bluuebunny/crossref_metadata_2025_split huggingface dataset:
Epidemic Situation and Forecasting if COVID-19 in Saudi Arabia using SIR model
Authors: Khoj Haitham , Mujallad Alaa F 
Published: May 2020

AbstractBackgroundSaudi Arabia is one of the countries affected by COVID-19 pandemic. This will lead to negative impacts in many sectors. Saudi Arabia not only plays an important role on the economical side because it is the leading country in oil production, but also because it is considered the heart of the Islamic countries. Although protective measures have been implemented in Saudi Arabia, the number of COVID-19 cases has increased.Aims of the studyThis study aimed to employ SIR model to forecast the peak of COVID-19 progression and an estimation of it is end in Saudi Arabia.MethodBased on the World Health Organization data on COVID-19 progression in Saudi Arabia from March 3rd to April 29th, 2020, we reliably estimate the constant parameters and make predictions on the inflection point and potential ending time. Susceptible, Infected, and Recovered are the main components of the SIR model that were used to run the analysis.ResultThe data showed an interesting result about the peak of the disease progression. It is projected to occur around the 20th day after running the model. According to the model, the peak time will be around the 20th of May. Then the cases will decrease until the 55th day, which is around June 20th.ConclusionThe result predicts a second peak and an estimation end of COVID-19 in Saudi Arabia. This data can inform the policy makers, who should try to contain the virus, to be prepared for what is coming next.Key Messages:
Prevalence and factors associated with effective helmet use among motorcyclists in Mysuru city of Southern India
Authors: Setty Naveen Kikkeri Hanumantha , Sukumar Gautham Melur , Majgi Sumanth Mallikarjun , Goel Akhil Dhanesh , Sharma Prem Prakash , Anand Manasa Brahmanandam 
Published: May 2020

ABSTRACTBackgroundHelmet use reduces the risk and severity of head injury and death due to road traffic crash among motorcyclists. The protective efficacy of different types of helmets varies. Wearing firmly fastened full face helmet termed as effective helmet use provides greatest protection. This study estimates the prevalence and factors associated with effective helmet use among motorcyclists in Mysuru, a tier II city in Southern India.MethodologyCross sectional road side observational study of 3499 motorcyclists (2134 motorcycle riders and 1365 pillion riders) at four traffic intersections was done followed by interview of random sample of 129 of the above riders. Effective helmet use proportion and effective helmet use per 100 person-minutes of observation was calculated. Multivariate logistic regression analysis was done to identify factors associated with effective helmet use.ResultsPrevalence of effective helmet use was 28 per 100 riders and 19.5 per 100 person-minutes of observation in traffic intersections. Specific prevalence rates was higher in riders (34.5%), female riders (51.3%), male pillion riders (30.5%) . Riders commuting for work and school and those ever stopped by the police in the past 3 months had significantly higher odds of effective helmet use.ConclusionThe effective helmet use among the motorcyclists in Mysore is very low. Strict enforcement and frequent checks by the police are necessary to increase the effective helmet use.
A retrospective comparison of deep learning to manual annotations for optic disc and optic cup segmentation in fundus photos
Authors: Fu Huazhu , Li Fei , Xu Yanwu , Liao Jingan , Xiong Jian , Shen Jianbing , Liu Jiang , Zhang Xiulan ,   for iChallenge-GON study group
Published: May 2020

ABSTRACTPurposeOptic disc (OD) and cup (OC) segmentation are fundamental for fundus image analysis. Manual annotation is time consuming, expensive, and highly subjective, while an automated system is invaluable to the medical community. The aim of this study is to develop a deep learning system to segment OD and OC in fundus photos, and evaluate how the algorithm compares against manual annotations.MethodsA total of 1200 fundus photos with 120 glaucoma cases were collected. The OD and OC annotations were labeled by seven licensed ophthalmologists, and glaucoma diagnoses were based on comprehensive evaluations of the subject medical records. A deep learning system for OD and OC segmentation was developed. The performances of segmentation and glaucoma discriminating based on the cup-to-disc ratio (CDR) of automated model were compared against the manual annotations.ResultsThe algorithm achieved an OD dice of 0.938 (95% confidence interval (CI), 0.934-0.941), OC dice of 0.801 (95% CI, 0.793-0.809), and CDR mean absolute error (MAE) of 0.077 (95% CI, 0.073-0.082). For glaucoma discriminating based on CDR calculations, the algorithm obtained an area under receiver operator characteristic curve (AUC) of 0.948 (95% CI, 0.920-0.973), with a sensitivity of 0.850 (95% CI, 0.794-0.923) and specificity of 0.853 (95% CI, 0.798-0.918).ConclusionsWe demonstrated the potential of the deep learning system to assist ophthalmologists in analyzing OD and OC segmentation and discriminating glaucoma from non-glaucoma subjects based on CDR calculations.Translational RelevanceWe investigate the segmentation of OD and OC by deep learning system compared against the manual annotations.
Elevated RDW is Associated with Increased Mortality Risk in COVID-19
Authors: Foy Brody H. , Carlson Jonathan C.T. , Reinertsen Erik , Valls Raimon Padros , Lopez Roger Pallares , Palanques-Tost Eric , Mow Christopher , Westover M. Brandon , Aguirre Aaron D. , Higgins John M. 
Published: May 2020

