bluuebunny/crossref_metadata_2025_split huggingface dataset:
Optimal lockdown strategies for SARS-CoV2 mitigation— an Indian perspective
Authors: Pathak Anagh , Mohan Varun Madan , Banerjee Arpan 
Published: August 2020

AbstractWe sought to identify optimal temporal windows for lockdown-based mitigation strategies on infectious disease spreads. An age-structured multi-compartmental Susceptible- Infected-Recovered (SIR) model was used to estimate infection spreads under parametric variation of lockdown intensity and duration from the data of SARS-CoV2 cases in India between January to July, 2020. The resulting parameter values were used to simulate lockdown outcomes for a wide range of start times and durations. Lockdowns were simulated as intervention strategies that modified weights assigned to social contact matrices for work, school and other places. Lockdown efficacy was assessed by the maximum number of infections recorded during a simulation run. Our analysis shows that lockdown outcomes depend sensitively on the timing of imposition and that it is possible to minimize lockdown durations while limiting case loads to numbers below the hospitalization thresholds. Such timing based effects arise naturally from the non-linear nature of SIR dynamics.NotationNTotal PopulationSNumber of susceptible individualsINumber of infected individualsRNumber of recovered/removed individualsβPer-individual disease transmission rateγRecovery rateτLockdown start-timeΔ Duration of lockdownpPost-lockdown coefficienthTotal number of hospital bedsξMaximum fraction of infected individualsξ0Hospitalization threshold
Evaluation of Convalescent Plasma Versus Standard of Care for the Treatment of COVID-19 in Hospitalized Patients: study protocol for a phase 2 randomized, open-label, controlled, multicenter trial
Authors: Diago-Sempere Elena , Bueno José Luis , Sancho-López Aránzazu , Rubio Elena Múñez , Torres Ferrán , de Molina Rosa Malo , Fernández-Cruz (AFC) Ana , de Diego Isabel Salcedo , Velasco-Iglesias Ana , Payares-Herrera Concepción , Flecha Inmaculada Casas , Avendaño-Solà Cristina , Palomino Rafael Duarte , Ramos-Martínez Antonio , Ruiz-Antorán Belén 
Published: August 2020

AbstractBackgroundCOVID-19 is a respiratory disease caused by a novel coronavirus (SARS-CoV-2) and causes substantial morbidity and mortality. At the time this clinical trial was planned, there were no available vaccine or therapeutic agents with proven efficacy, but the severity of the condition prompted the use of several pharmacological and non-pharmacological interventions.It has long been hypothesized that the use of convalescent plasma (CP) from infected patients who have developed an effective immune response is likely to be an option for the treatment of patients with a variety of severe acute respiratory infections (SARI) of viral etiology. The aim of this study is to assess the efficacy and safety of convalescent plasma in adult patients with severe COVID-19 pneumonia.Methods/DesignThe ConPlas-19 study is a multicenter, randomized, open-label controlled trial. The protocol has been prepared in accordance with the SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) guidelines. The study has been planned to include 278 adult patients hospitalized with severe COVID-19 infection not requiring mechanical ventilation (invasive or non-invasive). Subjects are randomly assigned in a 1:1 ratio (139 per treatment arm), stratified by center, to receive intravenously administered CP (single infusion) plus SOC or SOC alone, and are to be followed for 30 days. The primary endpoint of the study is the proportion of patients that progress to categories 5, 6 or 7 (on the 7-point ordinal scale proposed by the WHO) at day 15. Interim analyses for efficacy and/or futility will be conducted once 20%, 40%, and 60% of the planned sample size are enrolled and complete D15 assessment.DiscussionThis clinical trial is designed to evaluate the efficacy and safety of passive immunotherapy with convalescent plasma for the treatment of adult patients hospitalized with COVID-19. The results of this study are expected to contribute to establishing the potential place of CP in the therapeutics for a new viral disease.Trial registrationTrial registration at clinicaltrials.gov; Registration Number: NCT04345523; https://clinicaltrials.gov/ct2/show/NCT04345523; Registered on 30 March, 2020. First posted date: April 14, 2020.
The COVID-19 Consequences of College Class Continuity Calculator: A Tool to Provide Students and Administrators with Estimated Risks of Returning to Campus
Authors: Bewick Sharon , Ludden Erika , Robertson Suzanne , Demers Jeffrey 
Published: August 2020

