ObjectiveTo analyze the influencing factors of hepatic fibrosis in patients with Wilson's disease （WD） and to develop and validate a predictive model for hepatic fibrosis in WD that integrates traditional Chinese medicine （TCM） syndromes and modern biomarkers， thus providing a basis for early identification of high-risk populations and medical intervention. MethodsThe clinical data of 220 WD patients diagnosed in the Department of Encephalopathy， The First Affiliated Hospital of Anhui University of Chinese Medicine between January 2010 and December 2024 were retrospectively collected. Through a random number table， patients were assigned into a training set （154 patients） and a validation set （66 patients） in a 7∶3 ratio. Univariate COX regression， LASSO regression analysis， and multivariate COX regression analysis were performed to identify independent influencing factors for hepatic fibrosis in WD patients， and a clinical prediction model was established based on these factors. The discrimination， calibration， and clinical utility of the model were evaluated based on the area under the receiver operating characteristic curve （AUC）， calibration curves， and decision curve analysis （DCA）， respectively. ResultsA total of 220 patients were included. The syndrome of combined phlegm and stasis， triglycerides （TG）， laminin （LN）， and male gender were identified as independent risk factors for hepatic fibrosis in WD patients， while TCM intervention and high-density lipoprotein cholesterol （HDL-C） were protective factors. The C-index indicated excellent discriminative ability （training set： 3-year AUC=0.908， 5-year AUC=0.869， 7-year AUC=0.829； validation set： 3-year AUC= 0.898， 5-year AUC=0.780， 7-year AUC=0.743）. Calibration curves showed good consistency. DCA demonstrated the clinical net benefit probabilities across different risk thresholds at various time points. ConclusionThe predictive model established in this study has high accuracy and can be conveniently used for the early identification and risk prediction of hepatic fibrosis in patients with WD.

ObjectiveTo investigate the association between insulin resistance （IR） and cardiovascular metabolic indicators， including blood pressure， blood lipids， and serum uric acid， in a middle-aged population. MethodsBased on the baseline survey of the Hong Sifang cohort of China National Salt Industry Group， a total of 575 participants were included. According to the homeostasis model assessment of insulin resistance， the participants were divided into an IR group （n=116） and a non-IR group （n=459）. Differences between the two groups were compared using the t-test and chi-square test. Multivariate logistic regression analysis was performed to evaluate the associations between IR and abnormalities in metabolic indicators. ResultsA total of 575 participants were included， including 390 men and 185 women， with a mean age of （48.58 ± 5.14） years and a mean body mass index （BMI） of （24.16 ± 2.97） kg/m². Blood pressure， serum uric acid， and blood lipid levels were significantly higher in the IR group than in the non-IR group （P<0.001）. After adjusting for potential confounders including age， sex， and BMI， IR was independently associated with hypertension， dyslipidemia， and hyperuricemia， with odds ratios （OR） and 95% confidence intervals （CI） of 2.57 （1.61-4.09）， 3.81 （2.36-6.17）， and 2.68 （1.53-4.71）， respectively （all P<0.001）. ConclusionInsulin resistance is significantly associated with multiple cardiovascular metabolic risk factors in the middle-aged population. Monitoring IR may contribute to the early prevention and control of cardiovascular diseases.

Objective:To establish an evaluation model for the radon reduction effect of activated carbon adsorption in localized underground spaces, to guide the rational application of the activated carbon adsorption method for radon reduction in localized underground spaces.Methods:For both intermittent and continuous adsorption-based radon reduction method in localized underground spaces, a theoretical model was constructed for evaluating the of radon reduction effec. By means of this modle, the influence factors on the radon reduction effect were analyzed such as radon concentration, space volume, and air exchange rate with the external environment. Experimental validation of the theoretical model was conducted under typical conditions.Results:The radon exhalation rate from concrete surface in localized underground spaces was inversely linear relationship with the indoor radon concentration. However, the slope of this relationship was very small: when the radon concentration decreased from 2 018 Bq/m 3 to 0, the exhalation rate only increased slightly from 4.20 to 4.46 Bq·m -2·h -1, indicating a minimal change. At the same air exchange rate and in the same space volume, the initial radon concentration had little impact on the adsorption-based radon reduction effect, thus suggesting that the radon generation rate from the enclosure surface could be negligible in the evaluation. In contrast, the adsorption flow rate of a radon reduction device and the air exchange rate between the localized spaces and surrounding environment had significant fluence on radon reduction effect. In the same sealed space and at the same adsorption flow rate, the difference in effect between intermittent and continuous adsorption method was of insignificance. However, in localized spaces with connectivity to surrounding environment, the continuous adsorption significantly outperforms intermittent adsorption, achieving faster radon reduction and better suitability for larger spaces. Simulated experiments validated that the theoretical model for evaluating adsorption-based radon reduction effect was reliable. Conclusions:The research findings provide theoretical guidance on selecting appropriate adsorption-based radon reduction devices for localized underground spaces of different volumes and varing connectivity conditions, in order to reduce the radon concentration within localized spaces to expected levels.

