had an abnormal chest X-ray with improvement in all but 2 from admission. Eleven (10%) had restrictive spirometry. Blood test abnormalities had returned to baseline in the majority (104/110).InterpretationPatients with COVID-19 remain highly symptomatic at 8-12 weeks, however, clinical abnormalities requiring action are infrequent, especially in those without a supplementary oxygen requirement during their acute illness. This has significant implications for physicians assessing patients with persistent symptoms, suggesting that a more holistic approach focussing on rehabilitation and general wellbeing is paramount.FundingSouthmead Hospital Charity
Temporal Dynamics of Viral Load and False negative Rate Influence the Levels of Testing Necessary to Combat COVID19 Spread
Authors: Jarvis Katherine F. , Kelley Joshua B. 
Published: August 2020

AbstractColleges and other organizations are considering testing plans to return to operation as the COVID19 pandemic continues. Pre-symptomatic spread and high false negative rates for testing may make it difficult to stop viral spread. Here, we develop a stochastic agent-based model of COVID19 in a university sized population, considering the dynamics of both viral load and false negative rate of tests on the ability of testing to combat viral spread. Reported dynamics of SARS-CoV-2 can lead to an apparent false negative rate from ~17% to ~48%. Nonuniform distributions of viral load and false negative rate lead to higher requirements for frequency and fraction of population tested in order to bring the apparent Reproduction number (Rt) below 1. Thus, it is important to consider non-uniform dynamics of viral spread and false negative rate in order to model effective testing plans.
Severity Assessment of COVID-19 based on Clinical and Imaging Data
Authors: Quiroz Juan C. , Feng You-Zhen , Cheng Zhong-Yuan , Rezazadegan Dana , Chen Ping-Kang , Lin Qi-Ting , Qian Long , Liu Xiao-Fang , Berkovsky Shlomo , Coiera Enrico , Song Lei , Qiu Xiao-Ming , Liu Sidong , Cai Xiang-Ran 
Published: August 2020

ABSTRACTObjectivesThis study aims to develop a machine learning approach for automated severity assessment of COVID-19 patients based on clinical and imaging data.Materials and MethodsClinical data—demographics, signs, symptoms, comorbidities and blood test results—and chest CT scans of 346 patients from two hospitals in the Hubei province, China, were used to develop machine learning models for automated severity assessment of diagnosed COVID-19 cases. We compared the predictive power of clinical and imaging data by testing multiple machine learning models, and further explored the use of four oversampling methods to address the imbalance distribution issue. Features with the highest predictive power were identified using the SHAP framework.ResultsTargeting differentiation between mild and severe cases, logistic regression models achieved the best performance on clinical features (AUC:0.848, sensitivity:0.455, specificity:0.906), imaging features (AUC:0.926, sensitivity:0.818, specificity:0.901) and the combined features (AUC:0.950, sensitivity:0.764, specificity:0.919). The SMOTE oversampling method further improved the performance of the combined features to AUC of 0.960 (sensitivity:0.845, specificity:0.929).DiscussionImaging features had the strongest impact on the model output, while a combination of clinical and imaging features yielded the best performance overall. The identified predictive features were consistent with findings from previous studies. Oversampling yielded mixed results, although it achieved the best performance in our study.ConclusionsThis study indicates that clinical and imaging features can be used for automated severity assessment of COVID-19 patients and have the potential to assist with triaging COVID-19 patients and prioritizing care for patients at higher risk of severe cases.
Maraviroc inhibits SARS-CoV-2 multiplication and s-protein mediated cell fusion in cell culture
Authors: Risner Kenneth H. , Tieu Katie V. , Wang Yafei , Getz Michael , Bakovic Allison , Bhalla Nishank , Nathan Steven D. , Conway Daniel E. , Macklin Paul , Narayanan Aarthi , Alem Farhang 
Published: August 2020

