bluuebunny/crossref_metadata_2025_split huggingface dataset:
Individual-Level Heterogeneity in Mask-wearing During the COVID-19 Pandemic in Malaysia
Authors: Zhang Stephen X. , Looi Kim Hoe , Li Nicolas , Wan Xue , Li Jizhen 
Published: July 2021

ABSTRACTWearing a face mask has been a key approach to contain or slow down the spread of COVID-19 in the ongoing pandemic. However, there is huge heterogeneity among individuals in their willingness to wear face masks during an epidemic. This research aims to investigate the individual heterogeneity to wear face masks and its associated predictors during the COVID-19 pandemic when mask-wearing was not mandatory but individuals’ choices. Based on a survey of 708 Malaysian adults and a multivariate least-squares fitting analysis, the results reveal a significant variance among individuals in wearing masks, as 34% of the individual adults did not always wear masks in public places. Female, individuals who wash their hands more frequently, and those who reported more availability of personal protective equipment were more likely to practice mask-wearing. The identification of less compliant groups of mask-wearing has critical implications by enabling more specific health communication campaigns.
Comorbidity accounts for severe COVID-19 risk, but how do we measure it? Retrospective assessment of the performance of three measures of comorbidity using 4,607 hospitalizations
Authors: Monterde David , Carot-Sans Gerard , Cainzos-Achirica Miguel , Abilleira Sònia , Coca Marc , Vela Emili , Clèries Montse , Valero Damià , Comin-Colet Josep , Garcia-Eroles Luis , Sust Pol Perez , Arrufat Miquel , Lejardi Yolanda , Piera Jordi 
Published: July 2021

AbstractBackgroundComorbidity burden has been identified as a relevant predictor of critical illness in patients hospitalized with coronavirus disease 2019 (COVID-19). However, comorbidity burden is often represented by a simple count of few conditions that may not fully capture patients’ complexity.PurposeTo evaluate the performance of a comprehensive index of the comorbidity burden (Queralt DxS), which includes all chronic conditions present on admission, as an adjustment variable in models for predicting critical illness in hospitalized COVID-19 patients and compare it with two broadly used measures of comorbidity.Patients and methodsWe analyzed data from all COVID-19 hospitalizations reported in eight public hospitals of Catalonia (North-East Spain) between June 15 and December 8 2020. The primary outcome was a composite of critical illness that included the need for invasive mechanical ventilation, transfer to ICU, or in-hospital death. Predictors included age, sex, and comorbidities present on admission measured using three indices: the Charlson index, the Elixhauser index, and the Queralt DxS index for comorbidities on admission. The performance of different fitted models was compared using various indicators, including the area under the receiving operating characteristics curve (AUC).ResultsOur analysis included 4,607 hospitalized COVID-19 patients. Of them, 1,315 experienced critical illness. Comorbidities significantly contributed to predicting the outcome in all summary indices used. The AUC for prediction of critical illness was 0.641 (95% CI 0.624-0.660) for the Charlson index, 0.665 (0.645-0.681) for the Elixhauser index, and 0.787 (0.773-0.801) for Queralt DxS. Other metrics of model performance also showed Queralt DxS being consistently superior to the other indices.ConclusionIn our analysis, the ability of comorbidity indices to predict hospital outcomes in hospitalized COVID-19 patients increased with their exhaustivity. The comprehensive Queralt DxS index may improve the accuracy of predictive models for resource allocation and clinical decision-making in the hospital setting.
Prevalence of Long COVID symptoms in Bangladesh: A Prospective Inception Cohort Study of COVID-19 survivors
Authors: Hossain Mohammad Anwar , Hossain K M Amran , Saunders Karen , Uddin Zakir , Walton Lori Maria , Raigangar Veena , Sakel Mohamed , Shafin Rubayet , Hossain Mohammad Sohrab , Kabir Md. Feroz , Faruqui Rafey , Rana Shohag , Ahmed Md. Shahoriar , Chakrovorty Sonjit Kumar , Hossain Md. Anwar , Jahid Iqbal Kabir 
Published: July 2021

