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.

ObjectiveThis study aimed to elucidate the mechanistic role of Staphylococcus aureus in the pathogenesis of atopic dermatitis (AD), a chronic inflammatory skin disorder characterized by pruritus and barrier dysfunction. A key focus was screening traditional Chinese medicine (TCM) active components with dual antibacterial and antipruritic efficacy, followed by systematic evaluation of their in vitro antibacterial activity. Additionally, a novel drug delivery system was constructed to enable localized efficient drug delivery, inhibiting S. aureus proliferation and alleviating its induced pruritus, thereby providing new strategies for targeted AD therapy. MethodsMale C57BL/6J mice aged 6-8 weeks (body weight 18-22 g) were used to establish an AD model via repeated oxazolone sensitization. On day 14, microbial samples were collected from the lesional area (1 cm²) using sterile cotton swabs, followed by vortex mixing, serial dilution, and plating on 5% sheep blood agar plates (incubated at 37°C for 24 h). Single colonies with complete transparent β-hemolytic zones were isolated and identified as vancomycin-intermediate S. aureus (VISA) via 16S rRNA sequencing. An S. aureus mono-infection animal model was then established by applying gauze saturated with bacterial suspension (McFarland turbidity 0.1) to the nape and back skin of mice. The pruritic phenotype and inflammatory cell infiltration induced by S. aureus were evaluated using comprehensive approaches including behavioral assays (e.g., scratching frequency recording), hematoxylin-eosin (HE) staining, and toluidine blue staining. The in vitro antibacterial efficacy of the TCM monomer pseudolaric acid B (PAB) and double network hydrogel (DN) was separately assessed by disk diffusion assay, while the minimum inhibitory concentration (MIC) of PAB was determined via broth dilution method. Further validation of the pharmacodynamic characteristics of the composite system (PAB@DN, composed of PAB and DN) was conducted through behavioral assays, HE staining, and dermatitis scoring, with its drug release profile evaluated by mass spectrometry analysis. Based on scratching behavioral analysis and dermatitis scoring, the optimal ratio and concentration of PAB@DN were optimized. ResultsThe S. aureus load in AD lesional tissues was significantly higher than in normal skin ((5.3±0.33)×10⁶ CFU vs. (3.6±0.26)×10⁷ CFU, P<0.001). In the S. aureus mono-infection group, mice exhibited a 6.7-fold increase in scratching frequency compared to the control group. HE staining revealed marked epidermal thickening ((10.4±2.39) μm vs. (85.6±1.95) μm, P<0.000 1), and toluidine blue staining showed a 23-fold increase in mast cell degranulation. Pseudolaric acid B exhibited a significant concentration-dependent inhibitory effect onS. aureus growth, with its in vitro antibacterial effect being 57% that of the antibiotic cefepime (inhibition zone diameter: PAB (1.885±0.036) cm vs. cefepime (3.636±0.005) cm, P<0.000 1) and a minimum inhibitory concentration (MIC) of 1 g/L. The carrier double network hydrogel (DN) itself lacked direct antibacterial activity (no significant difference in inhibition zone diameter compared to the control) but effectively ameliorated the dry symptoms of AD-like lesions. The PAB@DN composite system demonstrated a synergistic effect compared to individual components, resulting in a 50% reduction in scratching behavior, an 86% decrease in dermatitis score, and a 60% reduction in epidermal thickening. It also reduced the S. aureus load in mouse skin by approximately 34%, with the optimal effective formulation being PAB at 1 g/L loaded onto DN. ConclusionS. aureus colonization plays a critical driving role in the onset and progression of AD. Using an S. aureus infection model, this study confirmed that the pseudolaric acidB-hydrogel composite delivery system (PAB@DN) can effectively alleviate S. aureus-induced pruritus and skin damage, providing experimental evidence for microbiota-targeted therapy of AD.

With the rapid development of large language models and related technologies, various forms of patient modeling have emerged, including virtual patients, synthetic patients, artificial intelligence patients, and digital twin patients. Although these forms all share the core objective of patient simulation, they exhibit significant differences in theoretical ontology, data sources, modeling logic, and application orientation. Based on the abstraction levels of simulation objects and system mapping logic, this study systematically compares five paradigms—standardized patients, virtual patients, synthetic patients, AI patients, and digital twin patients—to clarify their conceptual boundaries and technical positioning. The findings indicate that these paradigms represent distinct modeling pathways: behavioral reproduction, scenario simulation, data generation, cognitive interaction, and individual system mapping, respectively. By constructing a multidimensional comparative framework, this study provides a theoretical foundation for the conceptual standardization of patient modeling and the strategic deployment of intelligent healthcare systems.

