bluuebunny/crossref_metadata_2025_split huggingface dataset:
Tocilizumab for treatment of mechanically ventilated patients with COVID-19
Authors: Somers Emily C , Eschenauer Gregory A , Troost Jonathan P , Golob Jonathan L , Gandhi Tejal N , Wang Lu , Zhou Nina , Petty Lindsay A , Baang Ji Hoon , Dillman Nicholas O , Frame David , Gregg Kevin S , Kaul Dan R , Nagel Jerod , Patel Twisha S , Zhou Shiwei , Lauring Adam S , Hanauer David A , Martin Emily , Sharma Pratima , Fung Christopher M , Pogue Jason M 
Published: June 2020

ABSTRACTBackgroundSevere COVID-19 can manifest in rapid decompensation and respiratory failure with elevated inflammatory markers. This presentation is consistent with cytokine release syndrome in chimeric antigen receptor T cell therapy, for which IL-6 blockade is approved treatment.MethodsWe assessed effectiveness and safety of IL-6 blockade with tocilizumab in a single-center cohort of patients with COVID-19 requiring mechanical ventilation. The primary endpoint was survival probability post-intubation; secondary analyses included an ordinal illness severity scale integrating superinfections. Outcomes in patients who received tocilizumab compared to tocilizumab-untreated controls were evaluated using multivariable Cox regression with propensity score inverse probability weighting (IPTW).Findings154 patients were included, of whom 78 received tocilizumab and 76 did not. Median follow-up was 47 days (range 28-67). Baseline characteristics were similar between groups, although tocilizumab-treated patients were younger (mean 55 vs. 60 years), less likely to have chronic pulmonary disease (10% vs. 28%), and had lower D-dimer values at time of intubation (median 2.4 vs. 6.5 mg/dL). In IPTW-adjusted models, tocilizumab was associated with a 45% reduction in hazard of death [hazard ratio 0.55 (95% CI 0.33, 0.90)] and improved status on the ordinal outcome scale [odds ratio per 1-level increase: 0.59 (0.36, 0.95)]. Though tocilizumab was associated with an increased proportion of patients with superinfections (54% vs. 26%; p<0.001), there was no difference in 28-day case fatality rate among tocilizumab-treated patients with versus without superinfection [22% vs. 15%; p=0.42].InterpretationIn this cohort of mechanically ventilated COVID-19 patients, tocilizumab was associated with a decreased likelihood of death despite higher superinfection occurrence. Randomized controlled trials are urgently needed to confirm these findings.KEY POINTSQuestionCan therapy with the IL-6 receptor antagonist tocilizumab improve outcomes in patients with severe COVID-19 illness requiring mechanical ventilation?FindingsIn this observational, controlled study of 154 patients, receipt of tocilizumab was associated with a 45% reduction in the hazard of death, despite twice the frequency of superinfection (54% vs 26%), both of which were statistically significant findings.MeaningTocilizumab therapy may improve survival in patients with COVID-19 illness requiring mechanical ventilation. These results can inform clinical practice pending the results of randomized clinical trials.
Spreading of COVID-19 in Brazil: Impacts and uncertainties in social distancing strategies
Authors: Volpatto Diego T. , Resende Anna Claudia M. , dos Anjos Lucas , Silva João V. O. , Dias Claudia M. , Almeida Regina C. , Malta Sandra M. C. 
Published: June 2020

