ObjectiveTo address the challenges of unstructured classical Chinese expressions， nested entity relationships， and limited annotated data in famous traditional Chinese medicine（TCM） case records， this study proposes a joint relation extraction framework that integrates data augmentation and entity mapping， aiming to support the construction of TCM diagnostic knowledge graphs and clinical pattern mining. MethodsWe developed an annotation structure for entities and their relationships in TCM case texts and applied a data augmentation strategy by incorporating multiple ancient texts to expand the relation extraction dataset. A cascade binary tagging framework for relation triple extraction（CasRel） model for TCM semantics was designed， integrating a pre-trained bidirectional encoder representations from transformers（BERT） layer for classical TCM texts to enhance semantic representation， and using a head entity-relation-tail entity mapping mechanism to address entity nesting and relation overlapping issues. ResultsExperimental results showed that the CasRel model， combining data augmentation and entity mapping， outperformed the pipeline-based Bert-Radical-Lexicon（BRL）-bidirectional long short-term memory（BiLSTM）-Attention model. The overall precision， recall， and F₁-score across 12 relation types reached 65.73%， 64.03%， and 64.87%， which represent improvements of 14.26%， 7.98%， and 11.21% compared to the BRL-BiLSTM-Attention model， respectively. Notably， the F₁-score for tongue syndrome relations increased by 22.68%（69.32%）， and the prescription-syndrome relations performed the best with the F₁-score of 70.10%. ConclusionThe proposed framework significantly improves the semantic representation and complex dependencies in TCM texts， offering a reusable technical framework for structured mining of TCM case records. The constructed knowledge graph can support clinical syndrome differentiation， prescription optimization， and drug compatibility， providing a methodological reference for TCM artificial intelligence research.

This paper summarizes Professor LU Fang's clinical experience in treating systemic lupus erythematosus （SLE） based on the differentiation and treatment of heat-toxin and blood-stasis in the collaterals. SLE is generally characterized by deficiency in origin with excess in manifestation. The core pathogenesis is heat-toxin obstructing the collaterals. During the acute active stage， the predominant pattern is blazing heat-toxin causing blood stasis， while in the chronic remitting stage， the main pattern is toxic stasis blocking the collaterals with qi and yin deficiency. Clinical treatment follows the basic principle that treat with salty-cold herbs， when heat invades internally and that assist with acrid-dispersing herbs when stasis obstructs the collaterals. The self-formulated Yimian Decoction （抑免汤） serves as the base formula and is applied in stages. During the acute active stage， it is often combined with herbs for clearing heat and detoxifying， cooling blood and resolving stasis， and unblocking the collaterals. In the chronic remitting stage， it is often combined with herbs for activating blood circulation and unblocking the collaterals， as well as tonifying qi and nourishing yin.

This paper outlines the development of artificial intelligence （AI） and its applications in traditional Chinese medicine （TCM） research， and elucidates the roles and advantages of large language models， knowledge graphs， and natural language processing in advancing syndrome identification， prescription generation， and mechanism exploration. Using spleen-stomach diseases as an example， it demonstrates the empowering effects of AI in classical literature mining， precise clinical syndrome differentiation， efficacy and safety prediction， and intelligent education， highlighting an upgraded research paradigm that evolves from data-driven and knowledge-driven approaches to intelligence-driven models. To address challenges related to privacy protection and regulatory compliance in cross-institutional data collaboration， a "trusted federated evidence-based analysis platform for TCM spleen-stomach diseases" is proposed， integrating blockchain-based smart contracts， federated learning， and secure multi-party computation. The deep integration of AI with privacy-preserving computing is reshaping research and clinical practice in TCM spleen-stomach diseases， providing feasible pathways and a technical framework for building a high-quality， trustworthy TCM big-data ecosystem and achieving precision syndrome differentiation.