AbstractObjectiveThe objective of this study was to identify the prevalence of ‘Long COVID’ symptoms (LCS) in a large cohort of survivors and identify any potential associated risk factors.DesignA prospective survey was undertaken of an inception cohort of confirmed COVID-19 survivors (Aged 18 to 87 years).Participants and Setting14392 participants were recruited from 24 testing facilities across Bangladesh between June, and November 2020. All participants had a previously confirmed positive COVID-19 diagnosis, and reported persistent symptoms and difficulties in performing daily activities.Main Outcome MeasuresParticipants who consented, were contacted by face-to-face interview, and were interviewed regarding LCS, and restriction of activities of daily living using Post COVID-19 functional scale. Cardio-respiratory parameters were also measured.ResultsAmong 2198 participants, the prevalence of LCS at 12 weeks was 16.1%. Overall, eight LCS were identified and in descending order of prominence are: fatigue, pain, dyspnea, cough, anosmia, appetite loss, headache, and chest pain. COVID survivors experienced between 1 to 5 LCS with an overall duration period of 21.8 ± 5.2 weeks. SEM predicted the length of LCS to be related to younger age, female gender, rural residence, prior functional limitation and smoking.ConclusionIn this cohort of survivors, at 31 weeks post diagnosis, the prevalence of LCS was 16.1%. The risk factors identified for presence and longer length of LCS warrant further research and consideration to support public health initiatives.
A SARS-CoV-2 nucleocapsid protein TR-FRET assay amenable to high-throughput screening
Authors: Gorshkov Kirill , Vasquez Desarey Morales , Chiem Kevin , Ye Chengjin , Tran Bruce Nguyen , de la Torre Juan Carlos , Moran Thomas , Chen Catherine Z. , Martinez-Sobrido Luis , Zheng Wei 
Published: July 2021

AbstractDrug development for specific antiviral agents against coronavirus disease 2019 (COVID-19) is still an unmet medical need as the pandemic continues to spread globally. Although huge efforts for drug repurposing and compound screens have put forth, only few compounds remain in late stage clinical trials. New approaches and assays are needed to accelerate COVID-19 drug discovery and development. Here we report a time-resolved fluorescence resonance energy transfer-based assay that detects the severe acute respiratory syndrome coronavirus 2 (SARS-CoV‑2) nucleocapsid protein (NP) produced in infected cells. It uses two specific anti-NP monoclonal antibodies (MAbs) conjugated to donor and acceptor fluorophores that produces a robust ratiometric signal for high throughput screening of large compound collections. Using this assay, we measured a half maximal inhibitory concentration (IC50) for Remdesivir of 9.3 μM against infection with SARS-CoV-2 USA/WA1/2020 (WA-1). The assay also detected SARS-CoV-2 South African (Beta, β), Brazilian/Japanese variant P.1 (Gamma, γ), and Californian (Epsilon, ε), variants of concern or interest (VoC). Therefore, this homogeneous SARS-CoV-2 NP detection assay can be used for accelerating lead compound discovery for drug development and for evaluating drug efficacy against emerging SARS-CoV-2 VoC.
Generation of false positive SARS-CoV-2 antigen results with testing conditions outside manufacturer recommendations: A scientific approach to pandemic misinformation
Authors: Patriquin Glenn , Davidson Ross J. , Hatchette Todd F. , Head Breanne M. , Mejia Edgard , Becker Michael G. , Meyers Adrienne , Sandstrom Paul , Hatchette Jacob , Block Ava , Smith Nicole , Ross John , LeBlanc Jason J. 
Published: July 2021

AbstractObjectivesAntigen-based rapid diagnostics tests (Ag-RDTs) are useful tools for SARS-CoV-2 detection. However, misleading demonstrations of the Abbott Panbio COVID-19 Ag-RDT on social media claimed that SARS-CoV-2 antigen could be detected in municipal water and food products. To offer a scientific rebuttal to pandemic misinformation and disinformation, this study explored the impact of using the Panbio SARS-CoV-2 assay with conditions falling outside of manufacturer recommendations.MethodsUsing Panbio, various water and food products, laboratory buffers, and SARS-CoV-2-negative clinical specimens were tested, with and without manufacturer buffer. Additional experiments were conducted to assess the role of each Panbio buffer component (tricine, NaCl, pH, and tween-20), as well as the impact of temperatures (4°C, 20°C, and 45°C) and humidity (90%) on assay performance.ResultsDirect sample testing (without the kit buffer), resulted in false positive signals resembling those obtained with SARS-CoV-2-positive controls tested under proper conditions. The likely explanation of these artifacts is non-specific interactions between the SARS-CoV-2-specific conjugated and capture antibodies, as proteinase K treatment abrogated this phenomenon, and thermal shift assays showed pH-induced conformational changes under conditions promoting artifact formation. Omitting, altering, and reverse engineering the kit buffer all supported the importance of maintaining buffering capacity, ionic strength, and pH for accurate kit function. Interestingly, the Panbio assay could tolerate some extremes of temperature and humidity outside of manufacturer claims.ConclusionsOur data support strict adherence to manufacturer instructions to avoid false positive SARS-CoV-2 Ag-RDT reactions, otherwise resulting in anxiety, overuse of public health resources, and dissemination of misinformation.
Relating SARS-CoV-2 shedding rate in wastewater to daily positive tests data: A consistent model based approach
Authors: Petala M. , Kostoglou M. , Karapantsios Th. , Dovas Ch. , Lytras Th. , Paraskevis D. , Roilides E. , Koutsolioutsou-Benaki A. , Panagiotakopoulos G. , Sypsa V. , Metallidis S. , Papa A. , Stylianidis E. , Papadopoulos A. , Tsiodras S. , Papaioannou N. 
Published: July 2021