AbstractBrazil’s continental dimension poses a challenge to the control of the spread of COVID-19. Due to the country specific scenario of high social and demographic heterogeneity, combined with limited testing capacity, lack of reliable data, under-reporting of cases, and restricted testing policy, the focus of this study is twofold: (i) to develop a generalized SEIRD model that implicitly takes into account the quarantine measures, and (ii) to estimate the response of the COVID-19 spread dynamics to perturbations/uncertainties. By investigating the projections of cumulative numbers of confirmed and death cases, as well as the effective reproduction number, we show that the model parameter related to social distancing measures is one of the most influential along all stages of the disease spread and the most influential after the infection peak. Due to such importance in the outcomes, different relaxation strategies of social distancing measures are investigated in order to determine which strategies are viable and less hazardous to the population. The results highlight the need of keeping social distancing policies to control the disease spread. Specifically, the considered scenario of abrupt social distancing relaxation implemented after the occurrence of the peak of positively diagnosed cases can prolong the epidemic, with a significant increase of the projected numbers of confirmed and death cases. An even worse scenario could occur if the quarantine relaxation policy is implemented before evidence of the epidemiological control, indicating the importance of the proper choice of when to start relaxing social distancing measures.
Kalman Filter Based Short Term Prediction Model for COVID-19 Spread
Authors: Kumar Suraj , Singh Koushlendra Kumar , Dixit Prachi , Kumar Bajpai Manish 
Published: June 2020

AbstractCOVID-19 has emerged as global medical emergency in recentdecades. The spread scenario of this pandemic has shown many variations. Keeping all this in mind, this article is written after various studies and analysis on the latest data on COVID-19 spread, which also includes the demographic and environmental factors. After gathering data from various resources, all data are integrated and passed into different Machine Learning Models to check the fit. Ensemble Learning Technique,Random Forest, gives a good evaluation score on the test data. Through this technique, various important factors are recognised and their contribution to the spread is analysed. Also, linear relationship between various features is plotted through heatmap of Pearson Correlation matrix. Finally, Kalman Filter is used to estimate future spread of COVID19, which shows good result on test data. The inferences from Random Forest feature importance and Pearson Correlation gives many similarities and some dissimilarities, and these techniques successfully identify the different contributing factors. The Kalman Filter gives a satisfying result for short term estimation, but not so good performance for long term forecasting. Overall, the analysis, plots, inferences and forecast are satisfying and can help a lot in fighting the spread of the virus.
Evaluating the adequacy of Prima Covid-19 IgG/IgM Rapid Test for the assessment of exposure to SARS-CoV-2 virus
Authors: Di Lorenzo Giulia , Toniolo Paolo , Lurani Caterina , Foresti Luca , Carrisi Chiara 
Published: June 2020

1.AbstractThe outbreak of the SARS-CoV-2 in early 2020 found health authorities worldwide unprepared to control the pandemic. The adoption of accurate, rapid and inexpensive methods to identify infected subjects in the general population is of paramount relevance for the control of the disease. We evaluated one of the available serological tests, thePrima LabCovid-19 IgG/IgM Rapid Tests, on 739 volunteers. We first assessed the test’s reproducibility by administering it twice on the same day on 104 subjects obtaining and overall score of 93 percent. Since the intensity of the color in the test line regions varies depending on the concentration of Covid-19 antibodies in each sample and that the determination of the positivity depends strictly on the subjective assessment by the reader, after excluding the subjects whose color intensity was too tenuous to be deemed unquestionably positive by the reader the reproducibility increased to 96%. The test would not perform properly for 6 subjects for a very limited overall technical failure of 0.83%. For 138 subjects information was available regarding a previous Real Time PCR nasopharyngeal swab test performed elsewhere. The correspondence of positive results between the two tests was 90.58% (125/138). In spite of some limitation owing especially to the choice of a self selected population sample, we conclude thatPrima LabCovid-19 IgG/IgM Rapid Test represents a low-cost, easily applicable and reproducible tool in detecting SARS-Cov-2 diffusion in the general population.
Missing clinical trial data: the evidence gap in the safety of potential COVID-19 drugs
Authors: Rodgers Florence , Pepperrell Toby , Keestra Sarai , Pilkington Victoria 
Published: June 2020

