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.

OBJECTIVES: To investigate the effects of periodontitis induced by Prevotella nigrescens (Pn) combined with ligation on cognitive functions in mice. METHODS: Twenty-four C57BL/6J mice were randomly divided into control group, ligation group, and ligation + Pn treatment (P+Pn) group. Experimental periodontitis was induced by silk ligation of the first molars followed by topical application of Pn for 6 weeks. After modeling, alveolar bone resorption was assessed using micro-CT and histological analysis. Learning and memory abilities of the mice were evaluated using open field test (OFT), novel object recognition test (NORT), and Morris water maze test (MWM). Seven weeks after the start of modeling, the mice were sacrificed for examining histopathological changes in the hippocampus using HE and Nissl staining. RESULTS: After 6 weeks of molar ligation, micro-CT revealed horizontal alveolar bone resorption and furcation exposure in the mice, and histological analysis showed apical migration of the junctional epithelium, epithelial ridge hyperplasia, and lymphocyte infiltration, and these changes were obviously worsened in P+Pn group. Alveolar bone height decreased significantly in both ligation groups compared to the control group. Cognitive tests showed that the mice in both of the ligation groups traveled shorter distances in OFT, showed reduced novel object preference in NORT, and exhibited longer escape latencies in MWM, and the mice in P+Pn group had significantly poorer performances in the tests. Histologically, obvious neuronal cytoplasmic degeneration, necrosis, nuclear pyknosis, vacuolation, and reduced Nissl bodies and viable neurons were observed in the hippocampal regions of the mice in the two ligation groups. CONCLUSIONS Pn infection aggravates alveolar bone destruction, accelerates necrosis and causes morphological abnormalities of neuronal cells in the hippocampus to reduce cognitive functions of mice with periodontitis.
Animals ; Periodontitis/microbiology* ; Mice ; Mice, Inbred C57BL ; Cognition ; Alveolar Bone Loss ; Hippocampus/pathology* ; Male ; Inflammation ; Maze Learning

Objective To investigate the effects of periodontitis induced by Prevotella nigrescens(Pn)combined with ligation on cognitive functions in mice.Methods Twenty-four C57BL/6J mice were randomly divided into control group,ligation group,and ligation+Pn treatment(P+Pn)group.Experimental periodontitis was induced by silk ligation of the first molars followed by topical application of Pn for 6 weeks.After modeling,alveolar bone resorption was assessed using micro-CT and histological analysis.Learning and memory abilities of the mice were evaluated using open field test(OFT),novel object recognition test(NORT),and Morris water maze test(MWM).Seven weeks after the start of modeling,the mice were sacrificed for examining histopathological changes in the hippocampus using HE and Nissl staining.Results After 6 weeks of molar ligation,micro-CT revealed horizontal alveolar bone resorption and furcation exposure in the mice,and histological analysis showed apical migration of the junctional epithelium,epithelial ridge hyperplasia,and lymphocyte infiltration,and these changes were obviously worsened in P+Pn group.Alveolar bone height decreased significantly in both ligation groups compared to the control group.Cognitive tests showed that the mice in both of the ligation groups traveled shorter distances in OFT,showed reduced novel object preference in NORT,and exhibited longer escape latencies in MWM,and the mice in P+Pn group had significantly poorer performances in the tests.Histologically,obvious neuronal cytoplasmic degeneration,necrosis,nuclear pyknosis,vacuolation,and reduced Nissl bodies and viable neurons were observed in the hippocampal regions of the mice in the two ligation groups.Conclusion Pn infection aggravates alveolar bone destruction,accelerates necrosis and causes morphological abnormalities of neuronal cells in the hippocampus to reduce cognitive functions of mice with periodontitis.

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]

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.

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]

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.

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: Emerging evidence suggests that air pollution exposure may increase the risk of Parkinson’s disease (PD). We aimed to investigate the association between exposure to fine particulate matter (PM2.5) and the risk of incident PD nationwide. Methods: We utilized data from the Taiwan National Health Insurance Research Database, which is spatiotemporally linked with air quality data from the Taiwan Environmental Protection Administration website. The study population consisted of participants who were followed from the index date (January 1, 2005) until the occurrence of PD or the end of the study period (December 31, 2017). Participants who were diagnosed with PD before the index date were excluded. To evaluate the association between exposure to PM2.5 and incident PD risk, we employed Cox regression to estimate the hazard ratio and 95% confidence interval (CI). Results: A total of 454,583 participants were included, with a mean (standard deviation) age of 63.1 (9.9) years and a male proportion of 50%. Over a mean follow-up period of 11.1 (3.6) years, 4% of the participants (n = 18,862) developed PD. We observed a significant positive association between PM2.5 exposure and the risk of PD, with a hazard ratio of 1.22 (95% CI, 1.20–1.23) per interquartile range increase in exposure (10.17 μg/m3) when adjusting for both SO2 and NO2. Conclusion We provide further evidence of an association between PM2.5 exposure and the risk of PD. These findings underscore the urgent need for public health policies aimed at reducing ambient air pollution and its potential impact on PD.