bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Role of Air Conditioning in the Diffusion of Sars-CoV-2 in Indoor Environments: a First Computational Fluid Dynamic Model, based on Investigations performed at the Vatican State Children’s Hospital
Authors: Borro Luca , Mazzei Lorenzo , Raponi Massimiliano , Piscitelli Prisco , Miani Alessandro , Secinaro Aurelio 
Published: September 2020

AbstractBackgroundAbout 15 million people worldwide were affected by the Sars-Cov-2 infection, which already caused 600,000 deaths. This virus is mainly transmitted through exhalations from the airways of infected persons, so that Heating, Ventilation and Air Conditioning (HVAC) systems might play a role in increasing or reducing the spreading of the infection in indoor environments.MethodsWe modelled the role of HVAC systems in the diffusion of the contagion through Computational Fluid Dynamics (CFD) simulations of cough at the “Bambino Gesù” Vatican State Children’s Hospital.Both waiting and hospital rooms were modeled as indoor scenarios. A specific Infection-Index (η) parameter was used to estimate the amount of contaminated air inhaled by each person present in the simulated indoor scenarios. The potential role of exhaust air ventilation systems placed above the coughing patient’s mouth was also assessed.ResultsOur CFD-based simulations of the waiting room show that HVAC air-flow remarkably enhances infected droplets diffusion in the whole indoor environment within 25 seconds from the cough event, despite the observed dilution of saliva particles containing the virus. At the same time also their number is reduced due to removal through the HVAC system or deposition on the surfaces. The proper use of Local Exhaust Ventilation systems (LEV) simulated in the hospital room was associated to a complete reduction of infected droplets spreading from the patient’s mouth in the first 0.5 seconds following the cough event. In the hospital room, the use of LEV system completely reduced the η index computed for the patient hospitalized at the bed next to the spreader, with a decreased possibility of contagion.ConclusionsCFD-based simulations for indoor environment can be useful to optimize air conditioning flow and to predict the contagion risk both in hospitals/ambulatories and in other public/private settings.
Structural and functional connectomes in people with multiple sclerosis
Authors: Tozlu Ceren , Jamison Keith , Gauthier Susan A. , Kuceyeski Amy 
Published: September 2020

AbstractOne of the challenges in multiple sclerosis is that lesion volume does not correlate with symptom severity. Advanced techniques such as diffusion and functional MRI allow imaging of the brain’s connectivity networks, which may provide better insight as to brain-behavior relationships in impairment and compensation in multiple sclerosis. We aim to build machine learning models based on structural and functional connectomes to classify a) healthy controls versus people with multiple sclerosis and b) impaired versus not impaired people with multiple sclerosis. We also aim to identify the most important imaging modality for both classification tasks, and, finally, to investigate which brain regions’ connectome measures contribute most to the classification. Fifteen healthy controls (age=43.6± 8.6, 53% female) and 76 people with multiple sclerosis (age: 45.2 ± 11.4 years, 65% female, disease duration: 12.2 ± 7.2 years) were included. Twenty-three people with multiple sclerosis were considered impaired, with an Expanded Disability Status Scale of 2 or higher. Subjects underwent MRI scans that included anatomical, diffusion and resting-state functional MRI. Random Forest models were constructed using structural and static/dynamic functional connectome measures independently; single modality models were then combined for an ensemble prediction. The accuracy of the models was assessed by the area under the receiver operating curve. Models that included structural connectomes significantly outperformed others when classifying healthy controls and people with multiple sclerosis, having a median accuracy of 0.86 (p-value<0.05, corrected). Models that included dynamic functional connectome metrics significantly outperformed others when distinguishing people with multiple sclerosis by impairment level, having a median accuracy of 0.63 (p-value<0.05, corrected). Structural connectivity between subcortical, somatomotor and visual networks were most damaged by multiple sclerosis. For the classification of patients with multiple sclerosis into impairment severity groups, the most discriminatory metric was dwell time in a dynamic functional connectome state characterized by strong connectivity between and among somatomotor and visual networks. These results suggest that damage to the structural connectome, particularly in the subcortical, visual and somatomotor networks, is a hallmark of multiple sclerosis, and, furthermore, that increased functional coordination between these same regions may be related to severity of motor disability in multiple sclerosis. The use of multi-modal connectome imaging has the potential to shed light on mechanisms of disease and compensation in multiple sclerosis, thus enabling more accurate prognoses and possibly the development of novel therapeutics.
Optimising social mixing strategies to mitigate the impact of COVID-19 in six European countries: a mathematical modelling study
Authors: Ragonnet Romain , Briffoteaux Guillaume , Williams Bridget M. , Savulescu Julian , Segal Matthew , Abayawardana Milinda , Eggo Rosalind M. , Tuyttens Daniel , Melab Nouredine , Marais Ben J. , McBryde Emma S. , Trauer James M. 
Published: September 2020

