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.

AIM: To evaluate the postoperative clinical efficacy of non-diffractive extended depth of focus intraocular lens(EDOF IOL)in patients with highly myopic cataract(HMC).METHODS:A retrospective analysis was conducted on the clinical data of patients diagnosed with HMC at the hospital from January 2022 to December 2024. Patients were divided into an observation group [undergoing femtosecond laser-assisted cataract surgery(FLACS)combined with non-diffractive EDOF IOL implantation] and a control group(undergoing FLACS combined with aspheric monofocal IOL implantation)according to the type of implanted IOL. Postoperative visual acuity(LogMAR), visual quality, and patient satisfaction were compared between the two groups.RESULTS: A total of 33 patients(47 eyes)were finally included in this study, including 10 patients(17 eyes)in the observation group and 23 patients(30 eyes)in the control group. The observation group had a median age of 59.0(52.8, 63.8)y, with 8 males(13 eyes)and 2 females(4 eyes). The control group had a median age of 56.0(53.5, 60.0)y, with 13 males(17 eyes)and 10 females(13 eyes). At 3 mo postoperatively, the best-corrected distance visual acuity(BCDVA)was 0.10(0.08, 0.12)in the observation group and 0.20(0.10, 0.40)in the control group(P=0.586). However, the best-corrected intermediate visual acuity(BCIVA)[0.10(0.10, 0.10)vs 0.50(0.40, 0.90), P=0.032] and best-corrected near visual acuity(BCNVA)[0.20(0.18, 0.20)vs 0.60(0.45, 1.45), P=0.044] in the observation group were significantly better than those in the control group. The defocus curve showed that the uncorrected visual acuity(UCVA)in the observation group was relatively stable within the range of -2.00 to +1.00 D, which was superior to that in the control group. Postoperative questionnaires showed that the spectacle independence rate(76%)and overall satisfaction(88%)in the observation group were significantly higher than those in the control group(10% and 60%, respectively).CONCLUSION: Non-diffractive EDOF IOL significantly improves intermediate and near visual acuity, reduces spectacle dependence, and maintains distance visual acuity by extending the depth of focus, providing better postoperative visual quality and life satisfaction for HMC patients.

Malignant tumors remain one of the most critical global public threats to human health. The early diagnosis and precise therapeutic interventions are pivotal for improving patient survival rates and prognosis. Gold nanoclusters (Au NCs), distinguished by their ultra-small size (<3 nm), tunable optical properties, and exceptional biocompatibility, have emerged as transformative agents in precision oncology. This comprehensive review systematically summarizes the multifaceted applications of Au NCs in malignant tumor treatment. We discuss their roles as follows. (1) Intelligent delivery vehicles for targeted chemotherapy and controlled release through surface functionalization. (2) Therapeutic agents for chemodynamic therapy (CDT). This capability stems from their intrinsic enzyme-like catalytic activity or potent thioredoxin reductase (TrxR) inhibitory function, which disrupts the intracellular redox homeostasis and effectively activates downstream apoptotic pathways.(3) Direct therapeutic agents are characterized by their energy conversion capabilities: they can either convert absorbed light into heat to directly kill cancer cells, or transfer that photon energy to surrounding oxygen molecules to generate cytotoxic reactive oxygen species (ROS), leading to cell apoptosis or necrosis. (4) Potent radiosensitizers that enhance radiotherapy efficacy by enhancing localized radiation dose and promoting ROS generation. This review systematically summarizes the recent advances in Au NCs as intelligent delivery systems, direct chemotherapeutic agents, phototherapeutic agents, and efficient radiosensitizers in tumor treatment, elucidating how Au NCs overcome traditional therapeutic limitations through synergistic strategy. It establishes a robust theoretical foundation for next-generation nanotheranostic platforms. However, the translation of laboratory findings into functional clinical technologies confronts three significant challenges. First, although researchers can synthesize atomically precise Au NCs, achieving large-scale production of batches with completely consistent structure, size, and surface chemistry remains extremely challenging. To effectively control the final synthetic product, a deep understanding of the characteristics and formation mechanisms of Au NCs is essential. The traditional “trial-and-error” experimental approach faces inherent limitations when dealing with vast combinations of variables, which is time-consuming, labor-intensive, and struggles with systematic exploration and reproducibility. Machine learning has emerged as a powerful tool to bridge fundamental research and clinical application, which can guide experiments in reverse by predicting synthesis success through data mining and multi-variable analysis. In the future, we anticipate to achieve precise prediction and on-demand design of Au NCs’ structure and properties. Secondly, a systematic framework for evaluating the in vivo pharmacokinetics and long-term toxicity of Au NCs is absent. To address this gap, it is crucial to develop advanced imaging methodologies and integrated theranostic platforms. Au NCs, serving as both a therapeutic core and a highly promising photoluminescent material, are key to constructing such platforms through integration with other agents. These multifunctional systems are designed to achieve optimal synergistic therapy by combining multiple treatment modalities. Finally, the investigation of Au NCs is still largely confined to preclinical cellular and animal studies. Progress necessitates comprehensive clinical research to rigorously assess their safety and efficacy across a range of human cancer models, thereby ensuring broad clinical applicability. In summary, Au NCs-based platforms hold immense promise for translation into clinical anticancer therapy.

