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

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

Objective To compare the performance of 7 Chinese-developed GenAI models (DeepSeek, Wenxin Yiyan, Tongyi, Doubao, Kimi, Hunyuan, and iFLYTEK Spark) in responding to common diseases of the pediatric genitourinary system and to explore their potential applications.Methods A total of 80 questions covering basic disease information, diagnosis, treatment, and prognosis related to “hypospadias, hydronephrosis, and cryptorchidism” were developed as prompts.The models were tested under 3 modes:“quick thinking, ”“deep thinking, ” and “deep thinking + internet access.” The results were evaluated in a blinded review by two pediatric urologists.The responses were graded into 4 levels—A, B, C, and D—based on accuracy, completeness, and relevance.The proportions of responses rated as A and A+B were statistically analyzed and compared across the different models/modes.Results The “deep thinking” mode of DeepSeek demonstrated the best performance, with 88% of responses rated as A and 96% rated as A+B.DeepSeek (deep thinking + internet access), Doubao (quick thinking), Hunyuan (deep thinking), and Doubao (deep thinking + internet access) followed closely, also showing excellent response capabilities.Among the top 5 models/modes, significant differences were observed in the proportion of Alevel responses (P＜0.05), while differences in the proportion of A+B-level responses were not statistically significant (P＞0.05).Conclusion Certain Chinese-developed GenAI models, represented by DeepSeek (deep thinking), demonstrate significant application potential.However, they still cannot consistently provide recommendations rated at the A+B level (recommended grade or higher) with 100% accuracy.In clinical practice, both doctors and patients should view GenAI as an auxiliary tool for education and information acquisition rather than as a final decision-making authority.All information must be reviewed and confirmed by professional physicians.

OBJECTIVE To investigate the impact of inflammatory cytokines related to different endotypes of chronic rhinosinusitis on the transcriptome of nasal epithelial cells.METHODS Human primary nasal epithelial cells were cultured in vitro,and treated with type 1,type 2,and type 3 inflammatory cytokines IFN-γ(10 ng/ml),IL-4(50 ng/ml),and IL-17A(50 ng/ml),respectively.After 48 hours,cells were collected for RNA extraction and transcriptome analysis.Differentially expressed genes were identified,followed by functional annotation and pathway enrichment analysis.Additionally,common regulated genes among different cytokines were analyzed.RESULTS Treatment of primary nasal epithelial cells with IFN-γ,IL-4,and IL-17A induced significant transcriptomic alterations,with 650,540,and 192 differentially expressed genes,respectively,compared to the control group.Functional annotation and pathway enrichment analysis showed that IFN-γ was mainly associated with defense response to virus,antigen processing and presentation,leukocyte and lymphocyte mediated immunity,and NOD-like receptor signaling pathway;IL-4 was mainly related to the regulation of leukocyte chemotaxis,cell-cell adhesion,ECM-receptor interaction,and MAPK signaling pathway;IL-17A was involved in the regulation of neutrophil chemotaxis,response to molecule of bacterial origin,chemokine signaling pathway,and NF-κB signaling pathway.Genes commonly regulated by IFN-γ,IL-4,and IL-17A were identified,with DUOX2 being upregulated by all the three cytokines.CONCLUSION IFN-γ,IL-4,and IL-17A reshapes the transcriptomic profile of nasal epithelial cells,which mainly regulates signaling pathways related to defense response to virus,leukocyte chemotaxis,and response to bacteria,respectively.