AbstractBackgroundCoronavirus disease 2019 (COVID-19) is an acute respiratory illness with a high rate of hospitalization and mortality. Prognostic biomarkers are urgently needed. Red blood cell distribution width (RDW), a component of complete blood counts that reflects cellular volume variation, has been shown to be associated with elevated risk for morbidity and mortality in a wide range of diseases.MethodsWe retrospectively studied the relationship between RDW and COVID-19 mortality risk for 1198 adult patients diagnosed with SARS-CoV-2 at 4 Partners Healthcare Network Hospitals between March 4, 2020, and April 28, 2020.FindingsElevated RDW (> 14.5%) was associated with increased mortality in patients of all ages with a risk ratio of 2.5 (95% CI, 2.3 – 2.8). Stratified by age, the risk ratio was 6.2 (4.4 – 7.9, N = 312) < 50 years, 3.2 (2.5 – 4.1, N = 230) 50-60, 2.3 (1.6 – 3.1, N = 236) 60-70, 1.2 (0.7 – 1.8, N = 203) 70-80, and 1.9 (1.5 – 2.3, N = 216) > 80 years. RDW was significantly associated with mortality in Cox proportional hazards models adjusted for age, D-Dimer, absolute lymphocyte count, and common comorbidities (p < 1e-4 for RDW in all cases). Patients whose RDW increased during admission had a ~3-fold elevation in mortality risk compared to those whose RDW did not change.InterpretationElevated RDW at diagnosis and an increase in RDW during admission are both associated with increased mortality risk for adult COVID-19 patients at a large academic medical center network.FundingThis study was supported by funding from the One Brave Idea Initiative and the CRICO Risk Management Fund.
The race to find a SARS-CoV-2 drug can only be won by a few chosen drugs: a systematic review of registers of clinical trials of drugs aimed at preventing or treating COVID-19
Authors: Martinez-Vizcaino Vicente , Mesas Arthur E. , Cavero-Redondo Iván , Saz-Lara Alicia , Sequí-Dominguez Irene , Pascual-Morena Carlos , Álvarez-Bueno Celia 
Published: May 2020

ABSTRACTConsidering the massive amount of clinical trial registers aimed to find effective drugs for the prevention and treatment of COVID-19, it is challenging to have a comprehensive view of which drugs are being studied more extensively and when is expected that we will have consistent results regarding their effectiveness. This systematic review included all clinical trials on pharmacological therapy related to COVID-19 and SARS-CoV-2 registered at the International Clinical Trials Registry Platform (WHO-ICTRP) up to April 22, 2020. Clinical trials characteristics (country, design, sample size, main outcomes, expected completion data, type of participants, length of the interventions, main outcomes). How many trials and the accumulated sample size by drug or combination of drugs, and by month in 2020 was depicted. We identified 412 clinical trials registers addressing the effect of pharmacological treatments on COVID-19, predominantly from Asia and Europe (42.2% and 31.1% of clinical trials registers, respectively). The most main outcomes studied were clinical recovery (54.4% of the clinical trials registers, respiratory recovery (28.2%) mortality (27.4%), viral load/negativity (20.4%). During 2020, a huge amount of clinical trials are expected to be completed: 41 trials (60,366 participants) using hydroxychloroquine, 20 trials (1,588 participants) using convalescent’s plasma, 18 trials (6,830 participants) using chloroquine, 12 trials (9,938 participants using lopinavir/ritonavir, 11 trials (1,250 participants) using favipiravir, 10 trials (2,175 participants) using tocilizumab and 6 trials (13,540 participants) using Remdesivir. The distribution of the number of registered clinical trials among the different therapeutic options leads to an excess of sample size for some and a lack for others. Our data allow us to conclude that by the end of June we will have results of almost 20 trials involving 40000 patients for hydroxychloroquine and 5 trials with 4500 patients for remdesivir; however, low statistical power is expected from the 9 clinical trials testing the efficacy of favipiravir or the 5 testing tocilizumab, since they will recruit less than 1000 patients each one.
Functional connectivity of white matter as a biomarker of cognitive decline in Alzheimer’s disease
Authors: Gao Yurui , Sengupta Anirban , Li Muwei , Zu Zhongliang , Rogers Baxter P. , Anderson Adam W. , Ding Zhaohua , Gore John C. ,   for the Alzheimer’s Disease Neuroimaging Initiative
Published: May 2020

