bluuebunny/crossref_metadata_2025_split huggingface dataset:
Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity
Authors: Fallerini Chiara , Picchiotti Nicola , Baldassarri Margherita , Zguro Kristina , Daga Sergio , Fava Francesca , Benetti Elisa , Amitrano Sara , Bruttini Mirella , Palmieri Maria , Croci Susanna , Lista Mirjam , Beligni Giada , Valentino Floriana , Meloni Ilaria , Tanfoni Marco , Colombo Francesca , Cabri Enrico , Fratelli Maddalena , Gabbi Chiara , Mantovani Stefania , Frullanti Elisa , Gori Marco , Crawley Francis P. , Butler-Laporte Guillaume , Richards Brent , Zeberg Hugo , Lipcsey Miklos , Hultstrom Michael , Ludwig Kerstin U. , Schulte Eva C. , Pairo-Castineira Erola , Baillie John Kenneth , Schmidt Axel , Frithiof Robert ,   WES/WGS working group within the HGI,   GenOMICC Consortium,   GEN-COVID Multicenter Study, Mari Francesca , Renieri Alessandra , Furini Simone 
Published: September 2021

AbstractThe combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole exome sequencing data of about 4,000 SARS-CoV-2-positive individuals were used to define an interpretable machine learning model for predicting COVID-19 severity. Firstly, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthly, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management.
Asymptomatic SARS-CoV-2 infection and the demography of COVID-19
Authors: Wang Zengmiao , Wu Peiyi , Wang Jingyuan , Lourenço José , Li Bingying , Rader Benjamin , Laine Marko , Miao Hui , Wang Ligui , Song Hongbin , Bharti Nita , Brownstein John S. , Bjornstad Ottar N. , Dye Christopher , Tian Huaiyu 
Published: September 2021

AbstractAsymptomatic individuals carrying SARS-CoV-2 can transmit the virus and contribute to outbreaks of COVID-19, but it is not yet clear how the proportion of asymptomatic infections varies by age and geographic location. Here we use detailed surveillance data gathered during COVID-19 resurgences in six cities of China at the beginning of 2021 to investigate this question. Data were collected by multiple rounds of city-wide PCR test with detailed contact tracing, where each patient was monitored for symptoms through the whole course of infection. We find that the proportion of asymptomatic infections declines with age (coefficient =-0.006, P<0.01), falling from 56% in age group 0–9 years to 12% in age group >60 years. Using an age-stratified compartment model, we show that this age-dependent asymptomatic pattern together with the age distribution of overall cases can explain most of the geographic differences in reported asymptomatic proportions. Combined with demography and contact matrices from other countries worldwide, we estimate that a maximum of 22%–55% of SARS-CoV-2 infections would come from asymptomatic cases in an uncontrolled epidemic based on asymptomatic proportions in China. Our analysis suggests that flare-ups of COVID-19 are likely if only adults are vaccinated and that surveillance and possibly control measures among children will be still needed in the future to contain epidemic resurgence.
COVID-19 Bimodal Clinical and Pathological Phenotypes
Authors: Batah Sabrina S , Benatti Maíra N , Siyuan Li , Telini Wagner M , Barbosa Jamile , Menezes Marcelo B , Nadai Tales R , Sá Keyla S G , Vaswani Chirag M. , Gupta Sahil , Zamboni Dario S , Wada Danilo T , Calado Rodrigo T , Oliveira Renê D R , Louzada-Junior Paulo , Auxiliadora-Martins Maria , Veras Flávio P , Cunha Larissa D , Cunha Thiago M , Luppino-Assad Rodrigo , Balancin Marcelo L , Morais Sirlei S , Martins Ronaldo B , Arruda Eurico , Chahud Fernando , Koenigkam-Santos Marcel , Cetlin Andrea A , Cunha Fernando Q , Santos Claudia dos , Capelozzi Vera L , Fukuoka Junya , Duarte-Achcar Rosane , Fabro Alexandre T 
Published: September 2021

