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The causal association between cardiovascular proteins and diabetic nephropathy: a Mendelian randomization study.
Int Urol Nephrol
January 2025
Department of Nephrology, Jiangxi Medical College, The Second Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China.
Purpose: To clarify the causal association between cardiovascular proteins and diabetic nephropathy (DN) in Europeans.
Methods: The large genome-wide association study data of cardiovascular proteins and DN were used for this two-sample Mendelian randomization (MR) analysis. We took the Inverse variance weighted (IVW) as the primary method.
Machine Learning-Based predictive model for adolescent metabolic syndrome: Utilizing data from NHANES 2007-2016.
Sci Rep
January 2025
Department of Anesthesiology and Surgical Intensive Care Unit, Kunming Children's Hospital, Kunming, Yunnan, China.
Metabolic syndrome (Mets) in adolescents is a growing public health issue linked to obesity, hypertension, and insulin resistance, increasing risks of cardiovascular disease and mental health problems. Early detection and intervention are crucial but often hindered by complex diagnostic requirements. This study aims to develop a predictive model using NHANES data, excluding biochemical indicators, to provide a simple, cost-effective tool for large-scale, non-medical screening and early prevention of adolescent MetS.
View Article and Find Full Text PDFIntroducing and establishing birth companionship in labour ward of a tertiary care centre in North India: a quality improvement journey through and beyond COVID-19 pandemic.
BMJ Open Qual
January 2025
Professor Department of Obstetrics and Gynaecology, Lady Hardinge Medical College, New Delhi, India.
Background: Allowing a birth companion is the basic right of a mother and is identified as an important component of respectful maternity care. The implementation of this intervention has been a challenge in heavy-load public health facilities in India.
Local Problem: Despite the proven benefits of the presence of birth companions on maternal-fetal outcomes, there was no policy of allowing birth companions in our hospital.
Enhancing early breast cancer detection with APE1-triggered oligonucleotide probes and graphene oxide: The impact of variable AP site modification on sensitivity and specificity.
Talanta
January 2025
Guangdong Provincial Key Laboratory of New Drug Screening, Guangzhou Key Laboratory of Drug Research for Emerging Virus Prevention and Treatment, NMPA Key Laboratory for Research and Evaluation of Drug Metabolism, Guangdong-Hong Kong-Macao Joint Laboratory for New Drug Screening, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou, Guangdong, 510515, China. Electronic address:
There is a critical need for inclusive diagnostic platforms to enhance the accuracy of early breast cancer detection. Dysregulated microRNA-1246 (miR-1246), closely linked to the disease progression and recurrence, has emerged as a promising diagnostic and prognostic biomarker for BC. However, achieving simple, rapid, and ultrasensitive quantification of serum miRNAs remains significant challenge.
View Article and Find Full Text PDFInterpretation of COVID-19 Epidemiological Trends in Mexico Through Wastewater Surveillance Using Simple Machine Learning Algorithms for Rapid Decision-Making.
Viruses
January 2025
School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64849, Mexico.
Detection and quantification of disease-related biomarkers in wastewater samples, denominated Wastewater-based Surveillance (WBS), has proven a valuable strategy for studying the prevalence of infectious diseases within populations in a time- and resource-efficient manner, as wastewater samples are representative of all cases within the catchment area, whether they are clinically reported or not. However, analysis and interpretation of WBS datasets for decision-making during public health emergencies, such as the COVID-19 pandemic, remains an area of opportunity. In this article, a database obtained from wastewater sampling at wastewater treatment plants (WWTPs) and university campuses in Monterrey and Mexico City between 2021 and 2022 was used to train simple clustering- and regression-based risk assessment models to allow for informed prevention and control measures in high-affluence facilities, even if working with low-dimensionality datasets and a limited number of observations.
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