ObjectiveTo systematically assess the public health governance capacity in Zhejiang Province, to conduct an in-depth analysis of its strengths and weaknesses, so as to provide scientific basis and strategic recommendations for further enhancement. MethodsA systematic collection of policy documents, public information reports, and research literature related to public health governance capacity in Zhejiang Province from 2002 to 2023 was conducted (encompassing a total of 1 263 policy documents, 138 pieces of information reports and 631 research articles). Based on the evaluation criteria suitable for public health systems previously developed by the research team, the basic status and magnitude of change in public health governance capacity in Zhejiang Province was evaluated. Additionally, normative gap analyses were employed to identify the strengths and weaknesses. ResultsZhejiang Province ranked 4th nationwide in terms of public health governance capacity with a score of 733.4 points (1 000.0-point maximum). The province has effectively implemented the principle of health first (scoring 698.5 points in the assessment of health-first strategy implementation) and attached sufficient importance to health-related goals (scoring 658.2 points in the scientific rationality of goal setting). However, the implementation of inter-departmental coordination and incentive mechanisms only scored 178.7 points, the feasibility of management and monitoring mechanisms scored even lower at only 144.0 points, and the coverage of incentive mechanisms scored 286.0 points. ConclusionZhejiang Province has effectively implemented its health first strategy and attached great importance to health targets, but still needs to strengthen cross-departmental coordination mechanisms and health-oriented incentives.

ObjectiveCerebral ischemic injury triggers a complex pathological cascade characterized by excessive reactive oxygen species (ROS) accumulation, persistent oxidative stress, and sustained neuroinflammation in the injured brain microenvironment. These events collectively drive mitochondrial dysfunction, microglial overactivation, pro-inflammatory cytokine release, and progressive neuronal apoptosis, ultimately leading to severe and irreversible neurological deficits. However, conventional therapeutic strategies face critical limitations, including poor blood-brain barrier penetration, insufficient local drug concentration, uncontrolled drug release, and off-target systemic side effects. To address this pathological process, we rationally designed and fabricated an injectable ROS-responsive hydrogel loaded with polydopamine nanoparticles (PDA NPs) for spatiotemporally controlled antioxidation, anti-inflammation, and neuroprotection in the ischemic injury microenvironment. The present study aimed to systematically characterize the physicochemical properties, ROS-responsive drug release behavior, biocompatibility, and neuroprotective efficacy of this composite hydrogel system in vitro. MethodsPDA NPs were fabricated via oxidative self-polymerization. The ROS-responsive hydrogel was cross-linked using N1-(4-boronobenzyl)-N3-(4-boronophenyl)-N1,N1,N3,N3-tetramethylpropane-1, 3-diaminium (TSPBA) and polyvinyl alcohol (PVA). Morphology, particle size, Zeta potential, and structure of PDA NPs were characterized by dynamic light scattering (DLS), Zeta potential analysis, scanning electron microscopy (SEM), and transmission electron microscopy (TEM). Microstructure, rheological properties, shear-thinning behavior, and ROS-triggered release profiles of the hydrogel were examined by SEM and rheometry. Biocompatibility was evaluated using HT22 mouse hippocampal neurons with CCK-8 and live/dead staining. An oxygen-glucose deprivation/reoxygenation (OGD/R) model was established to simulate ischemic injury in vitro. ROS levels and neuronal apoptosis were detected by DHE staining and TUNEL assay. Microglial polarization and pro-inflammatory cytokine expression were analyzed using immunofluorescence and RT-qPCR in BV-2 microglia. Transwell co-culture was used to verify the indirect neuroprotection mediated by modulated microglia. ResultsCharacterization results confirmed that the as-prepared PDA NPs were monodispersed spherical nanoparticles with uniform diameter and negative surface potential, demonstrating favorable dispersibility and robust ROS-scavenging activity. The TSPBA-PVA hydrogel exhibited a highly porous interconnected network, suitable mechanical strength, and obvious shear-thinning behavior, supporting its application as an injectable implant. More importantly, the hydrogel displayed typical ROS-responsive degradation and on-demand PDA NP release in a ROS-concentration-dependent manner. In vitro cellular experiments demonstrated that the PDA NP-loaded hydrogel possessed excellent biocompatibility with HT22 cells. In the OGD/R model, the hydrogel significantly reduced intracellular ROS accumulation and markedly suppressed neuronal apoptosis. Furthermore, the composite hydrogel effectively redirected BV-2 microglia from the pro-inflammatory M1 toward the anti-inflammatory M2 phenotypes, downregulated the expression of pro-inflammatory cytokines including TNF-α, IL-1β, and IL-6, and reduced inflammatory damage. Transwell co-culture assays further validated that M2-polarized microglia mediated by the hydrogel significantly enhanced the survival of OGD/R-injured HT22 neurons and attenuated apoptosis. ConclusionIn this study, we successfully developed a novel injectable ROS-responsive hydrogel loaded with PDA NPs for synergistic antioxidative and anti-inflammatory neuroprotection. This intelligent hydrogel system enables ROS-triggered on-demand release of PDA NPs, efficiently scavenges excessive ROS, inhibits oxidative stress injury, modulates microglial polarization, and suppresses neuroinflammation, thereby exerting robust neuroprotective effects in vitro. This biomaterial platform provides a promising strategy for the targeted and controlled delivery of bioactive nanomaterials in the central nervous system diseases and establishes a solid experimental foundation for the development of in situ injectable therapies for ischemic brain injury.

