Objective To analyze the newly reported HIV-1 infection in several prefectures of Hubei Province，and analyze its influencing factors. Methods The limiting antigen avidity enzyme immunoassay(LAg-avidity EIA,LAg) was conducted on HIV-1 positive samples confirmed by Western blot of Hubei in 2017-2022. The demographic characteristics of the newly infected samples were analyzed by χ2 test.Logistic regression model was used to analyze influencing factors of new infection rate and predict the factors associated with the HIV-1 recent infection. Results There were 403 new cases of HIV-1 from 2017 to 2022 in several prefectures of Hubei Province, of which 77 were newly infected sorted by LAg,with a new infection rate of 19.11%. The newly confirmed HIV-1 persons of whom aged ≤24 years (40.00% new infection ratio), unmarried (29.41%), college or above (31.37%), and from Voluntary counseling and testing testing(VCT) clinics (40.00%) had a higher proportion of new infections, and the difference was statistically significant. Multivariate Logistic regression analysis showed that age ≤24 years old (aOR=4.346,95%CI: 1.342-14.075) and screening from the VCT clinic (aOR=6.761,95%CI: 1.460-31.319) were more likely to be newly infected. Conclusion The proportion of new HIV infection in several prefectures of Hubei province is relatively low in recent years.Further effective publicity and intervention measures for young students and the construction of VCT clinic should be continuously promoted to achieve early diagnosis and treatment.

This article provides a systematic interpretation and review of the Society of Critical Care Medicine 2024 Guidelines on Adult ICU Design. Developed based on the Grading of Recommendations Assessment, Development and Evaluation methodology, the guideline address five core themes: ICU layout, room design, infection control, infrastructure, and staff spaces, and put forward 17 evidence-based recommendations, including 5 good practice statements. This article briefly introduces the guideline development methods and evidence characteristics, and focuses on summarizing key recommendations, including high-visibility layouts, single-bed ward settings, natural lighting and noise reduction strategies, integrated infection prevention and control design, remote monitoring, and flexible capacity expansion capabilities. Furthermore, it delves into the applicability and localized implementation pathways within China's healthcare environment, analyzing limitations in terms of evidence quality, specialty applicability, and cost-effectiveness. Furthermore, by integrating the Chinese Guidelines for the Construction and Development of Critical Care Medicine (2025 Edition) and current domestic practices, the article proposes tiered and phased implementation recommendations. This article aims to provide domestic healthcare institutions with a scientific reference that balances international cutting-edge concepts with China's real-world conditions for the construction and renovation of ICUs, thereby promoting the development of intensive care environments centered on patient safety, healthcare worker well-being, and infection control.

Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

ObjectiveTo investigate the efficacy and safety of simplified diagnosis and treatment strategies and regimens in the treatment of patients with human immunodeficiency virus （HIV）/hepatitis C virus （HCV） co-infection. MethodsThis multicenter prospective real-world study was conducted among 198 patients with HIV/HCV co-infection who received hepatitis C treatment in 10 designated primary hospitals for hepatitis C in Yuxi， China from July 2023 to June 2024， and according to whether genotype detection was performed， they were divided into genotype detection group with 122 patients and non-genotype detection group with 76 patients. The patients were observed in terms of sustained virologic response at 12 weeks （SVR12）， safety， and liver biochemical parameters. Propensity score matching was used to match the two cohorts， using caliper matching （caliper value = 0.02） at a ratio of 1∶1. The independent-samples t test or the Wilcoxon signed-rank test was used for comparison of continuous data between two groups， and the chi-square test was used for comparison of categorical data between groups. ResultsAmong the 122 patients in the genotype detection group， 63.11% （77/122） had genotype 3， while the non-genotype detection group had 76 patients； both groups achieved an SVR12 rate of 100%. From baseline to 12 weeks after treatment， both groups had a significant increase in the serum level of albumin， significant reductions in the serum levels of total bilirubin， alanine aminotransferase， aspartate aminotransferase， and alpha-fetoprotein， and a significant reduction in FibroScan value （all P<0.05）. A total of 54 patients （27.27%） experienced at least one adverse reaction， and anemia was the most common adverse reaction （38 patients， 19.19%）， with an incidence rate of 50.00% （33/66） in patients with liver cirrhosis. HIV viral load remained <50 IU/mL before and after treatment， and there was no significant change in CD4⁺ T lymphocyte count after treatment （Z=-0.969， P=0.202）. Compared with the genotype detection group， the non-genotype detection group had a significantly shorter time from positive HCV RNA testing to treatment initiation （4±2 days vs 19±4 days， t=5.321， P<0.05） and significantly lower testing costs （618±97 yuan vs 789±129 yuan， t=3.661， P<0.05）. ConclusionFor patients with HIV/HCV co-infection， the simplified diagnosis and treatment strategies can achieve a relatively high SVR12 rate， improve biochemical indicators， and effectively reduce time cost and economic burden， with a favorable safety profile.

Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

BACKGROUND: Both medication and non-medication therapies are effective approaches to control blood pressure (BP) in hypertension patients. However, the association of joint changes in antihypertensive medication use and healthy lifestyle index (HLI) with BP control among hypertension patients is seldom reported, which needs to provide more evidence by prospective intervention studies. We examined the association of antihypertensive medication use and HLI with BP control among employees with hypertension in China based on a workplace-based multicomponent intervention program. METHODS: Between January 2013 and December 2014, a cluster randomized clinical trial of a workplace-based multicomponent intervention program was conducted in 60 workplaces across 20 urban areas in China. Workplaces were randomly divided into intervention (n = 40) and control (n = 20) groups. Basic information on employees at each workplace was collected by trained professionals, including sociodemographic characteristics, medical history, family history, lifestyle behaviors, medication status and physical measurements. After baseline, the intervention group received a 2-year intervention to achieve BP control, which included: (1) a workplace wellness program for all employees; (2) a guidelines-oriented hypertension management protocol. HLI including nonsmoking, nondrinking, adequate physical activity, weight within reference range and balanced diet, were coded on a 5-point scale (range: 0-5, with higher score indicating a healthier lifestyle). Antihypertensive medication use was defined as taking drug within the last 2 weeks. Changes in HLI, antihypertensive medication use and BP control from baseline to 24 months were measured after the intervention. RESULTS: Overall, 4655 employees were included (age: 46.3 ± 7.6 years, men: 3547 (82.3%)). After 24 months of the intervention, there was a significant improvement in lifestyle [smoking (OR = 0.65, 95% CI: 0.43-0.99; P = 0.045), drinking (OR = 0.52, 95% CI: 0.40-0.68; P < 0.001), regular exercise (OR = 3.10, 95% CI: 2.53-3.78; P < 0.001), excessive intake of fatty food (OR = 0.17, 95% CI: 0.06-0.52; P = 0.002), restrictive use of salt (OR = 0.26, 95% CI: 0.12-0.56; P = 0.001)]. Compare to employees with a deteriorating lifestyle after the intervention, those with an improved lifestyle had a higher BP control. In the intervention group, compared with employees not using antihypertensive medication, those who consistent used (OR = 2.34; 95% CI: 1.16-4.72; P = 0.017) or changed from not using to using antihypertensive medication (OR = 2.24; 95% CI: 1.08-4.62; P = 0.030) had higher BP control. Compared with those having lower HLI, participants with a same (OR = 1.38; 95% CI: 0.99-1.93; P = 0.056) or high (OR = 1.79; 95% CI: 1.27~2.53; P < 0.001) HLI had higher BP control. Those who used antihypertensive medication and had a high HLI had the highest BP control (OR = 1.88; 95% CI: 1.32-2.67, P < 0.001). Subgroup analysis also showed the consistent effect as the above. CONCLUSION These findings suggest that adherence to antihypertensive medication treatment and healthy lifestyle were associated with a significant improvement in BP control among employees with hypertension.

Objective To evaluate the surgical efficacy of unilateral pneumonectomy for the treatment of tuberculous destroyed lung, analyze the causes of severe postoperative complications, and explore clinical management strategies. Methods A retrospective analysis was conducted on the clinical data of patients with tuberculous destroyed lung who underwent unilateral pneumonectomy at the Public Health Clinical Center of Chengdu from 2017 to 2023. Postoperative severe complications were statistically analyzed. Patients were divided into a non-severe complication group and a severe-complication group, and the causes, management, and outcomes of complications were analyzed. Results A total of 134 patients were included, comprising 69 males and 65 females, with a mean age of 17-73 (40.43±12.69) years. There were 93 patients undergoing left pneumonectomy and 41 patients undergoing right pneumonectomy. Preoperative sputum smear was positive in 35 patients, all of which converted to negative postoperatively. There were 58 patients with hemoptysis preoperatively, and none experienced hemoptysis postoperatively. Postoperative incisional infection occurred in 8 (5.97%) patients, and postoperative pulmonary infection in 26 (19.40%) patients. Severe postoperative complications occurred in 17 (12.69%) patients, including empyema in 9 (6.72%) patients, bronchopleural fistula with empyema in 1 (0.75%) patient, severe pneumonia in 3 (2.24%) patients, postpneumonectomy syndrome in 1 (0.75%) patient, chylothorax in 1 (0.75%) patient, ketoacidosis in 1 (0.75%) patient, and heart failure with severe pneumonia in 1 (0.75%) patient. Perioperative mortality occurred in 2 (1.49%) patients, both of whom underwent right pneumonectomy. Multivariate logistic regression analysis revealed that a history of ipsilateral thoracic surgery, concomitant Aspergillus infection, and greater blood loss were independent risk factors for severe complications following unilateral pneumonectomy for tuberculous destroyed lung (P<0.05). Conclusion Unilateral pneumonectomy for patients with tuberculous destroyed lung can significantly improve the clinical cure rate, sputum conversion rate, and hemoptysis cessation rate. However, there is a certain risk of severe perioperative complications and mortality, requiring thorough perioperative management and appropriate management of postoperative complications.

