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 This study investigated the diversity of rodent populations and the infection status of Bartonella at three ports along the China-Mongolia border in 2022. Methods Rodents at three Belt and Road ports along the China-Mongolia border, namely Ganqimaodu, Erenhot, and Zhuengadabuqi, were morphologically identified. The Bartonella citrate synthase(gltA)gene was amplified by nested polymerase chain reaction(PCR), and PCR-positive products were sequenced. The resulting sequences were then analyzed for genetic characteristics. Phylogenetic analysis was performed using MEGA 11.0 software using Neighbor-Joining and Maximum-Likelihood method. Results A total of 94 rodents were captured, representing seven species from five families and six genera: Mus musculus, Meriones unguiculatus, Meriones meridianus, Spermophilus dauricus, Allactaga sibirica, Dipus sagitta, and Phodopus roborovskii. Among them, M. unguiculatus was the most abundant species, with 58 rodents, accounting for 61.70% of the total. Overall,11 positive Bartonella pathogen sequences were obtained from the three ports, with a positive detection rate of 11.70%(11/94). The infected rodent species included M. unguiculatus, M. meridianus, and A. sibirica, and two species of Bartonella were detected. Conclusions Rodents at Ganqimaodu, Erenhot, and Zhuengadabuqi ports along the China-Mongolia border were naturally infected with Bartonella. Rodent monitoring and pathogen prevention and control in this area should be strengthened.

Bone metastases are a common and serious complication of solid tumors such as breast cancer, prostate cancer, lung cancer, and kidney cancer, and occur at a high rate in patients with advanced cancer. This pathological process not only frequently leads to skeletal-related events such as severe bone pain, pathological fractures, spinal cord compression, and hypercalcemia—conditions that severely impact patients’ quality of life—but may also significantly increase the risk of death. Cancer bone metastasis is a complex, multistage cascade involving key steps such as the detachment of tumor cells from the primary tumor, their invasion and entry into the bloodstream, and their colonization, dormancy, and reactivation within the bone microenvironment. In recent years, exercise—as a safe and easily implementable non-pharmacological intervention—has demonstrated significant potential in inhibiting bone metastasis, improving the bone microenvironment, and alleviating related clinical symptoms. Its mechanisms of action may involve multiple levels. First, at the mechanical load level, mechanical signals generated by exercise can directly act on bone cells, inhibiting tumor cell infiltration and colonization by regulating endothelial cell permeability and osteoclast activity. Second, at the endocrine and paracrine levels, exercise can alter the expression profiles of microRNAs (such as miR-486 and miR-34b) carried by extracellular vesicles and particles (EVPs) in the circulation, thereby inhibiting tumor cell proliferation and migration by targeting cell cycle-related genes. Furthermore, exercise can remodel the immune microenvironment, enhance the cytotoxic activity of antitumor immune cells, and improve oxygen supply to tumor tissues, thereby alleviating hypoxia-induced immunosuppression. Finally, at the metabolic intervention level, exercise can induce systemic metabolic reprogramming, increasing the nutritional demands of normal tissues, thereby competing with tumor cells for nutrients and forming a “metabolic barrier” in distal organs such as bones. This article reviews the pathogenesis and classification of bone metastases, including osteolytic, osteogenic, and mixed types, and elaborates in detail on the complex processes by which tumor cells undergo colonization, dormancy, and reactivation within the bone microenvironment. Furthermore, based on a comprehensive analysis of human and animal studies on exercise interventions for bone metastases, this paper highlights the mechanisms by which exercise inhibits tumor formation, growth, and spread through the regulation of mechanical loading, extracellular vesicles and granules, as well as via immune and metabolic pathways, thereby interfering with bone metastasis. However, the array of exercise modalities and intensities available for patients grappling with the challenges of cancer-related bone metastases remains markedly constrained, and the formulation of tailored exercise prescriptions continues to be devoid of robust, evidence-based medical backing. This review aims to explore the effects of exercise on bone health in patients with bone metastases, analyze the factors and considerations that should be taken into account in exercise prescriptions, and provide new theoretical support and insights for developing personalized exercise programs for these patients and further advancing relevant clinical research. Future research directions should include the development of evidence-based, personalized exercise prescriptions and further clarification of the safety boundaries and best practice standards for exercise interventions, thereby promoting the advancement of relevant clinical research and ultimately improving patients’ clinical outcomes and quality of life.