As a life support system of maintaining life and health of patients,the quality and safety of medical gas system are crucial link that cannot be overlooked in medical safety.This article studied and arranged the test methods of main test parameters of medical gas system through took part in testing and acceptance work of supplying oxygen system and attracting system of medical center,and medical air system in many years.This article covered many key aspects such as gas source,pressure,flow rate,pipeline cleanliness,gas specificity,alarm device,pipeline airtightness,and grounding resistance.For each test item,the article systematically expounded corresponding test items and methods,which aimed at providing scientific basis and technical support for ensuring the safe and stable operation of medical gas systems,and reducing the operational risks of this systems,and helping to improve medical quality and the level of safety assurance for patients.At the same time,it can provide scientific test methods for test institutions,and ensure accuracy and effectiveness of test data when standardized testing work.

Objective To construct and validate an interpretable machine learning model integrating contrast-enhanced venous phase CT radiomics and clinical features for preoperative prediction of central lymph node metastasis(CLNM)of papillary thyroid carcinoma(PTC).Methods A case-control study was conducted on 243 histologically confirmed PTC patients.Among them,196 patients from the First Affiliated Hospital of Army Medical University were randomly allocated into a training set(n=137)and an internal validation set(n=59)at a 7:3 ratio,while the left 47 patients from No.958 Hospital of PLA Army were assigned into an external validation set.All participants completed standardized preoperative contrast-enhanced neck CT scanning.Quantitative radiomic features were systematically extracted from venous phase CT images through an open-source big data AI platform.Six machine learning classifiers,eXtreme Gradient Boosting(XGBoost),Support Vector Machine(SVM),Random Forest(RF),Logistic Regression(LR),k-Nearest Neighbors(KNN),and Decision Tree(DT)were implemented to construct clinical-radiomics integration models.The predictive performance was quantitatively assessed through receiver operating characteristic(ROC)curve analysis,with area under the curve(AUC)values calculated for training,internal validation,and external validation sets.Model interpretability was achieved using Shapley additive explanations(SHAP)framework,with particular focus on elucidating feature contributions in the best-performing model.Results The XGBoost model had an AUC value of 0.936(95%CI:0.895～0.976),0.832(95%CI:0.724～0.941),and 0.772(95%CI:0.632～0.912)in training,internal and external validation sets,respectively.SHAP analysis revealed age as the most influential clinical predictor,with younger patients showing higher CLNM risk(OR=0.968,P=0.042).Conclusion Our machine learning-based clinic-radiomic prediction model demonstrates satisfactory performance in predicting CLNM of PTC,providing valuable references for clinical decision-making.

Arsenic is a semi-metal,and lipid-soluble arsenic compounds are one of the widespread forms in the environment and food chain,but there is a lack of standards for lipid-soluble arsenic compounds,which is one of the bottlenecks in the current analytical detection and toxicological studies of organic arsenic.In this study,four saturated arsenic-containing hydrocarbons,AsHC 318,AsHC 332,AsHC 346,and AsHC 374(The number is relative molecular mass),were successfully synthesized in three steps by using dimethylarsinic acid,potassium iodide,sodium hydroxide,and four brominated alkanes(1-Bromotetradecane,1-bromopentadecane,1-bromohexadecane,and 1-bromooctadecane)as raw materials.The structures of these four saturated arsenic-containing hydrocarbons were characterized by proton nuclear magnetic resonance(1H NMR)spectroscopy,13C nuclear magnetic resonance(13C NMR)spectroscopy,and high-resolution mass spectrometry(HR-MS).The yields of the method were 8%-10%,and the synthesized compounds could be used in subsequent toxicity evaluation experiments to assess the toxic effects and mechanisms of action of arsenic-containing hydrocarbons.This study provided an effective method for synthesis of arsenic-containing hydrocarbons,enriching the synthesis methods of arsenic-containing hydrocarbons,and provided raw materials for the subsequent toxicological studies of arsenic-containing hydrocarbons.

