Tag: pubmed

Scand J Work Environ Health. 2021 Oct 19:3992. doi: 10.5271/sjweh.3992. Online ahead of print.

ABSTRACT

Objectives This is the first population-level study to examine inequalities in COVID-19 mortality according to working-age individuals’ occupations and the indirect occupational effects on COVID-19 mortality of older individuals who live with them. Methods We used early-release data for the entire population of Sweden of all recorded COVID-19 deaths from 12 March 2020 to 23 February 2021, which we linked to administrative registers and occupational measures. Cox proportional hazard models assessed relative risks of COVID-19 mortality for the working-aged population registered in an occupation in December 2018 and the older population who lived with them. Results Among working aged-adults, taxi/bus drivers had the highest relative risk of COVID-19 mortality: over four times that of skilled workers in IT, economics, or administration when adjusted only for basic demographic characteristics. After adjusting for socioeconomic factors (education, income and country of birth), there are no occupational groups with clearly elevated (statistically significant) COVID-19 mortality. Neither a measure of exposure within occupations nor the share that generally can work from home were related to working-aged adults’ risk of COVID-19 mortality. Instead of occupational factors, traditional socioeconomic risk factors best explained variation in COVID-19 mortality. Elderly individuals, however, faced higher COVID-19 mortality risk both when living with a delivery or postal worker or worker(s) in occupations that generally work from home less, even when their socioeconomic factors are taken into account. Conclusions Inequalities in COVID-19 mortality of working-aged adults were mostly based on traditional risk factors and not on occupational divisions or characteristics in Sweden. However, older individuals living with those who likely cannot work from home or work in delivery or postal services were a vulnerable group.

PMID:34665872 | DOI:10.5271/sjweh.3992

PLoS One. 2021 Oct 19;16(10):e0258858. doi: 10.1371/journal.pone.0258858. eCollection 2021.

ABSTRACT

Longstanding social and economic inequities elevate health risks and vulnerabilities for Black, Indigenous and People of Color (BIPOC) communities. Engagement of BIPOC communities in infectious disease research is a critical component in efforts to increase vaccine confidence, acceptability, and uptake of future approved products. Recent data highlight the relative absence of BIPOC communities in vaccine clinical trials. Intentional and effective community engagement methods are needed to improve BIPOC inclusion. We describe the methods utilized for the successful enrollment of BIPOC participants in the U.S. Government (USG)-funded COVID-19 Prevention Network (CoVPN)-sponsored vaccine efficacy trials and analyze the demographic and enrollment data across the efficacy trials to inform future efforts to ensure inclusive participation. Across the four USG-funded COVID-19 vaccine clinical trials for which data are available, 47% of participants enrolled at CoVPN sites in the US were BIPOC. White enrollment outpaced enrollment of BIPOC participants throughout the accrual period, requiring the implementation of strategies to increase diverse and inclusive enrollment. Trials opening later benefitted considerably from strengthened community engagement efforts, and greater and more diverse volunteer registry records. Despite robust fiscal resources and a longstanding collaborative and collective effort, enrollment of White persons outpaced that of BIPOC communities. With appropriate resources, commitment and community engagement expertise, the equitable enrollment of BIPOC individuals can be achieved. To ensure this goal, intentional efforts are needed, including an emphasis on diversity of enrollment in clinical trials, establishment of enrollment goals, ongoing robust community engagement, conducting population-specific trials, and research to inform best practices.

PMID:34665829 | DOI:10.1371/journal.pone.0258858

PLoS One. 2021 Oct 19;16(10):e0258689. doi: 10.1371/journal.pone.0258689. eCollection 2021.

ABSTRACT

BACKGROUND: Data to better understand and manage the COVID-19 pandemic is urgently needed. However, there are gaps in information stored within even the best routinely-collected electronic health records (EHR) including test results, remote consultations for suspected COVID-19, shielding, physical activity, mental health, and undiagnosed or untested COVID-19 patients. Observational and Pragmatic Research Institute (OPRI) Singapore and Optimum Patient Care (OPC) UK established Platform C19, a research database combining EHR data and bespoke patient questionnaire. We describe the demographics, clinical characteristics, patient behavior, and impact of the COVID-19 pandemic using data within Platform C19.