ABSTRACTBackgroundPatients with coronavirus disease-2019 (COVID-19) present varying clinical complications. Different viral load and host response related to genetic and immune background are probably the reasons for these differences. We aimed to sought clinical and pathological correlation that justifies the different clinical outcomes among COVID-19 autopsies cases.MethodsMinimally invasive autopsy was performed on forty-seven confirmed COVID-19 patients from May-July, 2020. Electronic medical record of all patients was collected and a comprehensive histopathological evaluation was performed. Immunohistochemistry, immunofluorescence, special stain, western blotting and post-mortem real-time reverse transcriptase polymerase chain reaction on fresh lung tissue were performed.ResultsWe show that 5/47 (10,6%) patients present a progressive decline in oxygenation index for acute respiratory distress syndrome (PaO2/FiO2ratio), low compliance levels, interstitial fibrosis, high α-SMA+ cells/protein expression, high collagens I/III deposition and NETs(P<0.05), named as fibrotic phenotype (N=5). Conversely, 10/47 (21,2%) patients demonstrated progressive increase in PaO2/FiO2ratio, high pulmonary compliance levels, preserved elastic framework, increase thrombus formation and high platelets and D-dimer levels at admission (P<0.05), named as thrombotic phenotype. While 32/47 (68,1%) had a mixed phenotypes between both ones.ConclusionsWe believe that categorization of patients based on these two phenotypes can be used to develop prognostic tools and potential therapies since the PaO2/FiO2ratio variation and D-dimer levels correlate with the underlying fibrotic or thrombotic pathologic process, respectively; which may indicate possible clinical outcome of the patient.
Development of a novel startle response task in Duchenne muscular dystrophy
Authors: Maresh Kate E. , Papageorgiou Andriani , Ridout Deborah , Harrison Neil A. , Mandy William , Skuse David , Muntoni Francesco 
Published: September 2021

AbstractDuchenne muscular dystrophy (DMD), an X-linked childhood-onset muscular dystrophy caused by loss of the protein dystrophin, can be associated with neurodevelopmental, emotional and behavioural problems. A DMD mouse model also displays a neuropsychiatric phenotype, including increased startle responses to threat which normalise when dystrophin is restored in the brain.We hypothesised that startle responses may also be increased in humans with DMD, which would have potential translational therapeutic implications. To investigate this, we first designed a novel discrimination fear-conditioning task and tested it in six healthy volunteers, followed by male DMD (n=11) and Control (n=9) participants aged 7-12 years. The aims of this methodological task development study were to: i) confirm the task efficacy; ii) optimise data processing procedures; iii) determine the most appropriate outcome measures.In the task, two neutral visual stimuli were presented: one ‘safe’ cue presented alone; one ‘threat’ cue paired with a threat stimulus (aversive noise) to enable conditioning of physiological startle responses (skin conductance response, SCR, and heart rate). Outcomes were the unconditioned physiological startle responses to the initial threat, and retention of conditioned responses in the absence of the threat stimulus.We present the protocol development and optimisation of data processing methods based on empirical data. We found that the task was effective in producing significantly higher physiological startle SCR in reinforced ‘threat’ trials compared to ‘safe’ trials (P<.001). Different data extraction methods were compared and optimised, and the optimal sampling window was derived empirically. SCR amplitude was the most effective physiological outcome measure when compared to SCR area and change in heart rate, with the best profile on data processing, the least variance, successful conditioned response retention (P=.01) and reliability assessment in test-retest analysis (rho=.86). The definition of this novel outcome will allow us to study this response in a DMD population.
Rehabilitation in Survivors of COVID-19 (RE2SCUE): a nonrandomized, controlled, and open protocol
Authors: Campos Maria Cristine , Nery Tatyana , de Bem Alves Ana Cristina , Speck Ana Elisa , Rocha Vieira Danielle Soares , Ceola Schneider Ione Jayce , Pereira Matos Maria Paula , Arcêncio Livia , Aguiar Aderbal Silva 
Published: September 2021

