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.

As a distinctive resource of Chinese civilization， traditional Chinese medicine （TCM） technology transfer faces significant opportunities under the background of digital and intelligent transformation， while also being constrained by unique challenges such as the complexity of its theoretical system， lengthy industrial chains， and multidimensional policy restrictions， resulting in a "high-value-high-threshold" paradox. At present， TCM technology transfer is deeply trapped in a "threefold reluctance" dilemma， i.e.， unwillingness to transfer， inability to transfer， and lack of capacity to transfer. Specifically， the disconnection between scientific research evaluation systems and market demand leads to low conversion rates of research achievements， unclear ownership and compliance risks suppress innovation incentives， and the absence of professional services intensifies supply-demand mismatches. This article systematically analyzes the specific characteristics of TCM technology transfer and proposes a breakthrough pathway centered on full-chain digital and intelligent transformation. By integrating technologies such as intelligent sorting systems， blockchain-based traceability， and AI diagnostic models， the TCM ecosystem spanning "cultivation-production-service" can be reconstructed. In terms of standardization， promoting the progression from "experience-based data conversion" to "data standardization" and further to "intelligent standardization" is advocated to resolve quality control challenges. For example， a "three-no-one-full" certification system can strengthen quality trust. Policy coordination should focus on optimizing mechanisms for the transformation of scientific and technological achievements， while exploring intellectual property securitization and risk-sharing models to stimulate research momentum. In terms of internationalization， reliance on the Belt and Road Initiative platform to promote the export of geo-authentic medicinal material brands and standards is recommended to build a dual-driven model of "technology plus culture". Looking ahead， through the construction of national-level databases， the cultivation of interdisciplinary talent， and the mutual recognition of international standards， a new paradigm of "scientific intelligent manufacturing" can be formed， providing systematic solutions for the modernization of TCM and global health governance.

Objective: Janus kinase (JAK) inhibitors are widely used to treat rheumatoid arthritis (RA); however, comparative analyses regarding their adverse events remain limited. This study aimed to compare the effects of different JAK inhibitors (tofacitinib, baricitinib, and upadacitinib) on hematological parameters in patients with RA in a real-world setting. Methods: This retrospective analysis included 552 patients with RA treated with JAK inhibitors between August 2015 and February 2024. Hematological parameters, including absolute neutrophil count (ANC) and platelet count, were assessed at baseline and after 6 months of treatment. Statistical analysis was performed to evaluate changes over time, and logistic regression analysis identified factors associated with hematologic alterations. Results: The 552 patients were divided into three groups: tofacitinib (n=264), baricitinib (n=143), and upadacitinib (n=145). No significant differences in baseline hematological parameters were observed across the groups. After 6 months, ANC decreased in all groups without significant differences among them (p=0.465). Patients receiving baricitinib had significantly higher platelet counts than those receiving tofacitinib (Pillai’s trace value of 0.063, p<0.001) or upadacitinib (Pillai’s trace value of 0.029, p<0.001).Multivariate analysis revealed that baricitinib was significantly associated with increased platelet counts (odds ratio, 2.009; 95% confidence interval, 1.212~3.331; p=0.007). Conclusion Although all three JAK inhibitors reduced ANC, baricitinib was associated with a substantial increase in platelet counts. These findings highlight the differences in adverse effect profiles among JAK inhibitors, emphasizing the importance of tailored monitoring in RA management.

Objective: To classify life stages for companion dogs and cats by identifying clusters in age-specific disease proportions derived from medical records, providing a data-driven foundation for health examination programs. Methods: We collected 505,667 medical records from 82 veterinary facilities in South Korea between 2020 and 2023. Diagnoses were standardized using GPT-4o and S-BioBERT. Following preprocessing, data from 27 facilities yielded 222,706 canine and 39,910 feline records for the final analysis. Principal component analysis and K-means clustering (K = 4) were applied to age-specific disease proportions to identify life stages.The 10 most highest-proportion diagnoses diseases were determined for each cluster. Results: Canine life stages were classified as ≤ 1 year, 2–5 years, 6–10 years, and 11–15+ years.Feline life stages were 1–2 years, 3–8 years, 9–12 years, and 13–15+ years. In dogs, developmental diseases were common in the youngest age group, while chronic diseases were more prevalent in older groups. In cats, oral and urinary diseases were high-ranking, conjunctivitis was most common in the early stage, and chronic diseases increased with age. Conclusions and Relevance: Age-specific diagnosis patterns support four practical life stages for dogs and cats in South Korea. These boundaries can inform evidence-based preventive examination schedules, animal health policy, and pet insurance product design.

