Objective: To explore the association between meat consumption and anxiety symptoms in first year junior high school students in Yunnan Province, and to provide theoretical support for preventing and relieving anxiety symptoms in junior high school students. Methods: From October to December 2022, a random cluster sampling method was used to select 8 500 first year junior high school students from 11 counties in Yunnan Province as the survey subjects for a questionnaire survey. The study used Food Frequency Questionnaire and the Chinese version of the Depression Anxiety Stress Scale-21 (DASS-21) to assess the meat consumption and anxiety symptoms of junior high school students.The distribution differences in anxiety symptoms among first year junior high school students with different demographic characteristics were analyzed statistically by using the Chi-square test,and the association between meat consumption and anxiety symptoms in students was analyzed by using a generalized linear model. Results: The detection rate of anxiety symptoms was 48.47%. After controlling for demographic variables and confounding factors, the consumption of livestock meat, poultry meat, processed meat, cured meat, barbecued meat and raw skin meat was statistically significant with anxiety symptoms ( β =-0.05, 0.04, 0.04, 0.08, 0.14, 0.17, all P <0.05). Stratified by ethnicity, The consumption of livestock meat, cured meat and barbecue was statistically correlated with anxiety symptoms in Han adolescents ( β =-0.07, 0.14, 0.22 ); the consumption of processed meat and raw skin meat was statistically correlated with anxiety symptoms in ethnic minority adolescents ( β =0.08, 0.18) (all P <0.05). Conclusions There is a statistical association between meat comsumption and the risk of anxiety symptoms in first year junior high school students in Yunnan Province. Guidance on meat consumption should be strengthened to prevent the occurrence of anxiety symptoms.

This paper has constructed a traditional Chinese medicine （TCM） specialized disease dataset platform for mixed hemorrhoids based on a multimodal large model， and the preliminary application has been validated. The platform uses StarRocks to establish a four-level data warehouse system， enabling the aggregation， cleaning， and standardization of multi-source heterogeneous data. Using DeepSeek-R1-Distill-Qwen-7B as the base model， domain fine-tuning is performed through low-rank adaptation （LoRA） technology. Combined with LLaMA-3.3 natural language processing and reasoning chain techniques， the platform enables intelligent parsing and structured extraction of unstructured TCM medical records. It accurately identifies six major categories and 28 subcategories of entities， including symptoms and syndromes， with a fine-tuned model F1 score of 93.8%. The platform has established a high-quality specialized disease dataset containing more than 50,000 medical records and has been applied in a real-world study involving 17,831 patients， preliminarily verifying the efficacy of TCM heritage surgery.

OBJECTIVE: To explore the genetic variants and phenotypic characteristics of patients with Neurofibromatosis type I (NF1). METHODS: Twenty two NF1 patients who presented at Enze Medical (Center) Group in Taizhou between 2018 and 2024 were selected as the study subjects. Clinical phenotype and family history were collected for the patients. Whole exome sequencing (WES) was carried out for the 22 probands to screen the variants of NF1 gene. Candidate variants were verified by Sanger sequencing of their family members. This study was approved by the Medical Ethics Committee of the Hospital (Ethics No.: K20230902). RESULTS: The 22 probands were diagnosed between the age of 5 months to 47 years old, and have all shown cafe au lait spots on their skin. Seventeen patients exhibited the phenotype at birth, and 11 had various degrees of neurofibromatosis. Among them, probands 1 and 13 underwent surgical resection of the tumor but had recurred, while proband 12 had amputation due to the huge size and serious impact of the neurofibroma and had no recurrence. Five patients had various degrees of scoliosis. In total 22 germline mutations and one somatic mutation were identified among the 22 families, with 5 variants unreported previously, including 1 nonsense mutation c.1603C>T (Q535*), 3 frameshift mutations [c.7268_7269delCA (Thr2423fs), c.2293del (Arg765Alafs*26), and c.5433_5438delinsGC (Phe1812ArgfsTer50)], and 1 deletion involving exons 41-44 of the NF1 gene and adjacent introns. Proband 13 was found to harbor germline mutation c.6796C>T (Gln2266Ter) and somatic mutation c.1019_1020del (Ser340Cysfs Ter12) in the peripheral blood and tumor tissue, respectively. Among the 22 NF1 probands, 6 had received treatment due to severe illness. Proband 1 had tumor resection in the right upper limb, but was found to have malignant lung tumor and died during follow-up. Proband 12 had multiple recurrence of neurofibroma in the left ring finger. Proband 4 underwent spinal correction surgery due to severe scoliosis. Proband 11 had died due to a central nervous system disease. Among the 22 germline mutations, 6 had led to the occurrence of truncated proteins, which may have a more severe impact on the phenotype. CONCLUSION This study investigated the genetic variants and clinical phenotypes of 22 NF1 families and identified 5 novel variants of the NF1 gene, which has expanded the genotypic and phenotypic spectra of the NF1. Preliminary studies have identified an association between truncated mutations, young age, and severe phenotypes, which may provide important clues for prognosis evaluation. For the clinical diagnosis and treatment of NF1, it is necessary to consider the phenotypic characteristics and genetic testing in combination with genetic counseling and long-term follow-up.
Humans ; Neurofibromatosis 1/pathology* ; Male ; Female ; Pedigree ; Adult ; Child ; Child, Preschool ; Middle Aged ; Adolescent ; Infant ; Young Adult ; Neurofibromin 1/genetics* ; Phenotype ; Asian People/genetics* ; Mutation ; Exome Sequencing ; East Asian People

