Tag: pubmed

Pediatr Diabetes. 2021 Jul 14. doi: 10.1111/pedi.13248. Online ahead of print.

ABSTRACT

BACKGROUND: Previous studies showed conflicting results on the association between maternal pre-pregnancy body mass index (BMI) and type 1 diabetes in the offspring, and the role of maternal pre-pregnancy physical activity is unclear. We aimed to assess whether maternal pre-pregnancy BMI and physical activity predict type 1 diabetes in their offspring.

METHODS: Prospective study including women participating in the Nurses’ Health Study II with follow-up from 1989 to 2011. Women repeatedly reported their BMI and physical activity, from which pre-pregnancy exposures were derived; and retrospectively reported their BMI at age 18 and physical activity at ages 18-22, considered early adulthood exposure. We estimated risk ratios (RR) and 95% confidence intervals (95%CI) using generalized estimating equations, adjusted for covariates. Findings at p < 0.05 were considered statistically significant.

RESULTS: We identified 276 cases of type 1 diabetes among offspring (n=70,168) with maternal pre-pregnancy information and 448 cases among offspring (n=111,692) with maternal early adulthood information. Pre-pregnancy and early adulthood maternal BMI and physical activity were not associated with offspring type 1 diabetes. The RR comparing overweight to normal weight mothers was 1.08 (95%CI: 0.73-1.59) and comparing obese to normal weight was 0.94 (95%CI: 0.49-1.79, p-trend: 0.98). Comparing highest to lowest quartile of maternal physical activity the RR was 0.90 (95%CI: 0.61-1.32; p-trend: 0.73). Maternal type 2 diabetes was associated with an increased risk of type 1 diabetes in the offspring (RR=1.87; 95%CI: 1.25-2.80).

CONCLUSIONS: Our findings do not support a relationship between maternal pre-pregnancy BMI or physical activity and the risk of type 1 diabetes in the offspring. This article is protected by copyright. All rights reserved.

PMID:34260806 | DOI:10.1111/pedi.13248

J Appl Microbiol. 2021 Jul 14. doi: 10.1111/jam.15218. Online ahead of print.

ABSTRACT

AIMS: Detection of bacterial contamination in healthcare and industry takes many hours if not days. Thermal imaging, the measurement of heat by an infrared camera, was investigated as a potential none-invasive method of detecting bacterial growth.

METHODS AND RESULTS: Infrared thermography can detect the presence of Escherichia coli and Staphylococcus aureus on solid growth media by an increase in temperature before they are visually observable. A heat decrease is observed after treatment with ultraviolet light and heat increased after incubation with dinitrophenol.

CONCLUSIONS: Infrared thermography can detect early growth of bacteria before they are detectable by other microbiology-based method. The heat observed is due to the cells being viable and metabolically active, as cells killed with ultraviolet light exhibit reduced increase in temperature and treatment with dinitrophenol increases heat detected.

SIGNIFICANCE AND IMPACT OF THE STUDY: Infrared thermography detects bacterial growth without the need for specialised temperature control facilities. The method is statistically robust and can be undertaken in situ, thus is highly versatile. These data support the application of infrared thermography in a laboratory, clinical and industrial setting for vegetative bacteria, thus may become into an important methodology for the timely and straightforward detection of early-stage bacterial growth.

PMID:34260801 | DOI:10.1111/jam.15218

Rev Med Virol. 2021 Jul;31(4):e2200. doi: 10.1002/rmv.2200. Epub 2020 Dec 4.

ABSTRACT

Population-based prevalence surveys of Covid-19 contribute to establish the burden of infection, the role of asymptomatic and mild infections in transmission, and allow more precise decisions about reopen policies. We performed a systematic review to evaluate qualitative aspects of these studies, assessing their reliability and compiling practices that can influence the methodological quality. We searched MEDLINE, EMBASE, bioRxiv and medRxiv, and included cross-sectional studies using molecular and/or serological tests to estimate the prevalence of Covid-19 in the general population. Survey quality was assessed using the Joanna Briggs Institute Critical Appraisal Checklist for Prevalence Studies. A correspondence analysis correlated methodological parameters of each study to identify patterns related to higher, intermediate and lower risks of bias. The available data described 37 surveys from 19 countries. The majority were from Europe and America, used antibody testing, and reached highly heterogeneous sample sizes and prevalence estimates. Minority communities were disproportionately affected by Covid-19. Important risk of bias was detected in four domains: sample size, data analysis with sufficient coverage, measurements in standard way and response rate. The correspondence analysis showed few consistent patterns for high risk of bias. Intermediate risk of bias was related to American and European studies, municipal and regional initiatives, blood samples and prevalence >1%. Low risk of bias was related to Asian studies, nationwide initiatives, reverse-transcriptase polymerase chain reaction tests and prevalence <1%. We identified methodological standards applied worldwide in Covid-19 prevalence surveys, which may assist researchers with the planning, execution and reporting of future population-based surveys.

PMID:34260777 | DOI:10.1002/rmv.2200

Psychooncology. 2021 Jul 14. doi: 10.1002/pon.5766. Online ahead of print.

