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

Background As a pillar industry in China, the manufacturing sector has a high incidence of non-fatal occupational injuries. The factors influencing non-fatal occupational injuries in this industry are closely related at various levels, including individual, equipment, environment, and management, making the analysis of these influencing factors complex. Objective To identify influencing factors of non-fatal occupational injuries among manufacturing workers, providing a basis for targeted interventions and surveillance. Methods A total of 2243 frontline workers from cable and shipbuilding enterprises were selected as study subjects to investigate the incidence of non-fatal occupational injuries and collect information at four levels: individual, equipment, management, and environment in past 12 months. Data balancing was performed using resampling, and LASSO regression was used to select factors of non-fatal occupational injuries. The influence degree and type of variables were judged based on the magnitude of the estimated coefficients of each variable, where variables with estimated coefficients > 0 are risk factors, and those <0 are protective factors. The area under the receiver operating characteristic (ROC)curve (AUC) was used to test the performance of the model, with an AUC value > 0.7 indicating good model performance. Results Among the 2243 frontline workers, males accounted for 77.7% (1742 out of 2243), with the main age range being 40-49 years old, representing 29.5% (661 out of 2243), 82.7% of the workers (1854 out of 2243) were married, and 55.6% (1248 out of 2243) had a junior middle school education level. The average monthly income for 51.0% (1144 out of 2243) of the workers was between 5000 and 6999 Chinese Yuan. The incidence of non-fatal occupational injuries among the manufacturing workers was 8.4% (189/2243) in the past 12 months. Among the 22 factors associated with the occurrence of non-fatal occupational injuries (P<0.05), 10 were individual-level factors, including gender, smoking, alcohol consumption, colleague relationships, average exercise duration, job burnout, work fatigue, musculoskeletal disorders, cardiovascular diseases, and neurological and sensory organ diseases; 3 were equipment-level factors, including equipment operability, hazardous workpieces, and safety hazards; 5 were environmental-level factors, including low temperatures, special operations, noise, workspace size, and dirty and disorderly environment; and 4 were management-level factors, including daily working hours, weekly working days, overtime, and pre-job technical training. The AUC value of the LASSO regression model was 0.704 and the final model retained a total of 10 variables. Among them, there were 7 risk factors for non-fatal occupational injuries (coefficient > 0), including safety hazards, musculoskeletal disorders, dangerous workpieces, job burnout, dirty and disorderly environment, smoking, and male gender; and 3 protective factors (coefficient < 0), including pre-job technical training, good colleague relationship, and long working days per week. Conclusion Manufacturing enterprises need to focus on the incidence of non-fatal occupational injuries and conduct targeted interventions for non-fatal occupational injuries by controlling potential safety hazards, providing pre-job technical training, reducing dangerous workpieces, rectifying working environment, and reasonably arranging working hours.

[Objective] To investigate the infection status and characteristics of HEV among voluntary blood donors in Ningbo, and to provide a basis for improving the blood screening strategy. [Methods] A total of 12 227 blood samples from voluntary blood donors in Ningbo from June 2022 to May 2023 were tested for HEV serology, enzymology, and nucleic acid testing. Furthermore, HEV gene sequencing was performed for genotyping analysis, and donors with reactive nucleic acid testing results were followed up to confirm their infection status. [Results] The reactivity rate of HEV Ag, anti-HEV IgM and anti-HEV IgG was 0.098%, 0.899% and 29.198%, respectively. There was no difference in the reactivity of anti-HEV IgM and anti-HEV IgG between genders, donation frequencies and donation types (P>0.05). The reactivity rate increased significantly with age (P<0.05). The rate of ALT disqualification (ALT>50U/L) was significantly higher than that in non-reactive samples (P<0.05). The HEV Ag reactivity rate (0.098%) was not correlated with gender, donation frequency, donation type or age. One HEV RNA positive case was found, with a positive rate of 0.008%(1/12 227). It was confirmed to be hepatitis E virus genotype 3 by sequencing analysis. Apart from HEV Ag reactivity, all other blood safety screening items were non-reactive, suggesting this case might be in the acute infection phase. The follow-up results showed that all indicators of the donor's previous blood donation were non-reactive. [Conclusion] Pre-donation ALT detection can reduce the risk of transfusion-transmitted HEV (TT-HEV) to a certain extent, and the effective way to prevent TT-HEV is to detect HEV RNA and serology of donor blood.

