Objective: To investigate the factors affecting pre-exposure prophylaxis (PrEP) use among men who have sex with men (MSM), so as to provide a basis for improving PrEP uptake among MSM and optimizing HIV prevention and control strategies. Methods: The MSM were recruited at a HIV testing site of the "Danlan Public Welfare" organization in Beijing Municipality using the convenient sampling method from March to May 2024. Sociodemographic information, awareness of basic AIDS knowledge, sexual orientation and behaviors, the use of PrEP and post-exposure prophylaxis (PEP) in the past six months were collected using the questionnaire survey. Multivariable logistic regression model and decision tree model were used to analyze the influencing factors for PrEP use among MSM. Results: A total of 414 participants were surveyed, with a mean age of (33.52±8.50) years. Among them, 345 individuals (83.33%) had a college degree and above. The predominant sexual orientation was homosexual, with 265 individuals, accounting for 64.01%. The majority reported having 1 to 2 same-sex partners in the past six months, with 182 individuals (43.96%). A total of 39 individuals (9.42%) had used PEP in the past six months. A total of 68 individuals (16.43%) had used PrEP during the same period. Multivariable logistic regression analysis revealed that educational attainment (college degree and above, OR=28.200, 95%CI: 3.162-251.519), the number of same-sex partners in the past six months (3-5 partners, OR=3.963, 95%CI: 1.065-14.755; ≥6 partners, OR=7.518, 95%CI: 1.829-30.910), and the use of PEP in the past six months (OR=18.606, 95%CI: 7.587-45.631) were significant factors associated with PrEP use among MSM. The decision tree model identified educational level, sexual orientation, and the number of same-sex partners in the past six months as factors affecting PrEP use among MSM. Among these, the number of same-sex partners in the past six months emerged as the primary influencing factor. The use of PrEP was 38.46% among MSM who had ≥3 same-sex partners in the past six months and identified as homosexual or heterosexual. Conclusion The use of PrEP among MSM is primarily associated with educational level, the number of same-sex partners in the past six months, use of post-exposure prophylaxis (PEP) in the past six months, and sexual orientation.

Objective To investigate the risk factors of subdelirium syndrome in adult ICU patients,then establish a risk prediction model and have it verified.Method A total of 380 adult ICU patients in our hospital between June 2022 and January 2024 were selected in this study.Among the patients,224(70％)were assigned to the model group and 114(30％)to the validation group.Independent risk factors were screened by comparison of the general data,disease and treatment,laboratory indicators and other relevant data between the patients with and without subdelirium.A risk prediction model was established with Logistic regression.Results A risk prediction model was finalised and established.It composed six risk factors of age(OR=1.023),APACHE Ⅱ score(OR=1.093),critical care pain observation tool(OR=2.216),duration of mechanical ventilation(OR=1.003),constraint(OR=2.615)and sepsis(OR=2.081).In internal validation,it was found that the calibration curve closely overlapped with the ideal curve and the area under the ROC curve was 0.816,with a predictive cut off value of 85 points.In external validation,the calibration curve was found closely overlapped with the ideal curve and the area under ROC curve was 0.808.Conclusion The risk prediction model for subdelirium syndrome in adult ICU patients established in the study has good consistency and prediction efficiency,thereby it provides a basis for early clinical screening and intervention of subdelirium syndrome.

