Traditional Chinese medicine (TCM) represents a paradigmatic approach to personalized medicine, developed through the systematic accumulation and refinement of clinical empirical data over more than 2000 years, and now encompasses large-scale electronic medical records (EMR) and experimental molecular data. Artificial intelligence (AI) has demonstrated its utility in medicine through the development of various expert systems (e.g., MYCIN) since the 1970s. With the emergence of deep learning and large language models (LLMs), AI's potential in medicine shows considerable promise. Consequently, the integration of AI and TCM from both clinical and scientific perspectives presents a fundamental and promising research direction. This survey provides an insightful overview of TCM AI research, summarizing related research tasks from three perspectives: systems-level biological mechanism elucidation, real-world clinical evidence inference, and personalized clinical decision support. The review highlights representative AI methodologies alongside their applications in both TCM scientific inquiry and clinical practice. To critically assess the current state of the field, this work identifies major challenges and opportunities that constrain the development of robust research capabilities-particularly in the mechanistic understanding of TCM syndromes and herbal formulations, novel drug discovery, and the delivery of high-quality, patient-centered clinical care. The findings underscore that future advancements in AI-driven TCM research will rely on the development of high-quality, large-scale data repositories; the construction of comprehensive and domain-specific knowledge graphs (KGs); deeper insights into the biological mechanisms underpinning clinical efficacy; rigorous causal inference frameworks; and intelligent, personalized decision support systems.
Medicine, Chinese Traditional/methods* ; Artificial Intelligence ; Humans ; Precision Medicine ; Decision Support Systems, Clinical

Objective To discuss the prediction value of the early efficacy of hepatic arterial infusion chemotherapy(HAIC)in treating stage Ⅱ-Ⅲ hepatocellular carcinoma(HCC).Methods The clinical data of 81 patients with stage Ⅱ-Ⅲ HCC,who received at least 3 times of HAIC at the Nanchang Municipal Central Hospital of China from November 2021 to March 2024,were retrospectively analyzed.CT or MRI was used to compare patient's local tumor response after each treatment cycle.Based on modified Response Evaluation Criteria in Solid Tumors(mRECIST),the curative effects of patients after receiving the first,the second,and the last HAIC treatment were compared between each other.The prediction value of the early efficacy of HAIC in treating patients with stage Ⅱ-Ⅲ HCC was analyzed.Results In the 67 patients,the efficacy of the last time HAIC was equal or similar to that of the first time HAIC,and in the remaining 14 patients the efficacy of the last time HAIC was different from that of the first time HAIC,with an efficacy prediction rate of 82.72％.The efficacy of the last time HAIC was equal or similar to that of the second time HAIC in 71 patients,and in the remaining 10 patients the efficacy of the last time HAIC was different from that of the second time HAIC,with an efficacy prediction rate of 87.65％.Conclusion In treating stage Ⅱ-Ⅲ HCC with HAIC,the early efficacy can be used to predict the final efficacy after completion of the total treatment course.

Drug development encompasses multiple processes,wherein protein subcellular localization is essential.It promotes target identification,treatment development,and the design of drug delivery systems.In this research,a deep learning framework called LocPro is presented for predicting protein subcellular localization.Specifically,LocPro is unique in(a)combining protein representations from the pre-trained large language model(LLM)ESM2 and the expert-driven tool PROFEAT,(b)implementing a hybrid deep neural network architecture that integrates convolutional neural network(CNN),fully connected(FC)layer,and bidirectional long short-term memory(BiLSTM)blocks,and(c)developing a multi-label framework for predicting protein subcellular localization at multiple granularity levels.Additionally,a dataset was curated and divided using a homology-based strategy for training and validation.Compar-ative analyses show that LocPro outperforms existing methods in sequence-based multi-label protein subcellular localization prediction.The practical utility of this framework is further demonstrated through case studies on drug target subcellular localization.All in all,LocPro serves as a valuable complement to existing protein localization prediction tools.The web server is freely accessible at https://idrblab.org/LocPro/.

