ObjectiveIn traditional Chinese medicine (TCM), the foundational doctrine that the eyes reflect the essence of the internal viscera establishes ocular observation as a cornerstone of diagnostic practice. Specifically, the morphological characteristics and coloration variations of the scleral microvasculature serve as critical clinical indicators for assessing the dynamic balance of Qi and Blood, as well as the pathological status of internal organs. Historically, however, TCM eye diagnosis has relied predominantly on the subjective clinical experience and visual acuity of individual practitioners, leading to inherent challenges in standardization and reproducibility. While automated computer-aided diagnostic systems offer a promising solution, existing vessel segmentation algorithms encounter significant domain-specific bottlenecks when applied to scleral imagery. These challenges primarily stem from the highly reflective and moist nature of the ocular surface, which generates severe reflective interference. Furthermore, the inherent low contrast of fine capillary networks against complex background textures, compounded by non-uniform illumination, frequently results in high false-positive rates, misdetections, and severe vessel fragmentation. To address these critical limitations and advance the objective quantification of TCM diagnostics, this paper proposes a novel, highly robust sclera vessel segmentation framework that innovatively integrates Frangi-Sato dual-filter adaptive enhancement with pixel-level reflection detection. MethodsThe proposed methodology systematically addresses the segmentation pipeline through three synergistic stages. First, to overcome the structural limitations of single-filter approaches, a multi-scale weighted fusion strategy is meticulously designed to harness the complementary extraction capabilities of both Frangi and Sato filters. This adaptive enhancement optimally balances the preservation of main vessel trunk continuity with the heightened sensitivity required for delineating delicate, low-contrast peripheral capillaries. Second, to tackle the persistent issue of reflective highlights, a sophisticated multi-feature synergistic reflection detection module is introduced. By jointly analyzing local information entropy, gradient field variations, and intensity statistical distributions, this module achieves precise, pixel-level identification and elimination of reflective artifacts without compromising the underlying vascular structures. Finally, a dual-level adaptive thresholding strategy, featuring an innovative “core protection” mechanism, is implemented. This critical step effectively suppresses complex background noise while rigorously preserving the structural and topological integrity of the intricate vessel network, preventing the structural breaks often seen in conventional binarization methods. ResultsThe efficacy of the proposed framework was rigorously evaluated using both self-constructed clinical datasets specifically acquired for TCM research and standardized public datasets. Extensive experimental results demonstrate that the proposed method consistently outperforms state-of-the-art traditional approaches and contemporary deep learning models. Specifically, the proposed method achieves a Dice similarity coefficient of approximately 0.71 on the private clinical dataset, and secures the best performance across the majority of quantitative metrics on both datasets. Notably, the framework exhibits exceptional robustness and generalization capabilities in highly challenging scenarios characterized by intense reflective interference, low signal-to-noise ratios, and cross-domain image variations. ConclusionThis study successfully realizes the high-integrity, automated segmentation of scleral vessel networks under complex clinical imaging conditions. By overcoming the fundamental algorithmic challenges of reflection interference and micro-vessel loss, the proposed methodology provides potential support for the digitization, objective standardization, and intelligent advancement of modern TCM eye diagnosis systems.