Objective: To investigate the prevalence of overweight and obesity among children and adolescents in Yangzhou City, and its association with screen behaviors, so as to provide scientific evidence for weight management among students. Methods: In May 2025, an electronic questionnaire survey was conducted among children and adolescents in Yangzhou City. A total of 3 722 participants were selected from grades 4 to 12 in 18 primary and secondary schools (108 classes) by using stratified cluster random sampling. The Chi square test was used to compare the differences in the detection rates of overweight and obesity among children and adolescents with 5 types of screen behaviors (watching TV, playing electronic games, scrolling short videos, screen based learning, electronic socializing) in different time groups each day (never, >0~<2 h, ≥2 h). Multivariate Logistic regression analysis was performed to examine the associations of five types of screen behaviors, presence of electronic devices in the bedroom, and screen use during meals on the weight status of children and adolescents. Results: The prevalence of overweight and obesity among children and adolescents was 37.3%. For all five types of screen behaviors, the differences in the distribution of overweight and obesity detection rates among children and adolescents across the three time spent categories were statistically significant ( χ 2=30.76- 70.78 , all P <0.01). After adjusting for confounding factors, multivariate Logistic regression analysis revealed that frequent or always using screens during meals( OR =1.63, 95％ CI =1.14~2.31), playing video games ( OR =1.28, 95% CI =1.11-1.48), browsing short videos ( OR =1.29, 95％ CI=1.09-1.54), and screen based learning ( OR =1.26, 95％ CI =1.10-1.44) were significantly associated with overweight and obesity among children and adolescents (all P <0.05). Conclusions Excessive screen use is positively correlated with the incidence of overweight and obesity in children and adolescents. Targeted interventions on screen behaviors among children and adolescents are therefore warranted.

BACKGROUND:Abutment screw loosening is one of the most common mechanical complications in implant restoration.Mechanical wear,as a potential cause of thread loosening,warrants attention due to its impact on mechanical performance and long-term stability.However,studies on the mechanical effects of thread wear in abutment screws remain limited,and no definitive conclusions have been reached.OBJECTIVE:To investigate the effect of different degrees of mechanical wear on the spatial stress distribution of the Morse taper connection implant system,with a view to providing a theoretical basis for the clinical assessment of the long-term stability of dental implants.METHODS:Three-dimensional finite element models of Morse taper implants with central screw thread wear levels of 0,0.1,1,10,and 100 μm were established using SolidWorks software,and simulation analysis with Ansys Workbench software was performed.The implant models were inserted into artificial bone blocks(simulating type Ⅱ bone,with a cortical bone thickness of 2 mm on the outer layer and cancellous bone inside).An alternating load of 300 N in the buccolingual direction was applied at the centroid of the abutment(forming an angle of 30° with the long axis of the implant).The von Mises stress,principal stress,displacement,and fatigue life of the abutment,central screw,implant,and bone tissue in the five groups of models were analyzed.RESULTS AND CONCLUSION:(1)As the degree of mechanical wear on the central screw thread increased,the von Mises stress,principal stress,and strain in the implant and abutment also increased.The stress in the model was concentrated at the top of the implant,at the shoulder level of the implant,at the neck of the abutment,and at the bottom edge of the abutment.(2)Under moderate wear conditions(≥ 10 μm),the fatigue life of the implant system decreased by 30％,and the maximum von Mises stress of the central screw decreased by 37％,with the stress still primarily concentrated at the transition area between the head and the body of the central screw.(3)Under significant wear conditions(≥ 100 μm),the von Mises stress of the central screw decreased by 98％,with the stress concentrated at the screw head,and the fatigue life of the implant system decreased by 63％.Therefore,when the wear level of the central screw thread reaches ≥ 10 μm,the risk of screw loosening is significantly increased,and the fatigue life of the implant system is markedly reduced,warranting clinical attention.

