Objective To explore the predictive effect of serum 25-hydroxyvitamin D3 [25(OH)D3] and blood lipid metabolism indexes on the occurrence of osteoporosis in type 2 diabetes mellitus (T2DM). Methods Totally 98 patients with T2DM in the hospital from January 2022 to January 2024 were classified into osteoporosis group (38 cases) and non-osteoporosis group (60 cases) by means of concurrent osteoporosis status. The levels of serum 25(OH)D3 and blood lipid metabolism indexes [high density lipoprotein (HDL), total cholesterol (TC), triglyceride (TG), low density lipoprotein (LDL), VLDL] were measured in study subjects. The association of serum 25(OH)D3 and blood lipid metabolism indexes with osteoporosis was explored by Logistic regression analysis. The predictive value of serum 25(OH)D3 and blood lipid metabolism indexes on osteoporosis was analyzed by receiver operating characteristic curve (ROC). Results Serum 25(OH)D3 and HDL levels in the osteoporosis group were lower while TG and LDL levels were higher than those in the non-osteoporosis group (P<0.05). The differences in the levels of TC and VLDL were insignificant between groups (P>0.05). After logistic regression analysis, the levels of serum 25(OH)D3, HDL, TG and LDL were closely related to the occurrence of osteoporosis (P<0.05). ROC curve indicated that the area under the curve (AUC), sensitivity and specificity of combined prediction of osteoporosis by serum 25(OH)D3, HDL, TG, and LDL were 0.943, 92.11% and 85.00%, and the efficiency of combined prediction was better than that of each index alone (P<0.05). Conclusion The levels of serum 25(OH)D3, HDL, TG and LDL in T2DM are closely related to osteoporosis. Early combined monitoring of the indicators can provide reference value for clinical prediction of osteoporosis occurrence in patients with T2DM.

Plant-derived exosome-like nanovesicles (PELNs), characterized by a natural lipid bilayer membrane, have rapidly emerged as a prominent research frontier in medicine owing to their unique biological properties and robust therapeutic potential. This review comprehensively examines the biological profiles, mechanistic functions, and recent engineering advancements of PELNs. In terms of composition, PELNs are uniquely enriched in plant-specific glycolipids, phosphatidylserine, secondary metabolites, and highly stable 2'-O-methylated miRNAs. This distinct molecular makeup endows them with exceptional biocompatibility, negligible immunogenicity, and the capacity for cross-species molecular communication. Mechanistically, PELNs demonstrate profound anti-inflammatory efficacy by suppressing the NF-κB and NLRP3 inflammasome pathways. They also serve as potent immune modulators, driving macrophage M1/M2 polarization and regulating T cell activity. Additionally, PELNs exhibit promising antitumor capabilities, targeting malignancies via reactive oxygen species (ROS) induction, TRAIL pathway activation, and tumor microenvironment remodeling. Crucially, the plant miRNAs encapsulated within PELNs remain highly stable in the gastrointestinal tract, allowing them to selectively alter gene expression in specific gut microbiota communities. This interaction deeply influences host immunity and metabolism, highlighting the vital role in cross-species regulation. Advancements in bioengineering have further expanded the clinical utility of PELNs. Targeted delivery efficiency can be significantly amplified via surface functionalization (e.g., folate and RGD sequences) and state-of-the-art drug loading technologies such as sonication and electroporation. Consequently, engineered PELNs surpass traditional synthetic nanocarriers in penetrating natural physiological barriers, particularly for oral and transdermal drug administration. Despite these advantages, clinical translation is currently hindered by the lack of standardized isolation protocols, challenges in scalable manufacturing, and the need for robust quality control frameworks. Looking forward, the integration of multi-omics approaches and AI-driven “molecular fingerprinting”—coupled with the design of synthetic biomimetic vesicles—will be instrumental in overcoming these bottlenecks, ultimately establishing PELNs as a next-generation platform for precision medicine and targeted nanotherapeutic delivery.

