[Objective]To investigate the prevalence rate of chronic non-communicable diseases(NCDs)and the association with quality of life in middle-aged and elderly patients with HIV/AIDS.[Methods]This cross-sectional study surveyed 432 patients with HIV/AIDS(aged≥45 years)in the Infectious Disease Center in Guangzhou Eighth People's Hospital of Guangzhou Medical University,and 366 participants were included in the analysis after quality control.A questionnaire and the EuroQol 5-Dimensional 3-level version(EQ-5D-3L)were used to investigate NCDs and quality of life and Tobit regression model was used to estimate the association between chronic diseases and quality of life.[Results]Among the 366 participants,29(7.9%)had cardiovascular disease,45(12.3%)had hypertension,122(33.3%)had hyperglycemia,151(41.3%)had hyperlipidemia,7(1.9%)had cancer,17(4.6%)had chronic kidney disease,38(10.4%)had chronic liver disease,21(5.7%)had musculoskeletal disorders,and 253(69.1%)suffered from at least one type of chronic diseases.The median(lower and upper quartiles)of EQ-5D utility index was 1.000(0.964～1.000).Multivariate Tobit regression results of the total population showed that cancer[ba=-0.08,95%CI(-0.15,-0.01),P=0.036],chronic kidney disease[ba=-0.07,95%CI(-0.12,-0.02),P=0.006],musculoskeletal disease[ba=-0.09,95%CI(-0.13,-0.05),P<0.001],and≥3 types of chronic diseases[ba=-0.05,95%CI(-0.08,-0.01),P=0.013]were negatively correlated with EQ-5D utility index.The stratified analysis results of different CD4+T cell levels showed that hypertension[ba=-0.07,95%CI(-0.12,-0.02),P=0.007],chronic kidney disease[ba=-0.10,95%CI(-0.18,-0.03),P=0.006],musculoskeletal disease[ba=-0.15,95%CI(-0.22,-0.07),P<0.001]and≥3 types of chronic diseases[ba=-0.09,95%CI(-0.09,-0.01),P<0.001]were negatively correlated with EQ-5D utility index in the group with CD4≤500(cells/μL),whereas cancer[ba=-0.11,95%CI(-0.20,-0.01),P=0.031]was negatively correlated with EQ-5D utility index in the group with CD4＞500(cells/μL).[Conclusions]The prevalence rate of chronic non-communicable diseases in middle-aged and elderly patients with HIV/AIDS is relatively high.The classification of NCDs such as cancer or chronic kidney disease or other chronic diseases and the numbers of NCDs categories are negatively correlated with quality of life.However,this association varies among patients with HIV/AIDS of different CD4+T cell levels.It is suggested that we should try to prevent and identify NCDs at an early stage,strengthen linkages and integration of health services for AIDS and chronic NCDs,and jointly manage and control AIDS with chronic diseases to improve the quality of life among people living with HIV/AIDS.

Objective:To construct and validate a depression risk prediction model for middle-aged and elderly patients with diabetes.Methods:Data were extracted from the fifth wave (2020) of the China Health and Retirement Longitudinal Study (CHARLS). A total of 900 diabetic patients were identified, and after excluding those with missing data or invalid questionnaires, 769 patients were included in the analysis. Patients were randomly divided into a training set and a validation set in a 7∶3 ratio. Univariate analysis and logistic regression analysis were performed to screen the optimal predictors of depression in diabetic patients, and a nomogram model was developed. The predictive performance of the model was assessed by the area under the receiver operating characteristic curve ( AUC). Model calibration and accuracy were evaluated using bootstrap resampling, calibration plots, and the Hosmer-Lemeshow test. The clinical utility was further assessed by decision curve analysis (DCA) and clinical impact curves (CIC) . Results:Among the 769 patients, 366 (47.59%) had depression. Logistic regression analysis showed that place of residence, pain, difficulty in toileting, difficulty in bathing, sleep duration, physical exercise, life satisfaction, and children's satisfaction were independent predictors of depression in diabetic patients. A nomogram was constructed based on these variables, yielding an AUC of 0.775. At the optimal cutoff value of 0.557, the model demonstrated a sensitivity of 59.1% and a specificity of 84.8%, indicating good discriminative ability. The Hosmer-Lemeshow test showed (χ 2=15.821, P=0.105), suggesting good agreement between predicted and observed outcomes. In the validation set, the AUC was 0.778, with Hosmer-Lemeshow (χ 2=8.557, P=0.575). DCA and CIC indicated favorable clinical applicability of the model. Conclusions:The depression risk prediction model constructed in this study demonstrated good predictive performance. It can assist clinicians in early identification of high-risk individuals with diabetes and provide a theoretical basis for targeted interventions.

