Machine learning represents a growing subfield of artificial intelligence with much promise in the diagnosis,treatment,and tracking of complex conditions,including neurodegenerative disorders such as Alzheimer’s and ...Machine learning represents a growing subfield of artificial intelligence with much promise in the diagnosis,treatment,and tracking of complex conditions,including neurodegenerative disorders such as Alzheimer’s and Parkinson’s diseases.While no definitive methods of diagnosis or treatment exist for either disease,researchers have implemented machine learning algorithms with neuroimaging and motion-tracking technology to analyze pathologically relevant symptoms and biomarkers.Deep learning algorithms such as neural networks and complex combined architectures have proven capable of tracking disease-linked changes in brain structure and physiology as well as patient motor and cognitive symptoms and responses to treatment.However,such techniques require further development aimed at improving transparency,adaptability,and reproducibility.In this review,we provide an overview of existing neuroimaging technologies and supervised and unsupervised machine learning techniques with their current applications in the context of Alzheimer’s and Parkinson’s diseases.展开更多
Alzheimer's disease(AD)is a neurodegenerative disease and a major threat to human health worldwide.The association between aluminum exposure and AD has been widely reported.Owing to the ubiquitous presence of alum...Alzheimer's disease(AD)is a neurodegenerative disease and a major threat to human health worldwide.The association between aluminum exposure and AD has been widely reported.Owing to the ubiquitous presence of aluminum in daily life;aluminum exposure can easily occur whenever and wherever possible.Thus;a rapid and sensitive reagent for detecting aluminum and assist in AD daily prevention for potential AD patient population is extremely needed.However;existing aluminum detection methods rely on precise instruments;which are impractical for household use.Herein;a series of aggregation-induced emission-based covalent-organic framework(AIE-COF)fluorescent probes has been designed with progressively tuned sizes and screened for aluminum detection.Among them;COF-N2 was found to have the highest response towards aluminum specifically;with a fluorescence intensity change of 19.14 times before and after chelation;which could determine the aluminum concentration by naked eye.Then;the molecular mechanism of COF-N2 fluorescence changes was explained and COF-N2 was used for both diagnose the aluminum distribution in various organs of APP/PS1 transgenic mice and quickly determine the aluminum content in daily necessities.The use of AIE-COF probes for aluminum detection provides a promising avenue for developing aluminum related AD clinical diagnosis and daily prevention tools.展开更多
Objective To determine the diagnostic significance of detecting the specific epithelial keratin CK-20 mRNA in peripheral venous blood from patients with bladder carcinomas. Methods Reverse transcription coupled with t...Objective To determine the diagnostic significance of detecting the specific epithelial keratin CK-20 mRNA in peripheral venous blood from patients with bladder carcinomas. Methods Reverse transcription coupled with two-step polymerase chain reaction (nested RT-PCR) was used to detect CK-20 mRNA expression in the peripheral blood from patients with blodder carcinomas. Results Detection of CK-20 mRNA expression was positive in 37 of 91 patients with bladder carcinoma (41 % ). Among 20 patients with distant metastasis, 17 were positive (85 % ). CK-20 mRNA was not detectable in the blood samples from 25 normal individuals. The frequency of positive CK-20 mRNA expression was signficantly higher in those with distant metastasis. Conclusion The presence of CK-20 mRNA expression in peripheral blood may be used as an early indicator of hematogenous metastasis of bladder carcinoma cells. 6 refs,1 tab.展开更多
文摘Machine learning represents a growing subfield of artificial intelligence with much promise in the diagnosis,treatment,and tracking of complex conditions,including neurodegenerative disorders such as Alzheimer’s and Parkinson’s diseases.While no definitive methods of diagnosis or treatment exist for either disease,researchers have implemented machine learning algorithms with neuroimaging and motion-tracking technology to analyze pathologically relevant symptoms and biomarkers.Deep learning algorithms such as neural networks and complex combined architectures have proven capable of tracking disease-linked changes in brain structure and physiology as well as patient motor and cognitive symptoms and responses to treatment.However,such techniques require further development aimed at improving transparency,adaptability,and reproducibility.In this review,we provide an overview of existing neuroimaging technologies and supervised and unsupervised machine learning techniques with their current applications in the context of Alzheimer’s and Parkinson’s diseases.
基金supported by the National Key R&D Program of China(2022YFB3805902)the National Natural Science Foundation of China(22131004,U21A20330,21975039 and 22077118)+3 种基金the“111”project(B18012)the Fundamental Research Funds for the Central Universities,Excellent Youth Team Program(2412023YQ001)the Finance Special Project on Medical and Health Talents of the Finance Department of Jilin Province(JLSWSRCZX2023-52)WBE Liver Fibrosis Foundation(2020009).
文摘Alzheimer's disease(AD)is a neurodegenerative disease and a major threat to human health worldwide.The association between aluminum exposure and AD has been widely reported.Owing to the ubiquitous presence of aluminum in daily life;aluminum exposure can easily occur whenever and wherever possible.Thus;a rapid and sensitive reagent for detecting aluminum and assist in AD daily prevention for potential AD patient population is extremely needed.However;existing aluminum detection methods rely on precise instruments;which are impractical for household use.Herein;a series of aggregation-induced emission-based covalent-organic framework(AIE-COF)fluorescent probes has been designed with progressively tuned sizes and screened for aluminum detection.Among them;COF-N2 was found to have the highest response towards aluminum specifically;with a fluorescence intensity change of 19.14 times before and after chelation;which could determine the aluminum concentration by naked eye.Then;the molecular mechanism of COF-N2 fluorescence changes was explained and COF-N2 was used for both diagnose the aluminum distribution in various organs of APP/PS1 transgenic mice and quickly determine the aluminum content in daily necessities.The use of AIE-COF probes for aluminum detection provides a promising avenue for developing aluminum related AD clinical diagnosis and daily prevention tools.
文摘Objective To determine the diagnostic significance of detecting the specific epithelial keratin CK-20 mRNA in peripheral venous blood from patients with bladder carcinomas. Methods Reverse transcription coupled with two-step polymerase chain reaction (nested RT-PCR) was used to detect CK-20 mRNA expression in the peripheral blood from patients with blodder carcinomas. Results Detection of CK-20 mRNA expression was positive in 37 of 91 patients with bladder carcinoma (41 % ). Among 20 patients with distant metastasis, 17 were positive (85 % ). CK-20 mRNA was not detectable in the blood samples from 25 normal individuals. The frequency of positive CK-20 mRNA expression was signficantly higher in those with distant metastasis. Conclusion The presence of CK-20 mRNA expression in peripheral blood may be used as an early indicator of hematogenous metastasis of bladder carcinoma cells. 6 refs,1 tab.