Objective To evaluate the effects of fasting and high-fat diet on the pharmacokinetics of rabeprazole sodium enteric-coated tablets in healthy Chinese subjects.Methods A single-center,randomized,open,two-agent,two-sequence,four-cycle,fully repeated crossover,single-dose trial design was used in this study,healthy subjects were assigned to receive single dose of rabeprazole sodium enteric-coated tablets 0.1 g in either fasting or high-fat diet state,and blood samples were taken at different time points,respectively.The concentrations of rabeprazole sodium enteric-coated in plasma were determined by liquid chromatography-tandem mass spectrometry(LC-MS/MS),the model method of the non-compartmental was used to calculate the pharmacokinetic parameters by Phoenix WinNonlin 8.2.Results The main pharmacokinetic parameters of rabeprazole sodium enteric-coated tablets in fasting state and high-fat diet state were as follows:Cmax were(339.63±156.47)and(318.86±132.13)ng·mL-1;t1/2 were(2.34±0.68)and(3.60±2.40)h;AUC0_t were(556.62±251.65)and(528.50±201.78)ng·mL-1·h;AUC0-∞ were(563.39±255.69)and(535.15±203.24)ng·mL-1·h;tmax were 3.65 and 6.99 h.After high-fat diet,the Cmax and AUC of rapeprazole sodium after high-fat and high-calorie diet decreased,Cmax decreased by 6.12％,AUC0-t decreased by 5.05％,AUC0-∞ decreased by 5.01％,andtmaxwas delayed by about 3.34 h.Cmax,AUC0-t and AUC0-∞ 90％confidence interval were 73.13％-115.10％,83.22％-112.28％and 83.40％-112.13％,respectively.Neither was between 85.00％-125.00％.Conclusion High-fat diet affects the absorption rate and degree of rabeprazole sodium enteric-coated,so it is suitable to be administered on an empty stomach.

Objective:To apply artificial intelligence（AI）in classifying ovarian tumors on ultrasound images，and compare the diagnostic results of several sonographers with varying seniority levels.Methods:A total of 645 patients diagnosed with adnexal masses via gynecological ultrasound examination at Qilu Hospital of Shandong University from January 2021 to December 2024 were enrolled. Three deep learning architectures，i.e.，Alexnet，Densenet121，and Resnet50 were developed and used to internally test the classification effectiveness of ovarian tumors，while the optimal model was selected for external testing. Two junior sonographers and two senior sonographers were recruited to independently diagnose ovarian tumors in the external test dataset. Subsequently，the benign and malignant results of the model's predictions were disclosed to each sonographer，and their revised diagnoses on the same external test data in combination with the best AI model were recorded.Results:The optimal model achieved an accuracy of 0.941，sensitivity of 0.936，and specificity of 0.944 on the internal test dataset，and maintained robust performance on the external test dataset with accuracy of 0.891，sensitivity of 0.880，and specificity of 0.907. Compared to junior sonographers，the optimal model demonstrated significantly higher sensitivity in discriminating benign from malignant ovarian tumors（0.880 vs. 0.723，0.602；all P<0.05）. No statistically significant difference was observed in diagnostic accuracy between the optimal model and senior sonographer 1（ P=0.05）. With assistance from the optimal model，junior sonographers achieved significant improvements in both sensitivity and specificity（sensitivity：0.723 vs. 0.843，0.602 vs. 0.819；specificity：0.778 vs. 0.833，0.685 vs. 0.741；all P<0.05）. Conclusions:The optimal model achieves comparable performance to that of senior sonographers in ovarian tumor classification. With model assistance，the diagnostic performance of junior sonographers is significantly improved.