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.

To explore the advantages of traditional Chinese medicine （TCM） and integrative TCM-Western medicine approaches in the treatment of diabetic microvascular complications （DMC）， refine key pathophysiological insights and treatment principles， and promote academic innovation and strategic research planning in the prevention and treatment of DMC. The 38th session of the Expert Salon on Diseases Responding Specifically to Traditional Chinese Medicine， hosted by the China Association of Chinese Medicine， was held in Beijing， 2024. Experts in TCM， Western medicine， and interdisciplinary fields convened to conduct a systematic discussion on the pathogenesis， diagnostic and treatment challenges， and mechanism research related to DMC， ultimately forming a consensus on key directions. Four major research recommendations were proposed. The first is addressing clinical bottlenecks in the prevention and control of DMC by optimizing TCM-based evidence evaluation systems. The second is refining TCM core pathogenesis across DMC stages and establishing corresponding "disease-pattern-time" framework. The third is innovating mechanism research strategies to facilitate a shift from holistic regulation to targeted intervention in TCM. The fourth is advancing interdisciplinary collaboration to enhance the role of TCM in new drug development， research prioritization， and guideline formulation. TCM and integrative approaches offer distinct advantages in managing DMC. With a focus on the diseases responding specifically to TCM， strengthening evidence-based support and mechanism interpretation and promoting the integration of clinical care and research innovation will provide strong momentum for the modernization of TCM and the advancement of national health strategies.

ObjectiveThis paper aims to conduct the feature analysis and correlation analysis on the ocular collateral features and differential metabolites in patients with chronic hepatitis B （CHB） complicated by glucose metabolism disorder （GMD），particularly those with the damp-heat syndrome type，by integrating shadowless scleral imaging and metabolomics technologies. MethodsA total of 313 patients were recruited from the Hepatology and Endocrinology Outpatient Departments of Public Health Clinical Center of Chengdu according to the inclusion/exclusion criteria，and they were divided into a CHB group and a CHB complicated by GMD groups （damp-heat syndrome group and non-damp-heat syndrome group）. All patients underwent high-definition ocular image acquisition and feature extraction using an intelligent analysis system for shadowless scleral imaging to analyze the differences in the counting of morphological feature scores of ocular collaterals among groups. By using a digital sampling method，24 patients from each group were randomly selected，along with 20 healthy volunteers，for untargeted metabolomic analysis of peripheral serum. Differential metabolites were identified，statistically analyzed，and subjected to potential biomarker analysis and pathway enrichment. Spearman method was performed to conduct the correlation analysis on the differential ocular collateral features and differential metabolites，followed by correlation network construction. ResultsCompared with those in the CHB group，patients with CHB complicated by GMD showed significant changes in ocular collateral feature scores such as "hillock"，"blood vessels"，and "pale dusky coloration" （P<0.05）. In comparison with those in the healthy group，metabolites including N-acetylglucosamine，acetylhomoserine，and myo-inositol （AUC>0.7） were identified as potential biomarkers for the disease. Compared with those in the CHB complicated by GMD group with non-damp-heat syndrome，patients with damp-heat syndrome exhibited significant changes in feature scores of "plaques"，"yellow coloration"，"spleen"，and "gallbladder" （P<0.05）. In comparison with those in the healthy group，metabolites such as O²′-4a-cyclic tetrahydrobiopterin，theobromine，xanthurenic acid，and L-glutamic acid 5-phosphate （AUC>0.7） were identified as potential biomarkers for the damp-heat syndrome type. The Spearman correlation analysis reveals weak to moderate linear correlations between the differential scleral collateral features and metabolites. By constructing a "disease-syndrome" network of ocular diagnosis and metabolites，"xanthurenic acid-gallbladder" and "theobromine-plaque/yellow coloration" were identified as specific molecular-phenotypic correlated biomarker clusters for CHB complicated by GMD with dampness-heat syndrome. ConclusionPatients with CHB complicated by GMD demonstrate differential ocular diagnostic features and serum metabolites corresponding to disease states and dampness-heat syndrome. These objective biomarkers can guide both clinical syndrome differentiation and medication. The macro-micro integration based on ocular feature clusters and potential metabolic biomarkers offers an innovative approach to a combined traditional Chinese and Western medicine diagnosis and treatment model for this disease.