AbstractObjectiveIn vivo functional changes in white matter during the progression of Alzheimer’s disease (AD) have not been previously reported. Our objectives are to measure changes in white matter functional connectivity (FC) in an aging population undergoing cognitive decline as AD develops, to establish their relationship to neuropsychological scores of cognitive abilities, and to assess their performance as predictors of AD.MethodsAnalyses were conducted using resting state functional MRI (rsfMRI) and neuropsychological data from 383 ADNI participants, including 136 cognitive normal (CN) controls, 46 with significant memory concern, 83 with early mild cognitive impairment (MCI), 37 with MCI, 46 with late MCI, and 35 with AD dementia. We used novel analyses of whole brain rsfMRI data to derive FC metrics between white matter tracts and discrete cortical volumes, as well as FC metrics between different white matter tracts, and their relationship to 6 cognitive measures. We then implemented supervised machine learning on white matter FCs to classify the participants and evaluated the performance.ResultsSignificant decreases were found in white matter FCs with prominent, specific, regional deficits appearing in late MCI and AD dementia patients relative to CN. These changes significantly correlated with behavioral measurements of impairments in cognition and memory. The sensitivity and specificity for distinguishing AD dementia and CN using white matter FCs were 0.83 and 0.81 respectively.Conclusions and RelevanceThe white matter FC decreased in late MCI and AD dementia patients compared to CN participants, and the white matter FC correlates with cognitive measures. White matter FC based classification shows promise for differentiating AD patients from CN. It is suggested that white matter FC may be a novel imaging biomarker of AD progression.
Identification and Analysis of Shared Risk Factors in Sepsis and High Mortality Risk COVID-19 Patients
Authors: Das Sayoni , Taylor Krystyna , Pearson Matthew , Kozubek James , Pawlowski Marcin , Jensen Claus Erik , Skowron Zbigniew , Møller Gert Lykke , Strivens Mark , Gardner Steve 
Published: May 2020

ABSTRACTBACKGROUNDCoronavirus disease 2019 (COVID-19) is a novel coronavirus strain disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The disease is highly transmissible and severe disease including viral sepsis has been reported in up to 16% of hospitalized cases. The admission characteristics associated with increased odds of hospital mortality among confirmed cases of COVID-19 include severe hypoxia, low platelet count, elevated bilirubin, hypoalbuminemia and reduced glomerular filtration rate. These symptoms correlate highly with severe sepsis cases. The diseases also share similar comorbidity risks including dementia, type 2 diabetes mellitus, coronary heart disease, hypertension and chronic renal failure. Sepsis has been observed in up to 59% of hospitalized COVID-19 patients.It is highly desirable to identify risk factors and novel therapy/drug repurposing avenues for late-stage severe COVID-19 patients. This would enable better protection of at-risk populations and clinical stratification of COVID-19 patients according to their risk for developing life threatening disease.METHODSAs there is currently insufficient data available for confirmed COVID-19 patients correlating their genomic profile, disease severity and outcome, co-morbidities and treatments as well as epidemiological risk factors (such as ethnicity, blood group, smoking, BMI etc.), a direct study of the impact of host genomics on disease severity and outcomes is not yet possible. We therefore ran a study on the UK Biobank sepsis cohort as a surrogate to identify sepsis associated signatures and genes, and correlated these with COVID-19 patients.Sepsis is itself a life-threatening inflammatory health condition with a mortality rate of approximately 20%. Like the initial studies for COVID-19 patients, standard genome wide association studies (GWAS) have previously failed to identify more than a handful of genetic variants that predispose individuals to developing sepsis.RESULTSWe used a combinatorial association approach to analyze a sepsis population derived from UK Biobank. We identified 70 sepsis risk-associated genes, which provide insights into the disease mechanisms underlying sepsis pathogenesis. Many of these targets can be grouped by common mechanisms of action such as endothelial cell dysfunction, PI3K/mTOR pathway signaling, immune response regulation, aberrant GABA and neurogenic signaling.CONCLUSIONThis study has identified 70 sepsis related genes, many of them for the first time, that can reasonably be considered to be potentially relevant to severe COVID-19 patients. We have further identified 59 drug repurposing candidates for 13 of these targets that can be used for the development of novel therapeutic strategies to increase the survival rate of patients who develop sepsis and potentially severe COVID-19.
A Computational Model for Estimating the Evolution of COVID-19 in Rondônia-Brazil
Authors: Junior Tito Dias , Machado Camila Bueno 
Published: May 2020