BACKGROUND:Abutment screw loosening is one of the most common mechanical complications in implant restoration.Mechanical wear,as a potential cause of thread loosening,warrants attention due to its impact on mechanical performance and long-term stability.However,studies on the mechanical effects of thread wear in abutment screws remain limited,and no definitive conclusions have been reached.OBJECTIVE:To investigate the effect of different degrees of mechanical wear on the spatial stress distribution of the Morse taper connection implant system,with a view to providing a theoretical basis for the clinical assessment of the long-term stability of dental implants.METHODS:Three-dimensional finite element models of Morse taper implants with central screw thread wear levels of 0,0.1,1,10,and 100 μm were established using SolidWorks software,and simulation analysis with Ansys Workbench software was performed.The implant models were inserted into artificial bone blocks(simulating type Ⅱ bone,with a cortical bone thickness of 2 mm on the outer layer and cancellous bone inside).An alternating load of 300 N in the buccolingual direction was applied at the centroid of the abutment(forming an angle of 30° with the long axis of the implant).The von Mises stress,principal stress,displacement,and fatigue life of the abutment,central screw,implant,and bone tissue in the five groups of models were analyzed.RESULTS AND CONCLUSION:(1)As the degree of mechanical wear on the central screw thread increased,the von Mises stress,principal stress,and strain in the implant and abutment also increased.The stress in the model was concentrated at the top of the implant,at the shoulder level of the implant,at the neck of the abutment,and at the bottom edge of the abutment.(2)Under moderate wear conditions(≥ 10 μm),the fatigue life of the implant system decreased by 30％,and the maximum von Mises stress of the central screw decreased by 37％,with the stress still primarily concentrated at the transition area between the head and the body of the central screw.(3)Under significant wear conditions(≥ 100 μm),the von Mises stress of the central screw decreased by 98％,with the stress concentrated at the screw head,and the fatigue life of the implant system decreased by 63％.Therefore,when the wear level of the central screw thread reaches ≥ 10 μm,the risk of screw loosening is significantly increased,and the fatigue life of the implant system is markedly reduced,warranting clinical attention.

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 investigate the influencing factors for cognitive function among aluminum workers, so as to provide the basis for intervention and prevention of cognitive function among aluminum-exposed populations. Methods: From July to August 2019, male aluminum workers in the electrolytic aluminum workshop of an aluminum factory in Shanxi Province were selected using the cluster sampling method. Demographic information, prevalence of chronic diseases, lifestyle behaviors, night shifts, and sleep quality were collected through questionnaire surveys. Blood aluminum levels were measured using inductively coupled plasma-mass spectrometry. Cognitive function was investigated using the Montreal Cognitive Assessment. Factors affecting cognitive function among aluminum workers were analyzed by a quantile regression model. Results: A total of 142 aluminum workers were surveyed, including 57 workers aged 20 to <40 years (40.14%) and 85 workers aged 40 to 60 years (59.86%). The median blood aluminum level was 38.23 (interquartile range, 21.82) μg/L. The median cognitive function score was 24.00 (interquartile range, 3.00) points. Quantile regression analysis revealed that older age (βQ5=-0.186, 95%CI: -0.269 to -0.102), lower educational level (βQ5=1.933, 95%CI: 1.029 to 2.838; βQ10=1.743, 95%CI: 0.480 to 3.006; βQ50=1.038, 95%CI: 0.141 to 1.935; βQ75=1.006, 95%CI: 0.437 to 1.575; βQ90=1.111, 95%CI: 0.291 to 1.930), smoking (βQ5=-2.056, 95%CI: -3.264 to -0.849), alcohol consumption (βQ5=-1.821, 95%CI: -3.247 to -0.396) and higher blood aluminum level (βQ5=-0.075, 95%CI: -0.110 to -0.040; βQ10=-0.078, 95%CI: -0.127 to -0.029; βQ50=-0.075, 95%CI: -0.110 to -0.040; βQ75=-0.057, 95%CI: -0.079 to -0.035; βQ90=-0.067, 95%CI: -0.099 to -0.035) were associated with cognitive function decline among aluminum workers. Conclusions Educational level and blood aluminum level are the main factors affecting the cognitive function among aluminum workers. Among those with lower cognitive function scores, age, smoking and alcohol consumption are also associated with cognitive function.

