Search

The problems of medical decision-making caused by medical uncertainty during thecoronavirus disease 2019 (COVID-19) pandemic were reviewed, and the potential mechanisms wereanalyzed. Medical uncertainty could lead to increased risks of ineffective treatments and wastes ofmedical resources. Regarding coping the public health emergencies, rapidly building data consortium iscrucial to break the boundaries of data silos, to improve the potentials of so-called “low-value data” andto provide evidence which could reduce medical uncertainty. Hence, the status of data consortium duringpublic health emergencies was summarized, and the potential implications in China was also discussed.

Background: Accumulated researches revealed that both fine particulate matter (PM2.5) and sunlight exposure may be a risk factor for obesity, while researches regarding the potential effect modification by sunlight exposure on the relationship between PM2.5 and obesity are limited. We aim to investigate whether the effect of PM2.5 on obesity is affected by sunlight exposure among the general population in China. Methods: A sample of 47,204 adults in China was included. Obesity and abdominal obesity were assessed based on body mass index, waist circumference and waist-to-hip ratio, respectively. The five-year exposure to PM2.5 and sunlight were accessed using the multi-source satellite products and a geochemical transport model. The relationship between PM2.5, sunshine duration, and the obesity or abdominal obesity risk was evaluated using the general additive model. Results: The proportion of obesity and abdominal obesity was 12.6% and 26.8%, respectively. Levels of long-term PM2.5 ranged from 13.2 to 72.1 μg/m3 with the mean of 46.6 μg/m3. Each 10 μg/m3 rise in PM2.5 was related to a higher obesity risk [OR 1.12 (95% CI 1.09-1.14)] and abdominal obesity [OR 1.10 (95% CI 1.07-1.13)]. The association between PM2.5 and obesity varied according to sunshine duration, with the highest ORs of 1.56 (95% CI 1.28-1.91) for obesity and 1.66 (95% CI 1.34-2.07) for abdominal obesity in the bottom quartile of sunlight exposure (3.21-5.34 hours/day). Conclusion: Long-term PM2.5 effect on obesity risk among the general Chinese population are influenced by sunlight exposure. More attention might be paid to reduce the adverse impacts of exposure to air pollution under short sunshine duration conditions. [ABSTRACT FROM AUTHOR]

Background: Most patients with chronic kidney disease (CKD) fail to achieve blood pressure (BP) management as recommended. Meanwhile, the effects of promising intervention and telehealth on BP control in CKD patients remain unclear. We aimed to evaluate the efficacy of telehealth for BP in CKD non-dialysis patients.Methods: Databases including MEDLINE, EMBASE, CENTRAL, CNKI, Wanfang, VIP and CBM were systematically searched for randomised controlled trials or quasi-randomised controlled trials on telehealth for BP control of CKD3-5 non-dialysis patients. We analysed systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP), serum creatinine, and estimated glomerular filtration rate (eGFR) with a fixed-effects model.Results: Three studies, with total 680 subjects, were included in our systematic review and two were included for meta-analysis. Pooled estimates showed decreased SBP (pooled mean difference (MD), -5.10; 95% confidence interval (CI), -11.34, 1.14; p > 0.05, p = 0.11), increased DBP (pooled MD, 0.45; 95% CI, -4.24, 5.13; p > 0.05, p = 0.85), decreased serum creatinine (pooled MD, -0.38; 95% CI, -0.83, 0.07; p > 0.05, p = 0.10) and maintained eGFR (pooled MD, 4.72; 95% CI, -1.85, 11.29; p > 0.05, p = 0.16) in the telehealth group. There was no significant difference from the control group. MAP (MD, 0.6; 95% CI, -6.61, 7.81; p > 0.05, p = 0.87) and BP control rate ( p > 0.05, p = 0.8), respectively, shown in two studies also demonstrated no statistical significance in the telehealth group.Conclusions: There was no statistically significant evidence to support the superiority of telehealth for BP management in CKD patients. This suggests further studies with improved study design and optimised intervention are needed in the future. [ABSTRACT FROM AUTHOR]