AbstractIn this work, the modified SEIR model was proposed to account separately for the tested and isolated cases, with severe and critical symptoms, from those not tested, with mild and moderate symptoms. Two parameters were estimated and evaluated for the cases registered in Rondônia, Brazil, between March 20 and April 22. The basic reproduction rate did not remain constant during the period, showing eventual variations due to social behavior. The results show that an increase in the proportion of testing to about 56% provided a significant decrease in confirmed cases, for the expansion of tested cases beyond the current testing criterion (20%) would help to identify and isolate also mild and moderate cases, generally referred to as asymptomatic.
BCG Vaccine Protection from Severe Coronavirus Disease 2019 (COVID19)
Authors: Escobar Luis E. , Molina-Cruz Alvaro , Barillas-Mury Carolina 
Published: May 2020

AbstractA series of epidemiological explorations has suggested a negative association between national BCG vaccination policy and the prevalence and mortality of COVID-19. However, these comparisons are difficult to validate due to broad differences between countries such as socioeconomic status, demographic structure, rural vs. urban settings, time of arrival of the pandemic, number of diagnostic tests and criteria for testing, and national control strategies to limit the spread of COVID-19. We review evidence for a potential biological basis of BCG cross-protection from severe COVID-19, and refine the epidemiological analysis to mitigate effects of potentially confounding factors (e.g., stage of the COVID-19 epidemic, development, rurality, population density, and age structure). A strong correlation between the BCG index, an estimation of the degree of universal BCG vaccination deployment in a country, and COVID-19 mortality in different socially similar European countries was observed (r2= 0.88;p= 8×10-7), indicating that every 10% increase in the BCG index was associated with a 10.4% reduction in COVID-19 mortality. Results fail to confirm the null hypothesis of no-association between BCG vaccination and COVID-19 mortality, and suggest that BCG could have a protective effect. Nevertheless, the analyses are restricted to coarse-scale signals and should be considered with caution. BCG vaccination clinical trials are required to corroborate the patterns detected here, and to establish causality between BCG vaccination and protection from severe COVID-19. Public health implications of a plausible BCG cross-protection from severe COVID-19 are discussed.Significance StatementThe COVID-19 pandemic is one of the most devastating in recent history. The bacillus Calmette-Guérin (BCG) vaccine against tuberculosis also confers broad protection against other infectious diseases, and it has been proposed that it could reduce the severity of COVID-19. This epidemiological study assessed the global linkage between BCG vaccination and COVID-19 mortality. Signals of BCG vaccination effect on COVID-19 mortality are influenced by social, economic, and demographic differences between countries. After mitigating multiple confounding factors, several significant associations between BCG vaccination and reduced COVID-19 deaths were observed. This study highlights the need for mechanistic studies behind the effect of BCG vaccination on COVID-19, and for clinical evaluation of the effectiveness of BCG vaccination to protect from severe COVID-19.
Severe obesity is associated with higher in-hospital mortality in a cohort of patients with COVID-19 in the Bronx, New York
Authors: Palaiodimos Leonidas , Kokkinidis Damianos G. , Li Weijia , Karamanis Dimitrios , Ognibene Jennifer , Arora Shitij , Southern William N. , Mantzoros Christos S. 
Published: May 2020

