Microbiota-gut-brain axis: Natural antidepressants molecular mechanism.

BACKGROUND: Major depressive disorder (MDD) is a severe mental health condition characterized by persistent depression, impaired cognition, and reduced activity. Increasing evidence suggests that gut microbiota (GM) imbalance is closely linked to the emergence and advancement of MDD, highlighting the potential significance of regulating the "Microbiota-Gut-Brain" (MGB) axis to impact the development of MDD. Natural products (NPs), characterized by broad biological activities, low toxicity, and multi-target characteristics, offer unique advantages in antidepressant treatment by regulating MGB axis. PURPOSE: This review was aimed to explore the intricate relationship between the GM and the brain, as well as host responses, and investigated the mechanisms underlying the MGB axis in MDD development. It also explored the pharmacological mechanisms by which NPs modulate MGB axis to exert antidepressant effects and addressed current research limitations. Additionally, it proposed new strategies for future preclinical and clinical applications in the MDD domain. METHODS: To study the effects and mechanism by which NPs exert antidepressant effects through mediating the MGB axis, data were collected from Web of Science, PubMed, ScienceDirect from initial establishment to March 2024. NPs were classified and summarized by their mechanisms of action. RESULTS: NPs, such as flavonoids,alkaloids,polysaccharides,saponins, terpenoids, can treat MDD by regulating the MGB axis. Its mechanism includes balancing GM, regulating metabolites and neurotransmitters such as SCAFs, 5-HT, BDNF, inhibiting neuroinflammation, improving neural plasticity, and increasing neurogenesis. CONCLUSIONS: NPs display good antidepressant effects, and have potential value for clinical application in the prevention and treatment of MDD by regulating the MGB axis. However, in-depth study of the mechanisms by which antidepressant medications affect MGB axis will also require considerable effort in clinical and preclinical research, which is essential for the development of effective antidepressant treatments.

An explainable machine-learning approach for revealing the complex synthesis path-property relationships of nanomaterials.

Machine learning (ML) models have recently shown important advantages in predicting nanomaterial properties, which avoids many trial-and-error explorations. However, complex variables that control the formation of nanomaterials exhibiting the desired properties still need to be better understood owing to the low interpretability of ML models and the lack of detailed mechanism information on nanomaterial properties. In this study, we developed a methodology for accurately predicting multiple synthesis parameter-property relationships of nanomaterials to improve the interpretability of the nanomaterial property mechanism. As a proof-of-concept, we designed glutathione-gold nanoclusters (GSH-AuNCs) exhibiting an appropriate fluorescence quantum yield (QY). First, we conducted 189 experiments and synthesized different GSH-AuNCs by varying the thiol-to-metal molar ratio and reaction temperature and time in reasonable ranges. The fluorescence QY of GSH-AuNCs could be systematically and independently programmed using different experimental parameters. We used limited GSH-AuNC synthesis parameter data to train an extreme gradient boosting regressor model. Moreover, we improved the interpretability of the ML model by combining individual conditional expectation, double-variable partial dependence, and feature interaction network analyses. The interpretability analyses established the relationship between multiple synthesis parameters and fluorescence QYs of GSH-AuNCs. The results represent an essential step towards revealing the complex fluorescence mechanism of thiolated AuNCs. Finally, we constructed a synthesis phase diagram exceeding 6.0 × 104 prediction variables for accurately predicting the fluorescence QY of GSH-AuNCs. A multidimensional synthesis phase diagram was obtained for the fluorescence QY of GSH-AuNCs by searching the synthesis parameter space in the trained ML model. Our methodology is a general and powerful complementary strategy for application in material informatics.