Ferroptosis-related protein biomarkers for diagnosis, differential diagnosis, and short-term mortality in patients with sepsis in the intensive care unit

Frontiers in Immunology, Journal Year: 2025, Volume and Issue: 16

Published: April 8, 2025

Sepsis is a disease with high mortality caused by dysregulated response to infection. Ferroptosis newly discovered type of cell death. Ferroptosis-related genes are involved in the occurrence and development sepsis. However, research on diagnostic value ferroptosis-related protein biomarkers sepsis serum limited. This study aims explore clinical proteins diagnosing predicting risk. A single-center, prospective, observational was conducted from January December 2023, involving 170 patients, 49 non-septic ICU 50 healthy individuals. Upon admission, biochemical parameters, GCS, SOFA, APACHE II scores were recorded, surplus stored at -80°C for biomarker analysis via ELISA. Diagnostic efficacy evaluated using ROC curve analysis. Baseline levels ACSL4, GPX4, PTGS2, CL-11, IL-6, IL-8, PCT, hs-CRP significantly differed among sepsis, non-septic, individuals (all p-value < 0.01). demonstrated differential performance (AUC: 0.6688 0.9945). IL-10 TNF-α showed good (AUC = 0.8955 0.7657, respectively). ACSL4 0.7127) associated mortality. Serum IL-6 above cut-off shorter survival times. positively correlated SOFA (Rho 0.354, 0.0001), 0.317, septic shock 0.274, 0.003) but negatively GCS score -0.218, 0.018). GPX4 0.204, 0.027) 0.233, 0.011) scores. have strong including ability predict 28-day may become new potential markers

Language: Английский

Integrating ensemble machine learning and multi-omics approaches to identify Dp44mT as a novel anti-Candida albicans agent targeting cellular iron homeostasis

Frontiers in Pharmacology, Journal Year: 2025, Volume and Issue: 16

Published: April 24, 2025

Candidiasis, mainly caused by Candida albicans, poses a serious threat to human health. The escalating drug resistance in C. albicans and the limited antifungal options highlight critical need for novel therapeutic strategies. We evaluated 12 machine learning models on self-constructed dataset with known anti-C. activity. Based their performance, optimal model was selected screen our separate in-house compound library unknown activity potential agents. of compounds confirmed through vitro susceptibility assays, hyphal growth biofilm formation assays. Through transcriptomics, proteomics, iron rescue experiments, CTC staining, JC-1 DAPI molecular docking, dynamics simulations, we elucidated mechanism underlying compound. Among models, best predictive an ensemble constructed from Random Forests Categorical Boosting using soft voting. It predicts that Dp44mT exhibits potent tests further verified this finding can inhibit planktonic growth, formation, albicans. Mechanistically, exerts disrupting cellular homeostasis, leading collapse mitochondrial membrane ultimately causing apoptosis. This study presents practical approach predicting com-pounds provides new insights into development homeostasis

Language: Английский