The 69-year-old male patient developed new-onset atrial fibrillation(AF)2 days after coronary artery bypass grafting(CABG),and it recurred 2 days later.The clinical pharmacist reviewed domestic and international literature,taking into account the patient's medical history,age,and symptoms,and after assessing the risks of thrombosis and bleeding,recommended a triple antithrombotic therapy with rivaroxaban,aspirin,and ticagrelor,which the physician accepted.During hospitalization,the patient maintained sinus rhythm(70-100 beats/min)without bleeding complications.When the patient was discharged,considering that prolonged triple antithrombotic therapy could increase the risk of bleeding and that related coagulation parameters could not be monitored outside the hospital,the clinical pharmacist recommended discontinuing ticagrelor and continuing antithrombotic therapy with aspirin and rivaroxaban.The patient was instructed to follow up one month after surgery for re-evaluation and adjustment of the antithrombotic treatment,which was adopted by the physician.Clinical pharmacists assisted physicians in developing individualized antithrombotic treatment plans for patients and provided pharmaceutical care to the patients'treatment process,which effectively controlled the condition and ensured that the patients'medication was safe and effective.This case could provide a reference for the management of antithrombotic therapy in patients with new-onset AF after CABG.

Aim To investigate the role of RNF8 in the proliferation,invasion and migration of hepatocellular carcinoma and in the promotion of epithelial-mesenchy-mal transition(EMT);to clarify the regulatory mecha-nism of RNF8 on hepatocellular carcinoma cells.Methods Immunohistochemistry was used to detect the expression of RNF8 and RhoA in human hepatocel-lular carcinoma tissues and adjacent tissues;Western blot and RT-PCR were used to detect the expression levels of RNF8 and RhoA in human normal hepatocytes and hepatocellular carcinoma cells.RNF8 was overex-pressed in HepG2 cells,and siRNA interference was used to downregulate the expression of RNF8.The cell experimental groups were as follows:control group(Control,normal HepG2 cells),RNF8 overexpression group,RNF8 low expression group(siRNA RNF8),RNF8 overexpression+Rhosin(20 μmol·L-1,RhoA blocker)group.The cell proliferation ability was detected by CCK-8 method;the cell migration ability was detected by scratch test;the cell invasion ability was detected by Transwell test;finally,the expression levels of RNF8,RhoA,PCNA,CyclinD1,N-cadherin,vimentin,Slug,and E-cadherin proteins and mRNA were detected by Western blot and RT-PCR.Results The expression of RNF8 and RhoA in liver cancer tissues and liver cancer cells significantly increased;after RNF8 knockdown,the proliferation,migration,in-vasion and EMT of liver cancer cells were significantly inhibited,while overexpression of RNF8 significantly increased the proliferation,migration,invasion ability of liver cancer cells and promoted EMT.RhoA showed a positive correlation with knockdown and overexpression of RNF8.When RNF8 was overexpressed and RhoA blocker was given at the same time,the phenomenon of overexpression of RNF8 increasing the proliferation,mi-gration,invasion ability and promoting EMT of liver cancer cells was significantly reversed.Conclusions RNF8 can promote the proliferation,migration,invasion and EMT of liver cancer cells,and at the same time promote the expression of RhoA.RNF8 promotes the progression of hepatocellular carcinoma by regulating RhoA to promote EMT.

Objective To construct a classification model for the Los Angeles grading of endoscopic reflux esophagitis based on the SimCLR algorithm's semi-supervised learning framework.Methods The designed learning framework was pre-trained on a large unlabeled dataset through self-supervised learning,and further finely tuned on a small labeled dataset according to the Los Angeles grading criteria.The performance test on the model was conducted on an independent dataset,and the proposed model was compared with the models of supervised learning algorithms and endoscopists in terms of accuracy,Matthews correlation coefficient,and Cohen's kappa value.Finally,Grad-CAM and t-SNE were used for the visualization of the model's interpretation.Results The SimCLR model with ResNet as the backbone network showed superior performance in accuracy(0.840),Matthews correlation coefficient(0.800),and Cohen's kappa value(0.960)than the traditional supervised learning model with ResNet as the backbone(0.680,0.601,and 0.870)as well as junior endoscopists(0.770,0.713,and 0.940),but there was still a slight gap compared with senior endoscopists(0.850,0.813,and 0.960).In addition,the results of t-SNE showed that self-supervised learning in SimCLR was more effective in clustering multi-dimensional samples than traditional supervised transfer learning.Conclusion Compared with traditional supervised learning methods,semi-supervised learning demonstrates outstanding performance even with only a small number of labeled endoscopic images.