Objective:To analyze the prospective associations between liver biomarkers and mortality among Chinese middle-aged and elderly populations and to evaluate the mortality risk predictive value.Methods:A total of 22 758 participants from the 3 rd resurvey of the China Kadoorie Biobank were included. Cox proportional hazard models were used to analyze the prospective associations of 5 liver biomarkers with mortality. These liver biomarkers included two liver imaging biomarkers (liver fat attenuation parameter, liver stiffness measurement) and three serum liver enzyme biomarkers [gamma-glutamyl transferase (GGT), ALT, and AST]. Restricted cubic spline was used to assess the nonlinear associations between biomarkers and mortality. The area used the receiver operating characteristic curve (AUC) to evaluate the predictive ability of the models after incorporating liver biomarkers into traditional prediction models for mortality. Results:The mean age of the participants was (65.2±9.1) years, with a median follow-up of 1.5 years, during which 307 deaths occurred. Compared to individuals without hepatic steatosis, those with severe hepatic steatosis had a 79% higher risk of mortality, with a HR of 1.79 (95% CI: 1.06-3.03). Compared to individuals without hepatic fibrosis, those with advanced fibrosis and cirrhosis had higher mortality risks of 48% and 91%, respectively (both P<0.05). For each standard deviation increase in GGT, the mortality risk increased by 10% ( HR=1.10, 95% CI: 1.05-1.15), with the positive association plateauing at higher GGT levels. AST exhibited a U-shaped association with mortality risk. The AUC of the prediction model adding liver biomarkers into traditional prediction factors was 0.718 (95% CI: 0.679-0.757), with an increase of 0.030 ( P<0.001) compared with the traditional model. Conclusions:Severe hepatic steatosis, higher levels of hepatic fibrosis, and elevated GGT levels are significantly associated with higher mortality risk. AST shows a U-shaped nonlinear association with mortality risk. Incorporating liver biomarkers into traditional risk prediction models enhance the ability to predict mortality.

Objective:To investigate the prospective associations between plasma metabolites and the risks of all-cause and cause-specific mortality among Chinese adults.Methods:This study analyzed plasma metabolomics data from 2 183 healthy adults in the China Kadoorie Biobank (CKB), measured using targeted mass spectrometry. Cox proportional hazards regression models were used to examine the associations between 630 metabolites and the risk of all-cause mortality. Cause-specific hazard regression models evaluated the associations between metabolites and cardiovascular disease (CVD) risks, cancer, and other-cause mortality. Stepwise regression was used to identify key metabolites independently associated with all-cause mortality, and the area under the receiver operating characteristic curve (AUC) was calculated to assess the improvement in predictive performance when these metabolites were added to traditional risk prediction models.Results:The mean age of the participants was (53.2±9.8) years, 65.1% of whom were female. During a median follow-up of 14.5 years, 231 deaths occurred. A total of 44 metabolites were significantly associated with the risk of all-cause mortality [false discovery rate (FDR)-adjusted P<0.05], primarily including triglycerides, ceramides, and amino acids. Additionally, 29 and 15 metabolites were found to be associated with cancer and other-cause mortality, respectively, but no metabolites were significantly associated with CVD mortality after FDR corrections. Adding 14 metabolites independently associated with all-cause mortality into the traditional prediction model significantly improved its predictive performance. Specifically, incorporating metabolites into the traditional model, which already included laboratory biomarkers, increased the AUC to 0.798 (95% CI: 0.755-0.843), an improvement of 0.088 compared to the traditional model ( P<0.001). Conclusions:Multiple metabolites are significantly associated with mortality risk and can substantially improve the accuracy of mortality risk prediction models. These findings provide new insights into the physiological mechanisms of aging and offer valuable clues for personalized health risk assessment.

Objective:To summarize the current status and research hotspots in volume management among patients with heart failure, and to predict future research trends.Methods:Literature related to volume management in heart failure patients published between January 1, 2004 and August 1, 2024 was retrieved from the China National Knowledge Infrastructure and Web of Science Core Collection databases. CiteSpace software was used to perform visual analysis of publication volume, authors, institutions, countries, and keywords.Results:A total of 5 008 articles were retrieved, of which 145 were Chinese and 202 were English publications. The overall publication trend showed a steady increase over the past two decades. The most prolific author was Fudim (7 publications), the leading institution was Mayo Clinic (14 publications), and the country with the highest output was the United States (91 publications). Keyword co-occurrence, clustering, and burst detection analyses revealed that current research hotspots in both Chinese and English literature mainly focus on the management and control of volume overload, exploration of nursing strategies, and patient self-management and home-based rehabilitation. Emerging trends include out-of-hospital volume overload control and intelligent volume management technologies.Conclusions:Research on volume management in heart failure patients is evolving toward diversification and integration. Clinical interventions and standardized guidelines have gained increasing attention. Home-based volume management and overload control continue to be key areas of interest. In the future, the integration of artificial intelligence and the development of individualized home self-management programs will likely become important directions to improve the quality of life in patients with heart failure.