Objective:To evaluate the effect of Bu-Zhong-Yi-Qi-Tang (BZYQT) combined with low molecular weight heparin (LMWH) on lower limb deep vein thrombosis (DVT) after fracture fixation in aged rabbits.Methods:Thirty-two elderly New Zealand rabbits of either sex, aged 36 months, weighing 3.5-4.0 kg, were divided into 4 groups ( n=8 each) using the random number table method: control group (C group), fracture fixation group (F group), low molecular weight heparin group (L group) and BZYQT combined with LMWH group (BL group). A model of fixation surgery for right femoral shaft fracture was established in anesthetized rabbits in F, L and BL groups. Starting from postoperative day 1, BL group received BZYQT at 3.6 ml/kg via gavage, F and L groups received normal saline at 3.6 ml/kg via gavage, L and BL groups received subcutaneous injection of LMWH at 200 IU/kg, and all the administrations were performed daily at 08: 00 for 7 consecutive days. On postoperative day 7, ultrasound examination was performed to assess lower limb DVT. Blood samples were collected from the marginal ear vein for determination of the serum concentrations of cortisol (COR), tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6) and endothelin-1 (ET-1) by enzyme-linked immunosorbent assay. Femoral vein specimens were obtained under anesthesia for microscopic examination of the pathological changes of the femoral vein lumen and endothelial tissues (by HE staining) and of the morphology of vascular endothelial cells (by the transmission electron microscopy). Results:Compared with C group, the concentrations of serum COR, TNF-α, IL-6 and ET-1 were significantly increased in F, L and BL groups, and the incidence of lower limb DVT within 7 days after surgery was significantly increased in F and L groups ( P<0.05). Compared with F group, the incidence of lower limb DVT within 7 days after surgery was significantly decreased in L and BL groups, and the concentrations of serum COR, TNF-α, IL-6 and ET-1 were significantly decreased ( P<0.05), and the pathological damage was significantly reduced in BL group. Compared with L group, the incidence of lower limb DVT and concentration of IL-6 were significantly decreased ( P<0.05), and pathological damage was significantly alleviated in BL group. Conclusions:BZYQT combined with LMWH can prevent the occurrence of lower limb DVT after fracture fixation in aged rabbits.

Objective:To develop a deep learning-based system for real-time recognition and classification of portal hypertensive gastropathy (PHG) and evaluate its ability to assist junior endoscopists.Methods:A total of 2 848 gastroscopy images from 832 patients with liver cirrhosis were selected from Digestive Endoscopy Center databases of Renmin Hospital of Wuhan University, Wuhan Hospital of Traditional Chinese and Western Medicine, and the Second Hospital of Jingzhou from January 2015 to October 2023. This system referred to 3 endoscopic features of Baveno Ⅱ scoring system. Three models were developed respectively for gastric antral vascular ectasia (GAVE), mosaic-like pattern (MLP), and red marks (RM). The specific classification references were as follows: (1) GAVE model: 0 no, 1 yes; (2) MLP model: 0 no, 1 mild, 2 severe; (3) RM model: 0 no, 1 isolated, 2 fused. The classification results for endoscopic characteristics of PHG of 3 endoscopy experts were taken as the gold standard. The yolov8-m model was used for training. The training dataset, validation dataset, and test dataset were allocated at a ratio of 8∶1∶1. The test dataset was used to evaluate the performance of models and their auxiliary effects on endoscopists. The accuracy, recall, precision, specificity and Kappa coefficient were calculated. Results:The accuracy, recall, specificity of GAVE model were 96.0% (48/50), 87.5% (7/8) and 97.6% (41/42). There was no significant difference between its accuracy and the gold standard ( χ2=316.226, P=1.000). The precision of GAVE1 and GAVE0 were 87.5% (7/8) and 97.6% (41/42) respectively. The accuracy of MLP model was 84.1% (132/157), and there was no significant difference compared with the gold standard ( χ2=3.286, P=0.193). The precision and recall of MLP2 were 88.2% (15/17) and 75.0% (15/20). The precision and recall of MLP1 were 77.9% (60/77) and 88.2% (60/68). The precision and recall of MLP0 were 90.5% (57/63) and 82.6% (57/69). The accuracy of RM model was 87.9% (123/140), and there was no significant difference compared with the gold standard ( χ2=2.891, P=0.409). The precision and recall of RM2 were 94.7% (18/19) and 78.3% (18/23). The precision and recall of RM1 were 72.2% (26/36) and 81.3% (26/32). The precision and recall of RM0 were 92.9% (79/85) and 92.9% (79/85). The mean accuracy of the three junior endoscopists, with and without the assistance of the GAVE model, MLP model, and RM model, respectively increased from 95.3% to 99.3%, from 83.9% to 91.9%, and from 81.9% to 83.1%. The overall consistency analysis of the 3 junior endoscopists with the gold standard indicated that the consistency of the GAVE model before and after assistance was extremely strong (both an overall Kappa of 1.000); the consistency before assistance of the MLP model was moderate (with an overall Kappa of 0.601), which increased to extremely strong after assistance (with an overall Kappa of 0.964); and the consistency of the RM model before and after assistance was also relatively strong (with an overall Kappa of 0.792 before and 0.798 after). Conclusion:The deep learning system accurately identifies and classifies PHG features and significantly enhances diagnostic performance of junior endoscopists.

