RESUMO
The hippocampus is one of the most commonly studied brain regions in the context of depression. The volume of the hippocampus is significantly reduced in patients with depression, which severely disrupts hippocampal neuroplasticity. However, antidepressant therapies that target hippocampal neuroplasticity have not been identified as yet. Chinese medicine (CM) can slow the progression of depression, potentially by modulating hippocampal neuroplasticity. Xiaoyaosan (XYS) is a CM formula that has been clinically used for the treatment of depression. It is known to protect Gan (Liver) and Pi (Spleen) function, and may exert its antidepressant effects by regulating hippocampal neuroplasticity. In this review, we have summarized the association between depression and aberrant hippocampal neuroplasticity. Furthermore, we have discussed the researches published in the last 30 years on the effects of XYS on hippocampal neuroplasticity in order to elucidate the possible mechanisms underlying its therapeutic action against depression. The results of this review can aid future research on XYS for the treatment of depression.
Assuntos
Antidepressivos , Medicamentos de Ervas Chinesas , Hipocampo , Plasticidade Neuronal , Hipocampo/efeitos dos fármacos , Hipocampo/fisiologia , Medicamentos de Ervas Chinesas/farmacologia , Medicamentos de Ervas Chinesas/uso terapêutico , Humanos , Plasticidade Neuronal/efeitos dos fármacos , Antidepressivos/farmacologia , Antidepressivos/uso terapêutico , Animais , Depressão/tratamento farmacológico , Depressão/fisiopatologia , Medicina Tradicional ChinesaRESUMO
This study systematically reviewed the application of large language models (LLMs) in medicine, analyzing 550 selected studies from a vast literature search. LLMs like ChatGPT transformed healthcare by enhancing diagnostics, medical writing, education, and project management. They assisted in drafting medical documents, creating training simulations, and streamlining research processes. Despite their growing utility in assisted diagnosis and improving doctor-patient communication, challenges persisted, including limitations in contextual understanding and the risk of over-reliance. The surge in LLM-related research indicated a focus on medical writing, diagnostics, and patient communication, but highlighted the need for careful integration, considering validation, ethical concerns, and the balance with traditional medical practice. Future research directions suggested a focus on multimodal LLMs, deeper algorithmic understanding, and ensuring responsible, effective use in healthcare.