Objective To construct a "disease-syndrome combination" mathematical representation model for pulmonary nodules based on oral microbiome data, utilizing a multimodal data algorithm framework centered on dynamic systems theory. Furthermore, to compare predictive models under various algorithmic frameworks and validate the efficacy of the optimal model in predicting the presence of pulmonary nodules. Methods A total of 213 subjects were prospectively enrolled from July 2022 to March 2023 at the Hospital of Chengdu University of Traditional Chinese Medicine, Sichuan Cancer Hospital, and the Chengdu Integrated Traditional Chinese and Western Medicine Hospital. This cohort included 173 patients with pulmonary nodules and 40 healthy subjects. A novel multimodal data algorithm framework centered on dynamic systems theory, termed VAEGANTF (Variational Auto Encoder-Generative Adversarial Network-Transformer), was proposed. Subsequently, based on a multi-dimensional integrated dataset of “clinical features-syndrome elements-microorganisms”, all subjects were divided into training (70%) and testing (30%) sets for model construction and efficacy testing, respectively. Using pulmonary nodules as dependent variables, and combining candidate markers such as clinical features, lesion location, disease nature, and microbial genera, the independent variables were screened based on variable importance ranking after identifying and addressing multicollinearity. Missing values were then imputed, and data were standardized. Eight machine learning algorithms were then employed to construct pulmonary nodule risk prediction models: random forest, least absolute shrinkage and selection operator (LASSO) regression, support vector machine, multilayer perceptron, eXtreme Gradient Boosting (XGBoost), VAE-ViT (Vision Transformer), GAN-ViT, and VAEGANTF. K-fold cross-validation was used for model parameter tuning and optimization. The efficacy of the eight predictive models was evaluated using confusion matrices and receiver operating characteristic (ROC) curves, and the optimal model was selected. Finally, goodness-of-fit testing and decision curve analysis (DCA) were performed to evaluate the optimal model. Results There were no statistically significant differences between the two groups in demographic characteristics such as age and sex. The 213 subjects were randomly divided into training and testing sets (7 : 3), and prediction models were constructed using the eight machine learning algorithms. After excluding potential problems such as multicollinearity, a total of 301 clinical feature information, syndrome elements, and microbial genera markers were included for model construction. The area under the curve (AUC) values of the random forest, LASSO regression, support vector machine, multilayer perceptron, and VAE-ViT models did not reach 0.85, indicating poor efficacy. The AUC values of the XGBoost, GAN-ViT, and VAEGANTF models all reached above 0.85, with the VAEGANTF model exhibiting the highest AUC value (AUC=0.923). Goodness-of-fit testing indicated good calibration ability of the VAEGANTF model, and decision curve analysis showed a high degree of clinical benefit. The nomogram results showed that age, sex, heart, lung, Qixu, blood stasis, dampness, Porphyromonas genus, Granulicatella genus, Neisseria genus, Haemophilus genus, and Actinobacillus genus could be used as predictors. Conclusion The “disease-syndrome combination” risk prediction model for pulmonary nodules based on the VAEGANTF algorithm framework, which incorporates multi-dimensional data features of “clinical features-syndrome elements-microorganisms”, demonstrates better performance compared to other machine learning algorithms and has certain reference value for early non-invasive diagnosis of pulmonary nodules.