ObjectiveTo investigate the effect of kynurenine-3-monooxygenase （KMO） on the function of fibroblast-like synoviocytes （FLS） in rheumatoid arthritis （RA）. MethodsKMO expression in synovial tissues from RA patients and healthy controls （HC） was analyzed using bioinformatics. Synovial tissues from RA patients and HC were collected， and KMO expression in FLS within synovial tissues was detected by immunofluorescence （IF）. In vitro experiments were performed using human rheumatoid arthritis fibroblast-like synoviocytes （MH7A）. Following KMO overexpression， KMO knockdown， and KMO inhibition with the specific inhibitor Ro61-8048 in MH7A cells， Western blot， CCK-8， EdU， Transwell and ELISA assays were used to evaluate alterations in cell viability， proliferation， migration and secretory function. ResultsBioinformatic analysis revealed significantly higher KMO gene expression in RA synovial tissue compared to the HC group （P<0.001）. IF results showed increased KMO expression in FLS from RA synovial tissues relative to the HC group （P<0.01）. Overexpression of KMO led to enhanced viability， proliferation， migration， and secretory capabilities of MH7A cells （all P<0.05）， whereas knockdown or inhibition of KMO with Ro61-8048 markedly suppressed these functions （all P<0.05）. ConclusionThe expression of KMO is upregulated in the synovial tissues of RA patients and promotes the activation of RA-FLS， suggesting that KMO may serve as a potential therapeutic target for RA treatment.

Oral cancer(OC)is a common malignant tumor and its incidence and mortality rate is increasing year by year.According to statistics,there are over 500,000 new cases worldwide every year,and most patients face treatment challenges.Electrospinning technology(ES)is a method for preparing nanofibers by driving the polymer in solution to form nanoscale fibers through an electric field.Since its discovery in the 1930s,it has received widespread attention and application.This article reviews the application of ES in the treatment of OC.The principles and preparation process of ES are induced,its application research in treating OC is elaborated,and the challenges faced by ES in OC treatment research are analyzed.Finally,the future development is discussed.

Aim To investigate the effect of Senecio scandens Buch.-Ham on inflammatory pain mediated by mast cell P2X7 receptor.Methods Using the ATP-induced foot inflammatory pain,immunofluores-cence and toluidine blue staining techniques were used to investigate whether Senecio scandens has inhibitory effect on P2X7 receptor on mast cells.Using the calci-um ion imaging experimental technology,to explore whether Senecio scandens inhibit the intracellular cal-cium ion enrichment caused by activation of P2X7 re-ceptor on mouse peritoneal mast cell.The whole-cell patch clamp technique was used to investigate whether senecio scandens could inhibit the inward current in-duced by activation of P2X7 receptor on mouse perito-neal mast cell.Results In vivo,Senecio scandens alle-viate ATP induced inflammatory pain(3.9 g·kg-1:P＜0.05),and significantly inhibited the infiltration of P2X7 receptor-positive mast cells(3.9 g·kg-1:P＜0.05).Knockout of mast cell can reduce the analgesic effect of Senecio scandens(3.9 g·kg-1:P=0.645).In vitro.The experiment results show that senecio scandens can significantly inhibit the calcium influx(300 mg·L-1:P＜0.05;1 g·L-1:P＜0.01;3 g·L-1:P＜0.01)and the inward current mediated by P2X7 receptor in mast cell(1 g·L-1:P＜0.01).Conclusion Senecio scandens alleviate inflammatory pain by inhibiting mast cell P2X7 receptor.

To formulate an expert consensus on the principles of preoperative hair removal and provide scientific guidance for standardized removal of hair before surgical procedures so as to reduce the incidence of surgical site infections.METHODS Led by the Hospital Management Institute of National Health Commission of the People's Republic of China,this consensus was reached with the joint efforts from the expects of relevant fields such as surgeries,interventional therapies,nursing,and infection prevention and control.The consensus facilitates the classification and evaluation of literatures by following the evidence grade formulated by Oxford Evidence-based Medicine Center and focuses on the association of preoperative hair removal with surgical site infection,it reaches the evidence grade of expert consensus and recommendation intensity by integrating with discussions on meetings and clinical experience of the expects from relevant fields.RESULTS A total of 6 items of consensus were reached by summarizing the latest evidence on the aspects including the indications for preoperative hair removal,tools,range,timing and places.CONCLUSION The consensus,to some extent,make supplements to and complete the exiting regulations and standards.It provides guidance for the medical institutions to carry out the preoperative hair removal.