METHODS: EHR data from Platform C19 were extracted from 14 practices across UK participating in the OPC COVID-19 Quality Improvement program on a continuous, monthly basis. Starting 7th August 2020, consenting patients aged 18-85 years were invited in waves to fill an online questionnaire. Descriptive statistics were summarized using all data available up to 22nd January 2021.

FINDINGS: From 129,978 invitees, 31,033 responded. Respondents were predominantly female (59.6%), white (93.5%), and current or ex-smokers (52.6%). Testing for COVID-19 was received by 23.8% of respondents, of which 7.9% received positive results. COVID-19 symptoms lasted ≥4 weeks in 19.5% of COVID-19 positive respondents. Up to 39% respondents reported a negative impact on questions regarding their mental health. Most (67%-76%) respondents with asthma, Chronic Obstructive Pulmonary Disease (COPD), diabetes, heart, or kidney disease reported no change in the condition of their diseases.

INTERPRETATION: Platform C19 will enable research on key questions relating to COVID-19 pandemic not possible using EHR data alone.

PMID:34665843 | DOI:10.1371/journal.pone.0258689

PLoS Biol. 2021 Oct 19;19(10):e3001413. doi: 10.1371/journal.pbio.3001413. eCollection 2021 Oct.

ABSTRACT

Light plays a fundamental role in the ecology of organisms in nearly all habitats on Earth and is central for processes such as vision and the entrainment of the circadian clock. The poles represent extreme light regimes with an annual light cycle including periods of Midnight Sun and Polar Night. The Arctic Ocean extends to the North Pole, and marine light extremes reach their maximum extent in this habitat. During the Polar Night, traditional definitions of day and night and seasonal photoperiod become irrelevant since there are only “twilight” periods defined by the sun’s elevation below the horizon at midday; we term this “midday twilight.” Here, we characterize light across a latitudinal gradient (76.5° N to 81° N) during Polar Night in January. Our light measurements demonstrate that the classical solar diel light cycle dominant at lower latitudes is modulated during Arctic Polar Night by lunar and auroral components. We therefore question whether this particular ambient light environment is relevant to behavioral and visual processes. We reveal from acoustic field observations that the zooplankton community is undergoing diel vertical migration (DVM) behavior. Furthermore, using electroretinogram (ERG) recording under constant darkness, we show that the main migratory species, Arctic krill (Thysanoessa inermis) show endogenous increases in visual sensitivity during the subjective night. This change in sensitivity is comparable to that under exogenous dim light acclimations, although differences in speed of vision suggest separate mechanisms. We conclude that the extremely weak midday twilight experienced by krill at high latitudes during the darkest parts of the year has physiological and ecological relevance.

PMID:34665816 | DOI:10.1371/journal.pbio.3001413

PLoS One. 2021 Oct 19;16(10):e0258554. doi: 10.1371/journal.pone.0258554. eCollection 2021.

ABSTRACT

At birth, human infants are poised to survive in harsh, hostile conditions. An understanding of the state of newborn skin development and maturation is key to the maintenance of health, optimum response to injury, healing and disease. The observational study collected full-thickness newborn skin samples from 27 infants at surgery and compared them to skin samples from 43 adult sites protected from ultraviolet radiation exposure, as the standard for stable, mature skin. Transcriptomics profiling and gene set enrichment analysis were performed. Statistical analysis established over 25,000 differentially regulated probe sets, representing 10,647 distinct genes, in infant skin compared to adult skin. Gene set enrichment analysis showed a significant increase in 143 biological processes (adjusted p < 0.01) in infant skin, versus adult skin samples, including extracellular matrix (ECM) organization, cell adhesion, collagen fibril organization and fatty acid metabolic process. ECM organization and ECM structure organization were the biological processes in infant skin with the lowest adjusted P-value. Genes involving epidermal development, immune function, cell differentiation, and hair cycle were overexpressed in adults, representing 101 significantly enriched biological processes (adjusted p < 0.01). The processes with the highest significant difference were skin and epidermal development, e.g., keratinocyte differentiation, keratinization and cornification intermediate filament cytoskeleton organization and hair cycle. Enriched Gene Ontology (GO) biological processes also involved immune function, including antigen processing and presentation. When compared to ultraviolet radiation-protected adult skin, our results provide essential insight into infant skin and its ability to support the newborn’s preparedness to survive and flourish, despite the infant’s new environment laden with microbes, high oxygen tension and potential irritants. This fundamental knowledge is expected to guide strategies to protect and preserve the features of unperturbed, young skin.