Objective To evaluate the predictive value of different machine learning models constructed in a multicenter environment for potential organ donors and verify their clinical application feasibility. Methods The study included 2 000 inpatients admitted to five domestic tertiary hospitals from January 2020 to December 2023, who met the criteria for potential organ donation assessment. They were randomly divided into a training set and an internal validation set (7∶3). Another 300 similar patients admitted to the First Affiliated Hospital of Harbin Medical University from January 2024 to April 2025 were included as an external validation set. The area under the curve (AUC), sensitivity, specificity, accuracy and F1-score of three models were compared, and the consistency of the potential organ donor determination process was tested. Multivariate logistic regression analysis was used to identify predictive factors of potential organ donors. Decision curve analysis (DCA) was employed to verify the resource efficiency of each model, and the threshold interval and intervention balance point were assessed. Results Apart from age, there were no significant differences in other basic characteristics among the centers (all P>0.05). The consistency of the potential organ donor determination process among researchers in each center was good [all 95% confidence interval (CI) lower limits >0]. In the internal validation set, the XGBoost model had the best predictive performance (AUC=0.92, 95% CI 0.89-0.94) and the best calibration (P=0.441, Brier score 0.099). In the external validation set, the XGBoost model also had the best predictive performance (AUC=0.91, 95% CI 0.88-0.94), outperforming logistic regression and random forest models. Multivariate logistic regression showed that mechanical ventilation had the greatest impact (odds ratio=2.06, 95% CI 1.54-2.76, P<0.001). DCA indicated that the XGBoost model had the highest net benefit in the threshold interval of 0.2-0.6. The “treat all” strategy only had a slight advantage at extremely low thresholds. The recommended threshold interval, which balances intervention costs and clinical benefits, considers ≥50% positive predictive value (PPV) and ≤50 referrals per 100 high-risk patients. Conclusions The XGBoost model established in a multicenter environment is accurate and well-calibrated in predicting potential organ donors. Combined with DCA, it may effectively guide the timing of clinical interventions and resource allocation, providing new ideas for the assessment and management of organ donation after brain death.

ObjectiveIn order to provide a reference for the optimization of preparation process of stir-fried Glycyrrhizae Radix et Rhizoma（sf-GRR）， the quality changes during the processing was studied. MethodsGlycyrrhizae Radix et Rhizoma was processed by stir-frying for 17 min， and samples were collected every 1 min during the processing. The appearance color of the samples was determined by visual analysis technology， the moisture and extract of the process samples were detected by the drying method and the hot extraction method of alcohol-soluble extract in the general rules of the 2020 edition of Chinese Pharmacopoeia（part Ⅳ）， and the contents of liquiritin apioside， liquiritin， isoliquiritin apioside， isoliquiritin， licoricesaponin G₂ and glycyrrhizic acid in the process samples were determined by high performance liquid chromatography（HPLC）. Then principal component analysis（PCA）， partial least squares-discriminant analysis（PLS-DA） and Spearman correlation analysis were used for clustering， discrimination and correlation analysis of the appearance color， moisture， extract and the contents of six internal components. Based on artificial neural network and random forest algorithm， the prediction model of processing degree of sf-GRR was established. On this basis， based on the five principles of quality marker（Q-Maker）， explore the monitoring Q-Maker of sf-GRR. ResultsThe color of Glycyrrhizae Radix et Rhizoma deepened after stir-frying， and the appearance color of the sample changed from light yellow to dark yellow during processing. During the stir-frying process， the moisture content showed a decreasing trend with the extension of processing time， while the extract content showed an increasing trend with the extension of processing time. After stir-frying， the contents of liquiritin apioside， liquiritin and licoricesaponin G₂ showed an overall decreasing trend， while the contents of isoliquiritin apioside and isoliquiritin increased， and the content of glycyrrhizic acid increased slightly. The correlation analysis showed that moisture was positively correlated with brightness（L^*） and red/green value（a^*）， and negatively correlated with yellow/blue value（b^*） and total color difference（E^*ab）. Isoliquiritin apioside and isoliquiritin had negative correlation with L^* and a^*， and positive correlation with b^* and E^*ab. The processing process of sf-GRR could be divided into two stages of the early stage（0-14 min） and the late stage（15-17 min）， and could be divided into three stages of the early stage（0-6 min）， the middle stage（7-14 min） and the late stage（15-17 min） by combining the moisture， extract， the contents of 6 components and color values. Based on artificial neural network analysis and random forest algorithm， isoliquiritin apioside， isoliquiritin， liquiritin and glycyrrhizic acid were selected as monitoring markers for sf-GRR. ConclusionBased on the analysis of the exterior-interior indicators of process samples of sf-GRR， this paper ultimately identifies four processing monitoring markers， which can provide a basis for optimizing the processing technology of sf-GRR.