Objective: To understand the impact of childhood trauma on psychological distress among upper grade elemetary school students, and to explore the mediating role of leisure activities in the relationship, so as to provide a basis for developing mental health intervention strategies. Methods: From August to November 2024, a combination of convenience sampling and stratified cluster random sampling was employed to recruit 1 373 fourth to sixth grade students from four primary schools in Harbin. The Childhood Trauma Questionnaire(CTQ), a self designed leisure activity scale (including active and passive leisure activities), and the Kessler Psychological Distress Scale (K10) were used to assess childhood trauma experiences, leisure activities, and levels of psychological distress. Spearman correlation analysis and linear regression analysis were conducted to explore the relationships among childhood trauma, leisure types, leisure time, and psychological distress. Based on the mediation analysis framework proposed by Hayes (Model 4), the mediating role of leisure types in the relationship between childhood trauma and psychological distress was examined. Results: Totally 19.1% of the upper elemetary school students exhibited psychological distress, while 30.2% had experienced childhood trauma. During school days, 64.6% of the students were reported of having leisure time concentrated between 1 and 5 hours per day, whereas 67.4% reported leisure time exceeding 5 hours per day on weekends. After controlling for potential demographic confounders such as gender, grade, ethnicity, household registration, being an only child, parents educational level, co residence, and whether parents are first time married,linear regression analysis showed that childhood trauma experience had positive predictive effect on psychological distress in upper primary school students( β =0.20, P <0.01). Leisure time showed no statistically significant association with psychological distress, both on school days ( β =-0.58 to -0.56) and weekends ( β =0.26- 0.98 )(all P >0.05). Active leisure activities were negatively associated with psychological distress ( β =-0.20), while passive leisure activities were positively associated with psychological distress ( β =0.29)(both P <0.01). Leisure type partially mediated the relationship between childhood trauma and psychological distress, accounting for 11.7% of the indirect effect. Conclusion Childhood trauma experiences positively predict psychological distress in upper elementary school students, and affect psychological distress through active leisure and passive leisure.

Objective:To evaluate the outcomes of intensive conservative treatment compared to conventional conservative treatment in patients with acute aortic syndrome(AAS).Methods:The study prospectively enrolled consecutive patients with AAS who were admitted to Beijing Anzhen Hospital, affiliated with Capital Medical University, and Beijing Dawanglu Emergency Rescue Hospital from January 2024 to December 2024. These patients with surgical contraindications or refused surgery for various reasons opted for conservative treatment. A total of 282 patients were included, and 15 patients with missing data or those who died without any treatment were excluded. Finally, 267 patients were enrolled, of whom 94 received intensive conservative treatment, and 173 received conventional conservative treatment, the inverse probability of treatment weighting (IPTW) was used to reduce the influence of confoundings. After adjusting of baseline datas via IPTW, the survival outcomes of the two groups were compared at 14 days, 30 days, and at the end of follow-up.Results:The results showed significant differences in acute phase survival rates between the enhanced conservative treatment group and the conventional conservative treatment group at 14 days(82.40%vs.53.20%, P<0.0001). Significant survival differences were also observed at 30 days and at 276-day mid-term follow-up (96.29% vs.51.60%, P<0.0001; 78.50% vs.48.50%, P<0.0001). In the subgroup analysis, for type A aortic dissection, the enhanced conservative treatment group had higher survival rates compared to the conventional conservative treatment group at 14, 30 and 276 days (63.46% vs.41.35%, P<0.05; 52.17% vs.37.90%, P<0.05; 50.00% vs. 31.97%, P<0.05). However, for type B aortic dissection, although the enhanced conservative treatment group had higher survival rates than the conventional conservative treatment group, no statistically significant differences were observed (96.29% vs. 80.00%, P=0.054; 95.65% vs.78.37%, P=0.067; 94.12% vs.74.20%, P=0.088). Conclusion:For patients diagnosed with AAS are forced to choose conservative treatment if emergency surgery is not possible in the first place, intensive conservative treatment strategies can significantly reduce the mortality in the acute phase compared with conventional conservative treatment. Mid-term follow-up, intensive conservative treatment still has a significant survival advantage.

Creutzfeldt-Jakob disease(CJD)is a rapidly progressive and fatal neurodegenerative disorder caused by prions,with certain infectivity and iatrogenic transmission risks.With the rapid progress and application of new dia-gnostic biomarkers and detection methods,as well as the construction and improvement of surveillance and reporting systems,the detection of CJD in patients domestically and internationally has shown an increasing trend year by year.Due to its long incubation period and heterogeneity of early symptoms,early identification and diagnosis of the disease is difficult,increasing the risk of transmission within medical institutions.Currently,there is a lack of con-sensus on the infection prevention and control of CJD.In order to timely identify and diagnose CJD as well as effec-tively block its transmission in medical institutions,this consensus summarizes 15 clinical concerns and formulates 24 specific recommendations based on the latest domestic and international research findings and clinical evidence,as well as combines with clinical practice,aiming to standardize healthcare-associated infection prevention and control measures for CJD and reduce its transmission risk in medical institutions.