The 69-year-old male patient developed new-onset atrial fibrillation(AF)2 days after coronary artery bypass grafting(CABG),and it recurred 2 days later.The clinical pharmacist reviewed domestic and international literature,taking into account the patient's medical history,age,and symptoms,and after assessing the risks of thrombosis and bleeding,recommended a triple antithrombotic therapy with rivaroxaban,aspirin,and ticagrelor,which the physician accepted.During hospitalization,the patient maintained sinus rhythm(70-100 beats/min)without bleeding complications.When the patient was discharged,considering that prolonged triple antithrombotic therapy could increase the risk of bleeding and that related coagulation parameters could not be monitored outside the hospital,the clinical pharmacist recommended discontinuing ticagrelor and continuing antithrombotic therapy with aspirin and rivaroxaban.The patient was instructed to follow up one month after surgery for re-evaluation and adjustment of the antithrombotic treatment,which was adopted by the physician.Clinical pharmacists assisted physicians in developing individualized antithrombotic treatment plans for patients and provided pharmaceutical care to the patients'treatment process,which effectively controlled the condition and ensured that the patients'medication was safe and effective.This case could provide a reference for the management of antithrombotic therapy in patients with new-onset AF after CABG.

Aim To investigate the role of RNF8 in the proliferation,invasion and migration of hepatocellular carcinoma and in the promotion of epithelial-mesenchy-mal transition(EMT);to clarify the regulatory mecha-nism of RNF8 on hepatocellular carcinoma cells.Methods Immunohistochemistry was used to detect the expression of RNF8 and RhoA in human hepatocel-lular carcinoma tissues and adjacent tissues;Western blot and RT-PCR were used to detect the expression levels of RNF8 and RhoA in human normal hepatocytes and hepatocellular carcinoma cells.RNF8 was overex-pressed in HepG2 cells,and siRNA interference was used to downregulate the expression of RNF8.The cell experimental groups were as follows:control group(Control,normal HepG2 cells),RNF8 overexpression group,RNF8 low expression group(siRNA RNF8),RNF8 overexpression+Rhosin(20 μmol·L-1,RhoA blocker)group.The cell proliferation ability was detected by CCK-8 method;the cell migration ability was detected by scratch test;the cell invasion ability was detected by Transwell test;finally,the expression levels of RNF8,RhoA,PCNA,CyclinD1,N-cadherin,vimentin,Slug,and E-cadherin proteins and mRNA were detected by Western blot and RT-PCR.Results The expression of RNF8 and RhoA in liver cancer tissues and liver cancer cells significantly increased;after RNF8 knockdown,the proliferation,migration,in-vasion and EMT of liver cancer cells were significantly inhibited,while overexpression of RNF8 significantly increased the proliferation,migration,invasion ability of liver cancer cells and promoted EMT.RhoA showed a positive correlation with knockdown and overexpression of RNF8.When RNF8 was overexpressed and RhoA blocker was given at the same time,the phenomenon of overexpression of RNF8 increasing the proliferation,mi-gration,invasion ability and promoting EMT of liver cancer cells was significantly reversed.Conclusions RNF8 can promote the proliferation,migration,invasion and EMT of liver cancer cells,and at the same time promote the expression of RhoA.RNF8 promotes the progression of hepatocellular carcinoma by regulating RhoA to promote EMT.

Objective To construct a classification model for the Los Angeles grading of endoscopic reflux esophagitis based on the SimCLR algorithm's semi-supervised learning framework.Methods The designed learning framework was pre-trained on a large unlabeled dataset through self-supervised learning,and further finely tuned on a small labeled dataset according to the Los Angeles grading criteria.The performance test on the model was conducted on an independent dataset,and the proposed model was compared with the models of supervised learning algorithms and endoscopists in terms of accuracy,Matthews correlation coefficient,and Cohen's kappa value.Finally,Grad-CAM and t-SNE were used for the visualization of the model's interpretation.Results The SimCLR model with ResNet as the backbone network showed superior performance in accuracy(0.840),Matthews correlation coefficient(0.800),and Cohen's kappa value(0.960)than the traditional supervised learning model with ResNet as the backbone(0.680,0.601,and 0.870)as well as junior endoscopists(0.770,0.713,and 0.940),but there was still a slight gap compared with senior endoscopists(0.850,0.813,and 0.960).In addition,the results of t-SNE showed that self-supervised learning in SimCLR was more effective in clustering multi-dimensional samples than traditional supervised transfer learning.Conclusion Compared with traditional supervised learning methods,semi-supervised learning demonstrates outstanding performance even with only a small number of labeled endoscopic images.