Objective To develop a machine learning model integrating preoperative chest CT radiomic features with clinical data for predicting 5-year postoperative recurrence risk in stage Ⅰ non-small cell lung cancer(NSCLC)patients undergoing surgical resection.Methods A total of 217 patients with pathologically confirmed stage Ⅰ NSCLC(selected from 778 initially screened cases based on our inclusion and exclusion criteria)treated in Army Medical Center of PLA between January 2014 and December 2019 were retrospectively enrolled,including 53 recurrence cases and 164 non-recurrence cases within 5-year follow-up.They were randomly divided into a training set(n=173)and a validation set(n=44)in a ratio of 8:2.Radiomic models were established based on extracted features from tumor-dominant regions of interest(ROI)on CT images,while clinical models were developed using demographic characteristics and preoperative laboratory examinations.A combined model was further constructed by integrating both feature sets,and model performance was compared to identify the optimal predictive model.Results This study screened the features from non-contrast CT images and ultimately selected 7 radiomic features for constructing radiomic model.Among 6 machine learning algorithms,the adaptive boosting(Adaboost)model demonstrated the best overall predictive performance,with an area under the curve(AUC)of 0.866(95%CI:0.808～0.923;accuracy:0.832,specificity:0.884)in the training set and of 0.806(95%CI:0.630～0.983;accuracy:0.795,specificity:0.971)in the validation set.Univariate and multivariate logistic regression analyses identified 4 clinical features for clinical model construction.The clinical model achieved an AUC value of 0.874(95%CI:0.821～0.928;accuracy:0.827,specificity:0.891)in the training set and 0.813(95%CI:0.677～0.948;accuracy:0.636,specificity:0.600)in the validation set.By integrating the 7 radiomic features and 4 clinical features using a feature-level fusion strategy,the combined model exhibited further improved predictive performance,with an AUC value of 0.953(95%CI:0.924～0.983;accuracy:0.884,specificity:0.860)and 0.852(95%CI:0.729～0.976;accuracy:0.682,specificity:0.629),respectively in the training set and the validation set.Conclusion The combined model integrating preoperative CT radiomic features with clinical risk factors may provide an evidence-based framework for evaluating 5-year postoperative recurrence risk in stage Ⅰ NSCLC patients.

Objective To investigate the value of ¹⁸F-FDG PET/CT metabolic parameters in the prognostic assessment of nasopharyngeal cancer patients. Methods The clinical data and PET/CT metabolic parameters of 185 nasopharyngeal cancer patients were retrospectively analyzed. The collected parameters were SUV_max, MTV, TLG, total metabolic tumor volume (TMTV) and whole-body total lesion glycolysis (WTLG). The ROC curve was used to determine the optimal cut-off values of PET/CT metabolic parameters. Univariate and multivariate Cox regression models were used to screen the independent prognostic factors. Kaplan–Meier curves were used to analyze the survival differences. Results The results of univariate Cox regression analysis showed that age, pathologic type, WTLG, TMTV, MTV, and TLG were closely associated with OS and PFS; and SUV_max was associated with PFS (P<0.05). Multivariate Cox regression analysis results showed that age, TMTV, and WTLG were the independent prognostic factors for OS and PFS (P<0.05). The combination of WTLG with T/N staging (AUC=0.781 and 0.781) and TMTV with T/N staging (AUC=0.800 and 0.790) yielded greater predictive accuracy than that of WTLG and TMTV alone (AUC=0.724 and 0.719) or T/N staging (AUC=0.593 and 0.575). Conclusion TMTV and WTLG are important prognostic predictors of nasopharyngeal carcinoma. TLG and MTV of primary lesions are prognostic factors for patients’ PFS and OS. SUV_max has limited prognostic value. Systemic metabolic indexes (TMTV and WTLG), when combined with T/N staging, can optimize prognostic stratification.

