Objective To address the safety challenges arising from the rapid development of nuclear energy and technology, assess the current status of health emergency response capabilities in nuclear radiation emergencies, and promote capacity enhancement. Methods A preliminary evaluation system for health emergency response capability in nuclear radiation emergencies was developed based on a literature review. Two rounds of Delphi expert consultation (n = 20) were conducted, and the analytic hierarchy process was employed to establish judgment matrices for assigning indicator weights. Results The finalized system included six primary indicators (radiation protection capability, triage capability, decontamination and evacuation capability, medical treatment capability, radiation detection capability, and radiation dose estimation capability), along with 29 secondary indicators, such as capability for setting up emergency zones, capability for protecting personnel from internal and external contamination, on-site first aid capability, and personal dose monitoring capability. The expert response rate was 0.95, and the expert authority coefficient reached 0.80. The Kendall’s coefficient of concordance was W = 0.288 (P<0.01) for the first round of expert consultation and W = 0.308 (P<0.01) for the second round. Both rounds demonstrated high agreement among experts, and the consultation questionnaires passed reliability and validity tests. Conclusion By integrating qualitative analysis and quantitative calculation, this study developed a scientifically sound and operationally feasible evaluation system. This system will help identify gaps in health emergency response capabilities and provide scientific guidance and a decision-making basis for optimizing emergency plans and improving the level of health emergency response in nuclear radiation emergencies.

ObjectiveTo systematically analyze the epidemiological characteristics and cause-of-death distribution among death cases with hepatitis C in Hongkou District of Shanghai, and to provide a scientific basis for optimizing healthcare resources allocation and targeted hepatitis C prevention and control measures. MethodsA retrospective cross-sectional study was conducted by integrating historical surveillance data from China Information System of Disease Prevention and Control and Shanghai Hongkou District Death Medical Registration System. Data on demographic characteristics, hepatitis C-related clinical and management records, and underlying causes of death for cases with hepatitis C between 2012 and 2024 in Hongkou District of Shanghai, were collected. Descriptive analyses were performed to analyze the epidemiological characteristics and cause-of-death distribution of death cases, and comparative analyses were conducted across different subgroups. ResultsA total of 204 hepatitis C-related deaths were identified in Hongkou District, Shanghai, from 2012 to 2024. The average age at death was (69.49±12.75) years The majority decedents were males (71.57%) and retired (73.53%). The top three underlying causes of death were malignant tumors (45.10%), cerebrovascular diseases (15.20%) and cardiovascular diseases (12.25%), collectively accounting for 72.55% of all deaths. Deaths attributed to hepatitis C accounted for 9.80% (20/204), with a mean age at death of (63.41±11.81) years. No statistically significant differences were observed in the proportion of hepatitis C-attributed deaths across different subgroups (all P>0.05). The proportion of premature deaths was 55.88% (114/204), with a mean age at death of (60.02±6.89) years. The proportion of premature deaths was higher among males (60.27%), laboratory-diagnosed patients (62.69%), patients with other liver diseases (72.06%), and those non-compliant with follow-up (70.97%) compared to their respective counterparts (all P<0.05). Additionally, homemakers /unemployed patients (100.00%) and employed patients (88.89%) had a significantly higher proportion of premature deaths compared to retired patients (42.67%) (P<0.001). There was a statistically significant difference in the distribution of causes of death between the premature death group and the non-premature death group (χ²=14.93， P=0.048). The top three causes of premature deaths were malignant tumors (50.00%), hepatitis C (12.28%) and cerebrovascular diseases (10.53%). Regarding the proportion of deaths occuring prematurely, other viral hepatitis had the highest percentage (75.00%), followed by diabetes mellitus (71.43%) and hepatitis C (70.00%). ConclusionThe majority of death cases with hepatitis C were males and retirees in Hongkou District, Shanghai. The leading cause of death was malignant tumors, while hepatitis C ranked as the fourth underlying cause, as well as served as the second leading cause of premature death following malignant tumors. Premature death was closely associated with gender, occupation, diagnostic classification, presence of other liver diseases, and follow-up compliance, highlighting the importance of enhanced health management and targeted interventions among high-risk groups.