OBJECTIVES: To evaluate the regulatory effects of lncRNA LINC00261 on proliferation, invasion, and metastasis of esophageal squamous cell carcinoma (ESCC) cells. METHODS: The differentially expressed RNAs in ESCC were identified using the GSE149612 dataset from the GEO database. PCR was used to detect LINC00261 expression levels in clinical ESCC and normal esophageal tissue samples and in multiple ESCC cell lines and normal esophageal epithelial cells (HEEC). In ESCC cells, the effects of overexpression of LINC00261 on cell proliferation, invasion, metastasis and apoptosis were analyzed using CCK-8 assay, clone formation assay, Transwell assay and flow cytometry. The potential targets of LINC00261 were predicted using bioinformatics tools including ENCORI and verified using dual-luciferase reporter assay and Western blotting. The effects of LINC00261 overexpression on ESCC were confirmed in a nude mouse model bearing ESCC xenograft. RESULTS: Analysis of the GSE149612 dataset revealed significantly lower LINC00261 expression in ESCC tissues and cell lines. In cultured ESCC cells, LINC00261 overexpression markedly suppressed cell proliferation, invasion, and metastasis and promoted cell apoptosis. Dual-luciferase reporter assays confirmed that LINC00261 targets the miR-23a-3p/ZNF292 axis. In the tumor-bearing mouse model, LINC00261 overexpression significantly inhibited ESCC xenograft proliferation and metastasis. CONCLUSIONS LINC00261 suppresses ESCC progression by targeting the miR-23a-3p/ZNF292 axis, suggesting a potential therapeutic strategy for ESCC treatment.
Humans ; MicroRNAs/genetics* ; Cell Proliferation ; Esophageal Neoplasms/genetics* ; Animals ; Esophageal Squamous Cell Carcinoma ; Mice, Nude ; RNA, Long Noncoding/genetics* ; Cell Line, Tumor ; Neoplasm Invasiveness ; Mice ; Carcinoma, Squamous Cell/genetics* ; Apoptosis ; Gene Expression Regulation, Neoplastic ; Neoplasm Metastasis

Purpose To investigate the feasibility of a deep learning framework to automatically analyze echocardiographic color Doppler videos in detecting valvular regurgitation.Materials and Methods This study retrospectively collected echocardiographic images of 1 109 patients with valvular regurgitation in the Fourth Medical Center of PLA General Hospital,from June 2015 to September 2019 as the training and validation sets.A prospective continuous collection of 1 562 echocardiography images was used as the test set in the Fourth Medical Center of PLA General Hospital from May 13 to June 13,2023,including 378 cases of mitral regurgitation and 223 cases of aortic regurgitation.This study developed deep learning networks to establish view classification model and valvular regurgitation recognition model,including the efficiency of section classification of deep learning models.Results The deep learning view classification model in this study could automatically identify two views for diagnosing mitral regurgitation and aortic regurgitation.The recognition accuracy for the parasternal long axis color Doppler view and the apical four chamber mitral color Doppler view was 1.00 and 0.93,respectively.The sensitivity,specificity,accuracy and area under the curve of the deep learning model for diagnosing mitral regurgitation were 0.847,0.852,0.849 and 0.930,respectively.The sensitivity,specificity,accuracy and area under the curve of the deep learning model in diagnosing aortic regurgitation were 0.857,0.861,0.859 and 0.940,respectively.Conclusion Deep learning algorithms can automatically identify valvular regurgitation and have the potential to become a screening tool for valvular heart disease.

Purpose To investigate the feasibility of a deep learning framework to automatically analyze echocardiographic color Doppler videos in detecting valvular regurgitation.Materials and Methods This study retrospectively collected echocardiographic images of 1 109 patients with valvular regurgitation in the Fourth Medical Center of PLA General Hospital,from June 2015 to September 2019 as the training and validation sets.A prospective continuous collection of 1 562 echocardiography images was used as the test set in the Fourth Medical Center of PLA General Hospital from May 13 to June 13,2023,including 378 cases of mitral regurgitation and 223 cases of aortic regurgitation.This study developed deep learning networks to establish view classification model and valvular regurgitation recognition model,including the efficiency of section classification of deep learning models.Results The deep learning view classification model in this study could automatically identify two views for diagnosing mitral regurgitation and aortic regurgitation.The recognition accuracy for the parasternal long axis color Doppler view and the apical four chamber mitral color Doppler view was 1.00 and 0.93,respectively.The sensitivity,specificity,accuracy and area under the curve of the deep learning model for diagnosing mitral regurgitation were 0.847,0.852,0.849 and 0.930,respectively.The sensitivity,specificity,accuracy and area under the curve of the deep learning model in diagnosing aortic regurgitation were 0.857,0.861,0.859 and 0.940,respectively.Conclusion Deep learning algorithms can automatically identify valvular regurgitation and have the potential to become a screening tool for valvular heart disease.

