ObjectiveAge-related macular degeneration （AMD） is one of the leading causes of low vision and blindness in people over 50 years old， and dry AMD （dAMD） is one type for which there is currently no clear treatment. On the basis of the diagnosis and clinical characteristics of dAMD in traditional Chinese and Western medicine， this paper evaluated the fitting degrees of existing animal models of dAMD with clinical characteristics according to the evaluation methods of animal models， and put forward suggestions and prospects. MethodsLiterature on animal models of dAMD was searched against database， and the characteristics of the models were assigned according to the diagnosis criteria of diseases and syndromes of traditional Chinese and Western medicine， and the fitting degrees of the models with clinical characteristics were analyzed and evaluated. ResultsAt present， the animal models of dAMD are mainly established targeting complement factors， chemokines， oxidative damage， lipid/glucose metabolism， and natural strains. Most of the models can simulate the major pathological changes of dAMD， showing the fitting degree of 25%-50% with clinical characteristics in Western medicine. However， the evaluation of traditional Chinese medicine （TCM） syndromes， especially the evaluation of secondary syndromes， is missing， and the models present low fitting degrees with the clinical characteristics in TCM. ConclusionExisting animal models of dAMD are mostly established under the guidance of Western diagnostic standards， which reproduce the main disease characteristics of Western medicine and lack observation of TCM syndromes. Future studies can pay attention to the intervention factors and evaluation systems of spleen deficiency Qi deficiency and liver-kidney Yin deficiency syndrome and build the animal model of dAMD with integration of disease and syndrome based on clinical characteristics of traditional Chinese and Western medicine.

ObjectiveAge-related macular degeneration （AMD） is one of the leading causes of low vision and blindness in people over 50 years old， and dry AMD （dAMD） is one type for which there is currently no clear treatment. On the basis of the diagnosis and clinical characteristics of dAMD in traditional Chinese and Western medicine， this paper evaluated the fitting degrees of existing animal models of dAMD with clinical characteristics according to the evaluation methods of animal models， and put forward suggestions and prospects. MethodsLiterature on animal models of dAMD was searched against database， and the characteristics of the models were assigned according to the diagnosis criteria of diseases and syndromes of traditional Chinese and Western medicine， and the fitting degrees of the models with clinical characteristics were analyzed and evaluated. ResultsAt present， the animal models of dAMD are mainly established targeting complement factors， chemokines， oxidative damage， lipid/glucose metabolism， and natural strains. Most of the models can simulate the major pathological changes of dAMD， showing the fitting degree of 25%-50% with clinical characteristics in Western medicine. However， the evaluation of traditional Chinese medicine （TCM） syndromes， especially the evaluation of secondary syndromes， is missing， and the models present low fitting degrees with the clinical characteristics in TCM. ConclusionExisting animal models of dAMD are mostly established under the guidance of Western diagnostic standards， which reproduce the main disease characteristics of Western medicine and lack observation of TCM syndromes. Future studies can pay attention to the intervention factors and evaluation systems of spleen deficiency Qi deficiency and liver-kidney Yin deficiency syndrome and build the animal model of dAMD with integration of disease and syndrome based on clinical characteristics of traditional Chinese and Western medicine.

Objective: To evaluate the effect of community comprehensive management model intervention among patients with dyslipidemia, so as to provide the reference for optimizing community management strategies and improving the target achievement rate for blood lipids among this population. Methods: From May to June 2023, a multi-stage stratified random sampling method was employed to select patients with dyslipidemia from primary healthcare institutions in Jiaxing City, Zhejiang Province. Eligible participants were randomly assigned to either a control group or an intervention group. The control group received routine management, while the intervention group was subjected to a community comprehensive management model in addition to the routine care. Both groups were followed up for 24 months. Data on demographic characteristics, lifestyle behaviors, physical examination indices, and blood biochemical indicators were collected at baseline and after the intervention through questionnaires, physical examinations, and laboratory tests. Changes in obesity rate, central obesity rate, target achievement rates for blood lipids, blood pressure, and blood glucose, as well as lifestyle modifications, were analyzed. Differences between the two groups before and after the intervention were assessed using generalized estimating equations (GEE). Results: The control group consisted of 560 patients, including 303 females (54.11%) and 430 individuals aged ≥65 years (76.79%). The intervention group also included 560 patients, with 300 females (53.57%) and 431 individuals aged ≥65 years (76.96%). Before the intervention, no statistically significant differences were observed between the two groups in terms of gender, age, educational level, history of chronic diseases, and atherosclerotic cardiovascular disease risk stratification (all P>0.05). After 24 months of intervention, interaction effects between group and time were observed for obesity rate, central obesity rate, target achievement rate for blood lipids, target achievement rate for blood glucose, composite target achievement rate, physical activity rate, and medication adherence (all P<0.05). Specifically, the intervention group demonstrated lower rates of obesity and central obesity, and higher target achievement rate of blood lipids, target achievement rate of blood glucose, composite target achievement rate, physical activity rate, and medication adherence compared to the control group. Conclusion The community comprehensive management model contributed to improvements in multiple metabolic parameters (including body weight, waist circumference, blood lipids, and blood glucose) among patients with dyslipidemia, and was associated with increased physical activity rate and medication adherence.