ABSTRACTDuring the COVID-19 pandemic, wastewater-based epidemiology (WBE) has been engaged to complement medical surveillance and in some cases to also act as an early diagnosis indicator of viral spreading in the community. Most efforts worldwide by the scientific community and commercial companies focus on the formulation of protocols for SARS CoV-2 analysis in wastewater and approaches addressing the quantitative relationship between WBE and medical surveillance are lacking. In the present study, a mathematical model is developed which uses as input the number of daily positive medical tests together with the highly non-linear shedding rate curve of individuals to estimate the evolution of virus shedding rate in wastewater along calendar days. A comprehensive parametric study by the model using as input actual medical surveillance and WBE data for the city of Thessaloniki (∼700,000 inhabitants, North Greece) during the outbreak of November 2020 reveals the conditions under which WBE can be used as an early warning tool for predicting pandemic outbreaks. It is shown that early warning capacity is different along the days of an outbreak and depends strongly on the number of days apart between the day of maximum shedding rate of infected individuals in their disease cycle and the day of their medical testing. The present data indicate for Thessaloniki an average early warning capacity of around 2 days. Moreover, the data imply that there exists a proportion between unreported cases (asymptomatic persons with mild symptoms that do not seek medical advice) and reported cases. The proportion increases with the number of reported cases. The early detection capacity of WBE improves substantially in the presence of an increasing number of unreported cases. For Thessaloniki at the peak of the pandemic in mid-November 2020, the number of unreported cases reached a maximum around 4 times the number of reported cases.HIGHLIGHTSModel estimates viral load evolution in wastewater from infected people dynamicsIdentifying actual conditions for which WBE can be used as an early warning toolEarly warning capacity increases with an increasing number of unreported casesIn Thessaloniki Nov20 outbreak, the early warning capacity of WBE was about 2 daysIn Thessaloniki Nov20 outbreak, unreported cases were up to 4 times reported cases
Pneumococcal exposure routes for infants, a nested cross-sectional survey in Nha Trang, Vietnam
Authors: Qian George , Toizumi Michiko , Clifford Sam , Le Lien Thuy , Papastylianou Tasos , Quilty Billy , Iwasaki Chihiro , Kitamura Noriko , Takegata Mizuki , Nguyen Trang Minh , Nguyen Hien Anh Thi , Dang Duc Anh , van Hoek Albert Jan , Yoshida Lay Myint , Flasche Stefan 
Published: July 2021

AbstractBackgroundInfants are at highest risk of pneumococcal disease. Their added protection through herd effects is a key part in the considerations on optimal pneumococcal vaccination strategies. Yet, little is currently known about the main transmission pathways to this vulnerable age group.Methods and findingsWe conducted a nested cross-sectional contact and nasopharyngeal swabbing survey in randomly selected infants across all 27 communes of Nha Trang, Vietnam. Bayesian logistic regression models were used to estimate age specific carriage prevalence in the population, a proxy for the probability that a contact of a given age could lead to pneumococcal exposure for the infant. We used another Bayesian logistic regression model to estimate the correlation between infant carriage and the probability that at least one of their reported contacts carried pneumococci, controlling for age and locality. In total 1583 infants between 4 and 13 months old participated, with 7428 contacts reported. Few infants (5%) attended day care and carriage prevalence was 22%. Most infants (61%) had less than a 25% probability to have had close contact with a pneumococcal carrier on the surveyed day. Pneumococcal infection risk and contact behavior were highly correlated: if adjusted for age and locality the odds of an infant’s carriage increased by 22% (95%CI:15-29) per 10 percentage points increase in the probability to have had close contact with at least one pneumococcal carrier. Two to six year old children contributed 51% (95%CI: 39-63) to the total pneumococcal exposure risks to infants in this setting.ConclusionsCross-sectional contact and infection studies can help identify pneumococcal transmission routes. In Nha Trang, preschool age children are the largest reservoir for pneumococcal transmission to infants.
Death profiling of hospitalized patients with COVID-19: Experience from a specialized hospital in Bangladesh
Authors: Shahed-Morshed Md. , Mosabbir Abdullah Al , Hossain Mohammad Sorowar 
Published: July 2021