A method for simultaneous determination of 21 kinds of Aconitum alkaloids(ATS)in biological specimens and infused liquor using ultra-performance liquid chromatography-tandem mass spectrometry(UPLC-MS/MS)was developed.The biological samples were pretreated with methanol-acetonitrile(1∶2,V/V)for protein precipitation,while infused liquors were diluted 100-fold with acetonitrile,followed by centrifugation,and filtration by a 0.22-μm membrane.Chromatographic separation was carried out on an EC-C18 column using gradient elution with the mixture of 10 mmol/L ammonium acetate and 0.2%formic acid as mobile phase A and acetonitrile as mobile phase B.With this method,all the analytes were separated within 9.5 min.The samples were detected in positive ESI mode with dynamic multiple reaction monitoring(MRM)and quantified via external standard calibration.The results showed that the concentrations of the analytes in the range of 2-1000 ng/mL had excellent linearity(R2>0.9992)with the peak area.The developed method was successfully used for detection of 21 kinds of aconitum alkaloids,with limits of detection of 0.5-2 ng/mL,quantification limits of 2-6 ng/mL,intra/inter-day precision≤6.0%,spiked recoveries of 89.4%-100.9%,extraction recoveries of 74.2%-104.4%,and matrix effects ranging from-11.1%to 9.2%in blood/urine.The method was applied to detection of 12 samples from 4 fatal aconite poisoning cases,and all 21 kinds of ATS with total alkaloid concentrations of 0.04-4.18 μg/mL in blood and 154.96-422.83 μg/mL in medicinal liquors were detected.Tissue distribution revealed that the order of concentrations from highest to lowest is as follows:urine(157.22 μg/mL)>gastric contents(51.37 μg/mL)>kidney(21.6 μg/g)>whole blood(4.18 μg/mL)>liver(0.03 μg/g).This method showed many advantages such as simple pretreatment,low detection limits,accurate quantification,broad analyte coverage,and superior anti-interference capability in complex matrices,proving ideal for forensic and toxicological analysis of aconitum alkaloids.

Forensic medicine has been designated as a first-level discipline,presenting new opportunities and challenges for the development of forensic medicine.Since the 1980s,the establishment of foren-sic medicine discipline and the cultivation of high-level forensic talents have become hot topics in the development of forensic medicine in China.Since the 13th Five-Year Plan,the forensic team of Shanxi Medical University has been aiming at the forefront,proposing the development goals of"Five First-class"and the discipline development path"Six Major Achievements".It has selected benchmark disci-plines,identified gaps in disciplinary development,unified thoughts,formulated completion timelines,concentrated superior resources,assigned tasks to individuals,and created an"Organized Fill-in-the-Blank Format"forensic medicine discipline construction model with the characteristics of the new era.The construction model of forensic medicine has achieved good results in the goals,discipline frame-work,scientific research,talent cultivation,discipline team and platform construction,forming a rela-tively complete discipline construction and management system,and accumulating valuable experience for the construction of first-level discipline and high-level talent cultivation of forensic medicine.

Objective To systematically evaluate the quality of Fengshiding capsules,to analyze existing problems based on national drug sampling and testing,and to offer references and suggestions for improving quality control and regulatory supervision of this product.Methods A total of 136 batches of Fengshiding capsules were subjected to standard quality tests and exploratory analyses.HPLC was used to establish the content determination of Angelica dahurica,Cynanchum paniculatum,and Glycyrrhizae Radix,as well as the limit test for anabasine in the preparation.Additionally,an UPLC method was also employed to establish the characteristic chromatogram of Fengshiding capsules.A screening method for artificial pigments was developed,and UPLC-MS/MS was used to detect the illegal addition of chemical drugs in Fengshiding capsules.Results All 136 batches of samples passed inspection according to the current quality standards.However,based on exploratory study evaluations,the content determination results of Cynanchum paniculatum,Angelica dahurica,and Glycyrrhiza Radix were below the proposed limits.The limit test for anabasine did not exceed the proposed threshold.Furthermore,the characteristic chromatograms revealed missing peaks in several samples,and some batches contained artificial pigments and residues of acetaminophen.Conclusions The overall quality of Fengshiding capsules is rated as"average"based on national drug sampling and testing.To enhance product quality,it is recommended to improve quality standards,ensure the use of high-quality raw herbal materials,promote stronger internal oversight by manufacturers,and intensify regulatory supervision.