OBJECTIVE: To investigate the regulatory effects of two traditional mineral medicines (TMMs), Gypsum Fibrosum (Shigao, GF) and Terra Flava Usta (Zaoxintu, TFU), on gut-beneficial bacteria in mice, and preliminarily explore their mechanisms of action. METHODS: Mice were randomly divided into 3 groups (n=10 per group): the control group (standard diet), the GF group (diet supplemented with 2% GF), and the TFU group (diet supplemented with 2% TFU). After 4-week intervention, 16S rRNA gene sequencing was used to analyze the changes in the gut microbiota (GM). Scanning electron microscopy, in combination with coumarin A tetramethyl rhodamine conjugate and Hoechst stainings, was used to observe the bacteria and biofilm formation. RESULTS: Principal coordinate analysis revealed that GF and TFU significantly altered the GM composition in mice. Further analysis revealed that GF and TFU affected different types of gut bacteria, suggesting that different TMMs may selectively modulate specific bacterial populations. For certain bacteria, such as Faecalibaculum and Ileibacterium, both GF and TFU exhibited growth-promoting effects, implying that they may be sensitive to TMMs and that different TMMs can increase their abundance through their respective mechanisms. Notably, Lactobacillus reuteri, a widely recognized and used probiotic, was significantly enriched in the GF group. Random forest analysis identified Ileibacterium valens as a potential indicator bacterium for TMMs' impact on GM. Further mechanistic studies showed that gut bacteria formed biofilm structures on the TFU surface. CONCLUSIONS This study provides new insights into the interaction between TMMs and GM. As safe and effective natural clays, GF and TFU hold promise as potential candidates for prebiotic development.
Animals ; Gastrointestinal Microbiome/drug effects* ; Bacteria/growth & development* ; Mice ; Biofilms/drug effects* ; Male ; RNA, Ribosomal, 16S/genetics*

Cemental tear is a rare and indetectable condition unless obvious clinical signs present with the involvement of surrounding periodontal and periapical tissues. Due to its clinical manifestations similar to common dental issues, such as vertical root fracture, primary endodontic diseases, and periodontal diseases, as well as the low awareness of cemental tear for clinicians, misdiagnosis often occurs. The critical principle for cemental tear treatment is to remove torn fragments, and overlooking fragments leads to futile therapy, which could deteriorate the conditions of the affected teeth. Therefore, accurate diagnosis and subsequent appropriate interventions are vital for managing cemental tear. Novel diagnostic tools, including cone-beam computed tomography (CBCT), microscopes, and enamel matrix derivatives, have improved early detection and management, enhancing tooth retention. The implementation of standardized diagnostic criteria and treatment protocols, combined with improved clinical awareness among dental professionals, serves to mitigate risks of diagnostic errors and suboptimal therapeutic interventions. This expert consensus reviewed the epidemiology, pathogenesis, potential predisposing factors, clinical manifestations, diagnosis, differential diagnosis, treatment, and prognosis of cemental tear, aiming to provide a clinical guideline and facilitate clinicians to have a better understanding of cemental tear.
Humans ; Dental Cementum/injuries* ; Consensus ; Diagnosis, Differential ; Cone-Beam Computed Tomography ; Tooth Fractures/therapy*

As global greenhouse gases continue rising, the urgency of more ambitious action is clearer than ever before. China is the world's biggest emitter of greenhouse gases and one of the countries affected most by climate change. The evidence about the impacts of climate change on the environment and human health may encourage China to take more decisive action to mitigate greenhouse gas emissions and adapt to climate impacts. This article aimed to review the evidence of environmental damages and health risks posed by climate change and to provide a new science-based perspective for the delivery of sustainable development goals. Over recent decades, China has experienced a strong warming pattern with a growing frequency of extreme weather events, and the impacts of climate change on China's environment and human health have been consistently observed, with increasing O ₃ air pollution, decreases in water resources and availability, land degradation, and increased risks for both communicable and non-communicable diseases. Therefore, China's climate policy should target the key factors driving climate change and scale up strategic measures to curb carbon emissions and adapt to inevitable increasing climate impacts. It provides new insights for not only China but also other countries, particularly developing and emerging economies, to ensure climate and environmental sustainability whilst pursuing economic growth.
Climate Change ; China ; Humans ; Greenhouse Gases ; Air Pollution ; Sustainable Development ; Environment