ObjectiveIn traditional Chinese medicine (TCM), the foundational doctrine that the eyes reflect the essence of the internal viscera establishes ocular observation as a cornerstone of diagnostic practice. Specifically, the morphological characteristics and coloration variations of the scleral microvasculature serve as critical clinical indicators for assessing the dynamic balance of Qi and Blood, as well as the pathological status of internal organs. Historically, however, TCM eye diagnosis has relied predominantly on the subjective clinical experience and visual acuity of individual practitioners, leading to inherent challenges in standardization and reproducibility. While automated computer-aided diagnostic systems offer a promising solution, existing vessel segmentation algorithms encounter significant domain-specific bottlenecks when applied to scleral imagery. These challenges primarily stem from the highly reflective and moist nature of the ocular surface, which generates severe reflective interference. Furthermore, the inherent low contrast of fine capillary networks against complex background textures, compounded by non-uniform illumination, frequently results in high false-positive rates, misdetections, and severe vessel fragmentation. To address these critical limitations and advance the objective quantification of TCM diagnostics, this paper proposes a novel, highly robust sclera vessel segmentation framework that innovatively integrates Frangi-Sato dual-filter adaptive enhancement with pixel-level reflection detection. MethodsThe proposed methodology systematically addresses the segmentation pipeline through three synergistic stages. First, to overcome the structural limitations of single-filter approaches, a multi-scale weighted fusion strategy is meticulously designed to harness the complementary extraction capabilities of both Frangi and Sato filters. This adaptive enhancement optimally balances the preservation of main vessel trunk continuity with the heightened sensitivity required for delineating delicate, low-contrast peripheral capillaries. Second, to tackle the persistent issue of reflective highlights, a sophisticated multi-feature synergistic reflection detection module is introduced. By jointly analyzing local information entropy, gradient field variations, and intensity statistical distributions, this module achieves precise, pixel-level identification and elimination of reflective artifacts without compromising the underlying vascular structures. Finally, a dual-level adaptive thresholding strategy, featuring an innovative “core protection” mechanism, is implemented. This critical step effectively suppresses complex background noise while rigorously preserving the structural and topological integrity of the intricate vessel network, preventing the structural breaks often seen in conventional binarization methods. ResultsThe efficacy of the proposed framework was rigorously evaluated using both self-constructed clinical datasets specifically acquired for TCM research and standardized public datasets. Extensive experimental results demonstrate that the proposed method consistently outperforms state-of-the-art traditional approaches and contemporary deep learning models. Specifically, the proposed method achieves a Dice similarity coefficient of approximately 0.71 on the private clinical dataset, and secures the best performance across the majority of quantitative metrics on both datasets. Notably, the framework exhibits exceptional robustness and generalization capabilities in highly challenging scenarios characterized by intense reflective interference, low signal-to-noise ratios, and cross-domain image variations. ConclusionThis study successfully realizes the high-integrity, automated segmentation of scleral vessel networks under complex clinical imaging conditions. By overcoming the fundamental algorithmic challenges of reflection interference and micro-vessel loss, the proposed methodology provides potential support for the digitization, objective standardization, and intelligent advancement of modern TCM eye diagnosis systems.

Objective:Based on the background of high-quality development,we analyze the operational efficiency of traditional Chinese medicine(TCM)hospitals in China's Yangtze River Economic Belt in 2021 and explore the impact of external environmental factors on operational efficiency,so as to provide a reference for promoting the high-quality development of TCM hospitals in the Yangtze River Economic Belt.Methods:The three-stage DEA model was used to analyze the operational efficiency of TCM hospitals in 11 provinces and cities in the Yangtze River Economic Zone in China in 2021.Results:After three-stage DEA analysis,the values of comprehensive efficiency,pure technical efficiency and scale efficiency of TCM hospitals in China's Yangtze River Economic Belt are 0.976,0.986 and 0.990,respectively.5 provinces and cities,Shanghai,Jiangsu,Hunan,Chongqing and Guizhou,are efficient before and after the adjustment,and the comprehensive efficiency of Zhejiang,Anhui,Hubei,Jiangxi,Sichuan and Yunnan have increased compared with that before the adjustment.Ranking of the average value of the comprehensive efficiency of TCM hospitals operation in the three major city clusters of the Yangtze River Economic Belt after adjustment:Chengdu-Chongqing city cluster(0.998)>city cluster in the Yangtze River Delta(0.964)>city cluster in the middle reaches of the Yangtze River(0.962).Conclusion:The operational efficiency of TCM hospitals in the Yangtze River Economic Zone has been underestimated,and the comprehensive efficiency is mainly affected by scale efficiency;there are differences in the operational efficiency of TCM hospitals in the three major urban agglomerations,and balanced development is needed between regions;the operational efficiency of TCM hospitals is affected by the external environment,and it is necessary to improve the external environment;it is necessary to strengthen the construction of digital and informatization of TCM,and to pay attention to the role of talents in TCM,so as to promote the high-quality development of TCM hospitals.

