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.

Metabolic associated fatty liver disease （MAFLD） is a chronic liver disease with a rapidly increasing incidence rate worldwide， and its complex pathogenesis is closely associated with O-linked β-N-acetylglucosamine （O-GlcNAc） modification. As a dynamic and reversible post-translational modification of proteins， O-GlcNAc modification is mainly regulated by O-GlcNAc transferase and O-GlcNAcase. O-GlcNAc modification can drive hepatic steatosis， exacerbate insulin resistance， and impair mitochondrial function， thereby leading to the aggravation of metabolic disorders， promoting inflammation response， and driving the progression of MAFLD to metabolic associated steatohepatitis and hepatic fibrosis. This article systematically reviews the latest research advances in the role of O-GlcNAc modification in the development and progression of MAFLD， in order to provide theoretical support and research direction for a deeper understanding of the pathological mechanism of MAFLD and the development of effective therapeutic strategies.

To analyze global trends and research hotspots in exercise intervention for overweight and obesity from 2010 to 2024, to provide novel perspectives for comprehensive management research on overweight and obesity.

Objective To investigate the epidemiological characteristics of imported malaria cases in Shanghai Municipality from 2020 to 2024, so as to provide insights into dynamic adjustment of imported malaria control strategies. Methods All data pertaining to malaria cases reported in Shanghai Municipality from 2020 to 2024 were collected from Infectious Diseases Reporting Information Management System and Parasitic Diseases Prevention and Control Information Management System of Chinese Center for Disease Control and Prevention, and epidemiological investigation reports of malaria cases. The type of malaria cases, source of acquiring infections, temporal, spatial and population distributions of malaria cases, type of initial diagnosis institutions, accuracy of initial diagnosis, time interval from disease onset to initial diagnosis, and the time interval from initial diagnosis to diagnosis were statistically analyzed. The monthly distribution of malaria cases was analyzed using a circular distribution method. Results A total of 124 malaria cases were reported in Shanghai Municipality from 2020 to 2024, with 14, 52, 22, 8 and 28 cases annually, respectively, and all cases were overseas imported cases. Plasmodium falciparum malaria was the predominant type, accounting for 82.26% (102/124) of all malaria cases, while P. ovale, P. vivax and P. malariae malaria accounted for 8.06% (10/124), 7.26% (9/124) and 2.42% (3/124) of reported cases, respectively. Malaria cases acquired infections from 28 countries in 4 continents, with African countries as a major source of acquiring infections (96.77%, 120/124). Malaria cases were reported across all 16 districts in Shanghai Municipality. There were peaks in numbers of reported malaria cases in May 2021 (9 cases) and November 2021 (9 cases); however, there was no concentration trend in the reporting time of malaria cases from 2020 to 2024 (Z = 2.45, P > 0.05). The reported malaria cases included 116 males and 8 females, with a male-to-female ratio of 14.5∶1.0, and had a median age of 39 (17) years. Worker was the primary occupation of reported malaria cases, accounting for 48.39% (60/124) of all reported cases. City-level medical institutions were the predominant initial diagnosis institutions (75.81%, 94/124). The overall accuracy of initial diagnosis was 74.19% (92/124), and the median time interval from disease onset to initial diagnosis was 0 (2) day (healthcare seeking on the day of disease onset), with 72.58% (90/124) of malaria cases seeking healthcare services within one day. The median time interval from initial diagnosis to diagnosis was 2 (3) days, with 41.94% (52/124) of malaria cases that were definitively diagnosed within one day following initial diagnosis. There were significant differences in the accuracy of initial malaria diagnosis (χ² = 15.01, P < 0.05), the time interval from disease onset to initial diagnosis (H = 29.30, P < 0.001), and the time interval from initial diagnosis to diagnosis (H = 22.92, P < 0.001) among years from 2020 to 2024, and the time interval from disease onset to initial diagnosis (Z = -5.09, P < 0.001) and the time interval from initial diagnosis to diagnosis (Z = -4.14, P < 0.001) were both shorter during the period from 2020 to 2022 than those recorded from 2023 to 2024. In addition, a total of 2 malaria deaths were reported in Shanghai Municipality during the period from 2020 to 2024, both caused by P. falciparum malaria. Conclusions The numbers of malaria cases fluctuated continuously in Shanghai Municipality from 2020 to 2024. It is recommended to continuously strengthen surveillance and response measures, improve malaria diagnosis and treatment capabilities, and conduct targeted health education activities among high-risk populations, such as labors going to overseas malaria hyper-endemic areas, so as to prevent re-establishment of imported malaria.

