Objective To establish an "human serum - porcine aortic endothelial cells (PAEC) - human platelets" in vitro model and explore the mechanism of xenotransplantation-related coagulation dysfunction mediated by immune complexes - platelet FcγRⅡa (CD32a) receptor. Methods Healthy human serum was co-incubated with PAEC to prepare the supernatant containing immune complexes, which was then used to stimulate healthy human platelets, or directly treated with the serum of xenogeneic liver transplant recipients. Flow cytometry was used to detect platelet activation markers CD62P and surface IgG binding levels, and the platelet adhesion function was evaluated by platelet-PAEC adhesion experiments. CD32a blocking antibody IV.3 and SYK blocker SKYIN 4 were used to clarify the signaling pathways. Results The supernatant from the co-incubation of healthy human serum and PAEC could significantly induce platelet activation and endothelial adhesion. The use of the serum from xenogeneic liver transplant recipients could also significantly induce platelet activation. Antibody IV.3 and SYK blocker SKYIN 4 could significantly inhibit these effects. Conclusions In xenotransplantation, the immune complexes formed by human serum antibodies and porcine endothelial antigens may induce abnormal platelet activation through the platelet CD32a receptor, which is an important mechanism of non-complement-dependent post-transplant coagulation dysfunction, providing a new target for the intervention of coagulation complications in xenotransplantation.

Hepatitis D virus （HDV）， as a defective virus， relies on the envelope protein of hepatitis B virus （HBV） to complete replication and transmission. Chronic hepatitis B （CHB） patients comorbid with HDV infection may experience significant acceleration of liver disease progression and a significantly higher risk of serious complications such as liver cirrhosis and hepatocellular carcinoma （HCC） compared with the patients with CHB alone， which poses a serious threat to the life and health of patients. At present， the coverage rate of HDV screening needs to be improved， and some patients with HBV/HDV co-infection have not been found in time. Therefore， strengthening the understanding of HDV among clinicians， expanding the scope of HDV screening， identifying patients with infection in a timely manner， and performing standardized antiviral therapy and long-term follow-up management are of great significance for improving the prognosis of patients， reducing disease burden， improving the quality of life of patients， and achieving the global goal of “eliminating viral hepatitis as a public health threat by 2030”.

AIM:To investigate the effect of fibroblast growth factor 21(FGF21)on high glucose-induced oxidative stress in retinal pigment epithelial(RPE)cells and to clarify the underlying molecular mechanisms.METHODS:Single-cell sequencing data from the GEO database were analyzed to determine the expression profile of the FGF21 receptor FGFR1 in RPE cells. Human ARPE-19 cells were cultured and randomly assigned to control, high glucose(30 mmol/L), and high glucose+FGF21 analog treatment groups, with additional siFGFR1 and PI3K inhibitor groups. Cell viability in different treatment groups was assessed using CCK-8 assay, intracellular reactive oxygen species(ROS)levels were quantified using DCFH-DA fluorescent probing combined with immunofluorescence staining and flow cytometry. Transcriptome sequencing was performed on cells from the high glucose group and high glucose+FGF21 group to analyze the enrichment level of the PI3K/Akt signaling pathway. Western blotting was performed to detect phosphorylation levels of PI3K/Akt pathway components.RESULTS:Single-cell sequencing revealed specific expression of FGFR1 in RPE cells of retinal tissues from diabetic model mice. Under In vitro experiments, high glucose(30 mmol/L)exposure reduced ARPE-19 cell viability by 49.7% and increased ROS levels by approximately 2-fold. Whereas treatment with the FGF21 analog(60 ng/mL)restored cell viability and attenuated high glucose-induced ROS accumulation. Mechanistic studies demonstrated that FGFR1 knockdown inhibited the antioxidative stress of FGF21. Further validation of the molecular mechanism revealed that high glucose significantly suppressed the PI3K/Akt pathway activation(the levels of p-Akt and p-PI3K were decreased by 33.9% and 36.6%, respectively), while FGF21 effectively reversed this inhibitory effect and restored the expression of p-Akt and p-PI3K. Treatment with the PI3K inhibitor LY294002 inhibited the cytoprotective effect of FGF21 and significantly increased the ROS-positive cells, these findings confirm that PI3K/Akt signaling is indispensable downstream mechanism for FGF21 to exert its effects.CONCLUSION:FGF21 alleviates high glucose-induced oxidative stress and cellular injury in RPE cells by activating the PI3K/Akt signaling pathway through its receptor FGFR1.

