Mapping Drug-Induced Neuropathy through In-Situ Motor Protein Tracking and Machine Learning.
Zhigao YiHuxin GaoXianglin JiXin-Yi YeoSuet Yen ChongYujie MaoBaiwen LuoChao ShenSanyang HanJiong-Wei WangSangyong JungPeng ShiHongliang RenXiaogang LiuPublished in: Journal of the American Chemical Society (2021)
Chemotherapy can induce toxicity in the central and peripheral nervous systems and result in chronic adverse reactions that impede continuous treatment and reduce patient quality of life. There is a current lack of research to predict, identify, and offset drug-induced neurotoxicity. Rapid and accurate assessment of potential neuropathy is crucial for cost-effective diagnosis and treatment. Here we report dynamic near-infrared upconversion imaging that allows intraneuronal transport to be traced in real time with millisecond resolution, but without photobleaching or blinking. Drug-induced neurotoxicity can be screened prior to phenotyping, on the basis of subtle abnormalities of kinetic characteristics in intraneuronal transport. Moreover, we demonstrate that combining the upconverting nanoplatform with machine learning offers a powerful tool for mapping chemotherapy-induced peripheral neuropathy and assessing drug-induced neurotoxicity.
Keyphrases
- drug induced
- liver injury
- chemotherapy induced
- machine learning
- high resolution
- adverse drug
- photodynamic therapy
- artificial intelligence
- case report
- big data
- high throughput
- emergency department
- oxidative stress
- risk assessment
- squamous cell carcinoma
- locally advanced
- binding protein
- drug delivery
- drug release
- electronic health record
- combination therapy
- single cell