AbstractObjectiveThis study aimed to evaluate the effects of physical rehabilitation for adults with sequelae after COVID-19.MethodsThis clinical, nonrandomized, controlled, and open study will examine 82 participants who have met the inclusion criteria and who will be divided into treatment and control groups according to participant preference. The intervention group will receive face-to-face care; the control group will receive remote educational guidance for 8 weeks, with pre-post evaluations. The primary outcomes are dyspnea, fatigue, and exercise capacity; the secondary outcomes are lung function, heart rate variability, handgrip strength, knee extensor strength and electrical activity, physical activity, functional limitation, cognitive function, depression and anxiety, and biochemical measures of hypoxia, inflammation, oxidative stress, blood glucose, and lactate blood tests. The survey will follow theStandard Protocol Items for Randomized Trialsguidelines, and the results will be reported according to theConsolidated Standards of Reporting Trialsguidelines. Effects will be assessed based on the intent-to-treat data collected. Analysis of covariance will be used for the initial and final evaluations, with a significance level of 5%.Results and ConclusionsThe results will show the effectiveness of rehabilitation in adults with post-COVID-19 sequelae.ImpactFatigue, dyspnea, cough, and muscle and joint pain are common sequelae of post-COVID-19 syndrome. Physical rehabilitation is one modality for treating these sequelae. This protocol can provide a treatment model for patients with post-COVID-19 sequelae.
SARS-CoV2 wild type and mutant specific humoral and T cell immunity is superior after vaccination than after natural infection
Authors: Richardson Jennifer R. , Götz Ralph , Mayr Vanessa , Lohse Martin J. , Holthoff Hans-Peter , Ungerer Martin 
Published: September 2021

AbstractObjectiveWe investigated blood samples from fully SARS-CoV2-vaccinated subjects and from previously positive tested patients up to one year after infection with SARS-CoV2, and compared short- and long-term T cell and antibody responses, with a special focus on the recently emerged delta variant (B.1.617.2).Methods and ResultsIn 23 vaccinated subjects, we documented high anti-SARS-CoV2 spike protein receptor binding domain (RBD) antibody titers. Average virus neutralization by antibodies, assessed as inhibition of ACE2 binding to RBD, was 2.2-fold reduced for delta mutant vs. wild type (wt) RBD. The mean specific antibody titers were lower one year after natural infection than after vaccination; ACE2 binding to delta mutant vs. wt RBD was 1.65-fold reduced. In an additional group, omicron RBD binding was reduced compared to delta.Specific CD4+ T cell responses were measured after stimulation with peptides pools from wt, alpha, beta, gamma, or delta variant SARS-CoV2 spike proteins by flow cytometric intracellular cytokine staining. There was no significant difference in cytokine production of IFN-γ, TNF-α, or IL-2 between vaccinated subjects. T cell responses to wt or mutant SARS-CoV2 spike were significantly weaker after natural occurring infections compared to those in vaccinated individuals.ConclusionAntibody neutralisation of the delta mutant was reduced compared to wt, as assessed in a novel inhibition assay with a finger prick blood drop. Strong CD4 T cell responses were present against wt and mutant SARS-CoV2 variants, including the delta (B.1.617.2) strain, in fully vaccinated individuals, whereas they were partly weaker 1 year after natural infection. Hence, immune responses after vaccination are stronger compared to those after naturally occurring infection, pointing out the need of the vaccine to overcome the pandemic.
INTESTINAL GLUCONEOGENESIS IS DOWNREGULATED IN PAEDIATRIC PATIENTS WITH COELIAC DISEASE
Authors: Karlson Olof , Arnell Henrik , Gudjonsdottir Audur H. , Agardh Daniel , Naluai Åsa Torinsson 
Published: September 2021