PURPOSE: To compare the effects of empirical and modified hemostatic resuscitation for liver blast injury combined with seawater immersion. METHODS: Thirty rabbits were subjected to liver blast injury combined with seawater immersion, and were then divided into 3 groups randomly (n = 10 each): group A (no treatment after immersion), group B (empirical resuscitation with 20 mL hydroxyethyl starch, 50 mg tranexamic acid, 25 IU prothrombin complex concentrate and 50 mg/kg body weight fibrinogen concentrate), and group C (modified resuscitation with additional 10 IU prothrombin complex concentrate and 20 mg/kg body weight fibrinogen concentrate based on group B). Blood samples were gathered at specified moments for assessment of thromboelastography, routine coagulation test, and biochemistry. Mean arterial pressure, heart rate, and survival rate were also documented at each time point. The Kolmogorov-Smirnov test was used to examine the normality of data distribution. Multigroup comparisons were conducted with one-way ANOVA. RESULTS: Liver blast injury combined with seawater immersion resulted in severe coagulo-fibrinolytic derangement as indicated by prolonged prothrombin time (s) (11.53 ± 0.98 vs. 7.61 ± 0.28, p＜0.001), activated partial thromboplastin time (APTT) (s) (33.48 ± 6.66 vs. 18.23 ± 0.89, p＜0.001), reaction time (R) (min) (5.85 ± 0.96 vs. 2.47 ± 0.53, p＜0.001), decreased maximum amplitude (MA) (mm) (53.20 ± 5.99 vs. 74.92 ± 5.76, p＜0.001) and fibrinogen concentration (g/L) (1.19 ± 0.29 vs. 1.89 ± 0.32, p = 0.003), and increased D-dimer concentration (mg/L) (0.38 ± 0.32 vs. 0.05 ± 0.03, p = 0.005). Both empirical and modified hemostatic resuscitation could improve the coagulo-fibrinolytic states and organ function, as indicated by shortened APTT and R values, decreased D-dimer concentration, increased fibrinogen concentration and MA values, lower concentration of blood urea nitrogen and creatine kinase-MB in group B and group C rabbits in comparison to that observed in group A. Further analysis found that the R values (min) (4.67 ± 0.84 vs. 3.66 ± 0.98, p = 0.038), APTT (s) (23.16 ± 2.75 vs. 18.94 ± 1.05, p = 0.001), MA (mm) (60.10 ± 4.74 vs. 70.21 ± 3.01, p < 0.001), and fibrinogen concentration (g/L) (1.68 ± 0.21 vs. 1.94 ± 0.16, p = 0.013) were remarkably improved in group C than in group B at 2 h and 4 h after injury. In addition, the concentration of blood urea nitrogen (mmol/L) (24.11 ± 1.96 vs. 21.00 ± 3.78, p = 0.047) and creatine kinase-MB (U/L) (85.50 ± 13.60 vs. 69.74 ± 8.56, p = 0.013) were lower in group C than in group B at 6 h after injury. The survival rates in group B and group C were significantly higher than those in group A at 4 h and 6 h after injury (p < 0.001), however, there were no statistical differences in survival rates between group B and group C at each time point. CONCLUSIONS Modified hemostatic resuscitation could improve the coagulation parameters and organ function better than empirical hemostatic resuscitation.
Animals ; Rabbits ; Resuscitation/methods* ; Liver/injuries* ; Seawater ; Blast Injuries/therapy* ; Fibrinogen/administration & dosage* ; Male ; Tranexamic Acid/administration & dosage* ; Immersion ; Hydroxyethyl Starch Derivatives/administration & dosage*

PURPOSE: To construct a decision-making app for pre-hospital damage control resuscitation (PHDCR) for severely injured patients, and to make a preliminary trial test on the effectiveness and usability aspects of the constructed app. METHODS: Decision-making algorithms were first established by a thorough literature review, and were then used to be learned by computer with 3 kinds of text segmentation algorithms, i.e., dictionary-based segmentation, machine learning algorithms based on labeling, and deep learning algorithms based on understanding. B/S architecture mode and Spring Boot were used as a framework to construct the app. A total of 16 Grade-5 medical students were recruited to test the effectiveness and usability aspects of the app by using an animal model-based test on simulated PHDCR. Twelve adult Bama miniature pigs were subjected to penetrating abdominal injuries and were randomly assigned to the 16 students, who were randomly divided into 2 groups (n = 8 each): group A (decided on PHDCR by themselves) and group B (decided on PHDCR with the aid of the app). The students were asked to complete the PHDCR within 1 h, and then blood samples were taken and thromboelastography, routine coagulation test, blood cell count, and blood gas analysis were examined. The lab examination results along with the value of mean arterial pressure were used to compare the resuscitation effects between the 2 groups. Furthermore, a 4-statement-based post-test survey on a 5-point Likert scale was performed in group B students to test the usability aspects of the constructed app. RESULTS: With the above 3 kinds of text segmentation algorithm, B/S architecture mode, and Spring Boot as the development framework, the decision-making app for PHDCR was successfully constructed. The time to decide PHDCR was (28.8 ± 3.41) sec in group B, much shorter than that in group A (87.5 ± 8.53) sec (p < 0.001). The outcomes of animals treated by group B students were much better than that by group A students as indicated by higher mean arterial pressure, oxygen saturation and fibrinogen concentration and maximum amplitude, and lower R values in group B than those in group A. The post-test survey revealed that group B students gave a mean score of no less than 4 for all 4 statements. CONCLUSION A decision-making app for PHDCR was constructed in the present study and the preliminary trial test revealed that it could help to improve the resuscitation effect in animal models of penetrating abdominal injury.
Animals ; Swine ; Resuscitation/methods* ; Mobile Applications ; Humans ; Algorithms ; Emergency Medical Services/methods* ; Male ; Decision Making ; Female