ABSTRACTBackgroundIf SARS-CoV-2 elimination is not feasible, strategies are needed to minimise the impact of COVID-19 in the medium-to-long term, until safe and effective vaccines can be used at the population-level.MethodsUsing a mathematical model, we identified contact mitigation strategies that minimised COVID-19-related deaths or years of life lost (YLLs) over a time-horizon of 15 months, using an intervention lasting six or 12 months, in Belgium, France, Italy, Spain, Sweden and the UK. We used strategies that either altered age- or location-specific contact patterns. The optimisation was performed under the constraint that herd immunity should be achieved by the end of the intervention period if post-infection immunity was persistent. We then tested the effect of waning immunity on the strategies.FindingsStrategies of contact mitigation by age were much more effective than those based on mitigation by location. Extremely stringent contact reductions for individuals aged over 50 were required in most countries to minimise deaths or YLLs. The median final proportion of the population ever-infected with SARS-CoV-2 after herd immunity was reached ranged between 30% and 43%, depending on the country and intervention duration. Compared to an unmitigated scenario, optimised age-specific mitigation was predicted to avert over 1 million deaths across the six countries. The optimised scenarios assuming persistent immunity resulted in comparable hospital occupancies to that experienced during the March-April European wave. However, if immunity was shortlived, high burdens were expected without permanent contact mitigation.InterpretationOur analysis suggests that age-selective mitigation strategies can reduce the mortality impacts of COVID-19 dramatically even when significant transmission occurs. The stringency of the required restrictions in some groups raises concerns about the practicality of these strategies. If post-infection immunity was short-lived, solutions based on a mitigation period designed to increase population immunity should be accompanied with ongoing contact mitigation to prevent large epidemic resurgence.
Development of a qualitative real-time RT-PCR assay for the detection of SARS-CoV-2: A guide and case study in setting up an emergency-use, laboratory-developed molecular assay
Authors: Anahtar Melis N. , Shaw Bennett , Slater Damien , Byrne Elizabeth , Botti-Lodovico Yolanda , Adams Gordon , Schaffner Stephen , Eversley Jacqueline , McGrath Graham , Gogakos Tasos , Lennerz Jochen , Marble Hetal Desai , Ritterhouse Lauren L. , Batten Julie , Georgantas N. Zeke , Pellerin Rebecca , Signorelli Sylvia , Thierauf Julia , Kemball Molly , Happi Christian , Grant Donald S. , Ndiaye Daouda , Siddle Katherine J. , Mehta Samar B. , Harris Jason , Ryan Edward T. , Pierce Virginia , LaRocque Regina , Lemieux Jacob E. , Sabeti Pardis , Rosenberg Eric , Branda John , Turbett Sarah E. 
Published: September 2020

ABSTRACTDeveloping and deploying new diagnostic tests is difficult, but the need to do so in response to a rapidly emerging pandemic such as COVID-19 is crucially important for an effective response. In the early stages of a pandemic, laboratories play a key role in helping health care providers and public health authorities detect active infection, a task most commonly achieved using nucleic acid-based assays. While the landscape of diagnostics is rapidly evolving, polymerase chain reaction (PCR) remains the gold-standard of nucleic acid-based diagnostic assays, in part due to its reliability, flexibility, and wide deployment. To address a critical local shortage of testing capacity persisting during the COVID-19 outbreak, our hospital set up a molecular based laboratory developed test (LDT) to accurately and safely diagnose SARS-CoV-2. We describe here the process of developing an emergency-use LDT, in the hope that our experience will be useful to other laboratories in future outbreaks and will help to lower barriers to fast and accurate diagnostic testing in crisis conditions.
Multi-trait association studies discover pleiotropic loci between Alzheimer’s disease and cardiometabolic traits
Authors: Bone William P. , Siewert Katherine M. , Jha Anupama , Klarin Derek , Damrauer Scott M. ,   the VA Million Veteran Project, Chang Kyong-Mi , Tsao Philip S. , Assimes Themistocles L. , Ritchie Marylyn D. , Voight Benjamin F. 
Published: September 2020

