Pharmacotranscriptomics has become a powerful approach for evaluating the therapeutic efficacy of drugs and discovering new drug targets. Recently, studies of traditional Chinese medicine (TCM) have increasingly turned to high-throughput transcriptomic screens for molecular effects of herbs/ingredients. And numerous studies have examined gene targets for herbs/ingredients, and link herbs/ingredients to various modern diseases. However, there is currently no systematic database organizing these data for TCM. Therefore, we built HERB, a high-throughput experiment- and reference-guided database of TCM, with its Chinese name as BenCaoZuJian. We re-analyzed 6164 gene expression profiles from 1037 high-throughput experiments evaluating TCM herbs/ingredients, and generated connections between TCM herbs/ingredients and 2837 modern drugs by mapping the comprehensive pharmacotranscriptomics dataset in HERB to CMap, the largest such dataset for modern drugs. Moreover, we manually curated 1241 gene targets and 494 modern diseases for 473 herbs/ingredients from 1966 references published recently, and cross-referenced this novel information to databases containing such data for drugs. Together with database mining and statistical inference, we linked 12 933 targets and 28 212 diseases to 7263 herbs and 49 258 ingredients and provided six pairwise relationships among them in HERB. In summary, HERB will intensively support the modernization of TCM and guide rational modern drug discovery efforts. And it is accessible through http://herb.ac.cn/.

Clinical trials and meta-analyses are considered high-level medical evidence with solid credibility. However, such clinical evidence for traditional Chinese medicine (TCM) is scattered, requiring a unified entrance to navigate all available evaluations on TCM therapies under modern standards. Besides, novel experimental evidence has continuously accumulated for TCM since the publication of HERB 1.0. Therefore, we updated the HERB database to integrate four types of evidence for TCM: (i) we curated 8558 clinical trials and 8032 meta-analyses information for TCM and extracted clear clinical conclusions for 1941 clinical trials and 593 meta-analyses with companion supporting papers. (ii) we updated experimental evidence for TCM, increased the number of high-throughput experiments to 2231, and curated references to 6 644. We newly added high-throughput experiments for 376 diseases and evaluated all pairwise similarities among TCM herbs/ingredients/formulae, modern drugs and diseases. (iii) we provide an automatic analyzing interface for users to upload their gene expression profiles and map them to our curated datasets. (iv) we built knowledge graph representations of HERB entities and relationships to retrieve TCM knowledge better. In summary, HERB 2.0 represents rich data type, content, utilization, and visualization improvements to support TCM research and guide modern drug discovery. It is accessible through http://herb.ac.cn/v2 or http://47.92.70.12.

Background: Esophageal squamous cell carcinoma (ESCC) is the predominant subtype of esophageal carcinoma in China. This study was to develop a staging model to predict outcomes of patients with ESCC. Methods: Using Cox regression analysis, principal component analysis (PCA), partitioning clustering, Kaplan–Meier analysis, receiver operating characteristic (ROC) curve analysis, and classification and regression tree (CART) analysis, we mined the Gene Expression Omnibus database to determine the expression profiles of genes in 179 patients with ESCC from GSE63624 and GSE63622 dataset. Results: Univariate cox regression analysis of the GSE63624 dataset revealed that 2404 protein‐coding genes (PCGs) and 635 long non‐coding RNAs (lncRNAs) were associated with the survival of patients with ESCC. PCA categorized these PCGs and lncRNAs into three principal components (PCs), which were used to cluster the patients into three groups. ROC analysis demonstrated that the predictive ability of PCG‐lncRNA PCs when applied to new patients was better than that of the tumor‐node‐metastasis staging (area under ROC curve [AUC]: 0.69 vs. 0.65, P < 0.05). Accordingly, we constructed a molecular disaggregated model comprising one lncRNA and two PCGs, which we designated as the LSB staging model using CART analysis in the GSE63624 dataset. This LSB staging model classified the GSE63622 dataset of patients into three different groups, and its effectiveness was validated by analysis of another cohort of 105 patients. Conclusions: The LSB staging model has clinical significance for the prognosis prediction of patients with ESCC and may serve as a three‐gene staging microarray.

Large language models can lighten the workload of clinicians and patients, yet their responses often include fabricated evidence, outdated knowledge, and insufficient medical specificity. We introduce a general retrieval-augmented question-answering framework that continuously gathers up-to-date, high-quality medical knowledge and generates evidence-traceable responses. Here we show that this approach significantly improves the evidence validity, medical expertise, and timeliness of large language model outputs, thereby enhancing their overall quality and credibility. Evaluation against 15,530 objective questions, together with two physician-curated clinical test sets covering evidence-based medical practice and medical order explanation, confirms the improvements. In blinded trials, resident physicians indicate meaningful assistance in 87.00% of evidence-based medical scenarios, and lay users find it helpful in 90.09% of medical order explanations. These findings demonstrate a practical route to trustworthy, general-purpose language assistants for clinical applications.