1 citations
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January 2023 in “IEEE access” This review examines advancements in deep learning methods for detecting dermatological conditions from dermoscopic images, summarizing available datasets and suggesting future research directions, but reports no new results.
11 citations
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February 1982 in “Mutation Research/Fundamental and Molecular Mechanisms of Mutagenesis” This study reports that treatment with X-rays or procarbazine induced dose-dependent mutations in melanocytes in mouse hair follicles, showing similar mutation rates to previous methods.
This study found that machine learning techniques, such as Random Forest, SVMs, and KNN, can significantly improve the early detection and determination of hair loss, potentially transforming treatment with more accurate and personalized approaches compared to traditional methods.
9 citations
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January 1983 in “Journal of Chromatography B Biomedical Sciences and Applications” This study found that enzyme activity related to cancer induction by polycyclic aromatic hydrocarbons can be increased by using a shampoo containing crude coal tar and significantly inhibited by imidazole compounds in human hair follicles.
4 citations
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September 2015 in “Bulletin of the Korean Chemical Society” This study presents a new method using UPLC-ESI-MS/MS for analyzing the constituents of a medicinal herbal complex extract, which includes herbs traditionally used to prevent hair loss and promote hair growth; no clinical results were reported.