Objective To explore the safety and feasibility of 5G remote robot-assisted thoracoscopic anatomic lobectomy and segmentectomy. Methods A retrospective analysis was conducted on patients who underwent 5G remote robot-assisted thoracoscopic anatomic lobectomy or segmentectomy between July and September 2024. The surgeries were performed collaboratively by Shanghai Chest Hospital, Affiliated Hospital of Yangzhou University, and The First Affiliated Hospital of Nanchang University. Perioperative outcomes were recorded and analyzed. Results Ten patients were included, comprising 4 anatomic lobectomies and 6 segmentectomies. The median age was 61.0 (56.0, 72.0) years. The median intraoperative bi-directional network latency was 52.5 (39.0, 54.0) ms. There were no instances of network interruption, robot-related adverse events, or conversions to open thoracotomy. The median postoperative chest tube drainage volume was 500.0 (375.0, 600.0) mL, and the median hospital stay was 5.5 (4.0, 6.0) days. No complications of Clavien-Dindo grade Ⅱor higher occurred. All patients were recurrence-free and alive during the 6-month postoperative follow-up period. Conclusion 5G remote robot-assisted thoracoscopic surgery demonstrates high safety and operational stability. This technology shows promising potential for clinical application and is worthy of further development and utilization.

Objective To explore the clinical value of preoperative assessment and intraoperative frozen section pathology in screening patients suitable for testicular preservation surgery, so as to provide reference for reducing unnecessary radical orchiectomy in patients with benign testicular tumors. Methods A retrospective analysis was conducted on 121 patients who underwent surgical treatment for testicular tumors in the First Affiliated Hospital of Xi'an Jiaotong University during Jan. 2021 and Sep. 2025. Data were collected, including demographic information, preoperative imaging, serum tumor markers, surgical procedures, intraoperative frozen section, postoperative pathology, and postoperative testicular volume changes. Results Among the 121 patients, 105 underwent radical orchiectomy, with 91 malignant and 14 benign cases confirmed by postoperative pathology; 16 received testicular tumor enucleation, all confirmed as benign. Benign lesions accounted for 24.79% (30/121) of all cases. In patients undergoing tumor enucleation, intraoperative frozen section results were fully consistent with final pathological findings. Conclusion Preoperative differentiation between benign and malignant testicular tumors is crucial for selecting the appropriate surgical approach. For patients with preoperative findings suggesting benign lesions, testicular tumor enucleation combined with intraoperative frozen section can achieve precise treatment while maximizing testicular preservation.

Both type 2 diabetes mellitus (T2DM) and metabolic associated fatty liver disease (MAFLD) fall within the spectrum of metabolic diseases, and they exhibit a bi-directional causal relationship and robust reciprocal association. Their shared pathological cornerstone is insulin resistance (IR), which involves the interplay of mitochondrial dysfunction and chronic inflammation, forming a cascading pathological process of “IR-mitochondrial dysfunction-inflammation”. This largely explains the notable upward trend in T2DM-MAFLD co-occurrence observed over recent years. Exercise intervention, as a safe and effective non-pharmacological approach, can improve the pathological progression of these patients at multiple levels. Following the logical framework of “pathogenesis-efficacy comparison-molecular mechanisms-clinical translation”, this article systematically compares the efficacy and molecular mechanisms of moderate-intensity continuous training (MICT), resistance exercise, high-intensity interval training (HIIT), and combined training. MICT reduces intrahepatic triglycerides by promoting lipolysis and improving cardiorespiratory fitness; resistance exercise increases muscle mass and basal metabolic rate, offering unique advantages in preserving muscle while reducing fat and improving insulin sensitivity; HIIT is a time-efficient exercise modality that enhances patients’ cardiorespiratory fitness and insulin sensitivity by alternating brief periods of vigorous exertion with recovery periods, with a prominent short-term triglyceride-lowering effect; combined training produces synergistic effects, comprehensively improving glucolipid metabolism and showing the best long-term adherence. Mechanistically, exercise exerts its beneficial effects through three common pathways: (1) AMPK-mediated lipid oxidation and mitochondrial biogenesis; (2) IRS/PI3K/Akt-mediated insulin signaling sensitization; and (3) Nrf2/ARE anti-oxidation and TGF-β/Smads anti-fibrosis regulation. Different exercise modalities activate these pathways with distinct emphases: MICT most directly and persistently activates the AMPK pathway; resistance exercise uniquely improves IRS/PI3K/Akt signaling through muscle mass gain; HIIT induces the highest AMPK activation intensity and triggers unique lactate-mediated signaling regulation; combined training integrates the above multiple mechanistic advantages. For clinical translation, multidisciplinary team collaboration is essential to ensure safety and adherence. Individualized prescriptions should be formulated according to the FITT-VP principle and patient phenotypes—frequency of 3-5 sessions/week of aerobic exercise combined with 2-3 sessions/week of resistance exercise; intensity of moderate-intensity (40%-60% heart rate reserve (HRR))aerobic exercise and 60%-80% of one-repetition maximum (1-RM) for resistance exercise; time of at least 150 min/week of moderate-intensity aerobic exercise, 30-60 min per session; type of combined training as the preferred modality; total volume of≥500-1 000 MET-min/week; and progression adjusted every 4-6 weeks—with real-time adjustments supported by wearable devices, ultimately forming a closed-loop management system from initial assessment to long-term follow-up. Notably, current studies have limitations such as small sample sizes and short intervention periods. Future research should focus on long-term follow-up, multi-omics biomarkers, and combined exercise-drug strategies. In conclusion, the systematic integration of structured, individualized, and sustainable exercise interventions into the multidisciplinary management pathway for patients with T2DM complicated by MAFLD is an urgent need in current clinical practice.