Objective To investigate the risk factors of subdelirium syndrome in adult ICU patients,then establish a risk prediction model and have it verified.Method A total of 380 adult ICU patients in our hospital between June 2022 and January 2024 were selected in this study.Among the patients,224(70％)were assigned to the model group and 114(30％)to the validation group.Independent risk factors were screened by comparison of the general data,disease and treatment,laboratory indicators and other relevant data between the patients with and without subdelirium.A risk prediction model was established with Logistic regression.Results A risk prediction model was finalised and established.It composed six risk factors of age(OR=1.023),APACHE Ⅱ score(OR=1.093),critical care pain observation tool(OR=2.216),duration of mechanical ventilation(OR=1.003),constraint(OR=2.615)and sepsis(OR=2.081).In internal validation,it was found that the calibration curve closely overlapped with the ideal curve and the area under the ROC curve was 0.816,with a predictive cut off value of 85 points.In external validation,the calibration curve was found closely overlapped with the ideal curve and the area under ROC curve was 0.808.Conclusion The risk prediction model for subdelirium syndrome in adult ICU patients established in the study has good consistency and prediction efficiency,thereby it provides a basis for early clinical screening and intervention of subdelirium syndrome.

AIM:To investigate the interaction mechanisms of estrogen receptor β(ERβ),nuclear factor-κB(NF-κB)and activator protein-1(AP-1)in colorectal cancer by analyzing the transcriptome data after tumor necrosis fac-tor α(TNF-α)treatment and combining it with NF-κB/p65 and ERβ cistrome data in colon cancer cell lines HT29 and SW480.METHODS:The TNF-α transcriptome was integrated with p65 and ERβ cistrome data.Protein interaction net-works of TNF-α,NF-κB/p65 and ERβ were constructed in colon cancer cell lines HT29 and SW480 using R.RE-SULTS:TNF-α regulated genes through p65 DNA binding,which were mainly enriched in the NF-κB and mitogen-acti-vated protein kinase(MAPK)pathways.Components of the NF-κB/p65 and MAPK pathways had potential interactions with AP-1 family proteins.ERβ overexpression did not significantly affect TNF-α-mediated gene regulation but may regu-late AP-1 activity through the MAPK and phosphatidylinositol 3-kinase(PI3K)/Akt pathways.Furthermore,ERβ de-creased p65 DNA binding sites in HT29 but increased p65 binding sites in SW480,suggesting cell line-specific regulation of NF-κB by ERβ.CONCLUSION:In colorectal cancer,NF-κB,ERβ and AP-1 have potential interactions:TNF-α can regulate AP-1 through NF-κB,while ERβ overexpression can alter NF-κB-mediated regulation,and the influence of ERβ on NF-κB may be gender-related.

Objective To investigate the progress of two-dimensional echocardiographic parameters in patients with different severity of aortic stenosis.Methods A retrospective analysis was per-formed on 96 patients diagnosed with aortic stenosis with at least 2 times of transthoracic echo-cardiography(interval ≥1 year)in Department of Cardiology,Fourth Medical Center of Chinese PLA General Hospital from March 2017 to December 2023.According to aortic stenosis severity,they were divided into a mild group(72 cases),a moderate group(14 cases)and a severe group(10 cases).Peak pressure gradient(PPG)across aortic valve,Vmax,mean aortic valve pressure gradient(ΔPm),pulmonary artery systolic pressure(PASP)were collected,and the changes and annual progress of these echocardiographic parameters at baseline and before and after follow-up were analyzed.Results The values of IVST,LVPWT,Vmax,aortic valve PPG and ΔPm were sig-nificantly increased in the mild,moderate and severe stenosis groups in turn(P＜0.05,P＜0.01).The values of Vmax,PPG and ΔPm were significantly lower in the mild stenosis group than the moderate and severe stenosis groups,and the LVPWT value was obviously lower in the mild ste-nosis group than the severe stenosis group(P＜0.05).The aortic valve PPG and ΔPm values at follow-up were significantly higher than those before the follow-up in the three stenosis groups(P＜0.05,P＜0.01).After follow-up,the Vmax values in mild and moderate stenosis groups were notably higher than before(P＜0.01).The PASP value at follow-up was significantly higher than before in the severe stenosis group(P＜0.05).The annual progression rate of Vmax,PASP,LVEF were gradually increased in the mild,moderate,and severe stenosis groups(P＞0.05).The annual progression rate of ΔPm was gradually increased in the three groups in turn(2.30±1.77 mm Hg/year vs 2.40±1.18 mm Hg/year vs 6.08±1.70 mm Hg/year,P＜0.05).Conclusion As the severity of baseline aortic stenosis increases,obvious changes are observed in cardiac structure and function.Before and after follow-up,the serious the aortic stenosis severity is,the faster the annual progression rates of Vmax,PPG,LVEF and PASP are.