Objective This study aims to explore the correlation between the grade of testicular tissue damage and clinical features which is used to evaluate the viability of testicular tissue after the orchidopexy for testicular torsion.Methods We conducted a retrospective,multicenter analysis of pathological specimens obtained from patients who under-went unilateral orchiectomy due to testicular torsion.Tissue viability was reassessed by the Mikuz grading scale.The corre-lations among tissue viability,duration of torsion symptoms,the angle of torsion,preoperative Doppler ultrasound image and intraoperative testicular resuscitation methods were analyzed.Results Eighty-five pathological specimens were en-rolled from 4 medical centers.The age range was 18 years(IQR 14-33),with a median torsion angle of 480°(IQR 180°-1080°).The interval from the torsion onset to surgical intervention was 18 hours(IQR 9-84).In the subgroup analysis of different torsion durations,the distribution of Grade I injuries was as follows.The subgroup within 12 hours ac-counted for 37.5％.The subgroup between 12 to 24 hours accounted for 14.28％.And the subgroup between 24 to 48 hours ac-counted for 6.45％.The subgroup exceeding 48 hours only accounted for 3.13％.Four patients received orchiectomy due to pre-operative ultrasound image indicating necrosis,correlating with Mikuz grade 3 tissue damage.The decision was made based on the failure of color recovery of affected testis in 57 cases,revealing 12.28％with grade 1 and 35.89％with grade Ⅱ damage.Twenty-four cases were identified as an Arda score of 3,with 4.16％showing grade 1 and 20.83％showing grade Ⅱ Mikuz damage.The method of Arda's grading could be used to determine the vitality of testicular tissue more accurately compared to the post-resusci-tation color observation method(P=0.032).Conclusion Pathological reassessment provides a precise determination of tis-sue viability in the affected testis.In some instances,the testicular tissue damage can be found potentially reversible.

AIM To establish an HPLC-MS/MS method for the simultaneous content determination of gallic acid,protocatechuic acid,oxypaeoniflorin,catechin,epicatechin,albiflorin,paeoniflorin,rutin,calycosin-7-glucoside,syringaldehyde,ferulic acid,coumarin,ononin,calycosin,cinnamic alcohol,cinnamic acid,benzoylpaeoniflorin,cinnamaldehyde,astragaloside,astragaloside Ⅲ,6-gingerol in Huangqi Guizhi Wuwu Decoction.METHODS The analysis was performed on a 30 ℃ thermostatic Thermo Scientific Hypersil GOLD column(150 mmx4.6 mm,3 μm),with the mobile phase comprising of 0.015％formic acid-acetonitrile flowing at 0.4 mL/min in a gradient elution manner,and electrospray ionization source was adopted in positive and negative ion modes with multiple reaction monitoring.RESULTS Twenty-one constituents showed good linear relationships within their own ranges(r＞0.990 5),whose average recoveries were 93.99％-108.52％with the RSDs of 1.04％-5.97％.CONCLUSION This simple,feasible,stable and reliable method can be used for the quality control of Huangqi Guizhi Wuwu Decoction.

For middle-aged and elderly patients with conditions such as spinal fractures and degenerative spinal diseases, spinal internal fixation is a core surgical procedure for reconstructing spinal stability, heavily relying on the biomechanical stability provided by pedicle screw systems. Whereas, these patients are often complicated by osteoporosis that can significantly compromise the stability of the bone-pedicle screw interface, leading to a marked increase in pedicle screw loosening and surgical failure rates. The bone cement-augmented pedicle screw technique, which involves injecting bone cement into the vertebral body or screw trajectory to optimize the mechanical properties of the bone-pedicle screw composite, has been proven to significantly enhance fixation strength and effectively prevent screw-related failures, thereby reducing the incidence of internal fixation failure in high-risk populations undergoing spinal fusion. However, the widespread clinical application of this technique has faced challenges such as inaccurate clinical decision-making (indication and contraindication selection), non-standardized operative practices, and insufficient awareness of complication prevention, resulting in considerable variability in clinical outcomes and even severe complications. To address this, Prof. Luo Fei from First Affiliated Hospital of Army Medical University initiated the project and the Chinese Association Orthopaedic Surgeons organized relevant experts to develop the Evidence-based clinical practice guideline for bone cement-augmented pedicle screw technique ( version 2025), based on current evidence. The guidelines put forward 8 recommendations regarding the clinical value, scope of application, and operational standards of the technique, aiming to provide evidence-based medical support and technical standardization for clinical decision-making.