AbstractBackgroundSeveral drugs are being repurposed for the treatment of the coronavirus disease 2019 (COVID-19) pandemic based on in vitro or early clinical findings. As these drugs are being used in varied regimens and dosages, it is important to enable synthesis of existing safety data from clinical trials. However, availability of safety information is limited by a lack of timely reporting of clinical trial results on public registries or through academic publication. We aimed to analyse the evidence gap in safety data by quantifying the number of missing clinical trial results for drugs potentially being repurposed for COVID-19 by conducting a rapid review of results posting on ClinicalTrials.gov and in academic publications.MethodsClinicalTrials.gov was searched for 19 drugs that have been identified as potential treatments for COVID-19. Relevant clinical trials for any prior indication were listed by identifier (NCT number) and checked for results and for timely result reporting (within 395 days of the primary completion date). Additionally, PubMed and Google Scholar were searched to identify publications of results not listed on the registry. A second, blinded search of 10% of trials was conducted to assess reviewer concordance.ResultsOf 3754 completed trials, 1516 (40.4%) did not post results on ClinicalTrials.gov or in the academic literature. 1172 (31.2%) completed trials had tabular results on ClinicalTrials.gov. A further 1066 (28.4%) completed trials had results from the literature search, but did not report results on ClinicalTrials.gov. Key drugs missing clinical trial results include hydroxychloroquine (37.0% completed trials unreported), favipiravir (77.8%) and lopinavir (40.5%).ConclusionsThere is an important evidence gap for the safety of drugs being repurposed for COVID-19. This uncertainty could cause a large burden of additional morbidity and mortality during the pandemic. We recommend caution in experimental drug use for non-severe disease and urge clinical trial sponsors to report missing results retrospectively.
Minimising exposure to droplet and aerosolised pathogens: a computational fluid dynamics study
Authors: Perella Paolo , Tabarra Mohammad , Hataysal Ertan , Pournasr Amir , Renfrew Ian 
Published: June 2020

BackgroundHazardous pathogens are spread in either droplets or aerosols produced during aerosol generating procedures (AGP). Adjuncts minimising exposure of healthcare workers to hazardous pathogens released during AGP may be beneficial. We used state-of-the-art Computational Fluid Dynamics modelling to optimise the performance of a custom-designed shield.MethodsWe modelled airflow patterns and trajectories of particles (size range 1–500µm) emitted during a typical cough using Computational Fluid Dynamics (ANSYS Fluent software), in the presence and absence of a protective shield enclosing the head of a patient. We modelled the effect of different shield designs, suction tube position, and suction flow rate on particle escape from the shield.ResultsUse of the shield prevented escape of 99.1–100% of particles, which were either trapped on the shield walls (16–21%) or extracted via suction (79–82%). At most, 0.9% particles remained floating inside the shield. Suction flow rates (40–160L min−1) had no effect on the final location of particles in a closed system. Particle removal from within the shield was optimal when a suction catheter was placed vertically next to the head of the patient. Addition of multiple openings in the shield reduced the purging performance from 99% at 160 L min−1 to 67% at 40 L min−1.ConclusionComputational fluid dynamics modelling provides information to guide optimisation of the efficient removal of hazardous pathogens released during AGP from a custom-designed shield. These data are essential to establish before clinical use and/or pragmatic clinical trials.
Suggestibility in functional neurological disorder: A meta-analysis
Authors: Wieder Lillian , Brown Richard J. , Thompson Trevor , Terhune Devin B. 
Published: June 2020

AbstractObjectiveResponsiveness to direct verbal suggestions (suggestibility) has long been hypothesized to represent a predisposing factor for functional neurological disorder (FND) but previous research has yielded conflicting results. The aim of this study was to quantitatively evaluate whether FND patients display elevated suggestibility relative to controls via meta-analysis.MethodsFour electronic databases were searched in November 2019, with the search updated in April 2020, for original studies assessing suggestibility using standardized behavioural scales or suggestive symptom induction protocols in FND patients and controls. The meta-analysis followed Cochrane, PRISMA, and MOOSE guidelines. Data extraction and study quality coding were performed by two independent reviewers. Standardized suggestibility scores and responsiveness to symptom induction protocols were used to calculate standardized mean differences (SMDs) between groups.ResultsOf 26,643 search results, 19 articles presenting 11 standardized suggestibility datasets (FND: n = 316; control: n = 360) and 11 symptom suggestibility datasets (FND: n = 1285; control: n = 1409) were included in random-effects meta-analyses. Meta-analyses revealed that FND patients displayed greater suggestibility than controls on standardized behavioural scales (SMD, 0.48 [95% CI, 0.15, 0.81]) and greater responsiveness to suggestive symptom induction (SMD, 1.39 [95% CI, 0.92, 1.86]). Moderation analyses presented mixed evidence regarding the extent to which effect sizes covaried with methodological differences across studies. No evidence of publication bias was found.ConclusionsThese results corroborate the hypothesis that FND is characterized by heightened responsiveness to verbal suggestion. Atypical suggestibility may confer risk for FND and be a cognitive marker that can inform diagnosis and treatment of this condition.
COVID-19 apparent reproductive number dropped during Spain’s nationwide dropdown, then spiked at lower-incidence regions
Authors: Santamaría L. , Hortal J. 
Published: June 2020