AbstractIdentification of genetic risk factors that are shared between Alzheimer’s disease (AD) and other traits, i.e., pleiotropy, can help improve our understanding of the etiology of AD and potentially detect new therapeutic targets. Motivated by previous epidemiological correlations observed between cardiometabolic traits and AD, we performed a set of bivariate genome-wide association studies coupled with colocalization analysis to identify loci that are shared between AD and eleven cardiometabolic traits. We identified three previously unreported pleiotropic trait associations at known AD loci as well as four novel pleiotropic loci. One associated locus was tagged by a low-frequency coding variant in the geneDOCK4and is potentially implicated in its alternative splicing. Statistical colocalization with expression quantitative trait loci identified by the Genotype-Tissue Expression (GTEx) project identified additional candidate genes, includingACE, the target of the hypertensive drug class of ACE-inhibitors. We found that the allele associated with decreasedACEexpression in brain tissue was also associated with increased risk of AD, providing human genetic evidence of a potential increase in AD risk from use of an established anti-hypertensive therapeutic. Overall, our results support a complex genetic relationship between AD and these cardiometabolic traits, and the candidate causal genes identified suggest that blood pressure and immune response play a role in the pleiotropy between these traits.
Safety and immunogenicity of ChAd63-KH vaccine in post kala azar dermal leishmaniasis patients in Sudan
Authors: Younis Brima M. , Osman Mohamed , Khalil Eltahir A.G. , Santoro Francesco , Furini Simone , Wiggins Rebecca , Keding Ada , Carraro Monica , Anas E.A. Musa , Abdarahaman Mujahid A.A. , Mandefield Laura , Bland Martin , Aebischer Toni , Gabe Rhian , Layton Alison M. , Lacey Charles J. N. , Kaye Paul M. , Musa Ahmed M. 
Published: September 2020

AbstractPost kala azar dermal leishmaniasis (PKDL) is a chronic, stigmatising skin condition occurring frequently after apparent clinical cure from visceral leishmaniasis. Given an urgent need for new treatments, we conducted a Phase IIa safety and immunogenicity trial of ChAd63-KH vaccine in Sudanese patients with persistent PKDL. LEISH2a (NCT02894008) was an open label three-phase clinical trial involving sixteen adult and eight adolescent patients with persistent PKDL (median duration 30 months; range 6 -180 months). Patients received a single intramuscular vaccination of 1×1010 viral particles (v.p.; adults only) or 7.5×1010 v.p. (adults and adolescents), with primary (safety) and secondary (clinical response and immunogenicity) endpoints evaluated over 42-120 days follow up. AmBisome® was provided to patients with significant remaining disease at their last visit. ChAd63-KH vaccine showed minimal adverse reactions in PKDL patients and induced potent innate and cell-mediated immune responses measured by whole blood transcriptomics and ELISpot. 7 patients (30.4%) monitored to study completion showed >90% clinical improvement and 6 (25%) showed partial improvement. A logistic regression model applied to blood transcriptomic data identified immune modules predictive of patients with >90% clinical improvement. A randomised controlled trial to determine whether these clinical responses were vaccine related and whether ChAd63-KH vaccine has clinical utility is underway.
Identifying COVID-19 cases in primary care settings
Authors: Mao Yinan , Tan Yi-Roe , Thein Tun Linn , Chai Yi Ann Louis , Cook Alex R , Dickens Borame L , Lew Yii Jen , Lim Fong Seng , Lim Jue Tao , Sun Yinxiaohe , Sundaram Meena , Soh Alexius , En Tan Glorijoy Shi , Wong Franco Pey Gein , Young Barnaby , Zeng Kangwei , Chen Mark , Seng Desmond Ong Luan 
Published: September 2020

