Effect of Machine Learning on Risk Stratification for Antiretroviral Treatment Failure in People Living with HIV DOI Creative Commons
Wenyuan Zhang, Lehao Ren, Kai Yang

et al.

Infection and Drug Resistance, Journal Year: 2025, Volume and Issue: Volume 18, P. 1761 - 1772

Published: April 1, 2025

Despite the widespread use of antiretroviral therapy (ART), HIV virologic failure remains a significant global public health challenge. This study aims to develop and validate nomogram-based scoring system predict incidence determinants in people living with (PLWH), facilitating timely interventions reducing unnecessary transitions second-line regimens. A total 9879 patients HIV/AIDS were included. The predictive model was developed using training cohort (N = 5,189) validated internally 2,228) externally 2,462) independent cohorts. Multivariable logistic regression, variables selected through least absolute shrinkage selection operator (LASSO) employed. final presented as nomogram transformed into user-friendly system. Key predictors included delayed ART initiation (6 points), poor adherence (7 discontinuation side effects (9 CD4+ T cell count (10 follow-up safety index (FSI) points). With cutoff 15.5 points, area under curve (AUC) for validation sets 0.807, 0.784, 0.745, respectively. demonstrated robust diagnostic performance across novel provides an accurate, well-calibrated tool predicting at individual level, offering valuable clinical utility optimizing management.

Language: Английский

Glutathiones’ life in multi-cancers: especially their potential micropetides in liver hepatocellular carcinoma DOI Creative Commons
Dandan Ma, Zhenguo Wu, Mengying Zhang

et al.

Discover Oncology, Journal Year: 2025, Volume and Issue: 16(1)

Published: Feb. 18, 2025

Glutathione plays critical roles in detoxifying xenobiotics, cell signaling, death and the antioxidant defence an emerging body of evidence, most abundant intracellular low molecular weight thiol tissues. However, all glutathione metabolism pertinent genes (GMPGs) expression their diagnostic/prognostic/micropeptide potential analyses have not been investigated to perform pan-cancers. We gained GMPGs from MsigDB 7.2, 12,123 samples were used reveal differentially expressed (DEGs) survival analysis 32 types cancers TCGA, GTEx, GEO datasets for first time. All statistical performed by R bioinformatics, such as DEGs, prognostic, diagnostic analysis, ceRNA, micropeptide prediction immune infiltration. In addition, we utilized siRNA technology target knockdown G6PD gene Huh7 hepatocellular carcinoma cells. was significantly poor prognosis liver (LIHC) predicted RBM26-AS1 encoded might LIHC. vitro experiments show that knockout cells reduces proliferation, migration, invasion capabilities. confirmed played a crucial role occurrence progression is positively associated with Th2 LIHC, regulating responses system. considered be new player involved LIHC interacting G6PD, key function cancer.

Language: Английский

Citations

0
Effect of Machine Learning on Risk Stratification for Antiretroviral Treatment Failure in People Living with HIV DOI Creative Commons
Wenyuan Zhang, Lehao Ren, Kai Yang

et al.

Infection and Drug Resistance, Journal Year: 2025, Volume and Issue: Volume 18, P. 1761 - 1772

Published: April 1, 2025

Despite the widespread use of antiretroviral therapy (ART), HIV virologic failure remains a significant global public health challenge. This study aims to develop and validate nomogram-based scoring system predict incidence determinants in people living with (PLWH), facilitating timely interventions reducing unnecessary transitions second-line regimens. A total 9879 patients HIV/AIDS were included. The predictive model was developed using training cohort (N = 5,189) validated internally 2,228) externally 2,462) independent cohorts. Multivariable logistic regression, variables selected through least absolute shrinkage selection operator (LASSO) employed. final presented as nomogram transformed into user-friendly system. Key predictors included delayed ART initiation (6 points), poor adherence (7 discontinuation side effects (9 CD4+ T cell count (10 follow-up safety index (FSI) points). With cutoff 15.5 points, area under curve (AUC) for validation sets 0.807, 0.784, 0.745, respectively. demonstrated robust diagnostic performance across novel provides an accurate, well-calibrated tool predicting at individual level, offering valuable clinical utility optimizing management.

Language: Английский

Citations

0