The"capitation payment with retained surplus and shared accountability for reasonable overruns"mechanism constitutes a pivotal institutional framework for advancing the high-quality development of County Medical Communities(CMCs).This study addresses two critical operational challenges:identifying the sources of medical insurance fund surplus and optimizing the governance of fund retention processes.Grounded in transaction cost theory,we develop an analytical framework examining the formation of medical insurance fund surplus through the dual lenses of intra-organizational dynamics within CMCs and external medical insurance payment mechanism design.Utilizing Deqing County,Zhejiang Province as an empirical case,this research proposes a five-pronged strategy:Clarifying generation channels of insurance fund surplus,scientifically determining regional medical insurance budgets,implementing bundled payment mechanisms for CMCs,adopting hybrid payment models integrating unified and differentiated approaches,and establishing performance-based incentive systems.These findings elucidate the formative pathways of medical insurance fund surplus while offering theoretical and practical insights for enhancing payment system reforms to support CMC development.

Objective:To explore the coordinated development status of the medical service supply-economy-society trinity systems in Zhejiang Common Prosperity Demonstration Zone,and to offer references for formulating policies conducive to the efficient and coordinated development of these three systems.Methods:Based on the panel data from 2013 to 2022,this research was conducted on the 11 prefectural-level cities in Zhejiang Province.Firstly,an evaluation index system for the three systems was established,and the entropy method was employed to determine the weights of each index and calculate the comprehensive evaluation index.Secondly,a triangular model was introduced to delineate the relative relationships among the three systems,and the coupling coordination degree model was utilized to disclose the coordination degree of the three systems.Finally,the spatial autocorrelation analysis method was applied to investigate the spatial autocorrelation of the coupling coordination degree of the three systems.Results:(1)On the whole,the development status of the three systems has improved over time,yet the comprehensive development level remains to be improved.(2)Overall,each prefectural-level city has transited from a medical service supply-society-dominated development to a balanced and coordinated development of the medical service supply-economy-society trinity systems.The coupling coordination degree of the three systems has risen from 0.468(on the verge of imbalance)in 2013 to 0.609(primary coordination)in 2022,presenting an upward trend in general.However,the coordination level remains to be improved and there exist imbalances among regions.(3)The coupling coordination degree of the three systems in Zhejiang Province exhibits a significant positive spatial correlation,and the spatial distribution characteristics are relatively stable. Conclusion:The coupling coordination degree of the medical service supply-economy-society trinity systems in Zhejiang Province awaits further enhancement. At the level of Zhejiang Province,favorable policy support should be provided,the layout of medical resources should be rationally planned,and high-quality economic and social development should be promoted. Each prefectural-level city should formulate strategies for medical service supply,economic,and social development in accordance with its own development level and local conditions,strengthen inter-city linkage and cooperation,and thereby elevate the coordinated development level of the three systems.

Objective To investigate the value of fractional-order calculus(FROC)and continuous-time random-walk(CTRW)diffusion models based on readout segmentation of long variable echo-trains(RESOLVE)in identifying benign and malignant lesions in the head and neck.Methods A retrospective analysis was conducted on 61 patients pathologically confirmed head and neck lesions,including 19 benign lesions(BL)and 42 malignant lesions(ML).The ML were further divided into a lymphoma subgroup(LS)with 9 cases(14 lesions)and a non-lymphoma malignant lesion subgroup(MLS)with 33 cases.The parameters of DFROC,βFROC,μFROC,DCTRW,αCTRW and βCTRW were obtained from the two diffusion models;Independent sample t-tests or U tests were used to compare the differences in each parameter between benign and malignant groups and among various subgroups,and the receiver operating characteristic(ROC)curve was used to analyze the diagnostic efficacy of each parameter.Area under the curve(AUC)was compared by DeLong test.Results DFROC,μFROC,DCTRW and αCTRW showed significant differences between benign and malignant,BL and LS,BL and MLS and LS and MLS,with αCTRW showed the highest diagnostic efficacy;βFROC showed differences between BL and LS,BL and MLS,whileβCTRW did not show differences between benign and malignant groups,and among subgroups.Conclusion FROC and CTRW diffusion models based on RESOLVE can distinguish between benign and malignant head and neck lesions with multiple parameters,and provide metrics reflecting tissue heterogeneity.