ABSTRACT

OBJECTIVE: To compare the risk of depression after diagnostic workup for prostate cancer (PCa), regardless of the histopathologic outcome, with that of a cancer-free population.

METHODS: A nationwide cohort of Danish men who had a prostatic biopsy sample in 1998-2011 was identified from the Danish Prostate Cancer Registry and compared to an age-matched cohort from the background population. Men with other cancers, major psychiatric disorder, or prior use of antidepressants were excluded. The risk of depression defined as hospital contact for depression or prescription for antidepressants was determined from cumulative incidence functions and multivariate Cox regression models.

RESULTS: Of 54,766 men who underwent diagnostic workup for PCa, benign results were found for 21,418 and PCa was diagnosed in 33,347. During up to 18 years of follow-up, the adjusted hazard of depression was higher in men with PCa than in the background population, with the highest risk in the two years after diagnosis (hazard ratio (HR) 2.77, 95% CI 2.66-2.87). Comorbidity and lowest or highest income were significant risk factors for depression and the cumulative incidence was substantially higher in men with metastatic or high-risk disease. In men with benign histopathology the HR for depression was 1.22 (95% CI 1.14-1.31) in the first two years but no different from the background population after that.

CONCLUSIONS: Diagnostic workup for PCa is associated with an increased risk of depression, mainly among men with a diagnosis of PCa. Clinicians should be aware of depressive symptoms in prostate cancer patients. This article is protected by copyright. All rights reserved.

PMID:34260790 | DOI:10.1002/pon.5766

Bioinformatics. 2021 Jul 14:btab514. doi: 10.1093/bioinformatics/btab514. Online ahead of print.

ABSTRACT

MOTIVATION: Identification and interpretation of noncoding variations that affect disease risk remain a paramount challenge in genome-wide association studies (GWAS) of complex diseases. Experimental efforts have provided comprehensive annotations of functional elements in the human genome. On the other hand, advances in computational biology, especially machine learning approaches, have facilitated accurate predictions of cell-type-specific functional annotations. Integrating functional annotations with GWAS signals has advanced the understanding of disease mechanisms. In previous studies, functional annotations were treated as static of a genomic region, ignoring potential functional differences imposed by different genotypes across individuals.

RESULTS: We develop a computational approach, Openness Weighted Association Studies (OWAS), to leverage and aggregate predictions of chromosome accessibility in personal genomes for prioritizing GWAS signals. The approach relies on an analytical expression we derived for identifying disease associated genomic segments whose effects in the etiology of complex diseases are evaluated. In extensive simulations and real data analysis, OWAS identifies genes/segments that explain more heritability than existing methods, and has a better replication rate in independent cohorts than GWAS. Moreover, the identified genes/segments show tissue-specific patterns and are enriched in disease relevant pathways. We use rheumatic arthritis (RA) and asthma (ATH) as examples to demonstrate how OWAS can be exploited to provide novel insights on complex diseases.

AVAILABILITY: The R package OWAS that implements our method is available at https://github.com/shuangsong0110/OWAS.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:34260700 | DOI:10.1093/bioinformatics/btab514

Clin Infect Dis. 2021 Jul 14:ciab630. doi: 10.1093/cid/ciab630. Online ahead of print.

ABSTRACT

Although interim results from several large placebo-controlled phase 3 trials demonstrated high vaccine efficacy (VE) against symptomatic COVID-19, it is unknown how effective the vaccines are in preventing people from becoming asymptomatically in- fected and potentially spreading the virus unwittingly. It is more difficult to evaluate VE against SARS-CoV-2 infection than against symptomatic COVID-19 because infection is not observed directly but rather is known to occur between two antibody or RT-PCR tests. Ad- ditional challenges arise as community transmission changes over time and as participants are vaccinated on different dates because of staggered enrollment of participants or crossover of placebo recipients to the vaccine arm before the end of the study. Here, we provide valid and efficient statistical methods for estimating potentially waning VE against SARS-CoV-2 infection with blood or nasal samples under time-varying community transmission, stag- gered enrollment, and blinded or unblinded crossover. We demonstrate the usefulness of the proposed methods through numerical studies mimicking the BNT162b2 phase 3 trial and the Prevent COVID U study. In addition, we assess how crossover and the frequency of diagnostic tests affect the precision of VE estimates.

PMID:34260716 | DOI:10.1093/cid/ciab630

Brief Bioinform. 2021 Jul 15:bbab262. doi: 10.1093/bib/bbab262. Online ahead of print.