Gene expression levels serve as valuable markers for assessing prognosis in cancer patients. To understand the mechanisms underlying prognosis and explore potential therapeutics across diverse cancers, we developed CancerPro (https:/medcode.link/cancerpro). This knowledge network platform integrates comprehensive biomedical data on genes, drugs, diseases and pathways, along with their interactions. By integrating ontology and knowledge graph technologies, CancerPro offers a user-friendly interface for analyzing pan-cancer prognostic markers and exploring genes or drugs of interest. CancerPro implements three core functions: gene set enrichment analysis based on multiple annotations; in-depth drug analysis; and in-depth gene list analysis. Using CancerPro, we categorized genes and cancers into distinct groups and utilized network analysis to identify key biological pathways associated with unfavorable prognostic genes. The platform further pinpoints potential drug targets and explores potential links between prognostic markers and patient characteristics such as glutathione levels and obesity. For renal and prostate cancer, CancerPro identified risk genes linked to immune deficiency pathways and alternative splicing abnormalities. This research highlights CancerPro's potential as a valuable tool for researchers to explore pan-cancer prognostic markers and uncover novel therapeutic avenues. Its flexible tools support a wide range of biological investigations, making it a versatile asset in cancer research and beyond., (© The Author(s) 2024. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.)

Background: Accumulated researches revealed that both fine particulate matter (PM 2.5 ) and sunlight exposure may be a risk factor for obesity, while researches regarding the potential effect modification by sunlight exposure on the relationship between PM 2.5 and obesity are limited. We aim to investigate whether the effect of PM 2.5 on obesity is affected by sunlight exposure among the general population in China., Methods: A sample of 47,204 adults in China was included. Obesity and abdominal obesity were assessed based on body mass index, waist circumference and waist-to-hip ratio, respectively. The five-year exposure to PM 2.5 and sunlight were accessed using the multi-source satellite products and a geochemical transport model. The relationship between PM 2.5 , sunshine duration, and the obesity or abdominal obesity risk was evaluated using the general additive model., Results: The proportion of obesity and abdominal obesity was 12.6% and 26.8%, respectively. Levels of long-term PM 2.5 ranged from 13.2 to 72.1 μg/m 3 with the mean of 46.6 μg/m 3 . Each 10 μg/m 3 rise in PM 2.5 was related to a higher obesity risk [OR 1.12 (95% CI 1.09-1.14)] and abdominal obesity [OR 1.10 (95% CI 1.07-1.13)]. The association between PM 2.5 and obesity varied according to sunshine duration, with the highest ORs of 1.56 (95% CI 1.28-1.91) for obesity and 1.66 (95% CI 1.34-2.07) for abdominal obesity in the bottom quartile of sunlight exposure (3.21-5.34 hours/day)., Conclusion: Long-term PM 2.5 effect on obesity risk among the general Chinese population are influenced by sunlight exposure. More attention might be paid to reduce the adverse impacts of exposure to air pollution under short sunshine duration conditions., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Chen, Yang, Li, Wang, Liang, Wang, Wang, Liang, Meng, Wang, Peng, Sun, Su, Kong, Wang and Zhang.)

The effective use of EHR data for clinical research is challenged by the lack of methodologic standards, transparency, and reproducibility. For example, our empirical analysis on clinical research ontologies and reporting standards found little-to-no informatics-related standards. To address these issues, our study aims to leverage natural language processing techniques to discover the reporting patterns and data abstraction methodologies for EHR-based clinical research. We conducted a case study using a collection of full articles of EHR-based population studies published using the Rochester Epidemiology Project infrastructure. Our investigation discovered an upward trend of reporting EHR-related research methodologies, good practice, and the use of informatics related methods. For example, among 1279 articles, 24.0% reported training for data abstraction, 6% reported the abstractors were blinded, 4.5% tested the inter-observer agreement, 5% reported the use of a screening/data collection protocol, 1.5% reported that team meetings were organized for consensus building, and 0.8% mentioned supervision activities by senior researchers. Despite that, the overall ratio of reporting/adoption of methodologic standards was still low. There was also a high variation regarding clinical research reporting. Thus, continuously developing process frameworks, ontologies, and reporting guidelines for promoting good data practice in EHR-based clinical research are recommended., (©2020 AMIA - All rights reserved.)