AbstractCase identification is an ongoing issue for the COVID-19 epidemic, in particular for outpatient care where physicians must decide which patients to prioritise for further testing. This paper reports tools to classify patients based on symptom profiles based on 236 SARS-CoV-2 positive cases and 564 controls, accounting for the time course of illness at point of assessment. Clinical differentiators of cases and controls were used to derive model-based risk scores. Significant symptoms included abdominal pain, cough, diarrhea, fever, headache, muscle ache, runny nose, sore throat, temperature between 37.5°C and 37.9°C, and temperature above 38°C, but their importance varied by day of illness at assessment. With a high percentile threshold for specificity at 0.95, the baseline model had reasonable sensitivity at 0.67. To further evaluate accuracy of model predictions, we firstly used leave-one-out cross-validation, which confirmed high classification accuracy with an area under the receiver operating characteristic curve of 0.92. For the baseline model, sensitivity decreased to 0.56. Secondly, in a separate ongoing prospective study of 237 COVID-19 and 346 primary care patients presenting with symptoms of acute respiratory infection, the baseline model had a sensitivity of 0.57 and specificity of 0.89, and in retrospective notes review of 100 COVID-19 cases diagnosed in primary care, sensitivity was 0.56. A web-app based tool has been developed for easy implementation as an adjunct to laboratory testing to differentiate COVID-19 positive cases among patients presenting in outpatient settings.
A multipurpose machine learning approach to predict COVID-19 negative prognosis in São Paulo, Brazil
Authors: Fernandes Fernando Timoteo , de Oliveira Tiago Almeida , Teixeira Cristiane Esteves , de Moraes Batista Andre Filipe , Costa Gabriel Dalla , Filho Alexandre Dias Porto Chiavegatto 
Published: September 2020

AbstractIntroductionThe new coronavirus disease (COVID-19) is a challenge for clinical decision-making and the effective allocation of healthcare resources. An accurate prognostic assessment is necessary to improve survival of patients, especially in developing countries. This study proposes to predict the risk of developing critical conditions in COVID-19 patients by training multipurpose algorithms.MethodsA total of 1,040 patients with a positive RT-PCR diagnosis for COVID-19 from a large hospital from São Paulo, Brazil, were followed from March to June 2020, of which 288 (28%) presented a severe prognosis, i.e. Intensive Care Unit (ICU) admission, use of mechanical ventilation or death. Routinely-collected laboratory, clinical and demographic data was used to train five machine learning algorithms (artificial neural networks, extra trees, random forests, catboost, and extreme gradient boosting). A random sample of 70% of patients was used to train the algorithms and 30% were left for performance assessment, simulating new unseen data. In order to assess if the algorithms could capture general severe prognostic patterns, each model was trained by combining two out of three outcomes to predict the other.ResultsAll algorithms presented very high predictive performance (average AUROC of 0.92, sensitivity of 0.92, and specificity of 0.82). The three most important variables for the multipurpose algorithms were ratio of lymphocyte per C-reactive protein, C-reactive protein and Braden Scale.ConclusionThe results highlight the possibility that machine learning algorithms are able to predict unspecific negative COVID-19 outcomes from routinely-collected data.
Health-Related Quality of Life in the Spironolactone to Reduce ICD Therapy (SPIRIT) Trial
Authors: Liberato Ana Carolina Sauer , Raitt Merritt H. , Zarraga Ignatius GE , MacMurdy Karen S. , Dougherty Cynthia M. 
Published: September 2020