ABSTRACT

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by the newly discovered coronavirus, SARS-CoV-2. Increased severity of COVID-19 has been observed in patients with diabetes mellitus (DM). This study aimed to identify common transcriptional signatures, regulators and pathways between COVID-19 and DM. We have integrated human whole-genome transcriptomic datasets from COVID-19 and DM, followed by functional assessment with gene ontology (GO) and pathway analyses. In peripheral blood mononuclear cells (PBMCs), among the upregulated differentially expressed genes (DEGs), 32 were found to be commonly modulated in COVID-19 and type 2 diabetes (T2D), while 10 DEGs were commonly downregulated. As regards type 1 diabetes (T1D), 21 DEGs were commonly upregulated, and 29 DEGs were commonly downregulated in COVID-19 and T1D. Moreover, 35 DEGs were commonly upregulated in SARS-CoV-2 infected pancreas organoids and T2D islets, while 14 were commonly downregulated. Several GO terms were found in common between COVID-19 and DM. Prediction of the putative transcription factors involved in the upregulation of genes in COVID-19 and DM identified RELA to be implicated in both PBMCs and pancreas. Here, for the first time, we have characterized the biological processes and pathways commonly dysregulated in COVID-19 and DM, which could be in the next future used for the design of personalized treatment of COVID-19 patients suffering from DM as comorbidity.

PMID:34260684 | DOI:10.1093/bib/bbab262

Rheumatology (Oxford). 2021 Jul 14:keab544. doi: 10.1093/rheumatology/keab544. Online ahead of print.

ABSTRACT

OBJECTIVES: Age at onset is useful in identifying chronic back patients at an increased risk of axial spondyloarthritis (axSpA). Yet, the majority of data on which the age at onset <45 years criterion was based originates from Europe. Therefore, it is unknown if this criterion applies in other parts of the world. We aimed to assess age at onset of axSpA and its relationship with HLA-B27 and gender across the world.

METHODS: Analyses were applied to patients from 24 countries across the world with an axSpA diagnosis and known age at onset of axial complaints. Cumulative probability plots were used to display the cumulative distribution of age at onset of axial symptoms. Linear regression models were built to assess the effect of HLA-B27 and gender on age at onset of axial symptoms.

RESULTS: 92% of 2,579 axSpA patients had an age at onset of axial symptoms <45 years, with only small variations across the geographical regions (Asia [n = 574; 94%], Europe & North America [n = 988; 92%], Latin America [n = 246; 89%], and Middle East & North Africa [n = 771; 91%]). Age at onset of axial symptoms was consistently lower in HLA-B27 positive patients (median 25[19-32] vs 31[22-39]) and male patients (median 25[19-33] vs 28[21-37]), but in multivariable models an additional statistically significant effect of male gender independent of HLA-B27 was only found in Asia.

CONCLUSION: Around the world, the large majority of axSpA patients had an age at onset of axial disease <45, with HLA-B27 and male gender associated with earlier disease onset.

PMID:34260699 | DOI:10.1093/rheumatology/keab544

PLoS One. 2021 Jul 14;16(7):e0254458. doi: 10.1371/journal.pone.0254458. eCollection 2021.

ABSTRACT

The issues of personality and its relations with the level of empathetic sensibility of medical doctors are broadly discussed in the literature. The aim of this study was an assessment of personality related predictors of empathy indicators in female and male students of medicine with consideration of gender differences. Methods applied were Empathic Sensitiveness Scale (ESS) and Personality Inventory (NEO-PI-R). The study included 153 participants, who were students of the fifth year of medical studies. Students filled in questionnaires during workshops in clinical psychological skills. Participation in the study was voluntary and anonymous. The statistical analysis was performed using Statistica 13 PL and PS IMAGO PRO (SPSS). Linear regression analysis with the interaction component was performed to explore the relationship between personality factors and gender and their interaction with the variable dependent level of empathy. The analysis showed that Extraversion, Openness and Agreeableness are associated with the level of Empathic Concern. Neuroticism, Extraversion, Agreeableness and Conscientiousness are associated with the level of Personal Distress. Extraversion, Openness, Agreeableness and Conscientiousness are associated with the level of Perspective-taking. The regression analysis with the interactive component showed that there is no relationship between gender and the level of empathy, therefore the interactions were insignificant. Empathetic sensibility is related to personality dimensions of the students of medicine. Although there has been no interaction among chief personality dimensions, empathy indicators and gender, detailed analysis of personality dimensions’ components has shown differences between men and women.

PMID:34260654 | DOI:10.1371/journal.pone.0254458

PLoS Pathog. 2021 Jul 14;17(7):e1009753. doi: 10.1371/journal.ppat.1009753. Online ahead of print.

ABSTRACT

To understand the diversity of immune responses to SARS-CoV-2 and distinguish features that predispose individuals to severe COVID-19, we developed a mechanistic, within-host mathematical model and virtual patient cohort. Our results suggest that virtual patients with low production rates of infected cell derived IFN subsequently experienced highly inflammatory disease phenotypes, compared to those with early and robust IFN responses. In these in silico patients, the maximum concentration of IL-6 was also a major predictor of CD8+ T cell depletion. Our analyses predicted that individuals with severe COVID-19 also have accelerated monocyte-to-macrophage differentiation mediated by increased IL-6 and reduced type I IFN signalling. Together, these findings suggest biomarkers driving the development of severe COVID-19 and support early interventions aimed at reducing inflammation.

PMID:34260666 | DOI:10.1371/journal.ppat.1009753