The regulatory processes in developmental biology research are significantly influenced by long non-coding RNAs (lncRNAs). However, the dynamics of lncRNA expression during human tooth development remain poorly understood. In this research, we examined the lncRNAs present in the dental epithelium (DE) and dental mesenchyme (DM) at the late bud, cap, and early bell stages of human fetal tooth development through bulk RNA sequencing. Developmental regulators co-expressed with neighboring lncRNAs were significantly enriched in odontogenesis. Specific lncRNAs expressed in the DE and DM, such as PANCR, MIR205HG, DLX6-AS1, and DNM3OS, were identified through a combination of bulk RNA sequencing and single-cell analysis. Further subcluster analysis revealed lncRNAs specifically expressed in important regions of the tooth germ, such as the inner enamel epithelium and coronal dental papilla (CDP). Functionally, we demonstrated that CDP-specific DLX6-AS1 enhanced odontoblastic differentiation in human tooth germ mesenchymal cells and dental pulp stem cells. These findings suggest that lncRNAs could serve as valuable cell markers for tooth development and potential therapeutic targets for tooth regeneration.
Humans ; RNA, Long Noncoding/metabolism* ; Odontogenesis/genetics* ; Tooth Germ/embryology* ; Cell Differentiation ; Gene Expression Regulation, Developmental ; Mesoderm/metabolism* ; Tooth/embryology* ; Gene Expression Profiling ; Sequence Analysis, RNA ; Dental Pulp/cytology*

OBJECTIVE: To investigate the relationship between physical activity and genetic risk and their combined effects on the risk of developing chronic obstructive pulmonary disease. METHODS: This prospective cohort study included 318,085 biobank participants from the UK. Physical activity was assessed using the short form of the International Physical Activity Questionnaire. The participants were stratified into low-, intermediate-, and high-genetic-risk groups based on their polygenic risk scores. Multivariate Cox regression models and multiplicative interaction analyses were used. RESULTS: During a median follow-up period of 13 years, 9,209 participants were diagnosed with chronic obstructive pulmonary disease. For low genetic risk, compared to low physical activity, the hazard ratios ( HRs) for moderate and high physical activity were 0.853 (95% confidence interval [ CI]: 0.748-0.972) and 0.831 (95% CI: 0.727-0.950), respectively. For intermediate genetic risk, the HRs were 0.829 (95% CI: 0.758-0.905) and 0.835 (95% CI: 0.764-0.914), respectively. For participants with high genetic risk, the HRs were 0.809 (95% CI: 0.746-0.877) and 0.818 (95% CI: 0.754-0.888), respectively. A significant interaction was observed between genetic risk and physical activity. CONCLUSION Moderate or high levels of physical activity were associated with a lower risk of developing chronic obstructive pulmonary disease across all genetic risk groups, highlighting the need to tailor activity interventions for genetically susceptible individuals.
Humans ; Pulmonary Disease, Chronic Obstructive/epidemiology* ; Exercise ; Male ; Female ; Middle Aged ; Prospective Studies ; Aged ; Genetic Predisposition to Disease ; Risk Factors ; United Kingdom/epidemiology* ; Incidence ; Adult

Objective To evaluate the clinical application value of the half-Fourier acquisition single-shot turbo spin echo(HASTE)sequence based on deep learning(DL)in pancreatic T2WI.Methods Data were collected from 41 patients who un-derwent both BLADE and DL-HASTE sequence scans during pancreatic T2WI at some hospital from February to July 2023.Qualitative assessments were made regarding overall image quality,pancreatic edge sharpness,pancreatic duct edge sharp-ness,pancreatic duct visua-lization(proximal,middle and distal segments)and lesion visibility of BLADE-sequence and DL-HASTE-sequence images.Quantitative assessments were carried out in terms of scan time,signal-to-noise ratio(SNR)and contrast-to-noise ratio(CNR).Statistical analyses were performed using SPSS 24.0.Results DL-HASTE sequence behaved significantly better than BLADE in pancreatic duct edge sharpness,pancreatic duct visualization,lesion visibility,scan time and CNR,while worse in pane-reatic edge sharpness(all P＜0.05).There were significant differences between DL-HASTE and BLADE sequences in overall image quality and SNR(all P＞0.05).Conclusion The DL-HASTE sequence maintains image quality while significantly shor-tening scan time,making it suitable for patients with irregular respiratory rates.[Chinese Medical Equipment Journal,2025,46(3):59-63]