ABSTRACTBackground & AimsNew York is the current epicenter of Coronavirus disease 2019 (COVID-19) pandemic. The underrepresented minorities, where the prevalence of obesity is higher, appear to be affected disproportionally. Our objectives were to assess the characteristics and early outcomes of patients hospitalized with COVID-19 in the Bronx and investigate whether obesity is associated with worse outcomes.MethodsThis retrospective study included the first 200 patients admitted to a tertiary medical center with COVID-19. The electronic medical records were reviewed at least three weeks after admission. The primary endpoint was in-hospital mortality.Results200 patients were included (female sex: 102, African American: 102). The median BMI was 30 kg/m2. The median age was 64 years. Hypertension (76%), hyperlipemia (46.2%), and diabetes (39.5%) were the three most common comorbidities. Fever (86%), cough (76.5%), and dyspnea (68%) were the three most common symptoms. 24% died during hospitalization (BMI <25 kg/m2: 31.6%, BMI 25-34 kg/m2: 17.2%, BMI≥35 kg/m2: 34.8%, p= 0.03). The multivariate analysis for mortality, demonstrates that BMI≥35 kg/m2 (OR: 3.78; 95% CI: 1.45 - 9.83; p=0.006), male sex (OR: 2.74; 95% CI: 1.25 - 5.98; p=0.011) and increasing age (OR: 1.73; 95% CI: 1.13 - 2.63; p=0.011) were independently associated with higher in hospital mortality. Similar results were obtained for the outcomes of increasing oxygen requirement and intubation.ConclusionsIn this cohort of hospitalized patients with COVID-19 in a minority-predominant population, severe obesity, increasing age, and male sex were associated with higher in-hospital mortality and in general worse in-hospital outcomes.
Comprehensive Testing Highlights Racial, Ethnic, and Age Disparities in the COVID-19 Outbreak: Epidemiological and Clinical Characteristic of Cases in Utah
Authors: Ahmed Sharia M , Shah Rashmee U. , Bale Margaret , Peacock Jordan B. , Berger Ben , Brown Alyssa , Mann Sara , West William , Martin Valerie , Fernandez Valerie , Grineski Sara , Brintz Benjamin , Samore Matthew H. , Ferrari Matthew J. , Leung Daniel T. , Keegan Lindsay T. 
Published: May 2020

AbstractThe United States (US), which is currently the epicenter for the COVID-19 pandemic, is a country whose demographic composition differs from that of other highly-impacted countries. US-based descriptions of SARS-CoV-2 infections have, for the most part, focused on patient populations with severe disease, captured in areas with limited testing capacity. The objective of this study is to compare characteristics of positive and negative SARS-CoV-2 patients, in a population primarily comprised of mild and moderate infections, identified from comprehensive population-level testing. Here, we extracted demographics, comorbidities, and vital signs from 20,088 patients who were tested for SARS-CoV-2 at University of Utah Health clinics, in Salt Lake County, Utah; and for a subset of tested patients, we performed manual chart review to examine symptoms and exposure risks. To determine risk factors for testing positive, we used logistic regression to calculate the odds of testing positive, adjusting for symptoms and prior exposure. Of the 20,088 individuals, 1,229 (6.1%) tested positive for SARS-CoV-2. We found that Non-White persons were more likely to test positive compared to non-Hispanic Whites (adjOR=1.1, 95% CI: 0.8, 1.6), and that this increased risk is more pronounced among Hispanic or Latino persons (adjOR=2.0, 95%CI: 1.3, 3.1). However, we did not find differences in the duration of symptoms nor type of symptom presentation between non-Hispanic White and non-White individuals. We found that risk of hospitalization increases with age (adjOR=6.9 95% CI: 2.1, 22.5 for age 60+ compared to 0-19), and additionally show that younger individuals (aged 019), were underrepresented both in overall rates of testing as well as rates of testing positive. We did not find major race/ethnic differences in hospitalization rates. In this analysis of predominantly non-hospitalized individuals tested for SARS-CoV-2, enabled by expansive testing capacity, we found disparities in both testing and SARS-CoV-2 infection status by race/ethnicity and by age. Further work on addressing racial and ethnic disparities, particularly among Hispanic/Latino communities (where SARS-CoV-2 may be spreading more rapidly due to increased exposure and comparatively reduced testing), will be needed to effectively combat COVID-19 in the US.