Objective:To investigate the factors influencing left atrial fibrosis in patients with atrial fibrillation(AF)and the association of Periostin protein,serum transforming growth factor-β2(TGF-β2)with left atrial fibrosis.Methods:We enrolled 100 AF patients admitted to Gansu Provincial People's Hospital between March 2021 and March 2023.They were divided into control group(＜10％,n=53)and fibrosis group(≥10％,n=47)according to their left atrial low voltage region.Univariate and multivariate Logistic regression were used to analyze the influ-encing factors of left atrial fibrosis in AF patients and construct a nomogram model.The diagnostic value of related factors and their combined detection for left atrial fibrosis in AF patients were analyzed by receiver operating char-acteristic curve(ROC).Spearman correlation analysis was used to analyze the association of Periostin protein,TGF-β2 with left atrial fibrosis in AF patients.Results:Compared to patients in the control group,those in the fibrosis group had significant higher left atrial diameter(LAD)[(37.08±3.19)mm vs.(33.45±2.45)mm],levels of ser-um uric acid(SUA)[(313.75±49.06)μmol/L vs.(279.88±38.15)μmol/L],Periostin protein[(83.27±3.98)ng/L vs.(75.21±3.04)ng/L],TGF-β2[(4346.84±321.34)ng/L vs.(4186.02±306.91)ng/L],and signifi-cant lower left atrial ejection fraction(LVEF)[(62.28±5.00)％vs.(67.24±3.07)％](P＜0.05 or＜0.01).Multivariate Logistic regression analysis showed that LAD(OR=1.663,95％CI 1.238～3.887,P=0.001),SUA(OR=1.586,95％CI 1.164～2.892,P＜0.001),Periostin protein(OR=1.997,95％CI 1.513～4.585,P=0.001),TGF-β2(OR=2.013,95％CI 1.543～5.864,P＜0.001)were independent risk factors for left atrial fi-brosis in AF patients,while LVEF was an independent protective factor(OR=0.524,95％CI 0.141～0.920,P=0.002).The nomogram model for left atrial fibrosis in AF patients:logit(P)=4.631+0.445 × LVEF+0.546 × LAD+0.575 × SUA+0.530 × Periostin protein+0.347 × TGF-β2.ROC curve showed that the area under the curve(AUC)of combined detection(0.893,95％CI 0.842～0.932)was significantly higher than SUA(AUC=0.637,95％CI 0.566～0.704),LVEF(AUC=0.701,95％CI 0.632～0.763),LAD(AUC=0.649,95％CI 0.579～0.715),Periostin protein(AUC=0.676,95％CI 0.606～0.740),TGF-β2(AUC=0.641,95％CI 0.570～0.707)alone(Z=5.265,6.399,6.379,6.040,6.483,P＜0.001 all).Spearman correlation analysis showed that Perios-tin protein and TGF-β2 were significantly positive correlated with left atrial fibrosis in AF patients(r=0.536,0.578,P＜0.001 all).Conclusion:Periostin protein and TGF-β2 were independent risk factors for left atrial fi-brosis in AF patients and were significantly positive correlated with it,a combination of above-mentioned indexes,cardiac function indexes and uric acid had good diagnostic value for left atrial fibrosis.

Objective:To construct a decision tree model to predict the risk of breast cancer in patients with pathological nipple discharge.Methods:A total of 157 patients with pathological nipple discharge,who were diagnosed and treated at Weifang Municipal Hospital of Traditional Chinese Medicine from January 2019 to April 2024 and met the inclusion criteria,were selected.A risk prediction model for concurrent breast cancer in patients with pathological nipple discharge was developed using Logistic regression analysis.A decision tree was then constructed,and the predictive performance of the model was assessed based on the area under the receiver operating characteristic curve(AUC).Re-sults:The incidence of concurrent breast cancer among patients with pathological nipple discharge was 24.2%.Accord-ing to the results of binary Logistic regression analysis,elevated CEA and CA 153 levels in nipple discharge,as well as bloody discharge,emerged as independent risk factors for the development of breast cancer in such patients(P<0.05).Based on these findings,a decision tree model was constructed to predict the risk of concurrent breast cancer in patients with pathological nipple discharge.The validation results showed that the Logistic regression model had an AUC value of 0.800,while the decision tree model achieved an AUC value of 0.889.Conclusions:The decision tree model,built upon the identified influencing factors,exhibits strong predictive power for the risk of developing concurrent breast can-cer in patients with pathological nipple discharge,thus facilitating more precise preoperative diagnoses by clinicians for these patients.