ObjectiveTo analyze the long-term trend of incidence and mortality of type 2 diabetic kidney disease （DKD） in China from 1990 to 2021. MethodsThe Joinpoint regression model was used to analyze the average annual percentage change （AAPC） of standardized incidence rate and standardized mortality rate， and the age-period-cohort （APC） model was constructed to analyze the longitudinal age change， period and cohort effect risk ratio （RR）. ResultsFrom 1990 to 2021， the standardized incidence rate of type 2 DKD in males and females showed an overall upward trend， with AAPC of 0.08% and 0.36%， respectively. The age-standardized mortality rate of the total population and female showed a downward trend， with AAPC of -0.61% and -1.03%， respectively. However， there was no significant difference in males. APC model showed that the age effect existed： the peak age was 75-79 years old， the mortality rate of females increased， and the mortality rate of males decreased after 80-84 years old. For the effect of time period， the risk of type 2 DKD incidence in females in 2017—2021 was 1.05 times that in 2002—2006， and the risk of death in males and females in 2017—2021 was 0.84 and 0.71 times that in 2002—2006， respectively. For cohort effects， the highest risk of disease was seen in men and women born in 1967—1971， and the highest risk of death was seen in men born in 1952—1956 and women born in 1912—1916. ConclusionFrom 1990 to 2021， the standardized incidence rate of type 2 DKD in China shows an upward trend， and the standardized mortality rate shows a downward trend. It is necessary to strengthen the health behavior publicity and education of type 2 DKD， and actively carry out early screening to reduce the disease burden.

Climate change has evolved from an environmental issue into a global public health crisis, posing severe challenges to healthcare systems. Issues such as shifts in patient disease patterns, increased care demands for vulnerable populations, and insufficient resilience in nursing systems are becoming increasingly prominent. As the frontline of healthcare delivery, nursing practice directly confronts multiple health risks triggered by climate change. Under the Healthy China 2030 strategy, the role of nursing in addressing climate change cannot be overlooked. Therefore, this paper systematically reviewed the impacts of climate change on nursing practice and corresponding domestic and international strategies, and proposed recommendations for localized development pathways. First, strengthen climate health literacy in nursing education by integrating the climate change system into curricula and clinical practice. Second, promote nursing policy participation in global health governance to establish a climate-adaptive nursing policy system with Chinese characteristics. Finally, establish a multidisciplinary nursing research framework to foster integration among nursing science, climate science, public health, traditional Chinese medicine, and other relevant fields. This paper aims to provide theoretical foundations for constructing a climate-adaptive nursing system with Chinese characteristics, thereby advancing the coordinated development of Healthy China initiative and climate governance.

BACKGROUND:Rapid and accurate segmentation of brain tissue in medical images is of great significance for three-dimensional biomechanical modeling and diagnosis of craniocerebral injuries.Currently,artificial intelligence(AI)-based baseline models exhibit excellent generalization capabilities on large-scale datasets.However,due to the specificity and complexity of craniocerebral tissues,these models have certain limitations in their application to craniocerebral tissue segmentation.Additionally,the scarcity of craniocerebral tissue samples makes it difficult for baseline models to achieve precise segmentation results through fine-tuning.OBJECTIVE:To construct a craniocerebral tissue segmentation model based on texture feature retrieval enhancement to improve segmentation accuracy under a small number of samples.METHODS:Segment Anything in Medical Images(MedSAM)model was selected as the basic framework,and texture features were combined with deep learning to build a brain tissue segmentation model based on texture feature retrieval enhancement(DP-MedSAM).Dice Coefficient and mean intersection over union(MIoU)were selected to evaluate the efficiency of image segmentation results.In comparison with the original MedSAM model,the ablation experiment systematically evaluated the influence of key components on the model performance.The sensitivities of MedSAM,the Segment Anything Model(SAM)for medical image segmentation(SAM-Med2D)and DP-MedSAM in the mandible,left optic nerve,and left parotid gland were compared.RESULTS AND CONCLUSION:(1)By verifying the impact of the number of point prompts on segmentation results on the HaN-Seg dataset,the experimental results indicated that the optimal Dice score was achieved with the addition of three points.(2)DP-MedSAM demonstrated performance improvements compared with MedSAM and SAM-Med2D on two datasets(HaN and Public Domain Database for Computational Anatomy).Especially on the Public Domain Database for Computational Anatomy dataset,in terms of the MIoU metric,DP-MedSAM outperformed MedSAM by 6.59％and SAM-Med2D by 37.35％;in terms of the Dice metric,DP-MedSAM outperformed MedSAM and SAM-Med2D by 4.34％and 25.32％,respectively.(3)The ablation experiment results showed that removing the texture feature extraction module in the DP-MedSAM model,relying solely on original image features,led to a significant decrease in results on the test set.Furthermore,removing the vector cache database and its retrieval enhancement function from the model,which deprived the ability of the model to perform similarity retrieval using an external knowledge base,further reduced model performance.(4)Under conditions of limited data resources,the DP-MedSAM model outperformed the other two models in all evaluation metrics.The DP-MedSAM model performed excellently when processing simple and moderately difficult samples,demonstrating a clear advantage over the other two models and indicating good generalization ability.Processing the fine structures of difficult samples placed higher demands on the model's segmentation capabilities.Although the performance of the DP-MedSAM model declined slightly,it still outperformed the other two models.(5)This study proposes an innovative craniocerebral tissue segmentation model,DP-MedSAM,which improves the baseline model's performance in capturing local details and global structural information in medical images by introducing target region texture feature extraction.Through vector similarity retrieval technology,DP-MedSAM can retrieve the feature vector most similar to the current target region from a pre-constructed vector database,providing more precise guiding information for the segmentation process.