AbstractCOVID-19 pandemic has rapidly spread worldwide. Spain has suffered one of the largest nationwide bursts, particularly in the highly populated areas of Madrid and Barcelona (two of the five largest conurbations in Europe). We used segmented regression analyses to identify shifts in the evolution of the apparent reproductive number (Rt) reported for 16 Spanish administrative regions. We associate these breaking points with a timeline of key containment measures taken by national and regional governments, applying time lags for the time from contagion to case detection, with their associated errors. Results show an early decrease of Rt that preceded the nationwide lockdown; a generalized, sharp decrease in Rt associated with such lockdown; a low impact of the strengthened lockdown, with a flattening of Rt evolution in high-incidence regions regions – but increases in Rt at low-incidence regions; and an increase in Rt, associated to the relaxation of the lockdown measures, in ten regions. These results evidence the importance of generalized lockdown measures to contain COVID-19 spread; and the limited effect of the subsequent application of a stricter lockdown (restrictions to all non-essential economic activities). Most importantly, they highlight the importance of maintaining strong social distancing measures and strengthening public health control during lockdown de-escalation.
A Statistical Model for Quantifying the Needed Duration of Social Distancing for the COVID-19 Pandemic
Authors: Rakocz Nadav , Fu Boyang , Halperin Eran , Sankararaman Sriram 
Published: June 2020

AbstractUnderstanding the effectiveness of strategies such as social distancing is a central question in attempts to control the COVID-19 pandemic. A key unknown in social distancing strategies is the duration of time for which such strategies are needed. Answering this question requires an accurate model of the transmission trajectory. A challenge in fitting such a model is the limited COVID-19 case data available from a given location. To overcome this challenge, we propose fitting a model of SARS-CoV-2 transmission jointly across multiple locations. We apply the model to COVID-19 case data from Spain, UK, Germany, France, Denmark, and New York to estimate the distribution for the time needed for social distancing to end to range from May 2020 to July 2021 (95% credible interval), where the median date is October, 2020. Our method is not specific to COVID-19, and it can also be applied to future pandemics.
Statistical Methods for Estimating Cure Fraction of COVID-19 Patients In India
Authors: Sreedevi E. P. , Sankaran P. G. 
Published: June 2020

AbstractThe human race is under the COVID-19 pandemic menace since beginning of the year 2020. Even though the disease is easily transmissible, a massive fraction of the affected people are recovering. Most of the recovered patients will not experience death due to COVID-19, even if they observed for a long period. They can be treated as long term survivors (cured population) in the context of lifetime data analysis. In this article, we present some statistical methods to estimate the cure fraction of the COVID-19 patients in India. Proportional hazards mixture cure model is used to estimate the cure fraction and the effect of covariates gender and age on lifetime. The data available on website ‘https://api.cvoid19india.org’ is used in this study. We can see that, the cure fraction of the COVID-19 patients in India is more than 90%, which is indeed an optimistic information.
COVID-SGIS: A smart tool for dynamic monitoring and temporal forecasting of Covid-19
Authors: de Lima Clarisse Lins , da Silva Cecilia Cordeiro , da Silva Ana Clara Gomes , Silva Eduardo Luiz , Marques Gabriel Souza , de Araújo Lucas Job Brito , Júnior Luiz Antônio Albuquerque , de Souza Samuel Barbosa Jatobá , de Santana Maíra Araújo , Gomes Juliana Carneiro , de Freitas Barbosa Valter Augusto , Musah Anwar , Kostkova Patty , dos Santos Wellington Pinheiro , da Silva Filho Abel Guilhermino 
Published: June 2020