AbstractAs schools prepare for the start of the Fall 2020 semester, many are struggling to make decisions regarding whether or not to return to on-campus classes or whether to remain fully online. Unfortunately, there is no ‘one-size-fits-all’ answer, and schools must balance their own risks against the costs of remote learning. We present a tool that integrates information about study body composition with predictions of COVID-19 infection rates in order to provide clarity and insight into the decisions facing colleges and universities nationwide. Our tool is freely available and currently hosted at the following location: https://bewicklab.shinyapps.io/covid-1/
Face masks to prevent transmission of respiratory diseases: Systematic review and meta-analysis of randomized controlled trials*
Authors: Ollila Hanna M. , Partinen Markku , Koskela Jukka , Savolainen Riikka , Rotkirch Anna , Laine Liisa T. 
Published: August 2020

AbstractObjectiveTo examine the effect of face mask intervention in respiratory infections across different exposure settings and age groups.DesignSystematic review and meta-analysis.Data sourcesPubMed, Cochrane Central Register of Controlled Trials, and Web of Science were searched for randomized controlled trials investigating the effect of face masks on respiratory infections published by November 18th, 2020. We followed PRISMA guidelines.Eligibility criteria for selecting studiesRandomized controlled trials investigating face masks in respiratory infections across different exposure settings. Two reviewers performed the search, extracted data, and assessed the risk of bias. Random effects meta-analysis with risk ratio, adjusted odds ratios, and number needed to treat were performed. Findings by source control or wearer protection, age groups, exposure settings, and role of non-compliance were evaluated.ResultsSeventeen studies were included, (N=11,601 cases and N=10,286 controls, follow-up from 4 days to 19 months). Fourteen trials included adults and children and three trials included children only. Twelve studies showed non-compliance in treatment and eleven in control group. Four studies supported the use of face masks. Meta-analysis across all studies with risk ratios found no association with number of infections (RR=0.957 [0.810 − 1.131], p=0.608). Meta-analysis using odds ratios adjusted for age, sex, and vaccination (when available) showed protective effect of face masks (OR=0.850 [0.736 − 0.982], p=0.027). Subgroup meta-analysis with adjusted odds ratios found a decrease in respiratory infections among adults (14 studies, OR = 0.829 [0.709 − 0.969], p=0.019) in source control setting (OR = 0.845 [0.7375 − 0.969], p=0.0159) and when face masks were used together with hand hygiene OR = 0.690 [0.568 − 0.838], p=0.0002). Overall between-study heterogeneity was large also in the subgroup analyses.ConclusionDespite the large between study heterogeneity, compliance bias and differences by environmental settings, the findings support the use of face masks to prevent respiratory infections. PROSPERO registration number CRD42020205523.
HPLC analysis ofWithania somnifera(Linn.) Dunal reveals two chemotypes in Indian germplasm and seasonal and genetic variability in Withaferin A -Content
Authors: Kaushik Nutan 
Published: August 2020

AbstractHPLC methods for profiling and estimation of withanolide inWithaniahave been developed to study seasonal, geographical and genetic variation in Withaferin-A content and identify chemotypes. Highest withanolide content was observed during the vegetative phase and slight decrease was observed during reproductive phase. However, the content drastically reduced at the maturity. No significant effect of location was observed on the withanolide content when same genotypes were grown at two diverse locations. Withaferin-A content in Indian population ofWithania somniferavaried from 0.22 to 3.38 % with a mean value of 1.099 in the leaves during vegetative stage.Whereas during maturity the content ranged from 0.0056 to 1.99 % with a mean value of 0.5942 %. In the roots, Withaferin-A content ranged from 0.0028 to 0.75 % with a mean value of 0.1242 during vegetative phase and 0.0008 to 0.1489 with a mean value of 0.00213 % at maturity of the plant. The population segregated into two chemotypes identified as chemotype-I and as an intermediate of Chemotype–I and Chemotype-II.
Noise-driven multistability versus deterministic chaos in phenomenological semi-empirical models of whole-brain activity
Authors: Piccinini Juan , Ipina Ignacio Perez , Laufs Helmut , Kringelbach Morten , Deco Gustavo , Perl Yonatan Sanz , Tagliazucchi Enzo 
Published: August 2020