BACKGROUND:Rapid and accurate segmentation of brain tissue in medical images is of great significance for three-dimensional biomechanical modeling and diagnosis of craniocerebral injuries.Currently,artificial intelligence(AI)-based baseline models exhibit excellent generalization capabilities on large-scale datasets.However,due to the specificity and complexity of craniocerebral tissues,these models have certain limitations in their application to craniocerebral tissue segmentation.Additionally,the scarcity of craniocerebral tissue samples makes it difficult for baseline models to achieve precise segmentation results through fine-tuning.OBJECTIVE:To construct a craniocerebral tissue segmentation model based on texture feature retrieval enhancement to improve segmentation accuracy under a small number of samples.METHODS:Segment Anything in Medical Images(MedSAM)model was selected as the basic framework,and texture features were combined with deep learning to build a brain tissue segmentation model based on texture feature retrieval enhancement(DP-MedSAM).Dice Coefficient and mean intersection over union(MIoU)were selected to evaluate the efficiency of image segmentation results.In comparison with the original MedSAM model,the ablation experiment systematically evaluated the influence of key components on the model performance.The sensitivities of MedSAM,the Segment Anything Model(SAM)for medical image segmentation(SAM-Med2D)and DP-MedSAM in the mandible,left optic nerve,and left parotid gland were compared.RESULTS AND CONCLUSION:(1)By verifying the impact of the number of point prompts on segmentation results on the HaN-Seg dataset,the experimental results indicated that the optimal Dice score was achieved with the addition of three points.(2)DP-MedSAM demonstrated performance improvements compared with MedSAM and SAM-Med2D on two datasets(HaN and Public Domain Database for Computational Anatomy).Especially on the Public Domain Database for Computational Anatomy dataset,in terms of the MIoU metric,DP-MedSAM outperformed MedSAM by 6.59％and SAM-Med2D by 37.35％;in terms of the Dice metric,DP-MedSAM outperformed MedSAM and SAM-Med2D by 4.34％and 25.32％,respectively.(3)The ablation experiment results showed that removing the texture feature extraction module in the DP-MedSAM model,relying solely on original image features,led to a significant decrease in results on the test set.Furthermore,removing the vector cache database and its retrieval enhancement function from the model,which deprived the ability of the model to perform similarity retrieval using an external knowledge base,further reduced model performance.(4)Under conditions of limited data resources,the DP-MedSAM model outperformed the other two models in all evaluation metrics.The DP-MedSAM model performed excellently when processing simple and moderately difficult samples,demonstrating a clear advantage over the other two models and indicating good generalization ability.Processing the fine structures of difficult samples placed higher demands on the model's segmentation capabilities.Although the performance of the DP-MedSAM model declined slightly,it still outperformed the other two models.(5)This study proposes an innovative craniocerebral tissue segmentation model,DP-MedSAM,which improves the baseline model's performance in capturing local details and global structural information in medical images by introducing target region texture feature extraction.Through vector similarity retrieval technology,DP-MedSAM can retrieve the feature vector most similar to the current target region from a pre-constructed vector database,providing more precise guiding information for the segmentation process.