Objective:To examine the regulatory effects of a potential antifibrotic tetrapeptide called N-acetyl-seryl-aspartyl-lysyl-proline (Ac-SDKP) on the expression of epidermal growth factor (EGFR) and signal transducer and activator of transcription 3 (STAT3) in lung tissues with fibrosis induced by silicosis in rats. This study aims to explore the potential therapeutic benefits of Ac-SDKP in the prevention and treatment of fibrotic lung diseases associated with silicosis.Methods:In January 2024, disease targets and Ac-SDKP active ingredients were predicted through GeneCards (https://www.genecards.org) and OMIM (https://www.omim.org) databases. Using R 4.2.1 software, we identified overlapping targets between pulmonary fibrosis and AC-SDKP. Cytoscape 3.10.2 was employed to visualize interactions between active chemical components and these targets, followed by GO enrichment analysis and KEGG pathway analysis using R 4.2.1. Forty healthy adult Wistar rats were selected to establish silicosis models through single-dose gavage with 50 mg/ml silica suspension (1. 0 ml per rat). The rats were randomly divided into four groups: model control group (4 weeks), silicosis model group (4 weeks), Ac-SDKP preventive treatment group (acquired via intraperitoneal injection of a micro-release pump containing Ac-SDKP [800 μg/ (kg·d) ] during modeling, maintained for 4 weeks), and Ac-SDKP anti-fibrosis treatment group (acquired via intraperitoneal injection of the same pump after 2 weeks of modeling, continued maintenance for 2 weeks). Each group contained 10 rats. The pathological changes in rat lung tissues were observed. Western blot technology was used to detect the protein expression levels of α-smooth muscle actin (α-SMA), epidermal growth factor receptor (EGFR), signal transduction and activation transcription factor 3 (STAT 3), caspase 3, and caspase 8 in lung tissues. Immunohistochemical techniques were employed to assess the expressions of EGFR, STAT3, caspase 3, and caspase 8. Overall differences between groups were compared using one-way ANOVA.Results:Compared with the control group in the silicosis model, rats in the 4-week group exhibited significant fibrotic nodules. The lung tissues of these rats showed statistically significant increases in α-SMA, EGFR, STAT 3, Caspase 3, and Caspase 8 protein expression ( P<0.05). In contrast, the Ac-SDKP prevention and anti-fibrosis treatment group demonstrated markedly reduced expression levels of these proteins compared to the 4-week silicosis model group, with statistically significant differences ( P<0.05). Immunohistochemical staining revealed that brownish-yellow expression of EGFR, STAT3, Caspase3, and Caspase8 was significantly enhanced in silicotic nodules within the silicosis model group. Conversely, this brownish-yellow expression was notably decreased in the Ac-SDKP prevention and anti-fibrosis treatment group compared to the 4-week silicosis model group. Conclusion:Ac-SDKP may exert antifibrotic effects on the lungs of rats with silicosis by regulating the EGFR/STAT3 pathway.

Objective To explore the integrating modeling strategy of prognosis prediction models,and provide support for the methodological selection of establishing effective clinical prediction models.Methods Based on the SEER liver cancer clinical follow-up data,the predictive performance and estimation accuracy of the classical Cox proportional hazard regression model,frequentist model averaging,and Bayesian model averaging methods were compared,and the applicability of model averaging methods was explored.Simulated studies were used to investigate the predictive performance of the models.Results Results of simulation analysis:the C index obtained by Bayesian model averaging was slightly higher than the Cox regression and frequentist model averaging.The 95%confidence interval of the C index tended to become narrower as the sample size increased.For variables with larger effect sizes,the Bayesian model averaging method obtained the smallest deviation of regression coefficients and the largest 95%interval coverage.Results of the case study:the validation set C index obtained by Cox regression,Bayesian model averaging and frequentist model averaging were 0.7845(95%CI:0.7613～0.8076),0.7851(95%CI:0.7619～0.8083)and 0.7845(95%CI:0.7613～0.8076),respectively.Conclusion Bayesian model averaging method can improve the ability of predicting prognosis when the sample size is small and the predictor variables are correlated.