ABSTRACTObjectiveUntreated coeliac disease (CD) patients have increased levels of blood glutamine and a lower duodenal expression of glutaminase (GLS). Intestinal gluconeogenesis (IGN) is a process through which glutamine is turned into glucose in the small intestine, for which GLS is crucial. Animal studies suggest impaired IGN may have long-term effects on metabolic control and be associated with development of type 2 diabetes and non-alcoholic fatty liver disease (NAFLD). The aim of this study was to thoroughly investigate IGN at the gene expression level in children with untreated coeliac disease.DesignQuantitative polymerase chain reaction (qPCR) was used to quantify expression of 11 target genes related to IGN using the delta-delta Ct method with three reference genes (GUSB, IPO8 and YWHAZ) in duodenal biopsies collected from 84 children with untreated coeliac disease and 58 disease controls.ResultsSignificantly lower expression of nine target genes involved in IGN was seen in duodenal biopsies from CD patients compared with controls: FBP1, G6PC, GLS, GPT1, PCK1, PPARGC1A, SLC2A2, SLC5A1, and SLC6A19. No significant differences in expression were seen for G6PC3 and GOT1.ConclusionChildren with untreated coeliac disease have lower expression of genes important for IGN. Further studies are warranted to disentangle whether this is a consequence of intestinal inflammation or due to an impaired metabolic pathway shared with other chronic metabolic diseases. Impaired IGN could be a mechanism behind the increased risk of NAFLD seen in CD patients.SIGNIFICANCE OF THIS STUDYWhat is already known about this subject?Genome-wide association studies have shown an association between coeliac disease (CD) and glutaminase (GLS).Intestinal gluconeogenesis (IGN) is a process with a recently described important function in energy homeostasis and metabolic disease. GLS is critical for IGN by enabling it to use glutamine, its main substrate.CD patients are at an increased risk of non-alcoholic fatty liver disease (NAFLD) as adults.What are the new findings?Nine genes involved in IGN are downregulated at the gene expression level in the small intestine of children with untreated CD, suggesting impairment of IGN.How might it impact on clinical practice in the foreseeable future?Impaired IGN might be a mechanism behind the increased risk of NAFLD seen in CD patients as adults.Early diagnosis and treatment of CD may restore IGN and prevent CD patients from NAFLD later in adulthood.
Fundamental limits on inferring epidemic resurgence in real time using effective reproduction numbers
Authors: Parag Kris V. , Donnelly Christl A. 
Published: September 2021

AbstractWe find that epidemic resurgence, defined as an upswing in the effective reproduction number (R) of the contagion from subcritical to supercritical values, is fundamentally difficult to detect in real time. Inherent latencies in pathogen transmission, coupled with smaller and intrinsically noisier case incidence across periods of subcritical spread, mean that resurgence cannot be reliably detected without significant delays of the order of the generation time of the disease, even when case reporting is perfect. In contrast, epidemic suppression (where R falls from supercritical to subcritical values) may be ascertained 5–10 times faster due to the naturally larger incidence at which control actions are generally applied. We prove that these innate limits on detecting resurgence only worsen when spatial or demographic heterogeneities are incorporated. Consequently, we argue that resurgence is more effectively handled proactively, potentially at the expense of false alarms. Timely responses to recrudescent infections or emerging variants of concern are more likely to be possible when policy is informed by a greater quality and diversity of surveillance data than by further optimisation of the statistical models used to process routine outbreak data.Author summaryThe timely detection of epidemic resurgence (i.e., upcoming waves of infected cases) is crucial for informing public health policy, providing valuable signals for implementing interventions and identifying emerging pathogenic variants or important population-level behavioural shifts. Increases in epidemic transmissibility, parametrised by the time-varying reproduction number, R, commonly signify resurgence. While many studies have improved computational methods for inferring R from case data, to enhance real-time resurgence detection, few have examined what limits, if any, fundamentally restrict our ability to perform this inference. We apply optimal Bayesian detection algorithms and sensitivity tests and discover that resurgent (upward) R-changes are intrinsically more difficult to detect than equivalent downward changes indicating control. This asymmetry derives from the often lower and stochastically noisier case numbers that associate with resurgence, and induces detection delays on the order of the disease generation time. We prove these delays only worsen if spatial or demographic differences in transmissibility are modelled. As these fundamental limits exist even if case data are perfect, we conclude that designing integrated surveillance systems that fuse potentially timelier data sources (e.g., wastewater) may be more important than improving R-estimation methodology and deduce that there may be merit (subject to false alarm costs) in conservative resurgence response initiatives.
Calibration of Two Validated SARS-CoV-2 Pseudovirus Neutralization Assays for COVID-19 Vaccine Evaluation
Authors: Huang Yunda , Borisov Oleg , Kee Jia Jin , Carpp Lindsay N. , Wrin Terri , Cai Suqin , Sarzotti-Kelsoe Marcella , McDanal Charlene , Eaton Amanda , Pajon Rolando , Hural John , Posavad Christine M. , Gill Katherine , Karuna Shelly , Corey Lawrence , McElrath M. Juliana , Gilbert Peter B. , Petropoulos Christos J. , Montefiori David C. 
Published: September 2021

