Objective: To map the research landscape of artificial intelligence (AI)-assisted tongue diagnosis through bibliometric analysis and to quantify its diagnostic accuracy and clinical interpretability through a diagnostic test accuracy (DTA) meta-analysis. Methods: For the bibliometric analysis, the Web of Science Core Collection (WoSCC) was queried for English-language articles and reviews on AI-assisted tongue diagnosis published between January 1, 2014 and December 31, 2025, and analysed using Bibliometrix, VOSviewer, and CiteSpace, with major output dimensions including annual publication output and disciplinary distribution, journal and citation characteristics, country/region and institutional collaboration, author networks, keyword co-occurrence, and keyword burst detection. For the DTA meta-analysis, four databases [Scopus, PubMed, Web of Science, and China National Knowledge Infrastructure (CNKI)] were searched in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses of Diagnostic Test Accuracy (PRISMA-DTA) guidelines. A bivariate random-effects model hierarchical summary receiver operating characteristic (HSROC) was used to pool sensitivity and specificity, with subgroup analyses by disease category, AI model architecture, and sample-size strata. Methodological quality was assessed with the Quality Assessment of Diagnostic Accuracy Studies version 2 (QUADAS-2) tool, and publication bias was evaluated by Deeks’ funnel plot asymmetry test. Results: A total of 198 publications met the bibliometric eligibility criteria. Annual output increased 24.5-fold (from 2 in 2014 to 49 in 2025), with the period 2022 – 2025 alone accounting for 65.2% of all publications. China contributed approximately 83.5% of all institutional affiliations, with Shanghai University of Traditional Chinese Medicine and Jiatuo Xu being the most productive institution and author, respectively. Keyword analysis identified four thematic clusters (AI and deep-learning architectures, image processing and segmentation, traditional Chinese medicine (TCM)-specific applications, and disease-specific applications) and a temporal evolution from traditional machine learning to deep learning and transformer-based, explainable, and multimodal AI architectures. Sixteen DTA meta-analysis studies (14 755 participants) covering metabolic and hepatic disorders, oncological and oral lesions, cardiovascular risk, diabetes, and other clinical applications were included in the DTA meta-analysis. The pooled sensitivity was 90.3% [95% confidence interval (CI): 86.7% – 93.1%] and the pooled specificity was 93.0% (95% CI: 90.6% – 94.7%); the area under the summary receiver operating characteristic (SROC) curve (AUC) was 0.961. Heterogeneity was substantial (I2 = 95.8% for sensitivity; I2 = 92.1% for specificity). Subgroup performance was broadly consistent across disease categories, AI architectures, and sample-size strata, and Deeks’ test indicated no significant publication bias (P = 0.258). Conclusion AI-assisted tongue diagnosis has progressed rapidly and shows pooled diagnostic performance comparable to established screening modalities, supporting its potential as a complementary and easily accessible decision-support tool.

