With the increasing popularity of herbal medicine, high standards of the high quality control of herbs becomes a necessity, with the herb recognition as one of the great challenges. Due to the complicated processing procedure of the herbs, methods of manual recognition that require chemical materials and expert knowledge, such as fingerprint and experience, have been used. Automatic methods can partially alleviate the problem by deep learning based herb image recognition, but most studies require powerful and expensive computation hardware, which is not friendly to resource-limited settings. In this paper, we introduce a deep learning-enabled mobile application which can run entirely on common low-cost smartphones for efficient and robust herb image recognition with a quite competitive recognition accuracy in resource-limited situations. We hope this application can make contributions to the increasing accessibility of herbal medicine worldwide.
Background: Due to their close relationship, the efficacy of major depressive disorder (MDD) drugs in the treatment of Alzheimer's disease (AD) has received widespread attention in recent years. Methods: In this study, we explored the potential therapeutic value of traditional Chinese medicine (TCM) and multitarget components on both MDD and AD by using a comprehensive strategy with network pharmacology and molecular docking technology. Results: In total, 234 MDD-related TCM prescriptions were analyzed and the 10 most commonly used Chinese herbs, correlating to 198 active ingredients, were identified. Through a comparative analysis of 144 prospective ingredient targets, 1095 MDD-related targets, and 1684 AD-related targets, network pharmacology identified 30 common targets, 9 key targets, and 7 representative compounds. The results of GO and KEGG enrichment analysis revealed that common targets were required to regulate multiple pathways related to MDD and AD. In addition, network analysis demonstrated that the combination of Xiangfu (Cyperi Rhizoma)-Gancao (Licorice)-Chaihu (Radix Bupleuri) constituted the major part of the representative ingredients and could be used to treat both diseases. Moreover, ALB, AKT1, ESR1, CASP3, and NOS3 were also chosen as prospective targets for synthetic multitarget ingredient screening. Further docking studies revealed that various natural chemicals exhibited binding affinity with the 5 targets, including quercetin, kaempferol, β-sitosterol, stigmasterol, isorhamnetin, naringenin, and 8-isopentenyl-kaempferol. Conclusion: Taken as a whole, the current study indicates a TCM combination with positive advantages in the combined treatment of AD and MDD.
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