Objective:To analyze the clinical characteristics, RAN-binding protein 2 ( RANBP2) gene variations, and prognosis in Chinese acute necrotizing encephalopathy (ANE) patients. Methods:A retrospective analysis of ANE cases registered in the Peking Union Medical College Hospital Encephalitis Registry System from 2022 to 2024, involving patients from Peking Union Medical College Hospital and other hospitals, was conducted. A descriptive study was performed on the clinical characteristics, treatments and prognosis, cerebrospinal fluid examination results, and imaging findings of these patients based on adjusted ANE diagnostic criteria. Whole-exome sequencing technology was used to detect gene mutations in these patients.Results:A total of 18 ANE cases were included, ranged in age from 2 to 72 [20(5, 43)] years. The male-to-female ratio was 4∶5. All patients were found with precipitating infections including COVID-19, influenza A virus and Mycoplasma pneumoniae infections. All patients presented with fever, with varying degrees of consciousness disturbance observed in 16 cases, and seizures in 10 cases. All patients underwent lumbar puncture, with normal or mildly elevated white cell counts [3(2, 13)×10 6/L] and mildly to moderately elevated protein levels [1.90(0.92, 4.65) g/L]. A total of 6 patients were found with extremely elevated interleukin-6 level [950(164, 2 000) pg/ml] in cerebrospinal fluid. Bilateral symmetric thalamic lesions were typical imaging features of ANE, while involvement of other areas such as cortical and subcortical white matter, brainstem, and cerebellum was also observed. A total of 14 patients performed genetic tests while 4 patients were identified with RANBP2 gene mutations (c.1754C>T in 3 cases, c.1966A>G in 1 case). All patients received immunotherapy, and 7 patients died at discharge while other patients presented with neurological sequelae of varying degrees. Conclusions:ANE is a rare and severe parainfectious encephalopathy that can occur in both children and adults. Clinically, it is characterized by rapidly progressing encephalopathy following systematic infection, with bilateral symmetric thalamic lesions. The detection of RANBP2 gene mutations could help make the diagnosis.

Objective:To explore the applicability and effectiveness of the "Hand as Foot" visual teaching method in bedside teaching of spinal surgical diseases, investigate its impact on enhancing student learning outcomes and clinical application abilities, and provide new directions and guidance for teaching methods.Methods:A total of 60 students from the five-year clinical medicine program enrolled in 2019 at Inner Mongolia Medical University were selected and randomly assigned to either an experimental group or a control group (30 students each) using a random number table. The teaching content covered bedside knowledge related to spinal surgery. The control group received traditional oral bedside teaching method, while the experimental group received the "Hand as Foot" visual teaching method. Both groups of students received eight 45-min bedside teaching sessions. After class, teaching effectiveness was evaluated through a theoretical knowledge examination and student classroom satisfaction was evaluated, including interest in learning, enthusiasm and participation, classroom activity, teacher-student interaction, and mastery of theoretical knowledge. SPSS 27.0 software was used for statistical analysis. Continuous variables were expressed as mean ± standard deviation and compared using the t-test; categorical variables were presented as numbers or percentages and compared using the chi-square test. Results:The experimental group scored (78.80±3.19) on the theoretical knowledge examination, while the control group scored (73.20±4.89), with a significant difference between the two groups ( t=3.72, P=0.001). In the classroom satisfaction survey, the experimental group also outperformed the control group in learning interest [(9.60±0.51) vs. (8.40±0.74)], enthusiasm and participation [(9.40±0.63) vs. (8.07±0.70)], classroom activity [(9.53±0.64) vs. (8.67±0.81)], teacher-student interaction [(9.53±0.52) vs. (8.33±0.72)], and mastery of theoretical knowledge [(8.60±0.91) vs. (7.20±0.77)], with all differences being statistically significant ( P<0.05). Conclusions:The "Hand as Foot" visual teaching method is suitable for bedside teaching of spinal diseases, allowing students to understand diseases more vividly. This method not only facilitates effective communication between teachers and students, enhances student understanding of relevant knowledge, and improves their confidence in learning, but also inspires a more proactive learning attitude.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.