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Why are PFASs so hard to replace?
Catabolism of extracellular glutathione supplies cysteine to support tumours
Abstract Restricting amino acids from tumours is an emerging therapeutic strategy with substantial promise 1 . Although typically considered an intracellular antioxidant with tumour-promoting capabilities 2 , glutathione (GSH), as a tripeptide of cysteine, glutamate and glycine, can be catabolized to release amino acids. The extent to which GSH-derived amino acids are essential to cancers is unclear. Here we show that depletion of intracellular GSH does not alter tumour growth and extracellular GSH is highly abundant in the tumour microenvironment, highlighting the potential importance of GSH outside tumours. Supplementation with GSH rescues cancer cell survival and growth in cystine-deficient conditions, and this rescue depends on the catabolic activity of γ-glutamyltransferases. Finally, pharmacological targeting of the activity of γ-glutamyltransferases prevents the breakdown of circulating GSH, reduces tumour cysteine levels and slows tumour growth. Our findings indicate a non-canonical role for GSH in supporting tumours by acting as a reservoir of amino acids. Depriving tumours of extracellular GSH or inhibiting its breakdown is potentially a therapeutically tractable approach for patients with cancer. Furthermore, these findings change our view of GSH and how amino acids, including cysteine, are supplied to cells.
Vision based feedback control of weatherstrip coextrusion with predictive dimension modeling
Stochastic allocation of photovoltaic energy resource and electric bus parking lot in distribution systems using an improved weighted average algorithm via sine–cosine strategy
Time-stratified daily walking speed measurement via smartphone and its predictive utility for mild cognitive impairment
Examining the immunomodulatory role of nanoparticles on mast cell activation
Integrating temporal morphophysiological and genomic markers for precise classification of flowering time in cannabis
France’s research-primate project goes against its own ethics panel
Prevalence, aetiology, and treatment needs of neonates with G6PD deficiency in Central Peninsular Malaysia
Airborne DNA can yield insights with the right techniques
Engineered blood clots stop bleeding in seconds
Atherogenic index of plasma as a predictor of cardiovascular outcomes in patients with myocardial infarction with nonobstructive coronary arteries
AI-optimized BPNN model for port safety risk prediction and management
Probabilistic cancer risk assessment from heavy metal exposure in iranian rice and pasta: a novel hybrid framework integrating INAA, ICP-AES, and ensemble machine learning
Abstract This study investigates cancer risk from heavy metal exposure in rice and pasta using experimental data and machine learning approaches, based on 19 experimental samples and 1,750 simulated exposure instances. Concentrations of toxic heavy metals were quantitatively measured in multiple rice varieties and pasta types using Instrumental Neutron Activation Analysis (INAA) and ICP-AES analytical techniques. The experimentally determined metal concentrations were integrated into the Excess Lifetime Cancer Risk (ELCR) framework, and Several machine learning models were developed for sensitivity analysis and feature prioritization within the ELCR framework. Rather than predicting an unknown outcome (as ELCR is mathematically deterministic), the models were designed to quantify the relative contribution of each exposure parameter to overall cancer risk under probabilistic uncertainty. Regression analysis identified exposure duration as the most influential risk factor (R² = 0.263, p < 0.001), followed by chromium bioavailability (R² = 0.125) and pasta consumption patterns. Ensemble methods provided robust ranking of feature importance, demonstrating how machine learning can complement deterministic risk models by enabling multi-dimensional sensitivity analysis and uncertainty decomposition. The calculated ELCR values ranged from 1.2 × 10⁻⁶ (acceptable) to 1.8 × 10⁻⁴ (unacceptable) depending on consumption scenarios Cancer risk estimates spanned from acceptable to unacceptable levels depending on consumption scenarios. The integration of experimental analytical chemistry with machine learning provides a robust methodology for dietary cancer risk assessment. This approach offers reliable data for food safety regulations and public health protection.