An outstanding open problem in neuroscience is to understand how neural systems are capable of producing and sustaining complex spatiotemporal dynamics. Computational models that combine local dynamics within vivomeasurements of anatomical and functional connectivity can be used to test potential mechanisms underlying this complexity. We compared two conceptually different mechanisms: noise-driven switching between equilibrium solutions (modeled by coupled Stuart-Landau oscillators) and deterministic chaos (modeled by coupled Rossler oscillators). We found that both models struggled to simultaneously reproduce multiple observables computed from the empirical data. This issue was especially manifest in the case of noise-driven dynamics close to a bifurcation, which imposed overly strong constraints on the optimal model parameters. In contrast, the chaotic model could produce complex behavior over an ampler range of parameters, thus being capable of capturing multiple observables at the same time with good performance. Our observations support the view of the brain as a non-equilibrium system able to produce endogenous variability. We presented a simple model capable of jointly reproducing functional connectivity computed at different temporal scales. Besides adding to our conceptual understanding of brain complexity, our results inform and constraint the future development of biophysically realistic large-scale models.The quote“What I cannot create, I do not understand”was found written in the blackboard of celebrated physicist Richard Feynman at the time of his death. This sentence suggests a way forward for neuroscientists interested in unravelling the principles behind the richness and complexity of spontaneous brain dynamics. Over the last decades, tremendous advances in neuroimaging enabled the construction of whole-brain activity models with real predictive power in the statistical sense. It is now possible tocreaterealistic complex dynamics, instead of passively screening for their presence in neuroimaging data. We contrasted two different types of building blocks (i.e. two choices of local dynamics) and tested their capacity to reproduce the empirical data, with the purpose of increasing our conceptual understanding of the mechanisms behind large-scale spontaneous activity in the human brain.
The Incubation Period of Severe Acute Respiratory Syndrome Coronavirus 2:A Systematic Review
Authors: Li Zhiyao , Zhang Yu , Wang Chenchen , Peng Liuqing , Gao Rongrong , Jing Jiarui , Ren Binzhi , Xu Jianguo , Wang Tong 
Published: August 2020

AbstractWhile the novel coronavirus continues to spread worldwide, the reported incubation period has varied between studies and is imprecise due to limited data. A literature search with certain selection criteria was conducted on May 30, 2020. In total, sixty-four articles were included, and 854 individual-level data were extracted from 30 studies for pooled analysis. Of these studies, 72% of them reported a median or mean incubation period of 4-7 days, while our estimated median was 4.9 days (95% confidence interval [CI]: 4.6-5.2). However, the inclusion of 81 asymptomatic and presymptomatic patients, as well as 31 cases with incubation periods exceeding 14 days, led to our estimation of 97.5 th percentile with 19.3 days (95% CI: 17.4-21.4), beyond the currently suggested 14-day quarantine period. Therefore, we appeal to prolong the quarantine duration, especially for areas that have insufficient testing resources, to protect susceptible populations from being infected.Article Summary LineThis article reviewed the COVID-19 studies involving incubation period and provided pooled estimation based on available data from these studies.The result showed that our estimated median incubation period is consistent with the estimates of formal studies but the 97.5 percentile is larger than ever on account of including a number of asymptomatic and presymptomatic patients.These finds suggested that we should properly prolong the isolation or quarantine period in order to identify more patients with longer incubation period and those without any symptoms.
Highly performing point-of-care molecular testing for SARS-CoV-2 with RNA extraction and isothermal amplification
Authors: Garneret Pierre , Coz Etienne , Martin Elian , Manuguerra Jean-Claude , Brient-Litzler Elodie , Enouf Vincent , González Obando Daniel Felipe , Olivo-Marin Jean-Christophe , Monti Fabrice , Van der Werf Sylvie , Vanhomwegen Jessica , Tabeling Patrick 
Published: August 2020