Objective: To systematically evaluate the prevalence and influencing factors of screen exposure among preschool children in China, so as to provide evidence for formulating scientific intervention strategies. Methods: Retrieve relevant studies on screen exposure among preschool children from PubMed, Web of Science, Embase, Cochrane Library, China Biomedical Literature Database, CNKI, VIP, and Wanfang databases from the time of estaldishment to June 29, 2025. Meta analysis was performed using Stata 16.0 software. Results: A total of 43 studies were included. Meta analysis showed that the prevalence of screen exposure among preschool children in China was 46.0% (95% CI = 38.9 %-53.1%, P <0.01). Girls, non only child, father s age<35 years, both parents having an educational level of high school or below, being cared for by grandparents, rural residence, parents having no exercise habit, parental support for the use of screen devices, and parental screen time>1 h/d were influencing factors for screen exposure among preschool children [ OR (95% CI ) were 0.85(0.78-0.92), 1.09(1.04-1.15), 1.45(1.22-1.71), 1.38(1.24- 1.54 ), 1.78(1.32-2.40), 1.39(1.16-1.65), 1.38(1.13-1.69), 1.67(1.40-1.98), 1.70(1.38-2.10), 1.59(1.04-2.43), all P <0.05]. Conclusion The prevalence of screen exposure among preschool children in China is relatively high, and relevant child health promotion strategies should be formulated to reduce its occurrence.

Objective To explore the integrating modeling strategy of prognosis prediction models,and provide support for the methodological selection of establishing effective clinical prediction models.Methods Based on the SEER liver cancer clinical follow-up data,the predictive performance and estimation accuracy of the classical Cox proportional hazard regression model,frequentist model averaging,and Bayesian model averaging methods were compared,and the applicability of model averaging methods was explored.Simulated studies were used to investigate the predictive performance of the models.Results Results of simulation analysis:the C index obtained by Bayesian model averaging was slightly higher than the Cox regression and frequentist model averaging.The 95%confidence interval of the C index tended to become narrower as the sample size increased.For variables with larger effect sizes,the Bayesian model averaging method obtained the smallest deviation of regression coefficients and the largest 95%interval coverage.Results of the case study:the validation set C index obtained by Cox regression,Bayesian model averaging and frequentist model averaging were 0.7845(95%CI:0.7613～0.8076),0.7851(95%CI:0.7619～0.8083)and 0.7845(95%CI:0.7613～0.8076),respectively.Conclusion Bayesian model averaging method can improve the ability of predicting prognosis when the sample size is small and the predictor variables are correlated.

Objective:To examine the regulatory effects of a potential antifibrotic tetrapeptide called N-acetyl-seryl-aspartyl-lysyl-proline (Ac-SDKP) on the expression of epidermal growth factor (EGFR) and signal transducer and activator of transcription 3 (STAT3) in lung tissues with fibrosis induced by silicosis in rats. This study aims to explore the potential therapeutic benefits of Ac-SDKP in the prevention and treatment of fibrotic lung diseases associated with silicosis.Methods:In January 2024, disease targets and Ac-SDKP active ingredients were predicted through GeneCards (https://www.genecards.org) and OMIM (https://www.omim.org) databases. Using R 4.2.1 software, we identified overlapping targets between pulmonary fibrosis and AC-SDKP. Cytoscape 3.10.2 was employed to visualize interactions between active chemical components and these targets, followed by GO enrichment analysis and KEGG pathway analysis using R 4.2.1. Forty healthy adult Wistar rats were selected to establish silicosis models through single-dose gavage with 50 mg/ml silica suspension (1. 0 ml per rat). The rats were randomly divided into four groups: model control group (4 weeks), silicosis model group (4 weeks), Ac-SDKP preventive treatment group (acquired via intraperitoneal injection of a micro-release pump containing Ac-SDKP [800 μg/ (kg·d) ] during modeling, maintained for 4 weeks), and Ac-SDKP anti-fibrosis treatment group (acquired via intraperitoneal injection of the same pump after 2 weeks of modeling, continued maintenance for 2 weeks). Each group contained 10 rats. The pathological changes in rat lung tissues were observed. Western blot technology was used to detect the protein expression levels of α-smooth muscle actin (α-SMA), epidermal growth factor receptor (EGFR), signal transduction and activation transcription factor 3 (STAT 3), caspase 3, and caspase 8 in lung tissues. Immunohistochemical techniques were employed to assess the expressions of EGFR, STAT3, caspase 3, and caspase 8. Overall differences between groups were compared using one-way ANOVA.Results:Compared with the control group in the silicosis model, rats in the 4-week group exhibited significant fibrotic nodules. The lung tissues of these rats showed statistically significant increases in α-SMA, EGFR, STAT 3, Caspase 3, and Caspase 8 protein expression ( P<0.05). In contrast, the Ac-SDKP prevention and anti-fibrosis treatment group demonstrated markedly reduced expression levels of these proteins compared to the 4-week silicosis model group, with statistically significant differences ( P<0.05). Immunohistochemical staining revealed that brownish-yellow expression of EGFR, STAT3, Caspase3, and Caspase8 was significantly enhanced in silicotic nodules within the silicosis model group. Conversely, this brownish-yellow expression was notably decreased in the Ac-SDKP prevention and anti-fibrosis treatment group compared to the 4-week silicosis model group. Conclusion:Ac-SDKP may exert antifibrotic effects on the lungs of rats with silicosis by regulating the EGFR/STAT3 pathway.