Glucagon-like peptide-1 （GLP-1） receptor agonists are a novel class of antidiabetic drugs that also possess lipid- lowering and cardiovascular protective effects， with liraglutide and semaglutide being their representative medications. Based on a systematic literature search， this review summarizes the lipid-lowering mechanisms by which liraglutide and semaglutide exert direct effects on the liver and kidney （regulating autophagy， key lipid metabolism pathways， reverse cholesterol transport， etc.）， direct actions on adipose tissue （affecting adipocyte proliferation and differentiation， expression of lipid metabolism proteins， and gene transcription）， activation of sympathetic pathways through the central nervous system， and modulation of the gut microbiota. Additionally， it summarizes the clinical evidence of their lipid-lowering effects in populations with type 2 diabetes mellitus， overweight individuals， and others. These findings indicate that GLP-1 receptor agonists exert lipid-lowering effects by acting on multiple tissues or systems， providing crucial evidence for further elucidating the molecular mechanisms of these drugs in lipid regulation and exploring potential new ideas for their clinical applications.

The etiology of tumors is complex and influenced by multiple factors, including the host and environmental conditions. Allergy is primarily driven by the immune response of helper T cell 2 (Th2). Research has shown that the Th2 immune response is closely related to tumors, which is specifically manifested through Th2 antibodies, allergy-related effector cells and mediators within the tumors, as well as tumor immune-related functions. This internal interaction mechanism will increase the complexity and challenges associated with the clinical diagnosis and treatment of tumors and allergy. The formation of allergic constitution is shaped by both congenital and acquired factors, and its physical state is closely linked to the occurrence and progression of allergic diseases. Therefore, this paper aims to explore the relationship between tumors and allergic reactions from the perspective of traditional Chinese medicine (TCM) constitution theory. Based on the four basic principles of the TCM constitution, including endowment inheritance theory, environment constraint theory, body-spirit composition theory, and life process theory, this exploration will focus on four aspects: genetic factors and internal disease causes, inflammatory environments and functional regulation, psychological disorders and emotional pathogenesis, as well as age structure and disease risk. Furthermore, from the perspective of constitution-disease relation of chronic disease prevention, this paper will discuss the significant importance of adjusting allergic constitution to improve both subjective symptoms and objective indicators of allergic reactions in tumor patients.

Artificial intelligence （AI） and population pharmacokinetics （PPK） technologies have demonstrated significant potential in the personalized medication of immunosuppressants after organ transplantation, enabling precise prediction of drug dosages. This article provides a comprehensive review of the application status of AI and PPK in the individualized administration of immunosuppressants after organ transplantation， focuses on monitoring blood drug concentration， predicting efficacy/adverse reactions， and establishing individualized dosing models for organ transplant recipients after immunosuppressant administration， and analyzes and compares the application characteristics of different methods in different organ transplant patients as well as the integration and future development of AI and PPK technologies. AI and PPK technologies can not only significantly reduce the dependence on human resources， but also greatly improve the level of individualized treatment of immunosuppressants after organ transplantation， and reduce the discomfort and burden caused by frequent blood concentration monitoring to patients.