Objective:To address large-scale nonlinear programming challenges in optimizing prognosis-guided intensity-modulated radiation therapy (IMRT) plans, to propose gradient-enhanced random contrastive interaction particle swarm optimization (GradRCIPSO). This gradient-enhanced swarm intelligence algorithm aims to enable global optimization of prognostic treatment plans in clinically efficient scenarios.Methods:The core concept of GradRCIPSO lied in achieving rapid global convergence by allowing particles to learn both swarm interaction and gradient information. Specifically, the interaction information was obtained from elite individuals in the swarm, enabling the particles to efficiently search the entire solution space, whereas the gradient information represents the direction of the steepest descent, enabling the particles to quickly explore the current neighborhood. To assess the effectiveness of the methodology, the IMRT plans for 10 cases of non-small cell lung cancer (NSCLC) were selected in this study. They were compared with the GradRCIPSO-generated prognosis-guided IMRT plans. Moreover, the interior-point method, sequential quadratic programming, active set, gradient descent method, and random contrastive interaction particle swarm optimization (RCIPSO) were employed as optimization engines and compared with GradRCIPSO in terms of optimization efficiency and accuracy.Results:GradRCIPSO successfully generated clinically viable prognosis-guided IMRT plans with comparable dosimetric statistics to original plans, while significantly reducing predicted total radiotherapy risk from 1.22(0.84, 1.51) to 0.93(0.80, 1.29) ( z=2.81, P<0.01). It demonstrated superior accuracy over the above four gradient-based method ( z=2.80-2.81, P<0.01) and achieved threefold acceleration versus RCIPSO while maintaining equivalent solution quality( P>0.05). Conclusions:The proposed GradRCIPSO demonstrates high feasibility and performance in optimizing prognosis-guided IMRT plans, laying the technical foundation for the broad clinical application of prognosis-guided IMRT plans for lung cancer.

Purpose To assess the utility of echocardiographic parameters in predicting adverse cardiovascular in patients with connective tissue disease.Materials and Methods A retrospective analysis was conducted on the clinical and echocardiographic records of patients with connective tissue disease from the Affiliated Hospital of Inner Mongolia Medical University(June 1st,2020 to June 1st,2023)who had complete medical data and at least twelve months of follow-up.Variables were screened based on univariate analysis,combined with clinical expertise and XGBoost feature weight analysis;this information was used to construct a Cox proportional hazards regression model designed to predict composite endpoint events of adverse cardiovascular outcomes.Internal validation was performed using the Bootstrap resampling method,and the model's performance was evaluated.Results The study included 123 participants.The incidence of positive events reached 39.02%(48/123).Mitral valve early diastolic annular velocity(reflecting left heart function)(HR=0.79,P=0.041)and tricuspid annular plane systolic excursion(reflecting right heart function)(HR=0.92,P=0.044)emerged as significant predictors for adverse cardiovascular outcomes.Compared with the clinical model,the model combined with left heart function parameters showed significant improvements in both risk classification and absolute accuracy for short-term and medium-term adverse prognosis(NRI365=0.054,IDI365=0.060,NRI730=0.064,IDI730=0.079,all P<0.05)and optimized risk classification for long-term adverse prognosis(NRI1 095=0.256,P<0.05).In contrast,the model combined with right heart function parameters improved risk classification at all time points(NRI365=0.054,NRI730=0.000,NRI1 095=0.135,all P<0.05).Conclusion Mitral valve early diastolic annular velocity and tricuspid annular plane systolic excursion,which reflect cardiac function,are factors for predicting adverse cardiovascular outcomes among individuals diagnosed with connective tissue disease.Multi-parameter combined models incorporating echocardiographic variables can provide incremental predictive value compared with clinical models.