AbstractIn an effort to identify therapeutic intervention strategies for the treatment of COVID-19, we have investigated a selection of FDA-approved small molecules and biologics that are commonly used to treat other human diseases. A investigation into 18 small molecules and 3 biologics was conducted in cell culture and the impact of treatment on viral titer was quantified by plaque assay. The investigation identified 4 FDA-approved small molecules, Maraviroc, FTY720 (Fingolimod), Atorvastatin and Nitazoxanide that were able to inhibit SARS-CoV-2 infection. Confocal microscopy with over expressed S-protein demonstrated that Maraviroc reduced the extent of S-protein mediated cell fusion as observed by fewer multinucleate cells in the context of drugtreatment. Mathematical modeling of drug-dependent viral multiplication dynamics revealed that prolonged drug treatment will exert an exponential decrease in viral load in a multicellular/tissue environment. Taken together, the data demonstrate that Maraviroc, Fingolimod, Atorvastatin and Nitazoxanide inhibit SARS-CoV-2 in cell culture.
Mitogenomic Phylogeny ofCallithrixwith Special Focus on Human Transferred Taxa
Authors: Malukiewicz Joanna , Cartwright Reed A. , Curi Nelson H.A. , Dergam Jorge A. , Igayara Claudia S. , Moreira Silvia B. , Molina Camila V. , Nicola Patricia A. , Noll Angela , Passamani Marcello , Pereira Luiz C.M. , Pissinatti Alcides , Ruiz-Miranda Carlos R. , Silva Daniel L. , Stone Anne C. , Zinner Dietmar , Roos Christian 
Published: August 2020

AbstractCallithrixmarmosets are a relatively young primate radiation, whose phylogeny is not yet fully resolved. These primates are naturally para- and allopatric, but three species with highly invasive potential have been introduced into the southeastern Brazilian Atlantic Forest by the pet trade. There, these species hybridize with each other and endangered, native congeners. We aimed here to reconstruct a robustCallithrixphylogeny and divergence time estimates, and identify the biogeographic origins of autochthonous and allochthonousCallithrixmitogenome lineages. We sequenced 49 mitogenomes from four species (C. aurita, C. geoffroyi, C. jacchus, C. penicillata) and anthropogenic hybrids (C. auritaxCallithrixsp.,C. penicillataxC. jacchus, Callithrixsp. xCallithrixsp.,C. penicillataxC. geoffroyi) via Sanger and whole genome sequencing. We combined these data with previously publishedCallithrixmitogenomes to analyze fiveCallithrixspecies in total.ResultsWe report the complete sequence and organization of theC. auritamitogenome. Phylogenetic analyses showed thatC. auritawas the first to diverge withinCallithrix3.54 million years ago (Ma), whileC. jacchusandC. penicillatalineages diverged most recently 0.5 Ma as sister clades. MtDNA clades ofC. aurita, C. geoffroyi, andC. penicillatashow intraspecific geographic structure, butC. penicillataclades appear polyphyletic. Hybrids, which were identified by phenotype, possessed mainlyC. penicillataorC. jacchusmtDNA haplotypes. The biogeographic origins of mtDNA haplotypes from hybrid and allochthonousCallithrixwere broadly distributed across naturalCallithrixranges. Our phylogenetic results also evidence introgression ofC. jacchusmtDNA intoC. aurita.ConclusionOur robustCallithrixmitogenome phylogeny showsC. auritalineages as basal andC. jacchuslineages among the most recent withinCallithrix. We provide the first evidence that parental mtDNA lineages of anthropogenic hybrid and allochthonous marmosets are broadly distributed inside and outside of the Atlantic Forest. We also show evidence of cryptic hybridization between allochthonousCallithrixand autochthonousC. aurita. Our results encouragingly show that further development of genomic resources will allow to more clearly elucidateCallithrixevolutionary relationships and understand the dynamics ofCallithrixanthropogenic introductions into the Brazilian Atlantic Forest.
Rapid Parallel Adaptation to Anthropogenic Heavy Metal Pollution
Authors: Papadopulos Alexander S.T. , Helmstetter Andrew J. , Osborne Owen G. , Comeault Aaron A. , Wood Daniel P. , Straw Edward A. , Mason Laurence , Fay Michael F. , Parker Joe , Dunning Luke T. , Foote Andrew D. , Smith Rhian J. , Lighten Jackie 
Published: August 2020

AbstractThe impact of human mediated environmental change on the evolutionary trajectories of wild organisms is poorly understood. In particular, species’ capacity to adapt rapidly (in hundreds of generations or less), reproducibly and predictably to extreme environmental change is unclear.Silene uniflorais predominantly a coastal species, but it has also colonised isolated, disused mines with phytotoxic, zinc-contaminated soils. Here, we found that rapid parallel adaptation to anthropogenic pollution has taken place without geneflow spreading adaptive alleles between populations of the mine ecotype. Across replicate ecotype pairs, we identified shared targets of selection with functions linked to physiological differences between the ecotypes, although the genetic response is only partially shared between mine populations. Our results are consistent with a complex, polygenic genetic architecture underpinning rapid adaptation. This shows that even under a scenario of strong selection and rapid adaptation, evolutionary responses to human activities may be idiosyncratic at the genetic level and, therefore, difficult to predict from genomic data.