Objective To investigate the role and mechanism of bergapten in treating osteoarthritis(OA)based on network pharmacology,molecular docking and in vitro experiments.Methods The functional targets of bergapten and the related targets of OA disease were obtained by searching Super-Pred,Swiss Target Prediction,Drug Bank.The obtained targets were intersected with Venny2.1.0,protein-protein interaction(PPI)analysis was performed using the STRING database,and molecular docking was conducted by AutoDock Vina.Subsequently,human chondrocyte C28/I2 cells were subjected in the following validation experiments.After the cells were treated with 0～50 μmol/L bergapten for 24,48 or 72 h,CCK-8 assay,RT-qPCR and immunofluorescence assay were used to detect the proliferation and apoptosis,and the expression of the molecules related to proliferation,inflammation and senescence(senescence associated secretory phenotype,SASP).Senescence β-galactosidase(SA-β-Gal)staining kit was employed to detect the change of SA-β-Gal level in the cells.Results There obtained 145 potential targets of bergapten for OA.PPI analysis revealed 4 potential core targets,HSP90AA1,EGFR,HIF1A,and PIK3CA,and they could form relatively stable complex with bergapten.CCK-8 assay showed that 10 μmol/L bergapten treatment for 48 h resulted in the highest proliferative activity of C28/12 cells(P＜0.05).Immunofluorescence assay,RT-qPCR and Western blotting indicated that bergapten treatment induced significant increases in the expression of cell proliferation markers such as PCNA,CCND1 and CCNE1(P＜0.05),while decreases in the expression of apoptosis markers Caspase3 and BAX(P＜0.05).Meanwhile,the expression levels of IL-1β-induced inflammatory factors IL-6 and TNF-α were reduced,while those of anti-inflammatory factors IL-4 and IL-10 were elevated after bergapten treatment(P＜0.05).The levels of SASP factors,such as P16,P21,MMP9 and MMP13 were also significantly declined(P＜0.05).SA-β-Gal staining displayed that the level of SA-β-Gal in C28/I2 cells was significantly enhanced after IL-1β induction(P＜0.05),while bergapten treatment could decrease the positive cell rate of the staining(P＜0.05).Conclusion Bergapten can reduce the expression of SASP factors such as IL-6,TNF-α,P16,P21,MMP9 and MMP13 in IL-1 β-stimulated chondrocytes,and thus exerts its anti-inflammatory and anti-aging effects in delaying the progression of OA.

Health behavior motivation significantly affects the quality of life of patients with cardiovascular diseases.Assessing the level of health behavior motivation can measure the health behavior of individuals,help to promote the participation rate of cardiac rehabilitation,reduce the readmission rate of patients,and promote patients'healthy lifestyles.This article reviews the content,characteristics and application of assessment tools for health behavior motivation of patients with cardiovascular diseases at home and abroad,with the aim of providing references for the development and application of such tools in China,and offering a basis for scientifically assessing their health behavior motivation level and formulating effective strategies to promote health behavior motivation.

The number of patients with chronic diseases in China has been increasing year by year,followed by the increase of treatment burden.It is particularly important to effectively evaluate the treatment burden of patients with chronic diseases.This paper reviews the main contents,application scope,advantages and disadvantages of the assessment tools for the treatment burden of patients with chronic diseases at home and abroad,so as to provide references and basis for medical staff to reasonably select assessment tools and conduct relevant clinical research.

Objective To comprehensively evaluate the quality difference of Huaihua powder obtained by the hospital's electromagnetic furnace decoction method and automatic decoction machine decoction method.Methods Five methods(induction cooker decoction(once decoction,twice decoction),normal pressure decoction(once decoction,twice decoction),and pressure decoction)were used to prepare the decoction.The HPLC method used a Shiseido CAPCELL PAK-C18(250 mm×4.6 mm,5 μm)column,with a UV detection wavelength of 254 nm,mobile phase of acetonitrile 0.1%phosphoric acid water,column temperature of 30℃,flow rate of 1.0 mL/min,and detection of index components including rutin,naringin,quercitrin,neohesperidin,quercetin,and menthone.The extraction rate of the index components was calculated and the cream yield of each decoction was detected.The evaluation of quality differences is based on the comprehensive score of cream yield(40%)and indicator components(60%).Results The HPLC fingerprint similarity of Huaihua powder prepared by 5 decoction methods was greater than 99%.There was no significant difference(P>0.05)in the yield of ointment and the content of indicator components between the induction cooker one decoction and the induction cooker twice decoction methods.The yield of ointment and the extraction rate of indicator components were significantly higher in twice normal pressure decoction using a decoction machine than once normal pressure decoction using a decoction machine(P<0.05).The index component score of machine under pressure decoction once was the highest,but the comprehensive score of twice normal pressure decoction was the highest,with a combined score of 92.13 and 89.58,respectively,far higher than the 36.01,18.79,and 15.60 of the other three decoction methods.Classify the decoction into three categories through cluster analysis.Orthogonal partial least squares-discriminant analysis was used to screen for rutin,naringin and neohesperidin as differential biomarkers.Conclusion The quality of Huaihua powder decocted by a decoction machine is significantly better than that by an induction cooker,and the twice decocted at normal pressure by the decoction machine are significantly better than those decocted at normal pressure once.Decoction under pressure once may be a better choice.