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.

Testicular histology based on testicular biopsy is an important factor for determining appropriate testicular sperm extraction surgery and predicting sperm retrieval outcomes in patients with azoospermia. Therefore, we developed a deep learning (DL) model to establish the associations between testicular grayscale ultrasound images and testicular histology. We retrospectively included two-dimensional testicular grayscale ultrasound from patients with azoospermia (353 men with 4357 images between July 2017 and December 2021 in The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China) to develop a DL model. We obtained testicular histology during conventional testicular sperm extraction. Our DL model was trained based on ultrasound images or fusion data (ultrasound images fused with the corresponding testicular volume) to distinguish spermatozoa presence in pathology (SPP) and spermatozoa absence in pathology (SAP) and to classify maturation arrest (MA) and Sertoli cell-only syndrome (SCOS) in patients with SAP. Areas under the receiver operating characteristic curve (AUCs), accuracy, sensitivity, and specificity were used to analyze model performance. DL based on images achieved an AUC of 0.922 (95% confidence interval [CI]: 0.908-0.935), a sensitivity of 80.9%, a specificity of 84.6%, and an accuracy of 83.5% in predicting SPP (including normal spermatogenesis and hypospermatogenesis) and SAP (including MA and SCOS). In the identification of SCOS and MA, DL on fusion data yielded better diagnostic performance with an AUC of 0.979 (95% CI: 0.969-0.989), a sensitivity of 89.7%, a specificity of 97.1%, and an accuracy of 92.1%. Our study provides a noninvasive method to predict testicular histology for patients with azoospermia, which would avoid unnecessary testicular biopsy.
Humans ; Male ; Azoospermia/diagnostic imaging* ; Deep Learning ; Testis/pathology* ; Retrospective Studies ; Adult ; Ultrasonography/methods* ; Sperm Retrieval ; Sertoli Cell-Only Syndrome/diagnostic imaging*

OBJECTIVES: To study clinical manifestations and gene mutation features of neonates with centronuclear myopathy. METHODS: A retrospective analysis was conducted on the medical data of 5 neonates with centronuclear myopathy diagnosed in the Neonatal Intensive Care Unit of Children's Hospital, Zhejiang University School of Medicine from January 2020 to August 2024. The data included gender, gestational age, birth weight, Apgar score, clinical manifestations, creatine kinase level, electromyography, genetic testing results and the outcomes of the infants. RESULTS: All 5 male neonates had a history of postpartum asphyxia and resuscitation. They all presented with hypotonia, myasthenia, and respiratory failure; two neonates also had swallowing dysfunction. Of the five neonates, three had normal creatine kinase levels, while two had slightly elevated levels. Electromyography was performed for three neonates, among whom two had myogenic damage. MTM1 gene mutations were identified by genetic testing in all five neonates, including two nonsense mutations and three missense mutations, among which one variant had not been previously reported. Four mutations were inherited from the mother, and the other one was a de novo mutation. The five neonates showed no clinical improvement following treatment, failed weaning from mechanical ventilation, and ultimately died after withdrawal of life-sustaining therapy. CONCLUSIONS Centronuclear myopathy caused by MTM1 gene mutation often has a severe phenotype and a poor prognosis, and it should be considered for neonates with hypotonia and myasthenia after birth. Genetic testing should be performed as soon as possible.
Humans ; Myopathies, Structural, Congenital/genetics* ; Male ; Infant, Newborn ; Retrospective Studies ; Mutation ; Female ; Protein Tyrosine Phosphatases, Non-Receptor/genetics*