ABSTRACTPurposeThe Spironolactone to Reduce ICD Therapy (SPIRIT) trial was designed to determine whether once daily spironolactone would: 1) reduce the incidence of ventricular tachycardia (VT) and ventricular fibrillation (VF), and 2) improve health related quality of life (HRQOL) and symptoms. The purpose of this paper is to describe the long term HRQOL outcomes in the SPIRIT trial and compare QOL in those who did or did not receive implantable cardioverter defibrillator (ICD) shocks during follow-up.MethodsNinety participants age 66±10 years, 96% men, 75% with NYHA class II, with an ICD at moderately high risk for recurrent VT/VF were randomized to spironolactone 25mg (N=44) or placebo (N=46). HRQOL was measured every 6 months for 24 months using 3 instruments: Patient Concerns Assessment (PCA), Short Form Health Survey-Veterans Version (SF-36V), and the Kansas City Cardiomyopathy Questionnaire (KCCQ). Linear mixed modeling was used to compare changes in HRQOL across 24-months. ANCOVA was used to compare HRQOL between those getting an ICD shock or not.ResultsOver 24-months, there were no differences in HRQOL between the spironolactone vs. placebo groups. Those who experienced at least 1 ICD shock vs. those with no ICD shocks, reported significantly lower HRQOL and more symptoms at 6-months and 24 months.ConclusionsSpironolactone had no significant impact on HRQOL. Patients receiving one or more ICD shocks reported significant reductions in HRQOL and higher symptoms.
Predicting and interpreting COVID-19 transmission rates from the ensemble of government policies
Authors: Sruthi C. K. , Biswal Malay Ranjan , Saraswat Brijesh , Joshi Himanshu , Prakash Meher K. 
Published: September 2020

AbstractSeveral questions resonate as the governments relax their COVID-19 mitigation policies - is it too early to relax them, were the policies as effective as they could have been. Answering these questions about the past or crafting newer policy decisions in the future requires a quantification of how policy choices affect the spread of the infection. Policy landscape as well as the infection trajectories from different states and countries diverged so fast that comparing and learning from them has not been easy. In this work, we standardize and pool together the ensemble of lockdown and graded re-opening policies adopted by the 50 states of USA in any given week between 9th March and 9th August. Using artificial intelligence (AI) on this pooled data, we build a predictive model (, ) for the weekly-averaged transmission rate of infections. Predictability conceptually raises the possibility of an evidence-based or data-driven mitigation policy-making by evaluating the relative merits of the different policy scenarios. Probing the predictions with interpretable AI highlights how factors such as the closing of bars or the use of masks influence transmission, effects which have been hard to decouple from the ensemble of policy instrument combinations. While acknowledging the limitations of our predictions as well as of the infection testing, we ask the theoretical question if the observed transmission rates in the states were as efficient as they could have been under various levels of restrictions, and if the mitigation policies of the states are ‘overdesigned’. The model can be further refined with a more detailed inclusion of geographies and policy compliances, as well as expanded as newer policies emerge.
Dynamic data-driven meta-analysis for prioritisation of host genes implicated in COVID-19
Authors: Parkinson Nicholas , Rodgers Natasha , Fourman Max Head , Wang Bo , Zechner Marie , Swets Maaike C. , Millar Jonathan E. , Law Andy , Russell Clark D. , Baillie J. Kenneth , Clohisey Sara 
Published: September 2020

AbstractThe increasing body of literature describing the role of host factors in COVID-19 pathogenesis demonstrates the need to combine diverse, multi-omic data to evaluate and substantiate the most robust evidence and inform development of therapies.Here we present a dynamic ranking of host genes implicated in human betacoronavirus infection (SARS-CoV-2, SARS-CoV, MERS-CoV, seasonal coronaviruses). Researchers can search and review the ranked genes and the contribution of different experimental methods to gene rank at https://baillielab.net/maic/covid19.We conducted an extensive systematic review of experiments identifying potential host factors. Gene lists from diverse sources were integrated using Meta-Analysis by Information Content (MAIC). This previously described algorithm uses data-driven gene list weightings to produce a comprehensive ranked list of implicated host genes.From 32 datasets, the top ranked gene was PPIA, encoding cyclophilin A, a drug-gable target using cyclosporine.Other highly-ranked genes included proposed prognostic factors (CXCL10, CD4, CD3E) and investigational therapeutic targets (IL1A) for COVID-19. Gene rankings also inform the interpretation of COVID-19 GWAS results, implicating FYCO1 over other nearby genes in a disease-associated locus on chromosome 3.As new data are published we will regularly update list of genes as a resource to inform and prioritise future studies.