OBJECTIVE: To explore the efficacy of a Thalassemia risk assessment software for the screening of thalassemia mutation carriers and distribution of thalassemia genotypes detected by screening. METHODS: A total of 6 040 individuals were evaluated at Leshan Maternal and Child Health Care Hospital between 2022 and 2024 using the commonly used clinical thalassemia risk assessment method and the thalassemia screening software, respectively, and the performance indicators of the two methods were compared and analyzed against the result of thalassemia gene testing. This study was approved by the Ethics Committee of our hospital (Ethics No.: LfyLL[2022]005). RESULTS: The high-risk rate by the thalassemia screening software was 11.19%, with a sensitivity of 95.12%, specificity of 93.28%, positive predictive value of 43.20%, negative predictive value of 99.72%, and the area under the ROC curve (AUC) was 0.942. The thalassemia gene detection rate of the high-risk samples screened was 4.83%. The high-risk screening rate of the conventional method was 2.50%, with a sensitivity of 51.22%, specificity of 93.28%, positive predictive value of 80.79%, negative predictive value of 97.40%, and the AUC was 0.754. The thalassemia gene detection rate of the high-risk samples was 2.02%. CONCLUSION The software can effectively detect thalassemia carriers and significantly reduce the missed detection compared with conventional method, thereby significantly improve the efficacy of screening.
Humans ; Thalassemia/diagnosis* ; Software ; Female ; Genetic Testing/methods* ; Male ; Mutation ; Adult ; Genotype ; ROC Curve ; Risk Assessment

ObjectiveTo construct a standardized diagnostic scale for Qi-Yin deficiency with blood stasis syndrome in diabetic macrovascular disease. MethodsLiterature related to Qi-Yin deficiency with blood stasis syndrome in diabetic macrovascular disease was retrieved from CNKI， VIP， and Wanfang databases. Diagnostic information from four diagnostic methods was extracted and standardized， with items having a frequency of ≥15 included in the item pool. A three-round Delphi expert consultation was conducted， screening items using support degree， mean score， rank sum， and coefficient of variation. Item weights were determined using analytic hierarchy process （AHP）， gactor analysis （FA）， and combined weighting method （CWM）. The optimal weighting method was selected by comparing the area under the receiver operating characteristic （ROC） curve （AUC）. The Youden index was calculated to establish the diagnostic cutoff value， which was proportionally scaled. ResultsA total of 102 studies were included. Thirty-five items were incorporated into the item pool. The authority coefficients for the three Delphi rounds were 0.82， 0.85， and 0.86， with coordination coefficients of 0.648， 0.538， and 0.506， respectively. Fifteen items were retained after screening. ROC curve analysis showed the AUC ranking as FA > CWM > AHP. The maximum Youden index was 0.814， corresponding to a diagnostic cutoff of 8.361 （scaled to 40 points）. The final scale adopted a structured diagnostic framework： the symptom dimension requires at least 2 items， and the tongue or pulse dimension requires at least 1 category. ConclusionThis study developed a standardized diagnostic scale for Qi-Yin deficiency with blood stasis syndrome in diabetic macrovascular disease. Core items were screened via the Delphi method， with factor analysis identified as the optimal weighting method through AUC comparison. The diagnostic threshold （40 points） and structured diagnostic framework provide a quantitatively clear， clinically practical tool.