ObjectiveTo propose an X-Gait motor rehabilitation assessment system based on a perception-cognition decoupling architecture to jointly achieve high-accuracy gait recognition and clinical interpretability for low clinical credibility and opaque decision-making workflows in existing intelligent gait assessment models. MethodsIn the perception layer, YOLOv11-Pose and PoseFormerV2 were integrated for 2D-to-3D human pose reconstruction. A long short-term memory network was adopted for temporal classification of normal, abnormal and pathological gaits. The layer-wise relevance propagation algorithm was used to identify discriminative joints and automatically quantify biomechanical indices including trunk forward inclination angle, stance phase percentage and stride symmetry. In the cognition layer, rehabilitation-oriented prompt engineering and prior knowledge graphs were combined to drive large language models, converting quantitative biomechanical measurements into clinically reasonable structured reports. Eight subjects were enrolled for bedside clinical validation to evaluate classification performance and clinical consistency. ResultsThe overall gait classification accuracy of X-Gait reached 94.1%. The 2D pose detection achieved 100% valid detection rate with an average confidence of 0.819. The mean per-joint position error of 3D pose reconstruction was 105.97 mm. In clinical validation of eight cases, outputs generated by explainable artificial intelligence combined with large language models showed high consistency with manual assessments from experienced rehabilitation physicians. ConclusionThe proposed X-Gait system based on perception-cognition decoupling realizes high-precision gait identification and clinically interpretable outputs.

Macrophages are innate immune cells connected with the development of inflammation. Retinoic acid has previously been proved to have anti-inflammatory and anti-arthritic properties. However, the exact mechanism through which retinoic acid modulates arthritis remains unclear. This study aimed to investigate whether retinoic acid ameliorates rheumatoid arthritis by modulating macrophage polarization. This study used retinoic acid to treat mice with adjuvant arthritis and evaluated anti-inflammatory effects by arthritis score, thermal nociceptive sensitization test, histopathologic examination and immunofluorescence assays. In addition, its specific anti-arthritic mechanism was investigated by flow cytometry, cell transfection and inflammatory signaling pathway assays in RAW264.7 macrophages in vitro. Retinoic acid significantly relieved joint pain and attenuated inflammatory cell infiltration in mice. Furthermore, this treatment modulated peritoneal macrophage polarization, increased levels of arginase 1, as well as decreased inducible nitric oxide synthase expression. In vitro, we verified that retinoic acid promotes macrophage transition from the M1 to M2 type by upregulating mitogen-activated protein kinase (MAPK) phosphatase 1 (MKP-1) expression and inhibiting P38, JNK and ERK phosphorylation in lipopolysaccharide-stimulated RAW264.7 cells. Notably, the therapeutic effects of retinoic acid were inhibited by MKP-1 knockdown. Retinoic acid exerts a significant therapeutic effect on adjuvant arthritis in mice by regulating macrophage polarization through the MKP-1/MAPK pathway, and play an important role in the treatment of rheumatic diseases.