Objective:To evaluate the outcomes of intensive conservative treatment compared to conventional conservative treatment in patients with acute aortic syndrome(AAS).Methods:The study prospectively enrolled consecutive patients with AAS who were admitted to Beijing Anzhen Hospital, affiliated with Capital Medical University, and Beijing Dawanglu Emergency Rescue Hospital from January 2024 to December 2024. These patients with surgical contraindications or refused surgery for various reasons opted for conservative treatment. A total of 282 patients were included, and 15 patients with missing data or those who died without any treatment were excluded. Finally, 267 patients were enrolled, of whom 94 received intensive conservative treatment, and 173 received conventional conservative treatment, the inverse probability of treatment weighting (IPTW) was used to reduce the influence of confoundings. After adjusting of baseline datas via IPTW, the survival outcomes of the two groups were compared at 14 days, 30 days, and at the end of follow-up.Results:The results showed significant differences in acute phase survival rates between the enhanced conservative treatment group and the conventional conservative treatment group at 14 days(82.40%vs.53.20%, P<0.0001). Significant survival differences were also observed at 30 days and at 276-day mid-term follow-up (96.29% vs.51.60%, P<0.0001; 78.50% vs.48.50%, P<0.0001). In the subgroup analysis, for type A aortic dissection, the enhanced conservative treatment group had higher survival rates compared to the conventional conservative treatment group at 14, 30 and 276 days (63.46% vs.41.35%, P<0.05; 52.17% vs.37.90%, P<0.05; 50.00% vs. 31.97%, P<0.05). However, for type B aortic dissection, although the enhanced conservative treatment group had higher survival rates than the conventional conservative treatment group, no statistically significant differences were observed (96.29% vs. 80.00%, P=0.054; 95.65% vs.78.37%, P=0.067; 94.12% vs.74.20%, P=0.088). Conclusion:For patients diagnosed with AAS are forced to choose conservative treatment if emergency surgery is not possible in the first place, intensive conservative treatment strategies can significantly reduce the mortality in the acute phase compared with conventional conservative treatment. Mid-term follow-up, intensive conservative treatment still has a significant survival advantage.

Objective:To investigate the efficacy of fibrosis-4 index (FIB-4), NAFLD fibrosis score (NFS), aspartate aminotransferase to platelet count ratio (APRI), liver stiffness value (LSM), and Agile 3+ score and their combined model in predicting advanced-stage liver fibrosis in patients with chronic hepatitis B (CHB) combined with metabolic-associated fatty liver disease (MAFLD).Methods:A multicenter retrospective cohort study was conducted on the BMOVE population.Nine hundred twenty CHB cases combined with MAFLD who underwent liver biopsy at seven medical centers in China from April 2006 to December 2023 were included. The patients were divided into advanced-stage liver fibrosis (159 cases) and non-advanced-stage liver fibrosis (761 cases) according to the Scheuer's scoring system.The area under the receiver operating characteristic curve (AUROC), decision curve, and calibration curve analysis were used to evaluate the efficacy of the firbrosis-4 index (FIB-4) score, NFS score, APRI index, LSM, and Agile 3+ score and their combined model in predicting advanced-stage fibrosis. The liver fibrosis grade of all patients was diagnosed by liver biopsy. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of each scoring model and combined model, as well as the proportion of correctly classified patients, were calculated based on different cutoff values.Results:AUROC analysis showed that Agile 3+ (0.814, 95% CI: 0.787-0.838) and LSM (0.805, 95% CI: 0.778-0.829) had similar accuracy and were superior to FIB-4 (0.721, 95% CI: 0.691-0.749), NFS (0.687, 95% CI: 0.656-0.716) and APRI ( 0.689, 95% CI: 0.658-0.718); however, HBV DNA level and HBV e antigen status had no effect on this outcome. Decision curve analysis showed that interventions based on LSM and Agile 3+ had provided higher net benefits compared with serological scores. Calibration curves showed that Agile 3+ had better predicitive accuracy than all other models. Agile 3+ had the highest PPV (0.54), minimal uncertainty interval (11.6%), and the highest proportion of correctly classified patients (76%); followed by LSM (PPV: 0.43, uncertainty interval: 15.5%, correct classification rate: 66%), and FIB-4 (PPV: 0.42, uncertainty interval: 26.1%, correct classification rate: 62.6%) in terms of identifying advanced-stage liver fibrosis. Combined model analysis demonstrated that FIB-4 combined with Agile 3+ had improved the correct classification rate and reduced the proportion of missed patients compared with FIB-4 combined with LSM. Conclusion:The Agile 3+ score is superior than LSM, FIB-4, NFS, and APRI index at identifying advanced-stage fibrosis in patients with CHB combined with MAFLD. This study supports the use of FIB-4 index combined with Agile 3+ for risk stratification in patients with CHB combined with MAFLD.