Objective To investigate the feasibility of simultaneous multi-slice(SMS)acquisition combined with single-shot echo-planar imaging(SSEPI)multi-model diffusion weighted imaging(DWI)for breast lesions.Methods Totally 108 cases of breast lesions were retrospectively enrolled and divided into malignant group(n=66)and benign group(n=42)based on pathology.3.0T MR scanner was used to acquire SSEPI and SMS-SSEPI multi-b values DWI,7 derived parameters were obtained through post-processing with mono-exponential,fractional-order calculus(FROC)and continuous-time random walk(CTRW)models.Then the imaging quality and derived parameters of SMS-SSEPI and SSEPI DWI were compared between groups.Spearman correlation analysis was performed to explore the relationships of corresponding parameters between SMS-SSEPI DWI and SSEPI DWI.Diagnostic performance of each parameter for distinguishing malignant and benign lesions was evaluated according to the area under the receiver operating characteristic curve(AUC).Results Background noise score of SMS-SSEPI DWI was lower than that of SSEPI DWI(P＜0.05),whereas no significant difference of overall imaging quality,normal anatomical structure depiction,lesion conspicuity,geometric distortion,signal-to-noise ratio(SNR)nor contrast-to-noise ratio(CNR)was found between SMS-SSEPI DWI and SSEPI DWI(all P＞0.05).Parameters derived from SMS-SSEPI DWI were all moderately to highly positively correlated with those from SSEPI DWI(rs=0.66-0.98).Malignant lesions exhibited significantly lower apparent diffusion coefficient(ADC),diffusion coefficient based on FROC(DFROC),fractional order derivative in space(βFROC),diffusion coefficient based on CTRW(DCTRW),temporal diffusion heterogeneity index(αCTRW)and spatial diffusion heterogeneity index(βCTRW)values,but higher spatial parameter(μFROC)value than benign lesions(all P＜0.05).AUC of SMS-SSEPI DWI derived parameters for differentiating malignant from benign lesions were 0.699-0.900,of those from SSEPI DWI were 0.654-0.887,while in both SMS-SSEPI DWI and SSEPI DWI,DFROC had the highest diagnostic efficacy(AUC=0.900,0.887).Conclusion SMS-SSEPI DWI could be used to effectively differentiate malignant and benign breast lesions.

With the increasing demand for dynamic,real-time and rapid qualitative analysis of chemical composition in areas such as emergency response and space exploration,chip-scale mass spectrometers have attracted significant attention.These devices are expected to drive the integration of mass spectrometry with micro/nano-fabrication and intelligent sensing technologies,fostering profound innovation and breakthroughs in analytical chemistry.As an excellent mass analyzer,the ion trap exhibits numerous advantages,and its miniaturization creates favorable conditions for the high-density integration of miniature mass spectrometers.However,the reduction in ion storage capacity may compromise its sensitivity and dynamic range,rendering the study of ion unidirectional ejection in highly miniaturized ion traps of significant practical importance.In this work,a research was conducted on achieving efficient ion unidirectional ejection while maintaining high mass resolution in the extremely miniature hyperbolic linear ion trap(M-HLIT)with a field radius of 1 mm,and an electric field compensation method was proposed,which combined asymmetric electrode stretching and unbalanced RF voltage to achieve high-precision optimization of the electric field composition.Simulations showed that in an ideal structure,this method achieved 100％unidirectional ejection efficiency with the mass resolution of 518,significantly outperforming traditional asymmetric structure method(365)and unbalanced voltage method(321).Following the introduction of ion ejection slots,further optimization through bidirectional stretching and electrical parameters improved the resolution to 790 while maintaining a unidirectional ejection efficiency of 93％.This method eliminated the requirement for additional excitation voltage,offering an ideal solution for the miniature mass analyzer with high detection performance of chip-level mass spectrometers.