Mesenchymal stem cells(MSCs)play a crucial role in tissue repair and regeneration,and the extracellular vesicle(EV)released by them holds great promise for applications in clinical biomarkers,vaccines,and drug delivery.However,MSCs-derived EV(MSCs-EV)face challenges such as low pro-duction yield,poor retention,and targeted delivery issues.There-fore,engineering MSCs-EV to enhance their performance and en-able visual research has become a hot topic.Curcumin(CUR),an active component in traditional chinese medicine,exhibits pharmacological effects but has limited bioavailability.Using MSCs-EV as a carrier for CUR delivery can address its solubility and bioavailability challenges.This article reviews the drug loading methods,engineering strategies of MSCs-EV,and their important applications in the delivery and treatment of CUR for cartilage injury diseases.It provides a basis for the clinical ap-plication of engineered MSCs-EV in CUR delivery for cartilage repair,offering potential solutions to the challenges in cartilage tissue repair.

Objective:To investigate the drug resistance gene characteristics, plasmid characteristics and genome tracing of Shigella sonnei causing a bacillary dysentery outbreak in Shandong Province. Methods:Sixty-five Shigella sonnei strains isolated from a 2021 outbreak in a county of Shandong Province were analyzed using antimicrobial susceptibility testing, whole genome sequencing (WGS), characterization of resistance and virulence genes, plasmid profiling, core genome multilocus sequence typing (cgMLST), and single nucleotide polymorphism (SNP) analysis. Results:All isolates had the same resistance phenotype and genotypes and were multidrug-resistant ESBL-producing Shigella sonnei, carrying important virulence genes. Plasmid analysis revealed a conserved genetic arrangement, pil( M/ N/ O2/ P)-tra( F/ H/ J/ K/ N/ O/ P/ Q)-IS Ecp1- blaCTX-M-14-Tn 903- yub( J/ I/ F/ G/ E/ D), and shared across strains from diverse regions and bacterial species. The cgMLST and SNP analyses demonstrated concordant clustering, with all 65 outbreak-related strains forming a single cluster alongside human-derived strains from Guangxi. Conclusion:The ESBL-producing Shigella sonnei responsible for the outbreak shares a homologous relationship with Guangxi human-derived strains, and the detected resistance plasmids and virulence genes underscore the need to strengthen drug resistance surveillance and genome tracing.

Objective:To develop a comprehensive training curriculum to enhance the effective implementation of the national initiative promoting dementia care and prevention.Methods:The Delphi method was utilized in an expert consultation that included 44 participants.The initial draft of the training curriculum was developed based on the current state of dementia care and prevention.This draft was subsequently evaluated for its importance, feasibility, and ease of dissemination.Experts offered targeted modifications and additional recommendations.Results:The recovery rate of the expert consultation questionnaire was 95.5%, with a recovery validity rate of 90.9%.The expert authority coefficient was 0.91, and the Kendall's coordination coefficient( W)for expert scoring was 0.316, with a significance level of P<0.001.Four course modules were ultimately identified: the foundation of memory clinic work, the complete management practice skills, group counseling techniques for caregivers, and practical skills for caregivers.The importance of these modules was rated with a mean of 4.92 to 4.95, and the coefficient of variation ranged from 0.044 to 0.063.Each module had a mean value of 4.92 to 4.95, with a coefficient of variation of 0.044 to 0.063; the mean value for practicality was between 4.78 and 4.92, with a coefficient of variation of 0.055 to 0.098; and the mean value for ease of generalization ranged from 4.28 to 4.65, with a coefficient of variation from 0.140 to 0.203.The four modules comprised a total of 55 specific course content items, with the mean value for each item ranging from 4.76 to 5.00 and a coefficient of variation from 0.000 to 0.121.The mean value of usefulness assigned to each entry ranged from 4.55 to 4.98, with a coefficient of variation from 0.031 to 0.150.Additionally, the mean value for ease of propagation assigned to each entry ranged from 4.00 to 4.83, with a coefficient of variation from 0.091 to 0.245. Conclusions:The developed training curriculum, which comprises four course modules and 55 items, demonstrated consistently high levels of importance, practicality, and ease of dissemination.These findings indicate that the curriculum is well-aligned with national initiatives aimed at enhancing dementia care and prevention.