AbstractObjectiveThe new kind of coronavirus SARS-Cov2 spread to countries in all continents in the World. The coronavirus disease 2019 (Covid-19) causes fever, cough, sore throat, and in severe cases shortness of breath and death. To evaluate strategies, it is necessary to forecast the number of cases and deaths, in order to aid the stakeholders in the process of making decisions against the disease. We propose a system for real-time forecast of the cumulative cases of Covid-19 in Brazil.Study DesignMonitoring of all Brazilian cities using oficial information from the National Notification System, from March to May 2020, concentrated on Brazil.io databases. Training and evaluation of ARIMA and other machine learning algorithms for temporal forecasting using correlation indexes (Pearson’s, Spearman’s, and Kendall’s) and RMSE(%). Validation from the relative errors of the following six days.MethodsOur developed software, COVID-SGIS, captures information from the 26 states and the Distrito Federal at the Brazil.io database. From these data, ARIMA models are created for the accumulation of confirmed cases and death cases by Covid-19. Finally, six-day forecasts graphs are available for Brazil and for each of its federative units, separately, with a 95% CI. In addition to these predictions, the worst and best scenarios are also presented.ResultsARIMA models were generated for Brazil and its 27 federative units. The states of Bahia, Maranhão, Piauí, Rio Grande do Norte and Amapá, Rondônia every day of the predictions were in the projection interval. The same happened to the states of Espírito Santo, Minas Gerais, Paraná and Santa Catarina. In Brazil, the percentage error between the predicted values and the actual values varied between 2.56% and 6.50%. For the days when the forecasts outside the prediction interval, the percentage errors in relation to the worst case scenario were below 5%. The states of Bahia, Maranhão, Piauí, Rio Grande do Norte, Amapá, and Rondônia every day of the predictions were in the projection interval. The same happened to the states of Espírito Santo, Minas Gerais, Paraná and Santa Catarina.ConclusionThe proposed method for dynamic forecasting may be used to guide social policies and plan direct interventions in a robust, flexible and fast way. Since it is based on information from multiple databases, it can be adapted to the different realities, becoming an important tool to guide the course of politics and action against Covid-19 pandemic worldwide.
A Mendelian randomization study of telomere length and blood-cell traits
Authors: Adams Charleen D. , Boutwell Brian B. 
Published: June 2020

AbstractWhether telomere attrition reducing proliferative reserve in blood-cell progenitors is causal has important public-health implications. Mendelian randomization (MR) is an analytic technique using germline genetic variants as instrumental variables. If certain assumptions are met, estimates from MR should be free from most environmental sources of confounding and reverse causation. Here, two-sample MR is performed to test whether longer telomeres cause changes to hematological traits. Summary statistics for genetic variants strongly associated with telomere length were extracted from a genome-wide association (GWA) study for telomere length in individuals of European ancestry (n=9190) and from GWA studies of blood-cell traits, also in those of European ancestry (n∼173,000 participants). A standard deviation increase in genetically influenced telomere length increased red blood cell (RBC) and white blood cell (WBC) counts, decreased mean corpuscular hemoglobin (MCH) and mean cell volume (MCV), and had no observable impact on mean corpuscular hemoglobin concentration (MCHC), red cell distribution width (RDW), hematocrit, or hemoglobin. Sensitivity tests for pleiotropic distortion were mostly inconsistent with glaring violations to the MR assumptions. Similar to how germline mutations in TERT can lead to bone-marrow failure, these data provide evidence that genetically influenced common variation in telomere length impacts hematologic traits in the population.