AbstractBackgroundThe information on characteristics and causes of mortality in deceased patients with coronavirus disease 2019 (COVID-19) is scarce in the literature. This study aimed to document the clinical profile with causes of death in deceased patients admitted in a COVID-19 dedicated hospital in Dhaka, Bangladesh.MethodsThis cross-sectional retrospective study included 108 COVID-19 associated deceased patients admitted in Kurmitola general hospital, Dhaka, Bangladesh between 25 March 2020 and 24 June 2020) Data were collected from hospital record. Causes of death were categorized into early and late with cut-off of 48 hours of hospitalization.ResultsAmong 809 hospitalized cases of COVID-19, 108 patient died (13.35%) over three months of study period. The mean age of the deceased patients was 60.22±13.94 years; 86.1% were male. About 85% had at least one comorbidity with diabetes mellitus (65.7%) was the most common one. The most common symptoms were breathlessness (88.0%), fever (65.7%) and cough (43.5%). Nearly 75% presented with severe disease. Patients had altered biochemical profiles and treated with different drugs including antibiotics and steroids. Young age and malnutrition were two characteristic features. Only one third got intensive care support. The most common cause of death was acute respiratory syndrome (95.37%). Septic shock & acute myocardial infarction were predominantly early and uremia, hepatic failure & hyperglycemic crisis were the predominant causes of late hospital death.ConclusionsThe findings of this study will help clinicians as well as policy makers to take necessary steps to prevent death from COVID-19 in Bangladeshi population.
Real time scalable data acquisition of COVID-19 in six continents through PySpark - a big data tool
Authors: Patel Tanvi S. , Patel Daxesh P. , Patel Chirag N. 
Published: July 2021

AbstractSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was declared as a global emergency in January 2020 due to its pandemic outbreak. To examine this Coronavirus disease 2019 (COVID-19) effects various data are being generated through different platforms. This study was focused on the clinical data of COVID-19 which relied on python programming. Here, we proposed a machine learning approach to provide a insights into the COVID-19 information. PySpark is a machine learning approach which also known as Apache spark an accurate tool for the searching of results with minimum time intervals as compare to Hadoop and other tools. World Health Organization (WHO) started gathering corona patients’ data from last week of the February 2020. On March 11, 2020, the WHO declared COVID-19 a global pandemic. The cases became more evident and common after mid-March. This paper used the live owid (our world in data) dataset and will analyse and find out the following details on the live COVID-19 dataset. (1) The daily Corona virus scenario on various continents using PySpark in microseconds of Processor time. (2) After the various antibodies have been implemented, how they impact new cases on a regular basis utilizing various graphs. (3) Tabular representation of COVID-19 new cases in all the continents.
Incorporating family disease history and controlling case-control imbalance for population based genetic association studies
Authors: Zhuang Yongwen , Wolford Brooke N , Nam Kisung , Bi Wenjian , Zhou Wei , Willer Cristen J , Mukherjee Bhramar , Lee Seunggeun 
Published: July 2021

AbstractIn the genome-wide association analysis of population-based biobanks, most diseases have low prevalence, which results in low detection power. One approach to tackle the problem is using family disease history, yet existing methods are unable to address type I error inflation induced by increased correlation of phenotypes among closely related samples, as well as unbalanced phenotypic distribution. We propose a new method for genetic association test with family disease history, TAPE (mixed-model-based Test with Adjusted Phenotype and Empirical saddlepoint approximation), which controls for increased phenotype correlation by adopting a two-variance-component mixed model, accounts for case-control imbalance by using empirical saddlepoint approximation, and is flexible to incorporate any existing adjusted phenotypes such as phenotypes from the LT-FH method. We show through simulation studies and analysis of UK-Biobank data of white British samples and the Korean Genome and Epidemiology Study (KoGES) of Korean samples that the proposed method is robust and yields better calibration compared to existing methods while gaining power for detection of variant-phenotype associations.
Lipid peroxidation induced ApoE receptor-ligand disruption as a unifying hypothesis underlying sporadic Alzheimer’s disease in humans
Authors: Ramsden Christopher E. , Keyes Gregory S. , Calzada Elizabeth , Horowitz Mark S. , Zamora Daisy , Jahanipour Jahandar , Sedlock Andrea , Indig Fred E. , Moaddel Ruin , Kapogiannis Dimitrios , Maric Dragan 
Published: July 2021

