Placental dysfunction, inflammation and degradation of fetal membranes has been hypothesized as a cause of preterm prelabor of rupture of membranes. To examine the effect of aspirin, an anti-inflammatory agent, on the prevalence of preterm prelabor rupture of membranes (PPRoMs). A retrospective analysis was conducted to examine the effect of aspirin on the prevalence of PPRoM. Aspirin (150 mg, nocte) was prescribed to women who were identified through a screening program at 11-13 weeks' gestation as being at high risk for developing early-onset preeclampsia. Women who were at low risk for developing preeclampsia did not receive aspirin. The prevalence of PPRoM was compared with an observational cohort. In the observational cohort, there were 3027 women, including 32 (1.1%) cases of PPRoM. The prevalence of PPRoM in the high risk group was 3.1% (4/128) and was statistically significantly higher compared to the low risk group (1.0%) (28/2899). The relative risk was 3.02 (95% CI 1.2-7.7; = .04). In the interventional cohort, there were 7280 women, with 114 (1.6%) cases of PPRoM. The prevalence of PPRoM in the high risk group who were treated with aspirin was 1.8% (14/766) compared to 1.5% (100/6516) in the low risk group (= .54). The prevalence of PPRoM in high risk patients in the observational group (who did not receive aspirin) compared with the high risk patients in the interventional group (who were treated with aspirin) was not statistically significant (= .31). PPRoM is significantly associated with a description of high risk for ePET; although, this algorithm is not a good screening tool for predicting PPRoM. Aspirin treatment of women deemed high risk for ePET is safe in the context of PPRoM and there may be some reduction in prevalence of PPRoM in treated high risk women; although, this study was not powered to demonstrate a small reduction in the prevalence of PPRoM. The findings merit further investigation through a larger prospective study with adequate sample size.
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http://dx.doi.org/10.1080/14767058.2019.1611768 | DOI Listing |
Sci Rep
December 2024
School of Physical Education, Southwest Petroleum University, Chengdu, 610500, China.
Stroke is one of the leading causes of death in developing countries, and China bears the largest global burden of stroke. This study aims to investigate the relationship between different dimensions of physical activity levels and stroke risk using a nationally representative database. We performed a cross-sectional analysis using data from the China Health and Retirement Longitudinal Study (CHARLS) 2020.
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December 2024
The School of Nursing, Fujian Medical University, No. 1 Xuefu North Road, Fuzhou, 350122, Fujian, China.
Diabetes Mellitus combined with Mild Cognitive Impairment (DM-MCI) is a high incidence disease among the elderly. Patients with DM-MCI have considerably higher risk of dementia, whose daily self-care and life management (i.e.
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December 2024
Weather Program Office, Ocean and Atmospheric Research, NOAA, Silver Spring, MD, USA.
Tropical cyclone risks are expected to increase with climate change. One such risk is extreme ocean waves generated by surface winds from these systems. We use synthetic databases of both historical (1980-2017) and future (2015-2050) tropical cyclone tracks to generate wind fields and force a computationally efficient wave model to estimate significant wave heights across all global tropical cyclone basins.
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December 2024
School of Pharmacy, Jiangxi Medical College, Nanchang University, Nanchang, 330006, People's Republic of China.
Cuproptosis, a newly identified form of cell death, has drawn increasing attention for its association with various cancers, though its specific role in colorectal cancer (CRC) remains unclear. In this study, transcriptomic and clinical data from CRC patients available in the TCGA database were analyzed to investigate the impact of cuproptosis. Differentially expressed genes linked to cuproptosis were identified using Weighted Gene Co-Expression Network Analysis (WGCNA).
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December 2024
Department of Applied Mathematics, Faculty of Mathematical Science, Ferdowsi University of Mashhad, Mashhad, Iran.
This study presents a web application for predicting cardiovascular disease (CVD) and hypertension (HTN) among mine workers using machine learning (ML) techniques. The dataset, collected from 699 participants at the Gol-Gohar mine in Iran between 2016 and 2020, includes demographic, occupational, lifestyle, and medical information. After preprocessing and feature engineering, the Random Forest algorithm was identified as the best-performing model, achieving 99% accuracy for HTN prediction and 97% for CVD, outperforming other algorithms such as Logistic Regression and Support Vector Machines.
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