The aging trend is intensifying currently,but there is still a lack of standardized diagnosis and treat-ment schemes for severe infections in elderly people.This paper focuses on the recommendations for immune-related clinical diagnosis and treatment routes as well as the idea of risk stratified diagnosis and treatment for elderly peo-ple,aiming to effectively prevent infectious diseases in elderly people and perform stratified management through systematic and scientific means of immune function monitoring and regulation,so as to enhance the standardized level of diagnosis and treatment as well as clinical treatment effect of infection in elderly people.

Chimeric antigen receptor (CAR) T (CAR-T) cell therapy has emerged as a promising treatment strategy for relapsed neurological autoimmune diseases (NADs). In response to this innovative approach, Chinese experts, combining clinical trial results and fully considering the actual medical needs in China, have developed the "Expert consensus on chimeric antigen receptor T cell therapy for neurological autoimmune diseases in China 2025". This consensus provides comprehensive guidelines on patients and cell product selection, treatment procedures, monitoring protocols, adverse event management, with the goal of standardizing clinical practice for CAR-T cell therapy in NADs. The consensus focuses on NADs, pays more attention to the actual situation of Chinese patients, and proposes implementation plans that are convenient for clinical practice, providing important guidance for the promotion of CAR-T cell therapy in the field of neurology in China.

Objective:To investigate the gut microbiota and metabolic profiles of patients with neuromyelitis optica spectrum disorder (NMOSD) and myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD), and to identify potential microbial biomarkers with diagnostic values.Methods:A total of 16 NMOSD patients, 6 MOGAD patients, and 22 age- and sex-matched healthy controls were recruited from Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology since June 2024. Fecal samples were subjected to metagenomic sequencing and untargeted metabolomics. Differential microbes were identified using LEfSe (linear discriminant analysis effect size), and receiver operating characteristic curve analysis was performed to evaluate diagnostic potential. Spearman correlation analysis was used to assess relationships between key microbes, metabolites, and serum antibody titers.Results:Distinct alterations in gut microbiota were observed in both disease groups compared with healthy controls. Ligilactobacillus salivarius was significantly enriched in both NMOSD and MOGAD patients and exhibited robust diagnostic accuracy (area under the curve=0.779 P=0.005). Metabolomics revealed that levels of ethosuximide and lysine-proline were elevated, while free fatty acids (15∶1) and 5, 6-dihydrothymine were reduced in the disease groups. Analysis results indicated that Ligilactobacillus salivarius abundance was positively correlated with aquaporin 4 antibody titers in NMOSD patients ( r=0.522, P=0.046). Conclusions:Patients with NMOSD and MOGAD have characteristic alterations in gut microbial and metabolic profiles.

Objective To evaluate the clinical application value of the half-Fourier acquisition single-shot turbo spin echo(HASTE)sequence based on deep learning(DL)in pancreatic T2WI.Methods Data were collected from 41 patients who un-derwent both BLADE and DL-HASTE sequence scans during pancreatic T2WI at some hospital from February to July 2023.Qualitative assessments were made regarding overall image quality,pancreatic edge sharpness,pancreatic duct edge sharp-ness,pancreatic duct visua-lization(proximal,middle and distal segments)and lesion visibility of BLADE-sequence and DL-HASTE-sequence images.Quantitative assessments were carried out in terms of scan time,signal-to-noise ratio(SNR)and contrast-to-noise ratio(CNR).Statistical analyses were performed using SPSS 24.0.Results DL-HASTE sequence behaved significantly better than BLADE in pancreatic duct edge sharpness,pancreatic duct visualization,lesion visibility,scan time and CNR,while worse in pane-reatic edge sharpness(all P＜0.05).There were significant differences between DL-HASTE and BLADE sequences in overall image quality and SNR(all P＞0.05).Conclusion The DL-HASTE sequence maintains image quality while significantly shor-tening scan time,making it suitable for patients with irregular respiratory rates.[Chinese Medical Equipment Journal,2025,46(3):59-63]