Objective To comprehensively evaluate the quality difference of Huaihua powder obtained by the hospital's electromagnetic furnace decoction method and automatic decoction machine decoction method.Methods Five methods(induction cooker decoction(once decoction,twice decoction),normal pressure decoction(once decoction,twice decoction),and pressure decoction)were used to prepare the decoction.The HPLC method used a Shiseido CAPCELL PAK-C18(250 mm×4.6 mm,5 μm)column,with a UV detection wavelength of 254 nm,mobile phase of acetonitrile 0.1%phosphoric acid water,column temperature of 30℃,flow rate of 1.0 mL/min,and detection of index components including rutin,naringin,quercitrin,neohesperidin,quercetin,and menthone.The extraction rate of the index components was calculated and the cream yield of each decoction was detected.The evaluation of quality differences is based on the comprehensive score of cream yield(40%)and indicator components(60%).Results The HPLC fingerprint similarity of Huaihua powder prepared by 5 decoction methods was greater than 99%.There was no significant difference(P>0.05)in the yield of ointment and the content of indicator components between the induction cooker one decoction and the induction cooker twice decoction methods.The yield of ointment and the extraction rate of indicator components were significantly higher in twice normal pressure decoction using a decoction machine than once normal pressure decoction using a decoction machine(P<0.05).The index component score of machine under pressure decoction once was the highest,but the comprehensive score of twice normal pressure decoction was the highest,with a combined score of 92.13 and 89.58,respectively,far higher than the 36.01,18.79,and 15.60 of the other three decoction methods.Classify the decoction into three categories through cluster analysis.Orthogonal partial least squares-discriminant analysis was used to screen for rutin,naringin and neohesperidin as differential biomarkers.Conclusion The quality of Huaihua powder decocted by a decoction machine is significantly better than that by an induction cooker,and the twice decocted at normal pressure by the decoction machine are significantly better than those decocted at normal pressure once.Decoction under pressure once may be a better choice.

Objective:To study the relationship between urinary arsenic methylation metabolism patterns and skin keratinization and pigmentation abnormalities in population exposed to arsenic through drinking water.Methods:Using a cross-sectional study method, a survey on endemic arsenic poisoning was conducted among permanent residents of drinking water endemic arsenic poisoning areas in Bayannur City, Inner Mongolia Autonomous Region in 2004 (before water improvement). In 2017 (after water improvement), 71 arsenic exposed individuals were followed up as survey subjects. According to the "Diagnosis of Endemic Arsenism" (WS/T 211-2015), the clinical grading of skin injuries (skin keratinization, pigmentation abnormalities) in the survey subjects was evaluated. Urine samples were collected for detection of arsenic methylation metabolite levels by high-performance liquid chromatography inductively coupled plasma mass spectrometry and calibrated with urinary creatinine. The changes and amplitudes of urinary arsenic methylation indicators before and after water improvement were calculated and analyzed according to the outcome of skin keratinization and pigmentation abnormalities which were divided into reduced, unchanged, and added groups.Results:(1) The changes in urinary total arsenic (TAs), inorganic arsenic (iAs), monomethyl arsenic (MMA), and dimethyl arsenic (DMA) levels in different outcome groups of skin keratinization were compared, and the differences were statistically significant ( H = 9.08, 8.77, 9.28, 8.57, P < 0.05). The changes in urinary TAs, iAs, MMA, DMA levels, iAs percentage (iAs%), DMA percentage (DMA%), and primary methylation index (PMI) in different outcome groups of skin pigmentation abnormalities were compared, and the differences were statistically significant ( H = 8.04, 10.67, 8.29, 9.14, 6.30, 9.10, 7.20, P < 0.05). (2) The comparison of amplitudes in urinary TAs, iAs, MMA, and DMA levels in different outcome groups of skin keratinization showed statistically significant differences ( H = 6.92, 7.34, 6.66, 6.16, P < 0.05). The amplitudes in urinary iAs level, iAs%, DMA%, and PMI in different outcome groups of skin pigmentation abnormalities were compared, and the differences were statistically significant ( H = 7.94, 7.61, 9.95, 7.22, P < 0.05). Conclusion:The changes pattern of urinary TAs, iAs, MMA, DMA, iAs%, DMA%, and PMI in population exposed to arsenic through drinking water is related to the transformation of skin keratinization and pigmentation abnormalities.