Charcoal-processed traditional Chinese herbal medicine has various therapeutic effects， including astringing， hemostasis， anti-diarrhea， clearing heat， tonifying， and warming the interior. This paper summarizes the clinical application features， compatible experiences， dosages， and precautions for over 20 types of charcoal-processed herbal medicine in the treatment of gynecological bleeding disorders caused by dysfunctions such as dysfunctional uterine bleeding， endometriosis， uterine incision pseudocavity， and vaginal bleeding resulting from threatened miscarriage. The charcoal-processed herbal medicine include Huangqin （Scutellaria Baicalensis） Charcoal， Dahuang （Rheum Palmatum） Charcoal， Cebai （Platycladus Orientalis） Charcoal， Diyu （Sanguisorba Officinalis） Charcoal， Daji （Cirsium Setosum） Charcoal， Xiaoji （Cirsium Japonicum） Charcoal， Shengdi （Rehmannia Glutinosa） Charcoal， Aiye （Artemisia Argyi） Charcoal， Paojiang （Zingiber Officinale） Charcoal， Xuduan （Dipsacus Asper） Charcoal， Duzhong （Eucommia Ulmoides） Charcoal， Qiancao （Rubia Cordifolia） Charcoal， Puhuang （Typha Angustifolia） Charcoal， Shanzha （Crataegus Pinnatifida） Charcoal， Jingjie （Schizonepeta Tenuifolia） Charcoal， Xueyu （Carthamus Tinctorius） Charcoal， Zonglyu （Areca Catechu） Charcoal， Wumei （Prunus Mume） Charcoal， Shudahuang （Rheum Officinale） Charcoal， Lianfang （Nymphaea Alba） Charcoal， Mianmaguanzhong （Clematis Armandii） Charcoal， and Oujie （Nelumbo Nucifera） Charcoal.

[Objective] To investigate the molecular prevalence and genotype of human parvovirus B19(B19) among blood donors in Lanzhou, and provide data support for monitoring the positive rate of B19 DNA in local blood donors. [Methods] A total of 7 644 blood donor samples collected from January to September 2022 were randomly screened using real-time fluorescent PCR, resulting in 23 samples testing positive for B19 DNA. The characteristics of the B19 DNA reactive donors including gender, age, blood donation recruitment and promotion mode, and donation frequency were analyzed using SPSS 22.0. Additionally, the VP1 gene fragment of B19 DNA reactive samples was sequenced and an evolutionary tree was constructed by the N-J method. [Results] The results showed that the positive rate of B19 DNA in Lanzhou was 0.30%, and the positive population mainly consisted of female individuals aged 18-30 years old who were first-time blood donors; furthermore, genotype 1a was identified as predominant. [Conclusion] The positive rate of B19 DNA is low among blood donors in Lanzhou, with genotype 1a being predominant. It is recommended to periodically monitor the B19 prevalence in blood donors and enhance prevention and control measures, thus improving blood quality and safety.