AIM:To establish a physiological-based pharmacokinetic(PBPK)model of ertapen-em in elderly patients with chronic kidney disease,and to analyze the pharmacokinetic/pharmacody-namic index f％ T＞MIC at different doses.METH-ODS:The physicochemical properties and pharma-cokinetic characteristics of ertapenem were collect-ed by reviewing the literature and databases,and a healthy adult model was established in PKSim? software,and then extrapolated to the PBPK model of the elderly.The clinical pharmacokinetic re-search data were used to optimize and validate the model,and the mean folding error(MFE)was used as the index to evaluate the prediction perfor-mance of the model.The final model was used to simulate the in vivo exposure of elderly patients with chronic kidney disease after administration,and the pharmacokinetic/pharmacodynamic index of commonly used clinical dosing regimens was an-alyzed,and the recommended dosing regimens were given.RESULTS:The MFE of the area under the curve(AUC0-t),peak concentration(Cmax)and peak time(Tmmax)predicted by the established PBPK model of ertapenem in adults were 0.92,0.79 and 1.02,respectively,and the predicted value of the optimized PBPK model of ertapenem in the elderly was also consistent with the observed value of 0.5＜MFE＜2 standards,all of which have good predictive performance.With f％ T＞MIC greater than 40％as the drug efficacy target,the minimum inhibitory concentration(MIC)is 0.5-1 μg/mL for sensitive bacteria,and elderly patients with chronic kidney disease can consider reducing the drug dose as ap-propriate.CONCLUSION:The PBPK model of ertap-enem in elderly patients with renal insufficiency has been successfully established,and the model has good prediction performance and provides a reference for clinical personalized medication in el-derly patients with renal insufficiency.

Objective:To analyze the clinical and prognostic characteristics of patients with immune checkpoint inhibitor (ICI) -related colitis.Methods:A retrospective observational research method was conducted. Clinical data from patients diagnosed with ICI-related colitis at Beijing Friendship Hospital between January 2016 and May 2024 were collected. Clinical severity was assessed using the common terminology criteria for adverse event (CTCAE) grading, Mayo Score, Truelove & Witts Score, and endoscopic severity was assessed using Mayo endoscopic score (MES), ulcerative colitis endoscopic index of severity (UCEIS), and MD Anderson Cancer Center endoscopic inflammation grading (MD grading). Spearman rank correlation analysis was performed to evaluate the correlation between different scoring systems.Results:A total of 15 patients were included, with 10 males and 5 females, and the median age was 64.0 (55.5, 71.0) years. Thirteen patients were treated with programmed cell death protein 1 (PD-1) monoclonal antibodies, and 2 patients were treated with programmed cell death-ligand 1 (PD-L1) monoclonal antibodies. The median onset time of the 15 patients was 66.5 (41.0, 168.0) days after ICI treatment. All patients had diarrhea, only 25% patients presented with abdominal pain, and 20%-33% patients had fever, abdominal distension, nausea, and vomiting. Most of the inflammatory indicators were non-specific. Among the 14 patients who underwent CT examinations, 2 patients had no abnormal manifestations, 10 patients had left colon involvement, mainly manifested as thickening of the colon wall, and 6 of these patients had full-thickness involvement. Among the 12 patients who underwent colonoscopy examination, except for one patient with no abnormalities, 11 patients had rectal and sigmoid colon involvement, the proportions of the descending colon, ascending colon, and terminal ileum involvement decreased successively, and the involved mucosa was mainly manifested as disappearance of vascular texture, mucosal congestion and edema, erosion to shallow ulcers, and lumen stenosis. The lesions in 5 patients were distributed continuously similar to ulcerative colitis, and the lesions in 6 patients were distributed segmentally, 1 of them had an isolated deep ulcer in the rectum under endoscopy. Correlation analysis revealed significant correlations between endoscopic scores (MES, UCEIS, and MD grading; all P < 0.001). Truelove & Witts score strongly correlated with the Mayo score ( ρ = 0.88, P < 0.001). Among clinical-endoscopic comparisons, the Mayo score exhibited the strong positive correlation with endoscopic scores (all P < 0.05), followed by the Truelove & Witts score (all P < 0.05), and CTCAE diarrhea grading only had positive correlation with MD grading ( P = 0.034). However, CTCAE colitis grading showed no significant correlation with endoscopic scores (all P > 0.05). During a mean follow-up of 13.5 months, 10 patients received corticosteroid therapy, and 5 received alternative treatments. Corticosteroid-treated patients showed favorable clinical responses, and rapid tapering feasible occurred in parts of mild-to-moderate patients without relapse. However, perforation occurred in 2 patients with small bowel involvement. Conclusions:The main clinical manifestations of patients with ICI-related colitis are diarrhea. Imaging studies suggest thickening of the colonic wall. Endoscopic findings show the highest involvement in the left colon. Truelove&Witts score and Mayo score is strongly correlated. Corticosteroid therapy exhibits a rapid response, and the prognosis of patients with small intestine involvement is poor.