AbstractVaccine-induced neutralizing antibodies (nAbs) are key biomarkers considered to be associated with vaccine efficacy. In United States Government-sponsored phase 3 efficacy trials of COVID-19 vaccines, nAbs are measured by two different validated pseudovirus-based SARS-CoV-2 neutralization assays, with each trial using one of the two assays. Here we describe and compare the nAb titers obtained in the two assays. We observe that one assay consistently yielded higher nAb titers than the other when both assays were performed on the World Health Organization’s anti-SARS-CoV-2 immunoglobulin International Standard, COVID-19 convalescent sera, and mRNA-1273 vaccinee sera. To overcome the challenge this difference in readout poses in comparing/combining data from the two assays, we evaluate three calibration approaches and show that readouts from the two assays can be calibrated to a common scale. These results may aid decision-making based on data from these assays for the evaluation and licensure of new or adapted COVID-19 vaccines.
Machine Learning to Predict 10-year Cardiovascular Mortality from the Electrocardiogram: Analysis of the Third National Health and Nutrition Examination Survey (NHANES III)
Authors: Kim Chang H. , Al-Kindi Sadeer , Tarabichi Yasir , Gohel Suril , Vyas Riddhi , Srinivasan Shankar 
Published: September 2021

ABSTRACTBackgroundThe value of the electrocardiogram (ECG) for predicting long-term cardiovascular outcomes is not well defined. Machine learning methods are well suited for analysis of highly correlated data such as that from the ECG.MethodsUsing demographic, clinical, and 12-lead ECG data from the Third National Health and Nutrition Examination Survey (NHANES III), machine learning models were trained to predict 10-year cardiovascular mortality in ambulatory U.S. adults. Predictive performance of each model was assessed using area under receiver operating characteristic curve (AUROC), area under precision-recall curve (AUPRC), sensitivity, and specificity. These were compared to the 2013 American College of Cardiology/American Heart Association Pooled Cohort Equations (PCE).Results7,067 study participants (mean age: 59.2 ± 13.4 years, female: 52.5%, white: 73.9%, black: 23.3%) were included. At 10 years of follow up, 338 (4.8%) had died from cardiac causes. Compared to the PCE (AUROC: 0.668, AUPRC: 0.125, sensitivity: 0.492, specificity: 0.859), machine learning models only required demographic and ECG data to achieve comparable performance: logistic regression (AUROC: 0.754, AUPRC: 0.141, sensitivity: 0.747, specificity: 0.759), neural network (AUROC: 0.764, AUPRC: 0.149, sensitivity: 0.722, specificity: 0.787), and ensemble model (AUROC: 0.695, AUPRC: 0.166, sensitivity: 0.468, specificity: 0.912). Additional clinical data did not improve the predictive performance of machine learning models. In variable importance analysis, important ECG features clustered in inferior and lateral leads.ConclusionsMachine learning can be applied to demographic and ECG data to predict 10-year cardiovascular mortality in ambulatory adults, with potentially important implications for primary prevention.