OBJECTIVE To investigate the efficacy and safety of ropivacaine combined with oxycodone for the analgesia of iliac fascia nerve block in patients undergoing hip replacement.METHODS Sixty-six patients who underwent hip replacement at the Central Hospital of Enshi Tujia and Miao Autonomous Prefecture from October 2023 to April 2024 were selected and randomly divided into observation group and control group,with 33 cases in each group.Before induction of anesthesia,ultrasound-guided iliac fascial nerve block was performed.Patients in the observation group were treated with 0.33%ropivacaine+0.1 mg/kg oxycodone injection mixture 30 mL,and patients in the control group were treated with 0.33%ropivacaine injection 30 mL.The time of first postoperative rescue analgesia,24 h postoperative analgesic drug consumption,sensory block and motor block effective and maintenance time,satisfaction degree,numerical rating scale(NRS)pain score,Ramsay sedation score,muscle strength score,heart rate(HR),mean arterial pressure(MAP),oxygen saturation(SpO2),sleep score,anxiety score,and the occurrence of adverse reactions in the two groups were all recorded.RESULTS Compared with the control group,the first rescue analgesia time after operation was significantly prolonged in the observation group,and 24 h postoperative analgesic drug consumption after operation decreased;the effective time of sensory block was significantly shortened,and the maintenance time of sensory block was significantly prolonged,and the satisfaction score was higher;the NRS pain score after iliac fascia nerve block was lower,HR and MAP were lower,and the anxiety score and sleep score 24 and 48 h after operation were lower(P＜0.05).In terms of safety,patients in both groups had adverse reactions after operation,such as hypertension,nausea,vomiting,and dizziness,but there was no significant difference in the incidence of adverse reactions between the two groups(P＞0.05).CONCLUSIONS Oxycodone combined with ropivacaine shows good efficacy and safety for iliac fascial nerve block analgesia in patients undergoing hip replacement,can significantly prolong the analgesic time of ropivacaine,reduce postoperative analgesic drug consumption,improve the sleep quality of patients,and promote the rapid recovery of patients.

The liver plays an important regulatory role in maintaining the dynamic balance of coagulation and anticoagulation in the body. Such dynamic balance is fragile in patients with liver cirrhosis， and the risk of bleeding can be increased due to reductions in coagulation factors and platelet count and excessive fibrinolysis； meanwhile， thrombus can be formed due to the increases in von Willebrand factor and coagulation factor Ⅷ， the reductions in anticoagulant protein C and anticoagulant protein S， the increase in thrombin-generating potential， and alterations in antifibrinolytic components. This article reviews the mechanisms of coagulation disorder in liver cirrhosis， so as to help clinicians with the prevention and treatment of bleeding or thrombotic disorders in patients with liver cirrhosis.

Objective To systematically describe and analyze the major factors influencing diabetes mellitus(DM)patients'foot self-care behaviors.Methods A computer-based search was conducted to retrieve observational studies on factors influencing DM patients'foot self-care behaviors from databases including PubMed,Web of Science,EMbase,Cochrane Library,CNKI,WanFang,VIP,CBM Datebase.The search was limited to articles published up to July 2024.Two separate researchers conducted an independent review,data extraction,and quality assessment of the retrieved articles,applying both inclusion and exclusion criteria.Subsequently,a Meta-analysis focusing on the influencing factors was performed utilizing Stata 17.0 software.Results A total of 17 articles met the criteria and were included in the study.The Meta-analysis results showed that good foot self-care was associated with education level,diabetes duration,place of residence,hypertension,insulin use,health education,regular follow-up visits,medication adherence,and self-monitoring of blood glucose.Conclusion There are multiple factors influencing DM patients'foot self-care.Healthcare providers can identify low levels of foot self-care behaviors early based on relevant factors and intervene accordingly to promote positive clinical outcomes for patients.