In recent years, the application of artificial intelligence (AI) in the field of biology has witnessed remarkable advancements. Among these, the most notable achievements have emerged in the domain of protein structure prediction and design, with AlphaFold and related innovations earning the 2024 Nobel Prize in Chemistry. These breakthroughs have transformed our ability to understand protein folding and molecular interactions, marking a pivotal milestone in computational biology. Looking ahead, it is foreseeable that the accurate prediction of various physicochemical properties of proteins—beyond static structure—will become the next critical frontier in this rapidly evolving field. One of the most important protein properties is thermodynamic stability, which refers to a protein’s ability to maintain its native conformation under physiological or stress conditions. Accurate prediction of protein stability, especially upon single-point mutations, plays a vital role in numerous scientific and industrial domains. These include understanding the molecular basis of disease, rational drug design, development of therapeutic proteins, design of more robust industrial enzymes, and engineering of biosensors. Consequently, the ability to reliably forecast the stability changes caused by mutations has broad and transformative implications across biomedical and biotechnological applications. Historically, protein stability was assessed via experimental methods such as differential scanning calorimetry (DSC) and circular dichroism (CD), which, while precise, are time-consuming and resource-intensive. This prompted the development of computational approaches, including empirical energy functions and physics-based simulations. However, these traditional models often fall short in capturing the complex, high-dimensional nature of protein conformational landscapes and mutational effects. Recent advances in machine learning (ML) have significantly improved predictive performance in this area. Early ML models used handcrafted features derived from sequence and structure, whereas modern deep learning models leverage massive datasets and learn representations directly from data. Deep neural networks (DNNs), graph neural networks (GNNs), and attention-based architectures such as transformers have shown particular promise. GNNs, in particular, excel at modeling spatial and topological relationships in molecular structures, making them well-suited for protein modeling tasks. Furthermore, attention mechanisms enable models to dynamically weigh the contribution of specific residues or regions, capturing long-range interactions and allosteric effects. Nevertheless, several key challenges remain. These include the imbalance and scarcity of high-quality experimental datasets, particularly for rare or functionally significant mutations, which can lead to biased or overfitted models. Additionally, the inherently dynamic nature of proteins—their conformational flexibility and context-dependent behavior—is difficult to encode in static structural representations. Current models often rely on a single structure or average conformation, which may overlook important aspects of stability modulation. Efforts are ongoing to incorporate multi-conformational ensembles, molecular dynamics simulations, and physics-informed learning frameworks into predictive models. This paper presents a comprehensive review of the evolution of protein thermodynamic stability prediction techniques, with emphasis on the recent progress enabled by machine learning. It highlights representative datasets, modeling strategies, evaluation benchmarks, and the integration of structural and biochemical features. The aim is to provide researchers with a structured and up-to-date reference, guiding the development of more robust, generalizable, and interpretable models for predicting protein stability changes upon mutation. As the field moves forward, the synergy between data-driven AI methods and domain-specific biological knowledge will be key to unlocking deeper understanding and broader applications of protein engineering.

Objective: To explore the correlation between traditional Chinese medicine (TCM) constitution and depressive symptom among school aged children and adolescents, so as to provide evidences for informing constitution based regulation and prevention of depressive symptom. Methods: From June to December 2024, a total of 4 729 students aged 6-14 were recruited by cluster random sampling from 10 primary schools in Baoding (Hebei Province), Heze and Liaocheng (Shandong Province). General information, TCM constitution and depressive symptom were collected. Restricted cubic spline (RCS) models were used to analyze related factors and threshold effects of depressive symptom. Binary Logistic regression was applied to examine the association between depressive symptom and TCM constitution, with subgroup analyses conducted. Results: The detection rate of depressive symptom among the included children and adolescents was 25.82%. RCS analyses indicated non linear associations between depressive symptom and age (inflection point at 10 years old), bedtime (inflection point at 22:00), and wake up time (inflection point at 6:30 ) (all P non linearity <0.01). Linear associations were observed with body mass index (BMI) and sleep duration (all P non linearity > 0.05 ). After adjusting for covariates such as age, BMI and sleep status, binary Logistic regression analyses showed that Yin deficient constitution ( OR =1.26, 95% CI =1.09-1.45) and Phlegm-dampness constitution ( OR =1.42, 95% CI =1.11-1.82) were significantly associated with depressive symptom among children and adolescents (all P <0.05). Conclusions Depressive symptom among school aged children and adolescents is primarily associated with Yin deficiency and Phlegm dampness constitutions in TCM constitution. Active attention should be paid to susceptible TCM constitution among children and adolescents. Targeted health guidance and interventions should be implemented to improve TCM constitution health status for preventing the occurrence of depressive symptom.

Nucleotide-binding oligomerization domain (NOD)-like receptor protein 3 (NLRP3) is a cytosolic pattern recognition receptor that recognizes multiple pathogen-associated molecular patterns and damage-associated molecular patterns. It is a cytoplasmic immune factor that responds to cellular stress signals, and it is usually activated after infection or inflammation, forming an NLRP3 inflammasome to protect the body. Aberrant NLRP3 inflammasome activation is reportedly associated with some inflammatory diseases and metabolic diseases. Recently, there have been mounting indications that NLRP3 inflammasomes play an important role in liver injuries caused by a variety of diseases, specifically hepatic ischemia/reperfusion injury, hepatitis, and liver failure. Herein, we summarize new research pertaining to NLRP3 inflammasomes in hepatic injury, hepatitis, and liver failure. The review addresses the potential mechanisms of action of the NLRP3 inflammasome, and its regulation in these liver diseases.
Humans ; NLR Family, Pyrin Domain-Containing 3 Protein/metabolism* ; Inflammasomes/physiology* ; Animals ; Liver Diseases/metabolism* ; Liver/metabolism* ; Reperfusion Injury/metabolism*