Machine Learning‐Engineered Nanozyme System for Synergistic Anti‐Tumor Ferroptosis/Apoptosis Therapy DOI
Tianliang Li, Bin Cao, Tianhao Su

et al.

Small, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 16, 2024

Abstract Nanozymes with multienzyme‐like activity have sparked significant interest in anti‐tumor therapy via responding to the tumor microenvironment (TME). However, consequent induction of protective autophagy substantially compromises therapeutic efficacy. Here, a targeted nanozyme system (Fe‐Arg‐CDs@ZIF‐8/HAD, FZH) is shown, which enhances synergistic ferroptosis/apoptosis by leveraging machine learning (ML). A novel ML model, termed sequential backward Tree‐Classifier for Gaussian Process Regression (TCGPR), proposed improve data pattern recognition following divide‐and‐conquer principle. Based on this, Bayesian optimization algorithm employed select candidates from extensive search space. Leveraging this fresh material discovery framework, strategy enhancing nanozyme‐based therapy, has been developed. The results reveal that FZH effectively exerts effects sequentially TME, having cascade reaction induce ferroptosis. Moreover, endogenous elevation high concentration nitric oxide (NO) serves as direct mechanism killing cells while concurrently suppressing induced oxidative stress (OS), therapy. Overall, improving proposed, underlying integration ML, experiments, and biological applications.

Language: Английский

Astragaloside IV prevents enterovirus 71-induced pyroptosis through the TCF12-TXNIP-Keap1/Nrf2 axis DOI Creative Commons
Jinfang Hao, Xiaoyan Zhang, Hui Wang

et al.

Journal of Functional Foods, Journal Year: 2024, Volume and Issue: 118, P. 106290 - 106290

Published: June 11, 2024

Language: Английский

Citations

0
Machine Learning‐Engineered Nanozyme System for Synergistic Anti‐Tumor Ferroptosis/Apoptosis Therapy DOI
Tianliang Li, Bin Cao, Tianhao Su

et al.

Small, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 16, 2024

Abstract Nanozymes with multienzyme‐like activity have sparked significant interest in anti‐tumor therapy via responding to the tumor microenvironment (TME). However, consequent induction of protective autophagy substantially compromises therapeutic efficacy. Here, a targeted nanozyme system (Fe‐Arg‐CDs@ZIF‐8/HAD, FZH) is shown, which enhances synergistic ferroptosis/apoptosis by leveraging machine learning (ML). A novel ML model, termed sequential backward Tree‐Classifier for Gaussian Process Regression (TCGPR), proposed improve data pattern recognition following divide‐and‐conquer principle. Based on this, Bayesian optimization algorithm employed select candidates from extensive search space. Leveraging this fresh material discovery framework, strategy enhancing nanozyme‐based therapy, has been developed. The results reveal that FZH effectively exerts effects sequentially TME, having cascade reaction induce ferroptosis. Moreover, endogenous elevation high concentration nitric oxide (NO) serves as direct mechanism killing cells while concurrently suppressing induced oxidative stress (OS), therapy. Overall, improving proposed, underlying integration ML, experiments, and biological applications.

Language: Английский

Citations

0