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 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.

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.

Three evaluation methods were recommended for the key properties of the intravascular catheter lubricating coating such as stability,lubricity and integrity,including insoluble particle test method,friction test procedure and appearance detection method.Fifteen batches of microcatheters produced by different manufacturers were selected for testing to clarify the three methods in test principle,step,result,characteristic.References were provided for the design,production,evaluation and regulation of intravascular catheters with lubricant coatings.[Chinese Medical Equipment Journal,2025,46(1):66-72]

Objective:To compare bowel function 12 months after surgery between side-to-end anastomosis (SEA) and end-to-end anastomosis (EEA) groups of patients who had undergone rectal cancer resection.Methods:This single-center, prospective, open-label, phase III randomized controlled trial was approved by the Ethics Committee of Peking University People's Hospital (2018PHB040-01) and registered at ClinicalTrials. org (NCT03669237). Inclusion criteria were as follows: (1) histologically confirmed rectal adenocarcinoma; (2) tumor located 0 to 12 cm from the anal verge; (3) age≥18 years; and (4) planned R0 resection with primary reconstruction. Exclusion criteria included: (1) emergency surgery; (2) cognitive impairment; (3) non-primary anastomosis; (4) history of left-sided colonic or anorectal surgery; and (5) preexisting chronic defecation dysfunction. Eligible rectal cancer patients scheduled for elective sphincter-preserving surgery at Peking University People's Hospital were prospectively enrolled between October 2018 and March 2021 and randomly assigned to either the EEA group or the SEA group via computer-generated numbers prior to entering the operating room. All patients underwent standard radical tumor resection. Bowel function was evaluated by the low anterior resection syndrome (LARS) questionnaire. It consists of five single-choice questions and yields a total score ranging from 0 to 42. Defecation function is categorized into three levels: no LARS (0-20 points), minor LARS (21-29 points), and major LARS (30-42 points). The primary endpoint was the LARS score 12 months after surgery. Secondary endpoints included LARS scores from 1 to 11 months and during long-term follow-up(>12 months). The final follow-up was completed in July 2022. All randomized patients were included in the intention-to-treat set (ITTS). The full analysis set (FAS) was defined as ITTS patients with valid outcome data. All primary statistical analyses were performed in the FAS, and results were further compared in the per-protocol set (PPS) based on the actual treatment received.Results:A total of 323 patients underwent eligibility assessment, of whom 71 did not meet the inclusion criteria and 52 declined to participate. Ultimately, 200 patients were randomized. Median age was 64 years and 85 were women. The SEA and EEA groups comprised 102 and 98 patients, respectively. A total of 181 patients (90.5%) were included in the FAS, and 170 (85.0%) were included in the PPS. Among these, the 12-month LARS score was evaluated in 178 patients (98.3%) in the FAS and in 167 (98.2%) in the PPS. Median LARS score at 1–12 months were significantly lower in the SEA group in both the FAS dataset [12 months:8 (interquartile range [IQR], 0–22) vs. 14 (IQR, 8–29); Z=2.687, P=0.007] and the PPS dataset [12 months: 8 (IQR, 0–22) vs. 14 (IQR, 6–29); Z=2.543, P=0.011]. During long-term follow-up, the median LARS score was also significantly lower in the SEA group in the FAS dataset [2 (IQR, 0–4) vs. 11 (IQR, 2–23); Z=2.968, P=0.003] and the PPS dataset [2 (IQR, 0–14) vs. 11 (2, 27); Z=2.687, P=0.007]. Conclusion:Compared with the EEA group, bowel function was superior in the SEA group 1 year after surgery and during long-term follow-up.