PMID:34665817 | DOI:10.1371/journal.pone.0258554

PLoS One. 2021 Oct 19;16(10):e0258731. doi: 10.1371/journal.pone.0258731. eCollection 2021.

ABSTRACT

Failure to thrive (FTT) impairs the expected normal physical growth of children. This study aimed to evaluate the effects of cyproheptadine hydrochloride on growth parameters in prepubertal children with FTT. The medical records of prepubertal children who were newly diagnosed with FTT at China Medical University Hospital between 2007 and 2016 were retrospectively examined. The patients were divided into two groups depending on whether they had (T-group) or had not (NT-group) received cyproheptadine hydrochloride (0.3 mg/kg daily) for at least 14 days. The mean length of the treatment period was 97.22 days (range: 14-532 days). Weight, height, and body mass index were adjusted for age using the median values in the growth charts for Taiwanese boys and girls as the reference. A total of 788 patients aged 3-11 years were enrolled, 50 in the T-group and 738 in the NT-group. No statistically significant difference in the median age-adjusted weight value was noted between the T-group and NT-group during the follow up period. In the T-group, age-adjusted weight and body mass index were inversely associated with age (P <0.001, P <0.001) and positively associated with medication duration (P = 0.026, P = 0.04). Our findings underscore the positive association between cyproheptadine hydrochloride treatment and weight gain among prepubertal children. Further prospective clinical studies with a. longer and consistent treatment course is warranted.

PMID:34665812 | DOI:10.1371/journal.pone.0258731

PLoS Comput Biol. 2021 Oct 19;17(10):e1009536. doi: 10.1371/journal.pcbi.1009536. Online ahead of print.

ABSTRACT

Ectopic beats (EBs) are cellular arrhythmias that can trigger lethal arrhythmias. Simulations using biophysically-detailed cardiac myocyte models can reveal how model parameters influence the probability of these cellular arrhythmias, however such analyses can pose a huge computational burden. Here, we develop a simplified approach in which logistic regression models (LRMs) are used to define a mapping between the parameters of complex cell models and the probability of EBs (P(EB)). As an example, in this study, we build an LRM for P(EB) as a function of the initial value of diastolic cytosolic Ca2+ concentration ([Ca2+]iini), the initial state of sarcoplasmic reticulum (SR) Ca2+ load ([Ca2+]SRini), and kinetic parameters of the inward rectifier K+ current (IK1) and ryanodine receptor (RyR). This approach, which we refer to as arrhythmia sensitivity analysis, allows for evaluation of the relationship between these arrhythmic event probabilities and their associated parameters. This LRM is also used to demonstrate how uncertainties in experimentally measured values determine the uncertainty in P(EB). In a study of the role of [Ca2+]SRini uncertainty, we show a special property of the uncertainty in P(EB), where with increasing [Ca2+]SRini uncertainty, P(EB) uncertainty first increases and then decreases. Lastly, we demonstrate that IK1 suppression, at the level that occurs in heart failure myocytes, increases P(EB).

PMID:34665814 | DOI:10.1371/journal.pcbi.1009536

PLoS Negl Trop Dis. 2021 Oct 19;15(10):e0009888. doi: 10.1371/journal.pntd.0009888. Online ahead of print.

ABSTRACT

BACKGROUND: Albendazole (ALB) is administered annually to millions of children through global deworming programs targeting soil-transmitted helminths (STHs: Ascaris lumbricoides, Trichuris trichiura and hookworms, Necator americanus and Ancylostoma duodenale). However, due to the lack of large individual patient datasets collected using standardized protocols and the application of population-based statistical methods, little is known about factors that may affect individual responses to treatment.