OBJECTIVE To investigate the mechanism of the inhibitory effect of total flavonoids from Taraxacum mongolicum on high-fat diet-induced obesity in mice through modulation of intestinal flora. METHODS Twenty-four C57BL/6J mice were randomly divided into blank group， model group and T. mongolicum total flavonoid group， with 8 mice in each group. Except for the blank group， the other 2 groups were given a high-fat diet， while T. mongolicum total flavonoid group was given T. mongolicum total flavonoid [400 mg/（kg·d）] intragastrically， once a day， for 8 consecutive weeks. During the experiment， the food intake of each group of mice was recorded. After the last medication， the body mass， fat weight， blood lipid level and pathological changes of liver and epididymal fat in mice were evaluated to observe the effect of T. mongolicum total flavonoid on the treatment of obesity in mice. The changes in abundance and structure of intestinal flora in mice were detected by amplicon sequencing； the effects of T. mongolicum total flavonoids on fat metabolism related genes were analyzed by qPCR. RESULTS Compared with model group， the body weight of mice in T. mongolicum total flavonoids group was decreased significantly （P＜0.05）； the levels of total lipid cholesterol， triglycerides， and LDL cholesterol were all decreased significantly （P＜0.01）， and the level of HDL cholesterol was increased significantly （P＜0.01）； the fat indexes of inguinal white adipose tissue and epididymal white wind_lz@hactcm.edu.cn adipose tissue were significantly reduced （P＜0.05）； significant improvement in hepatocellular steatosis and adipose cytopathy were significantly improved； mRNA expressions of COX7A1 and COX8B were significantly upregulated （P＜0.05）. The results of bacterial colony detection showed that compared with the model group， there was a rising trend in the diversity of the bacterial colony in T. mongolicum total flavonoids group， and the Sobs index characterization and β diversity were increased significantly （P＜0.05）. Relative abundances of Blautia， norank_f_Ruminococcaceae， Bilophila， Alistipes， classified_f_Ruminococcaceae， Parabacteroides， norank_f_Desulfovibrionaceae， Anaerotruncus were significantly up-regulated（P＜0.05）， while those of Faecalibaculum， Erysipelatoclostridium， GCA-900066575， Tuzzerella， Lactobacillus， norank_f_norank_o_RF39， achnospiraceae_FCS020_group were significantly down-regulated （P＜0.05）. CONCLUSIONS T. mongolicum total flavonoids can reduce body mass， fat weight and blood lipid levels， and repair the pathological damage to liver and epididymal fat in obese mice， which is related to improving intestinal flora disorders caused by high-fat diet.

