Objective: To systematically characterize the developmental trajectory and interdisciplinary integration of intelligent diagnosis in traditional Chinese medicine (TCM) through quantitative topic evolution analysis, we addressed the fragmentation of existing research and clarified the long-term research structure and evolutionary patterns of the field. Methods: A topic evolution analysis was performed on Chinese-language literature pertaining to intelligent diagnosis in TCM. Publications were retrieved from the China National Knowledge Infrastructure (CNKI), Wanfang Data, and China Science and Technology Journal Database (VIP), covering the period from database inception to July 3, 2025. A hybrid segmentation approach, based on cumulative publication growth trends and inflection point detection, was applied to divide the research timeline into distinct stages. Subsequently, the latent Dirichlet allocation (LDA) model was used to extract research topics, followed by alignment and evolutionary analysis of topics across different stages. Results: A total of 3 919 publications published between 2003 and 2025 were included, and the research trajectory was divided into five stages based on data-driven breakpoint detection. The field exhibited a clear evolutionary shift from early rule-based systems and tongue-pulse image and signal analysis (2006 – 2010), to machine-learning-based syndrome and prescription modeling (2011 – 2015), followed by deep-learning-driven pattern recognition and formula association (2016 – 2020). Since 2021, research has increasingly emphasized knowledge-graph construction, multimodal integration, and intelligent clinical decision-support systems, with recent studies (2024 – 2025) showing the emergence of large language models and agent-based diagnostic frameworks. Topic evolution analysis further revealed sustained cross-stage continuity in syndrome modeling and prescription association analysis, alongside the progressive consolidation of integrated intelligent diagnostic platforms. Conclusion By identifying key technological transitions and persistent core research themes, our findings offer a structured reference framework for the design of intelligent diagnostic systems, the construction of knowledge-driven clinical decision-support tools, and the alignment of AI models with TCM diagnostic logic. Importantly, the stage-based evolutionary insights derived from this analysis can inform future methodological choices, improve model interpretability and clinical applicability, and support the translation of intelligent TCM diagnosis from experimental research to real-world clinical practice.

Algae are a large group of photosynthetic organisms responsible for approximately half of the earth's total photosynthesis. In addition to their fundamental ecological roles as oxygen producers and as the food base for almost all aquatic life, algae are also a rich source of bioactive natural products, including several clinical drugs. Cytochrome P450 enzymes (P450s) are a superfamily of biocatalysts that are extensively involved in natural product biosynthesis by mediating various types of reactions. In the post-genome era, a growing number of P450 genes have been discovered from algae, indicating their important roles in algal life-cycle. However, the functional studies of algal P450s remain limited. Benefitting from the recent technical advances in algae cultivation and genetic manipulation, the researches on P450s in algal natural product biosynthesis have been approaching to a new stage. Moreover, some photoautotrophic algae have been developed into "photo-bioreactors" for heterologous P450s to produce high-value added pharmaceuticals and chemicals in a carbon-neutral or carbon-negative manner. Here, we comprehensively review these advances of P450 studies in algae from 2000 to 2021.