ObjectiveTo analyze the research hotspots and development trends in the field of spinal cord stimulation (SCS) for spinal cord injury (SCI). MethodsLiterature about SCS for SCI was retrieve from the Web of Science (WOS) Core Collection database, with a time range from January, 1999 to July, 2025. VOSviewer 1.6.20 and CiteSpace 6.4.R2 were used to analyze the annual publication volume, countries, authors, institutions, journals and keywords. ResultsA total of 636 literatures were included. From 1999 to 2025, the overall publication trend in this field showed an upward trajectory, with recent years fluctuating but tending to stabilize. The country with the most publications was the United States (429 papers), followed by Russia (98 papers) and China (70 papers). The institution with the highest number of publications was the University of California, Los Angeles (76 papers), the author with the most publications was V. Reggie Edgerton (70 papers), and the journal with the most publications was Journal of Clinical Medicine (31 papers). The most frequently cited study focused on exploring the combination of epidural spinal cord stimulation with task-specific training to restore motor function in patients with complete SCI. Keyword analysis showed that the research hotspots in this field were mainly focused on neuroregulation mechanisms, recovery of motor and autonomic nervous dysfunction, artificial intelligence, closed-loop stimulation and brain-computer interface technology innovations. In recent years, the research focus gradually shifted from basic mechanisms to personalized and precise multifunctional rehabilitation strategies. ConclusionThe field of SCS for SCI has undergone phases of basic mechanism exploration and clinical application expansion. Current research hotspots and future trends focus primarily on the development of new stimulation paradigms and combined innovative technologies.

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.

Rotor syndrome is an autosomal recessive disorder of bilirubin metabolism， and it is difficult to diagnose and differentiate due to its extreme rarity and a lack of specific clinical manifestations. In recent years， the development of genetic testing technology has enabled the early diagnosis of atypical patients. Literature search shows that only 19 cases with detailed clinical and genetic data have been reported. This article reports a case of a pregnant woman with an increase in direct bilirubin during pregnancy who was diagnosed with Rotor syndrome based on a bi-allelic mutation in the SLCO1B1 and SLCO1B3 genes and delivered successfully at last， and a retrospective analysis was performed for related articles， in order to facilitate the early accurate diagnosis of patients with Rotor syndrome and guide medications from the perspective of genetic mechanisms.

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.

ObjectiveTo investigate the current professional competency among full-time and part-time personnel of the nosocomial infection control administration in Shanghai, so as to provide a scientific basis for future training programmes. MethodsIn December 2024, a questionnaire survey was conducted by the Shanghai Nosocomial Infection Quality Control Center among full-time and part-time personnel of the nosocomial infection control administration across medical institutions at various levels and types in Shanghai using convenience sampling method. The questionnaire consisted of two parts: demographic information and professional competency assessment. The professional competency scale comprised four dimensions: fundamental cognition, basic skills, professional expertise, and personal qualities, totaling 35 items. ResultsA total of 1 179 questionnaires were distributed, with 1 144 valid responses collected, yielding an effective response rate of 97.03%. Statistically significant differences were observed among full-time and part-time personnel of the nosocomial infection control administration in terms of age (t=5.32, P=0.021), professional background (χ²=9.90, P=0.019), educational qualifications (χ²=19.10, P<0.001), professional titles (χ²=12.60, P=0.002), and the levels of medical institutions (χ²=111.08, P<0.001). The scores of full-time personnel of the nosocomial infection control administration in fundamental cognition [92 (82, 99) points] and basic skills [88 (78, 96) points] were significantly higher than those of part-time personnel(Z=-2.21, P=0.027;Z=-2.74, P=0.006). Statistically significant differences were found in fundamental cognition scores between full-time and part-time personnel of the nosocomial infection control administration regarding occupational safety protection, definition of healthcare-associated infection outbreaks, types of drug-resistant bacteria and their prevention and control strategies, and transmission routes of different infectious diseases (all P<0.05). Statistically significant differences were also observed in basic skills scores including proficient use of monitoring platforms, formulation and revision of standard operating procedures (SOPs), independent completion of targeted surveillance, guidance on basic infection control skills, guidance for key departments, and follow-up of personnel with occupational exposure (all P<0.05). However, no statistically significant differences were found in scores of professional knowledge and personal qualities (P>0.05). ConclusionThere are certain differences in professional competency between full-time and part-time personnel of the nosocomial infection control administration in Shanghai in terms of fundamental cognition and basic skills. Part-time personnel can effectively improve their professional competency through systematic training on basic infection control knowledge and practical skills, thereby comprehensively enhancing the overall quality of the nosocomial infection administration team.