Objective To determine the related substances in ornidazole active pharmaceutical ingredient(API)and injections using high performance liquid chromatography(HPLC)and to study the impurity profile of ornidazole using liquid chromatography-tandem mass spectrometry(LC-MS/MS)combined with forced degradation tests,aiming to clarify the sources of impurities and their correlation with the prescription and production process and providing technical support for the unified evaluation and quality control of this product.Methods A Phenomenex Luna C18column(4.6 mm×250 mm,5 μm)was used for the separation of ornidazole and its impurities,with 0.000 5%formic acid as mobile phase A and methanol as mobile phase B under gradient elution.The impurity content of 4 batches of APIs,3 batches of reference preparations,and 11 batches of domestic generic preparations were determined.The structure of unknown impurities was predicted using Jet Stream Ion Focusing Electrospray Ionization-Time of Flight Mass Spectrometry(AJS-TOF-MS/MS),and the sources of impurities were identified combined with forced degradation experiments,the prescription and the production process of various manufacturers.Results Ornidazole and its known impurities were well separated under the chromatographic conditions.The structures of five unknown impurities were inferred,and the sources of the impurities were identified.Conclusion This study provides a reference for impurity analysis,quality control,and overall evaluation of ornidazole API and injection.

In recent decades,the incidence of human papillomavirus(HPV)-associated oropharyngeal squamous cell carcinoma(OPSCC)has shown a marked increase.Significant changes have also occurred in the OPSCC diagnosis and treatment paradigm.Deter-mining HPV status prior to treatment is now essential,and radiotherapy/chemotherapy,immunotherapy,and minimally invasive surgical techniques have progressively emerged as key modalities for managing OPSCC.However,alongside these paradigm shifts,a comprehen-sive technical consensus guiding the entire diagnostic and therapeutic process for OPSCC patients is currently lacking.Given China's large population base and the rising incidence of OPSCC,an expert panel convened to develop a clinical technical consensus on OPSCC diagno-sis and management tailored to China's specific context.This consensus aims to further enhance and standardize understanding of OPSCC management techniques among relevant healthcare professionals.

OBJECTIVE: To explore the relationship between serum chloride levels and prognosis in patients with hepatic coma in the intensive care unit (ICU). METHODS: We analyzed 545 patients with hepatic coma in the ICU from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Associations between serum chloride levels and 28-day and 1-year mortality rates were assessed using restricted cubic splines (RCSs), Kaplan-Meier (KM) curves, and Cox regression. Subgroup analyses, external validation, and mechanistic studies were also performed. RESULTS: A total of 545 patients were included in the study. RCS analysis revealed a U-shaped association between serum chloride levels and mortality in patients with hepatic coma. The KM curves indicated lower survival rates among patients with low chloride levels (< 103 mmol/L). Low chloride levels were independently linked to increased 28-day and 1-year all-cause mortality rates. In the multivariate models, the hazard ratio ( HR) for 28-day mortality in the low-chloride group was 1.424 (95% confidence interval [ CI]: 1.041-1.949), while the adjusted hazard ratio for 1-year mortality was 1.313 (95% CI: 1.026-1.679). Subgroup analyses and external validation supported these findings. Cytological experiments suggested that low chloride levels may activate the phosphorylation of the NF-κB signaling pathway, promote the expression of pro-inflammatory cytokines, and reduce neuronal cell viability. CONCLUSION Low serum chloride levels are independently associated with increased mortality in patients with hepatic coma.
Humans ; Male ; Female ; Middle Aged ; Intensive Care Units ; Prognosis ; Chlorides/blood* ; Aged ; Coma/blood* ; Adult