Advancements in artificial intelligence(AI)and emerging technologies are rapidly expanding the exploration of chemical space,facilitating innovative drug discovery.However,the transformation of novel compounds into safe and effective drugs remains a lengthy,high-risk,and costly process.Comprehensive early-stage evaluation is essential for reducing costs and improving the success rate of drug development.Despite this need,no comprehensive tool currently supports systematic evaluation and efficient screening.Here,we present druglikeFilter,a deep learning-based framework designed to assess drug-likeness across four critical dimensions:1)physicochemical rule evaluated by systematic determination,2)toxicity alert investigated from multiple perspectives,3)binding affinity measured by dual-path analysis,and 4)compound synthesizability assessed by retro-route prediction.By enabling automated,multidimensional filtering of compound libraries,druglikeFilter not only streamlines the drug development process but also plays a crucial role in advancing research efforts towards viable drug candidates,which can be freely accessed at https://idrblab.org/drugfilter/.

Advancements in artificial intelligence (AI) and emerging technologies are rapidly expanding the exploration of chemical space, facilitating innovative drug discovery. However, the transformation of novel compounds into safe and effective drugs remains a lengthy, high-risk, and costly process. Comprehensive early-stage evaluation is essential for reducing costs and improving the success rate of drug development. Despite this need, no comprehensive tool currently supports systematic evaluation and efficient screening. Here, we present druglikeFilter, a deep learning-based framework designed to assess drug-likeness across four critical dimensions: 1) physicochemical rule evaluated by systematic determination, 2) toxicity alert investigated from multiple perspectives, 3) binding affinity measured by dual-path analysis, and 4) compound synthesizability assessed by retro-route prediction. By enabling automated, multidimensional filtering of compound libraries, druglikeFilter not only streamlines the drug development process but also plays a crucial role in advancing research efforts towards viable drug candidates, which can be freely accessed at https://idrblab.org/drugfilter/.

Drug development encompasses multiple processes, wherein protein subcellular localization is essential. It promotes target identification, treatment development, and the design of drug delivery systems. In this research, a deep learning framework called LocPro is presented for predicting protein subcellular localization. Specifically, LocPro is unique in (a) combining protein representations from the pre-trained large language model (LLM) ESM2 and the expert-driven tool PROFEAT, (b) implementing a hybrid deep neural network architecture that integrates convolutional neural network (CNN), fully connected (FC) layer, and bidirectional long short-term memory (BiLSTM) blocks, and (c) developing a multi-label framework for predicting protein subcellular localization at multiple granularity levels. Additionally, a dataset was curated and divided using a homology-based strategy for training and validation. Comparative analyses show that LocPro outperforms existing methods in sequence-based multi-label protein subcellular localization prediction. The practical utility of this framework is further demonstrated through case studies on drug target subcellular localization. All in all, LocPro serves as a valuable complement to existing protein localization prediction tools. The web server is freely accessible at https://idrblab.org/LocPro/.

Objective To evaluate the association between self-reported neighborhood walkability environments and mortality in China.Methods The Prospective Urban Rural Epidemiology study in China(PURE-China)recruited 47 931 participants aged 35-70 from 12 provinces in China between 2005 and 2009.Neighborhood environmental indicators were collected using the Neighborhood Environment Walkability Scale(NEWS)questionnaire,with higher scores indicating better walkable environments.The primary outcomes were all-cause mortality and cardiovascular mortality,using Cox fragile model to evaluate the association between community walkability and outcomes,as well as exploring mediating pathways.Results Of 35 490 participants included in this study,60%were female,with a mean(SD)age of 51.5(9.6)years.The median follow-up was 11.7 years.This study found an association between higher community walkability score and reduced risk of all-cause mortality,with the total score(HR=0.85;95%CI,0.80-0.89),land-use mix(HR=0.84;95%CI,0.79-0.88),and crime safety(HR=0.84;95%CI,0.80-0.89)showing the most significant associations.NEWS can affect long-term adverse outcomes through lifestyle.Conclusions In the Chinese population,favorable community walkability is associated with lower all-cause mortality risk,which may support policymakers to take actions to mitigate the adverse effects of poor community en-vironments on health.