AbstractIn order to respond to the urgent request of massive testing, developed countries perform nucleic acid amplification tests (NAAT) of SARS-CoV-2 in centralized laboratories. Real-time RT - PCR (Reverse transcription - Polymerase Chain Reaction) is used to amplify the viral RNA and enable its detection. Although PCR is 37 years old, it is still considered, without dispute, as the gold standard. PCR is an efficient process, but the complex engineering required for automated RNA extraction and temperature cycling makes it incompatible for use in point of care settings. In the present work, by harnessing progress made in the past two decades in DNA amplification, microfluidics and membrane technologies, we succeeded to create a portable test, in which SARS-CoV-2 RNA is extracted, amplified isothermally by RT - LAMP (Loop-mediated Isothermal Amplification), and detected using intercalating dyes or highly fluorescent probes. Depending on the viral load, the detection takes between twenty minutes and one hour. Using pools of naso-pharyngal clinical samples, we estimated a sensitivity comparable to RT-qPCR (up to a Cycle threshold of 39, equivalent to <0.1 TCID50 per mL) and a 100% specificity, for other human coronaviruses and eight respiratory viruses currently circulating in Europe. We designed and fabricated an easy-to-use portable device called “COVIDISC” to carry out the test at the point of care. The low cost of the materials along with the absence of complex equipment paves the way towards a large dissemination of this device. The perspective of a reliable SARS-CoV-2 point of care detection, highly performing, that would deliver on-site results in less than one hour, with a self-testing potential, opens up a new efficient approach to manage the pandemics.
The Cone Method: Inferring Decision Times from Single-Trial 3D Movement Trajectories in Choice Behavior
Authors: Ulbrich Philipp , Gail Alexander 
Published: August 2020

AbstractOngoing goal-directed movements can be rapidly adjusted following new environmental information, e.g. when chasing pray or foraging. This makes movement trajectories in go-before-you-know decision-making a suitable behavioral readout of the ongoing decision process. Yet, existing methods of movement analysis are often based on statistically comparing two groups of trial-averaged trajectories and are not easily applied to three-dimensional data, preventing them from being applicable to natural free behavior. We developed and tested thecone methodto estimate the point of overt commitment (POC) along a single two- or three-dimensional trajectory, i.e. the position where movement is adjusted towards a newly selected spatial target. In Experiment 1, we established a “ground truth” data set in which the cone method successfully identified the experimentally constrained POCs across a wide range of all but the shallowest adjustment angles. In Experiment 2, we demonstrate the power of the method in a typical decision-making task with expected decision time differences known from previous findings. The POCs identified by cone method matched these expected effects. In both experiments, we compared the cone method’s single trial performance with a trial-averaging method and obtained comparable results. We discuss the advantages of the single-trajectory cone method over trial-averaging methods and possible applications beyond the examples presented in this study. The cone method provides a distinct addition to existing tools used to study decisions during ongoing movement behavior, which we consider particularly promising towards studies of non-repetitive free behavior.
Prediction of type 2 diabetes mellitus onset using logistic regression-based scoreboards
Authors: Edlitz Yochai , Segal Eran 
Published: August 2020