Objective: To investigate the mechanism of Yishen Tongluo Formula in ameliorating renal pyroptosis in diabetic nephropathy mice by regulating the toll-like receptor 4 (TLR4)/myeloid differentiation factor 88 (MyD88)/nuclear factor-κB (NF-κB) signaling pathway. Methods: Sixty C57BL/6 male mice were randomly divided into control (10 mice) and intervention groups (50 mice) using random number table method. The diabetes nephropathy model was established by intraperitoneally injecting streptozotocin(50 mg/kg). After modeling, the intervention group was further divided into model, semaglutide (40 μg/kg), and high-, medium-, and low-dose Yishen Tongluo Formula groups (15.6, 7.8, and 3.9 g/kg, respectively) using random number table method. The high-, medium-, and low-dose Yishen Tongluo Formula groups were administered corresponding doses of medication by gavage, the semaglutide group received a subcutaneous injection of semaglutide injection, and the control group and model groups were administered distilled water by gavage for 12 consecutive weeks. Random blood glucose levels of mice in each group were monitored, and the 24-h urinary protein content was measured using biochemical method every 4 weeks; after treatment, the serum creatinine and urea nitrogen levels were measured using biochemical method. The weight of the kidneys was measured, and the renal index was calculated. Hematoxylin and eosin, periodic acid-Schiff, periodic Schiff-methenamine, and Masson staining were used to observe the pathological changes in renal tissue. An enzyme-linked immunosorbent assay was used to detect urinary β2-microglobulin (β2-MG), neutrophil gelatinase-associated lipocalin (NGAL), and kidney injury molecule-1 (KIM-1) levels. Western blotting and real-time fluorescence PCR were used to detect the relative protein and mRNA expression levels of nucleotide-binding domain leucine-rich repeat and pyrin domain-containing receptor 3 (NLRP3), Caspase-1, gasdermin D (GSDMD), interleukin-1β (IL-1β), and interleukin-18 (IL-18) in renal tissue. Immunohistochemistry was used to detect the proportion of protein staining area of the TLR4, MyD88, and NF-κB in renal tissue. Results: Compared with the control group, the random blood glucose, 24-h urinary protein, serum creatinine, urea nitrogen, and renal index of the model group increased, and the urine β2-MG, NGAL, and KIM-1 levels increased. The relative protein and mRNA expression levels of NLRP3, Caspase-1, GSDMD, IL-1β, and IL-18 in renal tissue increased, and the proportion of TLR4, MyD88, and NF-κB protein positive staining areas increased (P<0.05). Pathological changes such as glomerular hypertrophy were observed in the renal tissue of the model group. Compared with the model group, the Yishen Tongluo Formula high-dose group showed a decrease in random blood glucose after 12 weeks of treatment (P<0.05). The Yishen Tongluo Formula high- and medium-dose groups showed a decrease in 24-h urinary protein, creatinine, urea nitrogen, and renal index, as well as decreased β2-MG, NGAL, and KIM-1 levels. NLRP3, Caspase-1, GSDMD, IL-1 β, and IL-18 relative protein and mRNA expression levels were also reduced, and the proportion of TLR4, MyD88, and NF-κB protein positive staining areas was reduced (P<0.05). Pathological damage to renal tissue was ameliorated. Conclusion Yishen Tongluo Formula may exert protective renal effects by inhibiting renal pyroptosis and alleviating tubular interstitial injury in diabetic nephropathy mice by regulating the TLR4/MyD88/NF-κB signaling pathway.

Objective: To explore the plasma metabonomic characteristics of patients with type 2 diabetes mellitus and turbid toxin accumulation syndrome. Methods: One hundred and three patients with type 2 diabetes mellitus and turbid toxin accumulation syndrome were enrolled from November 2023 to February 2024 in the First Teaching Hospital of Tianjin University of Traditional Chinese Medicine and 54 healthy individuals were recruited. The general data of the two groups were analyzed, and the plasma metabolite content was detected using ultra-high performance liquid chromatography-Orbitrap mass spectrometry. Construct an orthogonal partial least squares discriminant analysis model to screen metabolites with significant intergroup changes. The variable importance in projection≥ 1, |log2FC|>1, and P<0.05 were used as the criteria for the screening of differential metabolites. Annotate differential metabolites using internal databases and the human metabolome database, and perform pathway analysis using MetaboAnalyst website. Results: There was no statistically significant difference in gender and age between the two groups.Seventeen potential differential metabolites were identified. The D-4′-phosphopantothenate, 2, 6-dichloroindophenol, 4-methylphenol, hypoxanthine, 11, 12-epoxyeicosatrienoic acids, oleamide, 3-phenyllactic acid contents were higher in patients with type 2 diabetes mellitus and turbid toxin accumulation syndrome than in healthy individuals(P<0.05); 3-anisic acid, 3-iodo-octadecanoic acid, mebendazole, β-alanine, citric acid, trans-aconitic acid, geranyl diphosphate, lysophosphatidylcholine(18∶2), phosphatidylethanolamine(18∶1), and caprolactam contents were lower in patients with type 2 diabetes mellitus and turbid toxin accumulation syndrome than in healthy individuals(P<0.05). Ten metabolic pathways were identified, including the key metabolic pantothenate and coenzyme A biosynthesis pathways. Conclusion Metabolic differences were observed between patients with type 2 diabetes mellitus and turbid toxin accumulation syndrome and healthy individuals, and the underlying mechanism may involve the pantothenate and coenzyme A biosynthesis pathways, jointly mediated by D-4′-phosphopantothenate and β-alanine.