Objective: To investigate the epidemic situation of notifiable infectious diseases in Zhejiang Province in 2024, so as to summarize the epidemic characteristics. Methods: Data of notifiable infectious diseases cases in Zhejiang Province from January 1 to December 31, 2024 were collected from the Infectious Disease Surveillance System of Chinese Disease Prevention and Control Information System. The epidemiological characteristics were analyzed according to the classification and transmission routes using the descriptive epidemiological method. Results: A total of 32 types of notifiable infectious diseases with 1 858 695 cases and 392 deaths were reported in Zhejiang Province in 2024, with a reported incidence of 2 804.73/105 and a reported mortality of 0.591 5/100 000. A total of 238 infectious disease public health emergencies were reported, of which 218 (91.60%) occurred in schools and kindergartens. There were 22 types of class A and B notifiable infectious diseases reported, with incidence of 470.62/100 000 and mortality of 0.591 5/100 000. Totally 10 types of class C notifiable infectious diseases, with a reported incidence of 2 334.11/105, and no deaths were reported. Classified by transmission route, respiratory infectious diseases had the highest reported incidence of 2 423.87/100 000, among which influenza exhibited the highest reported incidence of 2 024.22/100 000. The reported incidence of intestinal infectious diseases was 312.94/105, among which the incidence of other infectious diarrhea and hand-foot-mouth disease (HFMD) were high, with reported incidences of 169.52/100 000 and 136.18/100 000, respectively. Blood-borne and sexually transmitted infectious diseases accounted for the largest number of reported deaths, among which AIDS had the highest mortality of 0.424 0/100 000. Natural and insect-borne infectious diseases exhibited a low reported incidence of 1.37/105. The reported incidence of dengue fever was 0.40/100 000, and 95.08% of the cases were imported. Conclusions The reported incidence of respiratory and intestinal infectious diseases and the reported mortality of AIDS were high in Zhejiang Province in 2024. It is recommended to strengthen the prevention and control of infectious diseases such as influenza, other infectious diarrhea, and HMFD in schools and kindergartens.

Background Non-optimal temperatures pose significant threats to public health. Analyzing the association between temperature exposure and the number of emergency cases of acute alcohol intoxication can provide evidence for optimizing emergency resource allocation and response strategies. Objective To analyze the overall impact and lag effects of non-optimal temperatures on the number of 120 emergency calls for acute alcohol intoxication in Wuxi, and to assess the attributable risk, in order to provide empirical evidence for formulating climate-adaptive public health strategies. Methods Call records of acute alcohol intoxication from Wuxi's 120 emergency service, concurrent air pollutant data, and meteorological data (including daily mean temperature) were collected from January 1, 2014 to December 31, 2020. Distributed lag nonlinear modeling was used for time-series analysis, with cross-basis functions to capture the nonlinear relationship and lag effects between temperature and emergency volume. Confounding factors such as long-term trends, humidity, pollutants [ultimately including ozone (O₃) and fine particulate matter (PM_2.5)], day of the week, and holidays were controlled. The maximum lag period was set to 14 days. Single-day lag and cumulative lag effects of extreme temperatures were analyzed, followed by sensitivity analysis. Effects were quantified using relative risk (RR) and 95% confidence intervals (95%CI), and attributable fractions and numbers for different temperature ranges were calculated. Results A total of 10705 emergency cases of acute alcohol intoxication were included during the study period, with males accounting for 89.5% and individuals aged 18–50 years accounting for 83.4%. A non-linear "U-shaped" association was observed between daily mean temperature and the number of emergency calls, with an optimal temperature of 13.3 ℃. The effect of the extreme low temperature (1.88 ℃) was statistically significant from lag1 to lag3 after exposure (P<0.05), with the maximum effect at lag1 (RR=1.063, 95%CI: 1.002, 1.129). The extreme high temperature (32.3 ℃) showed an immediate effect, with maximum effect at lag0 (RR=1.374, 95%CI: 1.248, 1.512). The cumulative effect analysis showed that the risk associated with high temperature was the highest at lag0–3 (RR=2.000, 95%CI: 1.667, 2.399), while the risk associated with low temperature was the highest at lag0–14 (RR=1.462, 95%CI: 1.042, 2.051). The attribution analysis indicated that the mild heat (>24.7–30.8 ℃) and heat (>30.8–33.5 ℃) contributed the largest numbers of attributable cases, with 591 cases (95%CI: 56, 1032) and 190 cases (95%CI: 51, 309), respectively. The corresponding attributable fractions were 5.81% (95%CI: 0.80%, 9.94%) and 1.87% (95%CI: 0.49%, 2.96%), respectively. Conclusion Both extreme high and low temperatures significantly increase the number of 120 emergency calls for acute alcohol intoxication in Wuxi. High temperature exhibit an acute effect, while low temperature show a delayed effect. Mild heat have a significant impact on the emergency burden. It is recommended that emergency departments optimize resource allocation according to climatic characteristics and strengthen emergency responses during extreme weather to reduce alcohol-related health burden.