Objective:To investigate the distribution of iodine in drinking water and the iodine nutrition status of key populations in Boxing County of Shandong Province, and to provide a basis for continuous elimination of iodine deficiency disorders and scientific iodine supplementation.Methods:In 2021, in the historical iodine deficiency towns (districts, referred to as towns) in Boxing County, one water sample of centralized water supply terminal water of each administrative village (neighborhood committee) was collected for water iodine testing. Using stratified cluster sampling method, the historical iodine deficiency areas in Boxing County were divided into 5 areas according to east, west, south, north and center. One town was randomly selected from each area, 20 pregnant women and 40 non-boarding students aged 8 - 10 from a primary school were randomly selected from each town. Thirty pregnant women and 100 students were selected from each of the two historical high iodine towns, and random urine samples were collected from the above-mentioned population for urinary iodine detection and analysis.Results:A total of 329 water samples were collected, and the median water iodine was 10.9 μg/L. Of these, 267 samples (81.2%) were < 40 μg/L, 33 samples (10.0%) were among 40 - 100 μg/L, and 29 samples (8.8%) were > 100 μg/L. A total of 410 urine samples were collected from children, and the median urinary iodine was 162.0 μg/L. The median urinary iodine of children in different towns was compared, and the difference was statistically significant ( H = 86.34, P < 0.001). A total of 109 urine samples were collected from pregnant women, and the median urinary iodine was 57.4 μg/L. The median urinary iodine of pregnant women in different towns were compared, and the difference was statistically significant ( H = 38.35, P < 0.001). Conclusions:The distribution of iodine in drinking water in Boxing County is complex, with areas with high iodine, iodine deficiency, and suitable iodine sources coexisting. Children's iodine nutrition is at an appropriate level, but pregnant women's iodine nutrition is at a deficiency level. Relevant departments should strengthen health promotion, and each region should formulate iodized salt supply strategies based on actual conditions. At the same time, special attention should be paid to the iodine nutrition status of pregnant women.

Objective:To investigate the distribution of iodine in drinking water and the iodine nutrition status of key populations in Boxing County of Shandong Province, and to provide a basis for continuous elimination of iodine deficiency disorders and scientific iodine supplementation.Methods:In 2021, in the historical iodine deficiency towns (districts, referred to as towns) in Boxing County, one water sample of centralized water supply terminal water of each administrative village (neighborhood committee) was collected for water iodine testing. Using stratified cluster sampling method, the historical iodine deficiency areas in Boxing County were divided into 5 areas according to east, west, south, north and center. One town was randomly selected from each area, 20 pregnant women and 40 non-boarding students aged 8 - 10 from a primary school were randomly selected from each town. Thirty pregnant women and 100 students were selected from each of the two historical high iodine towns, and random urine samples were collected from the above-mentioned population for urinary iodine detection and analysis.Results:A total of 329 water samples were collected, and the median water iodine was 10.9 μg/L. Of these, 267 samples (81.2%) were < 40 μg/L, 33 samples (10.0%) were among 40 - 100 μg/L, and 29 samples (8.8%) were > 100 μg/L. A total of 410 urine samples were collected from children, and the median urinary iodine was 162.0 μg/L. The median urinary iodine of children in different towns was compared, and the difference was statistically significant ( H = 86.34, P < 0.001). A total of 109 urine samples were collected from pregnant women, and the median urinary iodine was 57.4 μg/L. The median urinary iodine of pregnant women in different towns were compared, and the difference was statistically significant ( H = 38.35, P < 0.001). Conclusions:The distribution of iodine in drinking water in Boxing County is complex, with areas with high iodine, iodine deficiency, and suitable iodine sources coexisting. Children's iodine nutrition is at an appropriate level, but pregnant women's iodine nutrition is at a deficiency level. Relevant departments should strengthen health promotion, and each region should formulate iodized salt supply strategies based on actual conditions. At the same time, special attention should be paid to the iodine nutrition status of pregnant women.