Lipid peroxide (LPO) accumulation and a depletion of intracellular antioxidants are hallmarks of ferroptosis, a controlled iron-dependent form of cell death. Iron chelators and radical scavengers can stop it, while erastin or iron overload can cause it. The main catechin in green tea extract (GTE), epigallocatechin-3-gallate (EGCG), has iron-chelating and antioxidant activities. Herein, we investigated the effects of EGCG-rich GTE on ferroptosis in iron-loaded hepatocytes. The contents of EGCG, total phenolics (TPC), and flavonoids (TFC), as well as ABTS^•+-scavenging activity and cytotoxicity, were determined. Human hepatoma (Huh7) cells were treated with ferric ammonium citrate (FAC) to induce ferroptosis and were co-treated with various concentrations of GTE. Labile iron pool (LIP), reactive oxygen species (ROS), LPO, glutathione (GSH), and glutathione peroxidase 4 (GPX-4) activity were then measured in the cells. One gram of GTE contained 26 mg of EGCG, with a TPC of 172.2 mg gallic acid equivalents and a TFC of 32.9 mg quercetin equivalents. GTE displayed concentration-dependent ABTS^•+-scavenging activity (IC₅₀ = 1.03 mg) that was equivalent to 0.29 mg of Trolox, reporting a Trolox-equivalent antioxidant capacity (TEAC) value of 0.29 mg. High-dose GTE (>100 µM EGCG equivalent) reduced cell viability below 80% (p < 0.05). Intracellular LIP, ROS, and LPO levels were markedly elevated, whereas GSH and GPX-4 activity levels were decreased (p < 0.05) in iron-loaded Huh7 cells. GTE treatment mitigated these alterations in a dose-dependent manner (p < 0.05). These cell-based in vitro findings indicate that EGCG-rich GTE can attenuate ferroptosis-associated oxidative stress in hepatocytes under iron-loading conditions. GTE may serve as a potential dietary antioxidant candidate; further mechanistic studies and in vivo experiments are required to determine its physiological relevance and translational applicability.

Lung cancer mortality is a critical issue in Northern Thailand, where incidence rates significantly exceed the national average. Key risk factors include tobacco smoking and seasonal air pollution from biomass burning, which releases carcinogenic polycyclic aromatic hydrocarbons (PAHs). This study aims to analyze the accumulation of 16 priority PAHs in human lung tissues to explore links between environmental exposure, smoking habits, and cancer development. Tissue samples (normal, para-cancerous, and adenocarcinoma) were collected from 30 surgical participants (15 lung cancer patients, 15 non-cancer controls) at Chiang Mai University Hospital. Compounds were extracted via ultrasonication and quantified using gas chromatography-mass spectrometry. Carcinogenic potency was evaluated using Benzo[a]pyrene equivalents, with concentrations compared across tissue types and patient groups using generalized estimating equations. High-molecular-weight PAHs were the dominant species, accounting for over 50% of total accumulation in all tissues, aligning with regional biomass burning profiles. Total concentrations and carcinogenic potency were significantly higher in smokers, particularly within normal lung tissues. Furthermore, high-molecular-weight PAH concentrations positively correlated with tissue cholesterol levels (R = 0.66). Source analysis indicated exposure primarily from pyrogenic sources, alongside petroleum emissions. In conclusion, high-molecular-weight carcinogens significantly accumulate in human lungs in Northern Thailand, reflecting substantial environmental exposure, while tissue lipid content modulates toxin bioaccumulation. Smoking facilitates compound retention in normal tissues, underscoring an urgent need for targeted public health strategies addressing both environmental pollution and tobacco use.

In this study, we investigate whether conversational AI agents, designed around diverse lived-experience profiles, can provide meaningful peer-like support and contribute to improvements in mental health. Building on prior work employing a digital platform that matches users with different human peer supporters, we developed AI-Peer2S. This platform allows users to interact with multiple AI peer supporters, each represented through a distinct lived-experience persona. A two-week mixed-method experiment study, consisting of a control period followed by an intervention period, was conducted with 19 participants to evaluate whether engagement with the AI peers was associated with changes in psychological and social assessments. Results showed that participants reported a significant reduction in social anxiety and depressive symptoms, while there were no significant changes on loneliness, generalized anxiety and perceived social support. Importantly, these changes did not occur during the control period. An analysis of the engagement patterns further showed that participants did not interact with AI peers uniformly but tended to explore multiple agents before settling on a preferred supporter. In addition, their temporal usage patterns indicated that while most interactions occurred in the morning, suggesting possible integration into daily routines, late-night use highlighted the potential role of AI peers as an accessible source of support when human support may be unavailable.