Objective:To compare bowel function 12 months after surgery between side-to-end anastomosis (SEA) and end-to-end anastomosis (EEA) groups of patients who had undergone rectal cancer resection.Methods:This single-center, prospective, open-label, phase III randomized controlled trial was approved by the Ethics Committee of Peking University People's Hospital (2018PHB040-01) and registered at ClinicalTrials. org (NCT03669237). Inclusion criteria were as follows: (1) histologically confirmed rectal adenocarcinoma; (2) tumor located 0 to 12 cm from the anal verge; (3) age≥18 years; and (4) planned R0 resection with primary reconstruction. Exclusion criteria included: (1) emergency surgery; (2) cognitive impairment; (3) non-primary anastomosis; (4) history of left-sided colonic or anorectal surgery; and (5) preexisting chronic defecation dysfunction. Eligible rectal cancer patients scheduled for elective sphincter-preserving surgery at Peking University People's Hospital were prospectively enrolled between October 2018 and March 2021 and randomly assigned to either the EEA group or the SEA group via computer-generated numbers prior to entering the operating room. All patients underwent standard radical tumor resection. Bowel function was evaluated by the low anterior resection syndrome (LARS) questionnaire. It consists of five single-choice questions and yields a total score ranging from 0 to 42. Defecation function is categorized into three levels: no LARS (0-20 points), minor LARS (21-29 points), and major LARS (30-42 points). The primary endpoint was the LARS score 12 months after surgery. Secondary endpoints included LARS scores from 1 to 11 months and during long-term follow-up(>12 months). The final follow-up was completed in July 2022. All randomized patients were included in the intention-to-treat set (ITTS). The full analysis set (FAS) was defined as ITTS patients with valid outcome data. All primary statistical analyses were performed in the FAS, and results were further compared in the per-protocol set (PPS) based on the actual treatment received.Results:A total of 323 patients underwent eligibility assessment, of whom 71 did not meet the inclusion criteria and 52 declined to participate. Ultimately, 200 patients were randomized. Median age was 64 years and 85 were women. The SEA and EEA groups comprised 102 and 98 patients, respectively. A total of 181 patients (90.5%) were included in the FAS, and 170 (85.0%) were included in the PPS. Among these, the 12-month LARS score was evaluated in 178 patients (98.3%) in the FAS and in 167 (98.2%) in the PPS. Median LARS score at 1–12 months were significantly lower in the SEA group in both the FAS dataset [12 months:8 (interquartile range [IQR], 0–22) vs. 14 (IQR, 8–29); Z=2.687, P=0.007] and the PPS dataset [12 months: 8 (IQR, 0–22) vs. 14 (IQR, 6–29); Z=2.543, P=0.011]. During long-term follow-up, the median LARS score was also significantly lower in the SEA group in the FAS dataset [2 (IQR, 0–4) vs. 11 (IQR, 2–23); Z=2.968, P=0.003] and the PPS dataset [2 (IQR, 0–14) vs. 11 (2, 27); Z=2.687, P=0.007]. Conclusion:Compared with the EEA group, bowel function was superior in the SEA group 1 year after surgery and during long-term follow-up.

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.

Three evaluation methods were recommended for the key properties of the intravascular catheter lubricating coating such as stability,lubricity and integrity,including insoluble particle test method,friction test procedure and appearance detection method.Fifteen batches of microcatheters produced by different manufacturers were selected for testing to clarify the three methods in test principle,step,result,characteristic.References were provided for the design,production,evaluation and regulation of intravascular catheters with lubricant coatings.[Chinese Medical Equipment Journal,2025,46(1):66-72]

ObjectiveExploring the formula rules of commonly used traditional Chinese medicines（TCMs） for epidemic treatment from the Qin and Han dynasties to the Qing dynasty through data mining， providing reference for the prevention and control of contemporary epidemics. MethodsThe articles on epidemic treatment in the electronic database of Chinese Medical Code V5.0 were systematically searched， and the contents such as source， dynasty， author， diagnosis， formula name， therapeutic method and efficacy， and composition of medicines from each article that met the inclusion criteria were extracted. Then， an Excel standardized database was established， and Python programs were used for data mining to summarize the frequency of commonly used medicines and perform hierarchical cluster analysis， Pearson correlation analysis， and association rule analysis. ResultsA total of 1 595 formulas were included， involving 558 TCMs. The efficacy of these medicines could be classified into two categories， namely， expeling pathogenic factors and reinforcing healthy Qi. According to the frequency deconstruction analysis， high-frequency medicines were mainly detoxification， Fu-organ dredging， aromatization and promoting blood circulation， followed by the medicines with the effect of treating the lungs， such as clearing the lungs and resolving phlegm， clearing heat and purging the lungs， relieving cough and asthma， and purging the lungs and relieving asthma. And the proportions of acrid-warm herbs and acrid-cold herbs varied in different periods. Hierarchical clustering and correlation analysis both suggested TCMs for expeling pathogenic factors and reinforcing healthy Qi often formed stable combinations with high association degrees. Association rule analysis showed that the core acrid-warm herb was mainly Ephedrae Herba， and the core acrid-cold herb was mainly Forsythiae Fructus， resulting in the core formulas of Maxing Shigantang and Yinqiaosan. ConclusionThroughout history， the prevention and control of epidemics have been based on the principle of "preserving healthy Qi and avoiding toxic Qi"， focusing on the treatment of the causes and characteristics of epidemics through detoxification， Fu-organ dredging， and aromatization， emphasizing the use of Rhei Radix et Rhizoma and other herbs to dredge Fu-organ， eliminate toxins and pathogens， and playing the role of actively intervene with symptomatic medication. And based on the external manifestations of the body's struggle between evil and righteousness， diagnose and treatment according to syndrome differentiation was performed.