Objective To investigate the effect of sodium-glucose cotransporter 2 inhibitor (empagliflozin, EMPA) on myocardial remodeling in a mouse uremic cardiomyopathy (UCM) model induced by 5/6 nephrectomy, through the phosphatidylinositol 3 kinase (PI3K)/protein kinase B (PKB/AKT)/p65 signaling pathway. Methods The animals were divided into three groups: Sham group (n=6), UCM group (n=8), and UCM＋EMPA group (n=8). A UCM model was established in C57BL/6N mice using the 5/6 nephrectomy. Starting from 5 weeks post-surgery, EMPA or a placebo was administered. After 16 weeks, blood pressure, serum creatinine, blood urea nitrogen, 24-hour urine glucose and urine sodium were measured. Cardiac structure and function were assessed by echocardiography. Hematoxylin-eosin (HE) staining and Masson trichrome staining were used to observe pathological changes in the heart and kidneys. Wheat germ agglutinin (WGA) staining was used to evaluate myocardial hypertrophy. The real-time quantitative PCR (RT-qPCR) was used to detect the expression levels of myocardial hypertrophy- and fibrosis-related mRNAs. Western blotting was used to detect the expression levels of PI3K, AKT and p65 in myocardial tissues. Results After 16 weeks, UCM group exhibited significantly higher blood pressure, serum creatinine, blood urea nitrogen than sham group (P＜0.01); UCM＋EMPA group exhibited lower blood pressure, serum creatinine, blood urea nitrogen, and higher 24 h urine sodium and glucose than UCM group (P＜0.05). Echocardiographic results showed ventricular remodeling in the UCM group, evidenced by left ventricular wall thickening, left ventricular enlargement, increased left ventricular mass, and decreased systolic function (P＜0.05); ventricular remodeling was alleviated (P＜0.05), though there was no significant improvement in systolic function in UCM＋EMPA group. HE and Masson stainings revealed myocardial degeneration, necrosis, and interstitial fibrosis in UCM group (P＜0.01); the myocardial pathology improved with reduced collagen deposition in UCM＋EMPA group (P＜0.01). WGA staining confirmed myocardial hypertrophy in UCM group (P＜0.01), while myocardial hypertrophy was alleviated in UCM＋EMPA group (P＜0.01). RT-qPCR results showed myocardial hypertrophy- and fibrosis-related genes (NPPA, NPPB, MYH7, COL1A1, COL3A1, TGF-β1) were upregulated in UCM group (P＜0.05), but downregulated in UCM＋EMPA group. Western blotting showed PI3K, p-AKT/AKT ratio, and p-p65/p65 ratio were increased in UCM group, but decreased in UCM＋EMPA group (P＜0.05). Conclusion EMPA can improve myocardial hypertrophy and fibrosis in the UCM mouse model, and it may play the role through inhibiting the PI3K/AKT/p65 signaling pathway.

Stasis-heat is a pathological factor associated with numerous exogenous and internal injuries, representing a pivotal mechanism in disease progression. Its primary cause is fire-heat toxicity. Based on the theory of qi and the holistic perspective of traditional Chinese medicine, this concept emphasizes that the biochemistry of all natural phenomena relies on the dynamic movement of qi ascending, descending, exiting, and entering. Within six qi, "fire" includes sovereign and ministerial fires. While physiological ministerial fire is the source power of life, pathological ministerial fire manifests as violent, intense energy that readily interacts with blood, leading to the formation of stasis-heat. Therefore, this article examines the formation and treatment of stasis-heat resulting from ministerial fire dysfunction. From the perspectives of ministerial fire gasification, the shape and quality of meridians, and the elevation of ministerial fire, it elucidates why the "pericardium-sanjiao-gallbladder" axis is regarded as pivotal. Furthermore, when the "pericardium-sanjiao-gallbladder" ministerial fire axis is unfavorable, and the stagnation of ministerial fire elevation and blockage is crucial to stasis-heat formation. Additionally, the depletion of essence and blood in the liver and kidneys, preventing the proper storage of ministerial fire, forms the pathological foundation. Drawing upon The Inner Canon of Yellow Emperor, this article explores therapeutic principles based on the rules of odor treatment: "when fires in the interior, the treatment of salty and cold, accompanied by bitter and pungent, acid to astringe, bitter to disperse." These principles are applied to achieve specific therapeutic goals: tempering the excess of ministerial fire to cool the nutritive level and transform stasis; adjusting the imbalance of elevation and depression to vent heat and unblock stasis; and restoring the misplaced fire by nourishing blood to expel stasis. Through these approaches, the article aims to reestablish the proper circulation of ministerial fire, dissipate blood stasis, and ultimately eliminate stasis-heat, thereby offering an integrated perspective on its pathogenesis and treatment.