OBJECTIVE To analyze the incidence of cyclosporine （CsA）-associated acute kidney injury （AKI） in hospitalized patients， identify influencing factors， and construct a risk prediction model. METHODS A single-center retrospective study was conducted， enrolling clinical data from hospitalized patients treated with CsA at the First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital from January 2018 to July 2024. The patients were classified into AKI group and non-AKI group based on the occurrence of CsA-related AKI. Univariate analysis and multivariate Logistic regression analysis were used to identify independent risk factors for CsA-related AKI， and a risk prediction model was constructed and its performance was evaluated. RESULTS A total of 439 patients were included， of whom 54 developed CsA-related AKI， with an incidence rate of 12.30%. The occurrence of CsA-associated AKI was positively correlated with concurrent bacterial pulmonary infection， cytomegalovirus viremia， respiratory failure， renal insufficiency， gastrointestinal bleeding， and peripheral central venous catheterization （odds ratios of 763.750， 16.944， 41.933， 236.806， 17.537 and 212.789， respectively； P ＜0.05）； while uric acid， prealbumin， and calcium levels were negatively associated with it （odds ratios of 0.983， 0.967 and 0.058， respectively； P ＜0.05）. The prediction model constructed based on the above factors yielded a χ 2 value of 10.254 （ P ＞0.05） in the Hosmer-Lemeshow test. The average area under the curve （AUC） from 10-fold cross-validation was 0.885. The AUC of the receiver operating characteristic curve was 0.883， with a sensitivity of 84.3% and a specificity of 80.4%， respectively， at the optimal cutoff value of 0.1. CONCLUSIONS Six factors， including concurrent bacterial pulmonary infection and cytomegalovirus viremia， are positively associated with the occurrence of CsA-related AKI； while uric acid， prealbumin， and calcium levels are negatively associated. The Logistic regression model constructed based on these factors demonstrates good predictive performance and can assist clinic in conducting early risk assessment and personalized interventions.

High-altitude retinopathy(HAR)is a retinal vascular disorder induced by exposure to high-altitude environments. With the increasing popularity of high-altitude travel, mountaineering, and special occupational activities, worldwide, the incidence of HAR has risen, particularly among individuals with rapid or short-term exposure to elevated altitudes. Most cases of HAR are self-limiting and gradually resolve with descent to lower altitudes. However, severe cases may result in permanent visual function impairment if not recognized and managed promptly. This review systematically summarizes the epidemiology, diagnostic progress, pathogenesis, and prevention and treatment strategies of HAR, with particular emphasis on hypoxia-associated molecular pathways involved in retinal damage, and evaluates current evidence for clinical interventions. Deepened understanding of its pathological mechanisms and clinical management strategies facilitates early identification, targeted intervention, and ocular health protection for populations exposed to high altitudes.

BACKGROUND: The clinical impact of post-percutaneous coronary intervention (PCI) quantitative flow ratio (QFR) in patients treated with PCI for chronic total occlusion (CTO) was still undetermined. METHODS: All CTO vessels treated with successful anatomical PCI in patients from PANDA III trial were retrospectively measured for post-PCI QFR. The primary outcome was 2-year vessel-oriented composite endpoints (VOCEs, composite of target vessel-related cardiac death, target vessel-related myocardial infarction, and ischemia-driven target vessel revascularization). Receiver operator characteristic curve analysis was conducted to identify optimal cutoff value of post-PCI QFR for predicting the 2-year VOCEs, and all vessels were stratified by this optimal cutoff value. Cox proportional hazards models were employed to calculate the hazard ratio (HR) with 95% CI. RESULTS: Among 428 CTO vessels treated with PCI, 353 vessels (82.5%) were analyzable for post-PCI QFR. 31 VOCEs (8.7%) occurred at 2 years. Mean value of post-PCI QFR was 0.92 ± 0.13. Receiver operator characteristic curve analysis shown the optimal cutoff value of post-PCI QFR for predicting 2-year VOCEs was 0.91. The incidence of 2-year VOCEs in the vessel with post-PCI QFR < 0.91 (n = 91) was significantly higher compared with the vessels with post-PCI QFR ≥ 0.91 (n = 262) (22.0% vs. 4.2%, HR = 4.98, 95% CI: 2.32-10.70). CONCLUSIONS Higher post-PCI QFR values were associated with improved prognosis in the PCI practice for coronary CTO. Achieving functionally optimal PCI results (post-PCI QFR value ≥ 0.91) tends to get better prognosis for patients with CTO lesions.