AbstractType 2 diabetes mellitus (T2DM) accounts for ∼90% of all cases of diabetes which are estimated with an annual world death rate of 1.6 million in 2016. Early detection of T2D high-risk patients can reduce the incidence of the disease through a change in lifestyle, diet, or medication. Since populations of lower socio-demographic status are more susceptible to T2D and might have limited resources for laboratory testing, there is a need for accurate yet accessible prediction models based on non-laboratory parameters. This paper introduces one non-laboratory model which is highly accessible to the general population and one highly precise yet simple laboratory model. Both models are provided as an accessible scoreboard form and also as a logistic regression model. We based the models on data from 44,879 non-diabetic, UK Biobank participants, aged 40-65, predicting the risk of T2D onset within the next 7.3 years (SD 2.3). The non-laboratory prediction model for T2DM onset probability incorporated the following covariates: sex, age, weight, height, waist, hips-circumferences, waist-to-hip Ratio (WHR) and Body-Mass Index (BMI). This logistic regression model achieved an Area Under the Receiver Operating Curve (auROC) of 0.82 (0.79-0.85 95% CI) and an odds ratio (OR) between the upper and lower prevalence deciles of x77 (28-98). We further analysed the contribution of laboratory-based parameters and devised a blood-test model based on just five blood tests. In this model, we included age, sex, Glycated Hemoglobin (HbA1c%), reticulocyte count, Gamma Glutamyl-Transferase, Triglycerides, and HDL cholesterol to predict T2D onset. This logistic-regression model achieved an auROC of 0.89 (0.86-0.91) and a deciles’ OR of x87 (27-152). Using the scoreboard results, the Anthropometrics model classified three risk groups, a group with 1%(1-2%); a group with 9% (7-11%) probability, and a group with a 15% (7-23%) risk of developing T2D. The Five blood tests scoreboard model, further classified into four risk groups: 0.9% (0.7%-1%); 8%(6-11%); 18%(14-22%) and a high risk group of 38%(23-54%) of developing T2D. We analysed several more comprehensive models which included genotyping data and other environmental factors and found that it did not provide cost efficient benefits over the five blood tests model. The Five blood tests and anthropometric models, both in their logistic regression form and scoreboard form, outperform the commonly used non-laboratory models, the Finnish Diabetes Risk Score (FINDRISC) and the German Diabetes Risk Score (GDRS). When trained using our data, the FINDRISC achieved an auROC of 0.75 (0.71-0.78), and the GDRS auROC resulted in 0.58 (0.54-0.62), respectively.
SARS-CoV-2 Seroprevalence in Relation to Timing of Symptoms
Authors: Ebinger Joseph E. , Botwin Gregory J. , Albert Christine M. , Alotaibi Mona , Arditi Moshe , Berg Anders H. , Binek Aleksandra , Botting Patrick , Fert-Bober Justyna , Figueiredo Jane C. , Grein Jonathan D. , Hasan Wohaib , Henglin Mir , Hussain Shehnaz K. , Jain Mohit , Joung Sandy , Karin Michael , Kim Elizabeth H. , Li Dalin , Liu Yunxian , Luong Eric , McGovern Dermot P.B. , Merchant Akil , Merin Noah , Miles Peggy B. , Nguyen Trevor-Trung , Raedschelders Koen , Rashid Mohamad A. , Riera Celine E. , Riggs Richard V. , Sharma Sonia , Sobhani Kimia , Sternbach Sarah , Sun Nancy , Tourtellotte Warren G. , Eyk Jennifer E. Van , Braun Jonathan G. , Cheng Susan 
Published: August 2020

AbstractOf individuals with SARS-CoV-2 IgG antibody testing performed, those who contemporaneously experienced a cluster of Covid-19 relevant symptoms in the 1-2 months preceding the antibody assay were more likely to test positive whereas those who experienced the symptom clustering in the prior 3-6 months were more likely to test negative. These findings suggest that antibodies likely wane over a period of months, particularly in relation to the timing of symptoms.
Impacts of people’s learning behavior in fighting the COVID-19 epidemic
Authors: Cheng Baolian , Wang Yi-Ming 
Published: August 2020

This work presents a mathematical model that captures time-dependent social-distancing effects and presents examples of the consequences of relaxing social-distancing restrictions in the fight against the novel coronavirus epidemic. Without social distancing, the spread of COVID-19 will grow exponentially, but social distancing and people’s learning behavior (isolating, staying at home, wearing face masks, washing hands, restricting the size and frequency of group gatherings, etc.) can significantly impede the epidemic spread, flatten the infection curve, and change the final outcome of the COVID-19 outbreak. Our results demonstrate that strict social distancing and people’s learning behavior can be effective in slowing the spread rate and significantly reducing the total number of infections, daily infection rate, peak of daily infections, and duration of the epidemic. Under strict social distancing, the rise and fall of infections would be nearly symmetric about the peak of of daily infections, and the epidemic spread would be essentially over within 60 days. Relaxing social distancing and people learning behaviors will significantly increase the total and daily numbers of infections and prolong the course of the outbreak. These results have immediate applications for the implementation of various social-distancing policies and general significance for ongoing outbreaks and similar infectious disease epidemics in the future (LA-UR 20-22877).DisclaimerThis material is not final and is subject to be updated any time. Contact information:bcheng@lanl.gov.)