Objective:To study the relationship between the levels of multiple elements in urine and the risk of arsenic poisoning in populations exposed to drinking water arsenic in Inner Mongolia Autonomous Region (Inner Mongolia).Methods:From April 2023 to January 2024, a case-control study method was used to select 128 individuals with a residence time of ≥10 years in drinking water arsenic exposed areas in Inner Mongolia as study subjects. Eighty-one individuals diagnosed with arsenic poisoning were selected as the case group, and 47 healthy individuals were selected as the control group for urine sample collection and questionnaire survey. Inductively coupled plasma mass spectrometry was employed to determine the levels of 10 elements (chromium, manganese, cobalt, nickel, copper, zinc, arsenic, molybdenum, cadmium and lead) in urine. The levels of each element in urine were divided into four groups ( Q1, Q2, Q3, and Q4 groups) based on quartiles. The associations between the levels of various elements in urine and the risk of arsenic poisoning were studied using binary logistic regression model and restricted cubic spline (RCS). Results:The age of the control group and the case group [ M ( Q1, Q3)] were 61 (53, 69) and 61 (56, 67) years old, respectively. There were 19 and 43 males, and 28 and 38 females, respectively. There was no statistically significant differences in age and and gender composition between the two groups ( Z = - 0.39, P = 0.700; χ 2 = 1.91, P = 0.167). The levels of urinary copper and cadmium of the case group were higher than those of the control group, and the differences were statistically significant ( Z = - 2.66, - 2.16, P < 0.05). The results of univariate logistic regression analysis showed that urinary copper was an influencing factor for arsenic poisoning ( P = 0.017). The results of multivariate logistic regression analysis revealed that after adjusting for covariates, urinary copper and arsenic were independent influencing factors of arsenic poisoning ( P < 0.05). Taking Q1 group as a reference, urinary copper in Q3 group [ OR (95% CI) = 8.23 (1.81, 37.39), P = 0.006] increased the risk of arsenic poisoning, while urinary arsenic in Q2, Q3, and Q4 groups [ OR (95% CI) = 0.24 (0.06, 0.92), 0.12 (0.03, 0.53), 0.15 (0.04, 0.63), P < 0.05] decreased the risk of arsenic poisoning. After adjusting for covariates, RCS did not show a dose-response relationship between urinary copper, urinary arsenic, and arsenic poisoning ( P > 0.05). Conclusion:Urinary arsenic and copper are associated with the risk of arsenic poisoning in the drinking water arsenic exposed areas of Inner Mongolia, copper exposure may contribute significantly to arsenic poisoning.

Health behavior motivation significantly affects the quality of life of patients with cardiovascular diseases.Assessing the level of health behavior motivation can measure the health behavior of individuals,help to promote the participation rate of cardiac rehabilitation,reduce the readmission rate of patients,and promote patients'healthy lifestyles.This article reviews the content,characteristics and application of assessment tools for health behavior motivation of patients with cardiovascular diseases at home and abroad,with the aim of providing references for the development and application of such tools in China,and offering a basis for scientifically assessing their health behavior motivation level and formulating effective strategies to promote health behavior motivation.

The number of patients with chronic diseases in China has been increasing year by year,followed by the increase of treatment burden.It is particularly important to effectively evaluate the treatment burden of patients with chronic diseases.This paper reviews the main contents,application scope,advantages and disadvantages of the assessment tools for the treatment burden of patients with chronic diseases at home and abroad,so as to provide references and basis for medical staff to reasonably select assessment tools and conduct relevant clinical research.