Revolutionizing Prostate Cancer Therapy: Artificial intelligence – based Nanocarriers for Precision Diagnosis and Treatment

Critical Reviews in Oncology/Hematology, Journal Year: 2025, Volume and Issue: unknown, P. 104653 - 104653

Published: Feb. 1, 2025

Language: Английский

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Omics-Based Interaction Analysis Reveals Interplay of Chemical Pollutant (Ozone) and Photoradiation (UVSSR) Stressors in Skin Damage

Biology, Journal Year: 2025, Volume and Issue: 14(1), P. 72 - 72

Published: Jan. 14, 2025

The skin acts as the first line of defense against various environmental stressors, such solar ultraviolet radiation, visible light, pollution particles and ozone. Simultaneous exposure to different stressors is common in everyday life but has been less studied than single stressors. Herein, combined effects a chemical pollutant (ozone) UV radiation stressor (UVSSR) were investigated on 3D pigmented living equivalent model. Our results demonstrate that lightness (L* value) was significantly decreased by either UVSSR or ozone alone co-exposure further exacerbated surface darkness, suggesting these had significant joint effect. Conventional differential expression analysis showed that, among groups, dysregulated most genes, followed alone. Omics-based interaction framework (OBIF) interactive genes following displayed cooperative effect functions related barrier; with synergistic enriched biological pathways chronic inflammatory response apoptotic signaling pathway. In summary, combination UVSSR-induced phenotypic changes skin; underlying mechanism determined using transcriptome analysis, showing additive impacts damage, cellular stress responses. These findings shed light how exacerbates damage indicate identified OBIF may drive progression induced chemical/photoradiation co-exposure.

Language: Английский

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Computer-Aided Discovery of Synergistic Drug–Nanoparticle Combinations for Enhanced Antimicrobial Activity

ACS Applied Materials & Interfaces, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 17, 2025

Antibiotic resistance is a critical global public health challenge driven by the limited discovery of antibiotics, rapid evolution mechanisms, and persistent infections that compromise treatment efficacy. Combination therapies using antibiotics nanoparticles (NPs) offer promising solution, particularly against multidrug-resistant (MDR) bacteria. This study introduces an innovative approach to identifying synergistic drug–NP combinations with enhanced antimicrobial activity. To carry this out, we compiled two groups data sets predict minimal concentration (MC) zone inhibition (ZOI) various combinations. CatBoost regression models achieved best 10-fold cross-validation R2 scores 0.86 0.77, respectively. We then adopted machine learning (ML)-reinforced genetic algorithm (GA) identify NPs. The proposed was first validated reproducing previous experimental results. As proof concept for discovering combinations, Au NPs were identified as highly when paired chloramphenicol, achieving minimum bactericidal (MBC) 71.74 ng/mL Salmonella typhimurium fractional inhibitory index 6.23 × 10–3. These findings present effective strategy providing combating drug-resistant pathogens advancing targeted therapies.

Language: Английский

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Hallmarks of artificial intelligence contributions to precision oncology

Nature Cancer, Journal Year: 2025, Volume and Issue: unknown

Published: March 7, 2025

Language: Английский

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