Sacroiliac complex injuries are commonly seen in high-energy pelvic fractures. The injuries make a big difference in treatment patterns due to the diverse injury types, posing considerable challenges in formulating optimal treatment strategies, and hence are persistent clinical difficulties in orthopedic trauma. The clinical management of sacroiliac complex injuries presents several key challenges such as a non-negligible rate of missed diagnoses in associated vascular and visceral injuries, absence of standardized protocols for surgical approaches and reduction-fixation strategies across different injury patterns, and ongoing controversies regarding surgical indications and optimal timing for patients combined with concomitant lumbosacral plexus injuries. Currently, no systematic clinical guidelines are available for the diagnosis and treatment of sacroiliac complex injuries both domestically and internationally. To this end, the Pelvic and Acetabular Surgery Group, Orthopedic Branch, China International Exchange and Promotive Association for Medical and Health Care and Orthopedic Physician Branch, Chinese Medical Doctor Association organized a panel of domestic experts in the field to develop the Clinical guideline for the diagnosis and treatment of sacroiliac complex injuries ( version 2025), based on evidence-based medicine and adhering to the principles of scientific rigor, clinical applicability, and innovation. These guidelines provided 11 recommendations covering diagnosis, therapeutic principles and techniques, management protocols for lumbosacral plexus injuries, outcome evaluation, and postoperative rehabilitation pathways, etc., aiming to standardize the clinical management of sacroiliac complex injuries.

OBJECTIVE: To evaluate the dynamic changes of glucocorticoid (GC) dose and the feasibility of GC discontinuation in rheumatoid arthritis (RA) patients under the background of Chinese medicine (CM). METHODS: This multicenter retrospective cohort study included 1,196 RA patients enrolled in the China Rheumatoid Arthritis Registry of Patients with Chinese Medicine (CERTAIN) from September 1, 2019 to December 4, 2023, who initiated GC therapy. Participants were divided into the Western medicine (WM) and integrative medicine (IM, combination of CM and WM) groups based on medication regimen. Follow-up was performed at least every 3 months to assess dynamic changes in GC dose. Changes in GC dose were analyzed by generalized estimator equation, the probability of GC discontinuation was assessed using Kaplan-Meier curve, and predictors of GC discontinuation were analyzed by Cox regression. Patients with <12 months of follow-up were excluded for the sensitivity analysis. RESULTS: Among 1,196 patients (85.4% female; median age 56.4 years), 880 (73.6%) received IM. Over a median 12-month follow-up, 34.3% (410 cases) discontinued GC, with significantly higher rates in the IM group (40.8% vs. 16.1% in WM; P<0.05). GC dose declined progressively, with IM patients demonstrating faster reductions (median 3.75 mg vs. 5.00 mg in WM at 12 months; P<0.05). Multivariate Cox analysis identified age <60 years [P<0.001, hazard ratios (HR)=2.142, 95% confidence interval (CI): 1.523-3.012], IM therapy (P=0.001, HR=2.175, 95% CI: 1.369-3.456), baseline GC dose ⩽7.5 mg (P=0.003, HR=1.637, 95% CI: 1.177-2.275), and absence of non-steroidal anti-inflammatory drugs use (P=0.001, HR=2.546, 95% CI: 1.432-4.527) as significant predictors of GC discontinuation. Sensitivity analysis (545 cases) confirmed these findings. CONCLUSIONS RA patients receiving CM face difficulties in following guideline-recommended GC discontinuation protocols. IM can promote GC discontinuation and is a promising strategy to reduce GC dependency in RA management. (Trial registration: ClinicalTrials.gov, No. NCT05219214).
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Arthritis, Rheumatoid/drug therapy* ; Glucocorticoids/therapeutic use* ; Medicine, Chinese Traditional ; Retrospective Studies