Structured AbstractBackgroundSporadic Alzheimer’s disease (sAD) lacks a unifying hypothesis that can account for the lipid peroxidation observed early in the disease, enrichment of ApoE in the central core of neuritic plaques, the hallmark plaques and tangles, and the selective vulnerability of entorhinal-hippocampal structures.ObjectiveWe hypothesized that (1) high expression of ApoER2 (receptor for ApoE and Reelin) could help explain the selective anatomical vulnerability; and (2) lipid peroxidation of ApoE and ApoER2 contributes to sAD pathogenesis, by disrupting ApoE delivery and Reelin-ApoER2 signaling cascades.MethodsWe conductedin vitrobiochemical experiments, single-marker immunohistochemistry (IHC), and multiplex fluorescence-IHC (MP-IHC) in postmortem specimens from 26 individuals who died cognitively normal, with Mild Cognitive Impairment or with sAD.ResultsIn biochemical experiments, Lys- and His-enriched peptides within the binding domains of ApoE and ApoER2 and their corresponding recombinant proteins, were susceptible to attack by reactive lipid aldehydes, generating lipid-protein adducts and crosslinked ApoE-ApoER2 complexes. Usingin situhybridization alongside IHC and MP-IHC, we observed that: (1) ApoER2 is strongly expressed in terminal zones of the entorhinal-hippocampal ‘perforant path’ projections that underlie memory; (2) ApoE and lipid aldehyde-modified ApoE, Reelin, ApoER2 and several downstream components of Reelin-ApoER2 signaling cascades accumulated in the immediate vicinity of neuritic plaques in perforant path terminal zones in sAD cases; and (3) several ApoE/Reelin-ApoER2 pathway markers—including the ApoER2 ligand binding domain, Disabled homolog-1 (Dab1), and Thr19-phosphorylated PSD95 (marker of synaptic disassembly)—were higher in sAD cases than controls and positively correlated with histological progression and cognitive deficits.ConclusionResults provide proof-of-concept that ApoE and ApoER2 are vulnerable to lipid aldehyde induced adduction and crosslinking and demonstrate derangements in multiple ApoE/Reelin-ApoER2-Dab1 axis components in perforant path terminal zones in AD. Findings provide the foundation for a unifying hypothesis implicating lipid peroxidation of ApoE particles and ApoE receptors in sAD in humans.
Mathematical modelling of SARS-CoV-2 variant outbreaks reveals their probability of extinction
Authors: Schiøler Henrik , Knudsen Torben , Brøndum Rasmus Froberg , Stoustrup Jakob , Bøgsted Martin 
Published: July 2021

AbstractWhen a virus spreads, it may mutate into, e.g., vaccine resistant or fast spreading lineages, as was the case for the Danish Cluster-5 mink variant (belonging to the B.1.1.298 lineage), the British B.1.1.7 lineage, and the South African B.1.351 lineage of the SARS-CoV-2 virus. A way to handle such spreads is through a containment strategy, where the population in the affected area is isolated until the spread has been stopped. Under such circumstances, it is important to monitor whether the mutated virus is extinct via massive testing for the virus sub-type. If successful, the strategy will lead to lower and lower numbers of the sub-type, and it will eventually die out. An important question is, for how long time one should wait to be sure the sub-type is extinct? We use a hidden Markov model for infection spread and an approximation of a two stage sampling scheme to infer the probability of extinction. The potential of the method is illustrated via a simulation study. Finally, the model is used to assess the Danish containment strategy when SARS-CoV-2 spread from mink to man during the summer of 2020, including the Cluster-5 sub-type. In order to avoid further spread and mink being a large animal virus reservoir, this situation led to the isolation of seven municipalities in the Northern part of the country, the culling of the entire Danish 17 million large mink population, and a bill to interim ban Danish mink production until the end of 2021.