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Objective And Methods: Our objective was to investigate the role of patient pharmacogenetic variability in determining site of action target attainment during tuberculous meningitis (TBM) treatment. Rifampin and isoniazid PBPK model that included SLCO1B1 and NAT2 effects on exposures respectively were obtained from literature, modified, and validated using available cerebrospinal-fluid (CSF) concentrations. Population simulations of isoniazid and rifampin concentrations in brain interstitial fluid and probability of target attainment according to genotypes and M. tuberculosis MIC levels, under standard and intensified dosing, were conducted.
Results: The rifampin and isoniazid model predicted steady-state drug concentration within brain interstitial fluid matched with the observed CSF concentrations. At MIC level of 0.25 mg/L, 57% and 23% of the patients with wild type and heterozygous SLCO1B1 genotype respectively attained the target in CNS with rifampin standard dosing, improving to 98% and 91% respectively with 35 mg/kg dosing. At MIC level of 0.25 mg/L, 33% of fast acetylators attained the target in CNS with isoniazid standard dosing, improving to 90% with 7.5 mg/kg dosing.
Conclusion: In this study, the combined effects of pharmacogenetic and M. tuberculosis MIC variability were potent determinants of target attainment in CNS. The potential for genotype-guided dosing during TBM treatment should be further explored in prospective clinical studies.
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http://dx.doi.org/10.1016/j.tube.2022.102271 | DOI Listing |
PLoS One
September 2025
School of Electrical and Information Engineering, Hunan Institute of Technology, Hengyang, Hunan, China.
Knowledge tracing can reveal students' level of knowledge in relation to their learning performance. Recently, plenty of machine learning algorithms have been proposed to exploit to implement knowledge tracing and have achieved promising outcomes. However, most of the previous approaches were unable to cope with long sequence time-series prediction, which is more valuable than short sequence prediction that is extensively utilized in current knowledge-tracing studies.
View Article and Find Full Text PDFPLoS One
September 2025
The George Institute for Global Health, Imperial College London, London, United Kingdom.
Background: Tobacco use remains a major public health challenge in sub-Saharan Africa, with significant gendered dimensions. Place of residence is an important determinant, as rural and urban contexts shape exposure, access, and consumption patterns. This study investigates rural-urban disparities in tobacco use among women in sub-Saharan Africa, with a focus on quantifying the relative contributions of socioeconomic factors.
View Article and Find Full Text PDFMedicine (Baltimore)
September 2025
Department of Respiratory and Critical Care Medicine, Hejiang People's Hospital, Luzhou City, Sichuan Province, China.
Chronic obstructive pulmonary disease (COPD) requires effective management that often depends on the knowledge level of primary family caregivers. This study assessed caregiver knowledge using a validated scale and identified key factors associated with higher COPD knowledge. A cross-sectional survey was conducted from April 2020 to January 2024 at a tertiary hospital in Southwest China.
View Article and Find Full Text PDFPLoS One
September 2025
Center for Studies of Education and Psychology of Ethnic Minorities in Southwest China, Southwest University, Chongqing, China.
Background: Educational hypogamy, where women marry men with lower educational attainment, reflects evolving gender roles and societal norms. In China, the rapid expansion of education, coupled with persistent traditional values, provides a unique context to study this phenomenon.
Methods: Using data from the 2013, 2015, 2017, 2018, and 2021 waves of the China General Social Survey (CGSS), this study applies logistic regression models and Random Forest machine learning techniques to analyze the impact of education on women's selection of hypogamy.
Health Inf Sci Syst
December 2025
School of Information Science and Automation, Northeastern University, Shenyang, 110819 China.
Accurate prediction of drug-target interactions (DTIs) is crucial for improving the efficiency and success rate of drug development. Despite recent advancements, existing methods often fail to leverage interaction features at multiple granular levels, resulting in suboptimal data utilization and limited predictive performance. To address these challenges, we propose CF-DTI, a coarse-to-fine drug-target interaction model that integrates both coarse-grained and fine-grained features to enhance predictive accuracy.
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