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Development and evaluation of patient-centred polygenic risk score reports for glaucoma screening.
BMC Med Genomics
January 2025
Department of Ophthalmology, Flinders Medical and Health Research Institute, Flinders University, Adelaide, SA, Australia.
Background: Polygenic risk scores (PRS), which provide an individual probabilistic estimate of genetic susceptibility to develop a disease, have shown effective risk stratification for glaucoma onset. However, there is limited best practice evidence for reporting PRS and patient-friendly reports for communicating PRS effectively are lacking. Here we developed patient-centred PRS reports for glaucoma screening based on the literature, and evaluated them with participants using a qualitative research approach.
View Article and Find Full Text PDFIdentification of Lignans.
Methods Mol Biol
January 2025
College of Pharmacy and Institute of Pharmaceutical Sciences and Technology, Hanyang University, Ansan, Gyeonggi-do, Republic of Korea.
Lignans have garnered significant interest in various fields of pharmaceuticals, nutrition, and pesticides due to their diverse biological activities. This chapter primarily focuses on the extraction and purification methods of lignans from Schisandra, followed by an extensive examination of qualitative and quantitative analytical techniques, including thin-layer chromatography (TLC) and high-performance liquid chromatography (HPLC). Additionally, methods for structural analysis using nuclear magnetic resonance (NMR) are briefly summarized.
View Article and Find Full Text PDF(+)/(-)-Corydecumbenpines A and B, two pairs of dimeric phthalide-benzophenanthridine alkaloids with cytotoxicity from Corydalis decumbens.
Phytochemistry
January 2025
State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, 100191, China. Electronic address:
Four pairs of previously undescribed enantiomers, including two pairs of dimeric phthalide-benzophenanthridine alkaloid and two pairs phthalides derivatives, corydecumbenpines A-D, were isolated from the rhizomes of Corydalis decumbens. Their planar structures were elucidated by extensive spectroscopic analysis including UV, IR, HRESIMS, 1D and 2D NMR. Optically pure compounds were obtained by chiral chromatography and their absolute configurations were determined by single-crystal X-ray crystallography and comparison of experimental and calculated ECD spectra.
View Article and Find Full Text PDFNutrition Users' Guides: RCTs Part 2 - structured guide for interpreting and applying study results from randomised controlled trials on therapy or prevention questions.
BMJ Nutr Prev Health
August 2024
Department of Nutrition, College of Agriculture and Life Sciences, Texas A&M University, College Station, Texas, USA.
This article continues from a prior commentary on evaluating the risk of bias in randomised controlled trials addressing nutritional interventions. Having provided a synopsis of the risk of bias issues, we now address how to understand trial results, including the interpretation of best estimates of effect and the corresponding precision (eg, 95% CIs), as well as the applicability of the evidence to patients based on their unique circumstances (eg, patients' values and preferences when trading off potential desirable and undesirable health outcomes and indicators (eg, cholesterol), and the potential burden and cost of an intervention). Authors can express the estimates of effect for health outcomes and indicators in relative terms (relative risks, relative risk reductions, OR or HRs)-measures that are generally consistent across populations-and absolute terms (risk differences)-measures that are more intuitive to clinicians and patients.
View Article and Find Full Text PDFEstimation of leaf area index by combining multi-source remote sensing data and machine learning optimization model.
Front Plant Sci
January 2025
Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.
The Leaf Area Index (LAI) is an essential parameter that affects the exchange of energy and materials between the vegetative canopy and the surrounding environment. Estimating LAI using machine learning models with remote sensing data has become a prevalent method for large-scale LAI estimation. However, existing machine learning models have exhibited various flaws, hindering the accurate estimation of LAI.
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