Objective To establish the theoretical model for morphology of lipid droplets adhering to inner vascular wall, so as to provide the theoretical model for the study and analysis of the overall morphology of lipid droplets on inner vascular wall of patients with fat embolism. MethodsThe model of the droplet with variable radius on inner wall of the cylindrical tube was established to describe morphology of lipid droplets on inner vascular wall, and accuracy of the theoretical model was verified by Surface Evolver (SE) software simulation results. According to the theoretical model, the influencing patterns of lipid droplet volume and contact angle on dimensionless adhesion area Sb0 and blockage ratio κ of lipid droplets in blood vessels were analyzed. Results The theoretical model could predict contour parameters of adhesion morphology for lipid droplets on inner vascular wall, including the height and arc radius of lipid droplet at azimuth angle of 0 and π/2. The relative errors between contour parameters obtained from the theoretical model and corresponding parameters obtained from the SE simulation were smaller than 10%. For lipid droplets with the same dimensionless volume V0, Sb0 of inner vascular wall decreased with contact angle increasing, and blockage ratio κ increased with contact angle increasing. At the same contact angle, the smaller V0 , the smaller Sb0 and κ would be. Conclusions The established theoretical model with variable radius can well describe morphological characteristics of lipid droplets on inner vascular wall. The influening pattern of volume, contact angle and other parameters on height, adhesion area and cross-sectional area of lipid droplets can be accurately and quickly obtained through the theoretical model, indicating that the larger the contact angle of lipid droplets or the smaller the dimensionless volume, the lower the probability of embolism. The research findings provide theoretical support for the analysis on related diseases.

Objective:To explore the value of Care Evaluation Scale (CES) in cancer patients for predicting survival time in patients with advanced breast cancer.Methods:From November 2018 to June 2020, the convenient sampling was used to select 140 patients with advanced breast cancer who were treated in Qingdao Central Hospital Affiliated to Qingdao University and 140 home caregivers as the research objects. Researchers contacted home caregivers of patients with advanced breast cancer to set up a time and place to conduct one-on-one surveys of research objects using the Cancer Patient CES. Pearson correlation was used to analyze the correlation between survival time and CES score in patients with advanced breast cancer. Multiple stepwise regression analysis was used to analyze the influencing factors of CES scores in patients with advanced breast cancer. A total of 140 questionnaires were distributed, of which 133 were valid, and the effective recovery rate was 95.00% (133/140) .Results:Among the 133 patients with advanced breast cancer, 76 were Luminal A, 19 were Luminal B, 12 were ERBB 2+ and 26 were Basal-like. Luminal type A advanced breast cancer patients had the longest survival time, and the differences were statistically significant compared with those of ERBB 2+ and Basal-like advanced breast cancer patients ( P<0.05) . There was no statistically significant difference in survival time between Luminal B type advanced breast cancer and Luminal A, ERBB 2+ and Basal-like advanced breast cancer patients ( P>0.05) . Luminal type A advanced breast cancer patients had the highest CES score, and the differences were statistically significant compared with those of Luminal type B, ERBB 2+ and Basal-like advanced breast cancer patients ( P<0.05) . There was no statistically significant difference in the CES score between Luminal B advanced breast cancer and Luminal A, ERBB 2+ and Basal-like advanced breast cancer ( P>0.05) . Pearson correlation analysis found that the survival time of patients with advanced breast cancer was positively correlated with the CES score ( r=0.892, P<0.05) . The influencing factors of CES empowerment in patients with advanced breast cancer included physical care provided by doctors, mental health care, environment, explanation or instructions by doctors to family members, collaboration and persistence, consideration for family members, availability, cost, explanation or instructions by doctors to patients and physical care provided by nurses. Conclusions:CES in cancer patients can effectively predict the survival time of patients with advanced breast cancer. The molecular types of patients with advanced breast cancer are different and the effect of predicting survival time is also different.