METHODOLOGY/PRINCIPAL FINDINGS: We re-analyzed 645 individual patient data from three standardized clinical trials designed to assess the efficacy of a single 400 mg oral dose of ALB against STHs in schoolchildren from different study sites, each with varying history of drug pressure based on duration of mass drug administration programs: Ethiopia, low; Lao People’s Democratic Republic (PDR), moderate; Pemba Island (Tanzania), high. Using a Bayesian statistical modelling approach to estimate individual responses (individual egg reduction rates, ERRi), we found that efficacy was lower in Pemba Island, particularly for T. trichiura. For this STH, the proportion of participants with a satisfactory response (ERRi ≥50%), was 65% in Ethiopia, 61% in Lao PDR but only 29% in Pemba Island. There was a significant correlation between ERRi and infection intensity prior to drug administration (ERRi decreasing as a function of increasing infection intensity). Individual age and sex also affected the drug response, but these were of negligible clinical significance and not consistent across STHs and study sites.

CONCLUSIONS/SIGNIFICANCE: We found decreased efficacy of ALB against all the STHs analyzed in Pemba Island (Tanzania), an area with high drug pressure. This does not indicate causality, as this association may also be partially explained by differences in infection intensity prior to drug administration. Notwithstanding, our results indicate that without alternative treatment regimens, program targets will not be achievable on Pemba Island because of inadequate efficacy of ALB.

TRIAL REGISTRATION: The study was registered on Clinicaltrials.gov (ID: NCT03465488) on March 7, 2018.

PMID:34665810 | DOI:10.1371/journal.pntd.0009888

PLoS Med. 2021 Oct 19;18(10):e1003793. doi: 10.1371/journal.pmed.1003793. eCollection 2021 Oct.

ABSTRACT

BACKGROUND: The importance of infectious disease epidemic forecasting and prediction research is underscored by decades of communicable disease outbreaks, including COVID-19. Unlike other fields of medical research, such as clinical trials and systematic reviews, no reporting guidelines exist for reporting epidemic forecasting and prediction research despite their utility. We therefore developed the EPIFORGE checklist, a guideline for standardized reporting of epidemic forecasting research.

METHODS AND FINDINGS: We developed this checklist using a best-practice process for development of reporting guidelines, involving a Delphi process and broad consultation with an international panel of infectious disease modelers and model end users. The objectives of these guidelines are to improve the consistency, reproducibility, comparability, and quality of epidemic forecasting reporting. The guidelines are not designed to advise scientists on how to perform epidemic forecasting and prediction research, but rather to serve as a standard for reporting critical methodological details of such studies.

CONCLUSIONS: These guidelines have been submitted to the EQUATOR network, in addition to hosting by other dedicated webpages to facilitate feedback and journal endorsement.

PMID:34665805 | DOI:10.1371/journal.pmed.1003793

PLoS Comput Biol. 2021 Oct 19;17(10):e1009464. doi: 10.1371/journal.pcbi.1009464. Online ahead of print.

ABSTRACT

Gene selection in unannotated large single cell RNA sequencing (scRNA-seq) data is important and crucial step in the preliminary step of downstream analysis. The existing approaches are primarily based on high variation (highly variable genes) or significant high expression (highly expressed genes) failed to provide stable and predictive feature set due to technical noise present in the data. Here, we propose RgCop, a novel regularized copula based method for gene selection from large single cell RNA-seq data. RgCop utilizes copula correlation (Ccor), a robust equitable dependence measure that captures multivariate dependency among a set of genes in single cell expression data. We raise an objective function by adding a l1 regularization term with Ccor to penalizes the redundant co-efficient of features/genes, resulting non-redundant effective features/genes set. Results show a significant improvement in the clustering/classification performance of real life scRNA-seq data over the other state-of-the-art. RgCop performs extremely well in capturing dependence among the features of noisy data due to the scale invariant property of copula, thereby improving the stability of the method. Moreover, the differentially expressed (DE) genes identified from the clusters of scRNA-seq data are found to provide an accurate annotation of cells. Finally, the features/genes obtained from RgCop can able to annotate the unknown cells with high accuracy.

PMID:34665808 | DOI:10.1371/journal.pcbi.1009464