An assay was established for detection of toxigenic Clostridioides difficile by combining microfluidic chip analysis with immunochromatography,and its performance was evaluated and compared with those of the Xpert C.difficile/Epi and VIDAS CD AB tests.Primer pairs were designed according to the tcdB and tpi genes in C.difficile.The specificity,limit of detection,reproducibility,and stability were evaluated.A total of 215 stool samples from patients with diarrhea were collected and tested in parallel with the Xpert C.difficile/Epi,VIDAS CDAB,and our assay.C.difficile was isolated from samples,and the tcdB gene was identified when discrepant results were obtained from the three above assays.Our assay showed no cross-reaction with other diarrhea-associated pathogens.Its reproducibility was 100％in testing of two standard plasmids containing tcdB and tpi genes at two concentrations(105 and 102 copies/μL).Two standard plasmids were detected after the PCR and immunochromatography reagents had been stored for 3,6,9,and 12 months,and all the results were posi-tive.The limit of detection was 10 copies/μL for toxigenic C.difficile.Testing of 33 samples positive for C.difficile with our assay(33/215,15.3％)yielded findings statistically coherent with those of the Xpert C.difficile/Epi test(kappa value=0.965).The sensitivity,specificity,positive predictive value,and negative predictive value of our assay,with respect to Xpert C.difficile/Epi as the standard,were 94.3％,100.0％,100.0％,and 98.9％;these values were significantly higher than those of VIDAS CDAB(60.0％,98.9％,91.3％,and 92.7％)(Kappa=0.714,OR=157.50,95％CI:62.03-847.28,P=0.013).In conclusion,our newly developed assay is specific,stable,and reproducible,and may be used for rapid and accu-rate detection of toxigenic C.difficile.The assay could be used for C.difficile infection screening in outpatient and emergen-cy,community medical service center,and epidemiological settings.

The purpose of this study was to establish a luciferase immunosorbent assay(LISA)using the Crimean-Congo hemorrhagic fever virus(CCHFV)glycoprotein C(Gc),a specific antigen,for the detection of CCHFV IgG antibodies.Three antigenic fragments based on CCHFV glycoprotein C were designed,and three recombinant plasmids were constructed by liga-tion with the NanoLuc luciferase(NLuc)expression vector pNLF1-N through molecular cloning.The accuracy of the sequences in the recombinant plasmids was confirmed through sequencing.The recombinant plasmids were transfected into eukaryotic cells to obtain fusion proteins containing specific antigens and luciferase,and the expression of the fusion proteins was verified by western blotting,thereby facilitating the establishment of the CCHFV-LISA detection technique.The assay's sensitivity,specificity,and stability were evaluated and compared with those of a commercial CCHFV IgG antibody test kit.Three recom-binant antigen fragments of CCHFV Gc—NLuc-Gc-Full,NLuc-Gc-C1,and NLuc-Gc-C2—were expressed,with molecular weights of 80.1 kDa,62.8 kDa,and 53.9 kDa,respectively.The optimal fragment for CCHFV detection was NLuc-Gc-C2.The sensitivity of the CCHFV-LISA was 90.9％,and the specificity was 100％;the findings were highly concordant with those for the commercial CCHFV enzyme-linked immunosorbent assay kit.Repeatability tests indicated no statistically significant differ-ences in inter-and intra-assay variability within the same batch.The LISA was highly specific,sensitive,and user-friendly in detecting IgG antibodies against the CCHFV.Therefore,this method may facilitate serological diagnosis and epidemiological studies in endemic regions,and provide essential technical support for disease surveillance and early warning.

Neurodegenerative diseases are a group of chronic progressive diseases characterized by inflammation,degenera-tion and apoptosis.Chronic neuroinflammation is gradually becoming a potential pathogenic and predisposing factor.As a widely expressed non-histone nucleoprotein,HMGB1 participates in inflammatory process of human body through receptors of advanced glycation end products and Toll-like receptors while maintaining chromosome homeostasis.As a key factor of neuroinflammation,HMGB1 is widely involved in development of neurodegenerative diseases and may become a biomarker and a potential therapeutic target of neurodegenerative diseases.This article reviews the role of HMGB1 in neurodegenerative diseases and tries to provide ground-work for basic research and clinical application for targeting HMGB1 in the treatment of neurodegenerative diseases.