A series of 74 dihydroalkoxybenzyloxopyrimidines (DABOs), a class of highly potent non-nucleoside reverse transcriptase inhibitors (NNRTIs), was retrieved from the literature and studied by comparative molecular field analysis (CoMFA) in order to derive three-dimensional quantitative structure-activity relationship (3D-QSAR) models. The CoMFA study has been performed with a training set of 59 compounds, testing three alignments and four charge schemes (DFT, HF, AM1, and PM3) and using defaults probe atom (Csp (3), +1 charge), cutoffs (30 kcal.mol (-1) for both steric and electrostatic fields), and grid distance (2.0 A). The best model ( N = 59), derived from Alignment 1 and PM3 charges, shows q (2) = 0.691, SE cv = 0.475, optimum number of components = 6, r (2) = 0.930, SEE = 0.226, and F-value = 115.544. The steric and electrostatic contributions for the best model were 43.2% and 56.8%, respectively. The external predictive ability (r (2) pred = 0.918) of the resultant best model was evaluated using a test set of 15 compounds. In order to design more potent DABO analogues as anti-HIV/AIDS agents, attention should be taken in order to select a substituent for the 4-oxopyrimidine ring, since, as revealed by the best CoMFA model, there are a steric restriction at the C2-position, a electron-rich group restriction at the C6-position ( para-substituent of the 6-benzyl group), and a steric allowed region at the C5-position.
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Biodegradation
December 2024
Department of Civil engineering, Islamic Azad university, Mashhad Branch, Iran.
The widespread use of pesticides, including diazinon, poses an increased risk of environmental pollution and detrimental effects on biodiversity, food security, and water resources. In this study, we investigated the impact of Potentially Toxic Elements (PTE) including Zn, Cd, V, and Mn on the degradation of diazinon in three different soils. We investigated the capability and performance of four machine learning models to predict residual pesticide concentration, including adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), radial basis function (RBF), and multi-layer perceptron (MLP).
View Article and Find Full Text PDFJ Am Med Inform Assoc
December 2024
AI for Health Institute, Washington University in St Louis, St Louis, MO 63130, United States.
Objective: Early detection of surgical complications allows for timely therapy and proactive risk mitigation. Machine learning (ML) can be leveraged to identify and predict patient risks for postoperative complications. We developed and validated the effectiveness of predicting postoperative complications using a novel surgical Variational Autoencoder (surgVAE) that uncovers intrinsic patterns via cross-task and cross-cohort presentation learning.
View Article and Find Full Text PDFUnlabelled: Diabetic macular edema (DME) is a leading cause of visual impairment and blindness among diabetic patients, its prevalence is continuing to increase worldwide. Faricimab, a bispecific antibody, represents a new generation of treatments for DME.
Purpose: This study presents an indirect comparison of the effectiveness and safety of faricimab versus other treatment options for DME.
J Health Popul Nutr
December 2024
Department of Nursing, Changde Hospital, Xiangya School of Medicine, Central South University(The first people's hospital of Changde city), Changde, Hunan Province, China.
Purpose: The association between nutritional risk status assessment and hospital mortality in older patients remains controversial. The aim of this study was to assess the relationship between nutritional risk on admission and in-hospital mortality, and explore the best Nutritional Risk Status Screening 2002 (NRS2002) threshold for predicting in-hospital mortality of older inpatients in China.
Method: The elderly inpatients were recruited from a hospital in Hunan Province, China.
Implement Sci Commun
December 2024
Department of Health Promotion and Behavioral Sciences, School of Public Health, The University of Texas Health Science Center at Houston, 7000 Fannin Street, Houston, TX, 77030, USA.
Background: All for Them is a theory-based and evidence-informed multilevel, multicomponent program delivered through schools to increase HPV vaccination among medically underserved youth across Texas. Given the potential logistical challenges of program implementation, understanding how to best support the implementation and sustainment of the program is critical. The overall goals of this study are twofold: 1) develop a multifaceted implementation strategy, Implementing All for Them (IM-AFT); and 2) evaluate the impact of IM-AFT on implementation outcomes for schools and healthcare providers to successfully implement All for Them in their respective settings.
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