The objective of this study was to develop a rapid and precise detection technique for por-cine Senecavirus A(SVA)employing reverse transcription recombinase polymerase amplification(RT-RAA)in conjunction with CRISPR Cas13a technology.Additionally,the study aimed to opti-mize the assay's reaction conditions to enhance amplification efficiency.Eight RT-RAA primer sets were designed based on the conserved gene sequence of porcine SVA,and a series of reaction condi-tions were evaluated to refine the RT-RAA reaction system.Subsequently,CRISPR-derived RNA(crRNA)sequences were developed and selected to construct the RT-RAA-CRISPR reaction sys-tem.The method's specificity was determined by examining six prevalent porcine pathogenic nucleic acids,while its sensitivity was assessed using SVA cRNA standards quantified by digital PCR.The method's stability and the consistency of clinical sample analysis were also evaluated.The findings revealed that the optimized RT-RAA and CRISPR reaction systems exhibited the highest amplifi-cation efficiency at a reaction temperature of 37 ℃.Among the eight crRNAs,five were identified as exhibiting the strongest detection signals.The formulated RT-RAA-CRISPR Cas13a method demonstrated exceptional specificity,showing no cross-reactivity with other common porcine disea-ses,including ASFV,PRRSV,PEDV,PCV2,CSFV,and PRV.The method achieved high sensitivi-ty,detecting as low as 0.86 copies/μL of SVA,and exhibited stable fluorescence output,robust re-producibility,and the ability to complete clinical sample analysis within 50 minutes.Consistency e-valuation with six positive and 58 negative samples indicated 100％agreement in outcomes.These results substantiate that the study successfully established a rapid and specific RT-RAA-CRISPR Cas13a detection method for the on-site identification of porcine Senecavirus A,demonstrating high specificity and sensitivity,and holds promise for application in SVA monitoring and control initia-tives.

Objective:To construct and validate a depression risk prediction model for middle-aged and elderly patients with diabetes.Methods:Data were extracted from the fifth wave (2020) of the China Health and Retirement Longitudinal Study (CHARLS). A total of 900 diabetic patients were identified, and after excluding those with missing data or invalid questionnaires, 769 patients were included in the analysis. Patients were randomly divided into a training set and a validation set in a 7∶3 ratio. Univariate analysis and logistic regression analysis were performed to screen the optimal predictors of depression in diabetic patients, and a nomogram model was developed. The predictive performance of the model was assessed by the area under the receiver operating characteristic curve ( AUC). Model calibration and accuracy were evaluated using bootstrap resampling, calibration plots, and the Hosmer-Lemeshow test. The clinical utility was further assessed by decision curve analysis (DCA) and clinical impact curves (CIC) . Results:Among the 769 patients, 366 (47.59%) had depression. Logistic regression analysis showed that place of residence, pain, difficulty in toileting, difficulty in bathing, sleep duration, physical exercise, life satisfaction, and children's satisfaction were independent predictors of depression in diabetic patients. A nomogram was constructed based on these variables, yielding an AUC of 0.775. At the optimal cutoff value of 0.557, the model demonstrated a sensitivity of 59.1% and a specificity of 84.8%, indicating good discriminative ability. The Hosmer-Lemeshow test showed (χ 2=15.821, P=0.105), suggesting good agreement between predicted and observed outcomes. In the validation set, the AUC was 0.778, with Hosmer-Lemeshow (χ 2=8.557, P=0.575). DCA and CIC indicated favorable clinical applicability of the model. Conclusions:The depression risk prediction model constructed in this study demonstrated good predictive performance. It can assist clinicians in early identification of high-risk individuals with diabetes and provide a theoretical basis for targeted interventions.

Objective:To evaluate the effect of Bu-Zhong-Yi-Qi-Tang (BZYQT) combined with low molecular weight heparin (LMWH) on lower limb deep vein thrombosis (DVT) after fracture fixation in aged rabbits.Methods:Thirty-two elderly New Zealand rabbits of either sex, aged 36 months, weighing 3.5-4.0 kg, were divided into 4 groups ( n=8 each) using the random number table method: control group (C group), fracture fixation group (F group), low molecular weight heparin group (L group) and BZYQT combined with LMWH group (BL group). A model of fixation surgery for right femoral shaft fracture was established in anesthetized rabbits in F, L and BL groups. Starting from postoperative day 1, BL group received BZYQT at 3.6 ml/kg via gavage, F and L groups received normal saline at 3.6 ml/kg via gavage, L and BL groups received subcutaneous injection of LMWH at 200 IU/kg, and all the administrations were performed daily at 08: 00 for 7 consecutive days. On postoperative day 7, ultrasound examination was performed to assess lower limb DVT. Blood samples were collected from the marginal ear vein for determination of the serum concentrations of cortisol (COR), tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6) and endothelin-1 (ET-1) by enzyme-linked immunosorbent assay. Femoral vein specimens were obtained under anesthesia for microscopic examination of the pathological changes of the femoral vein lumen and endothelial tissues (by HE staining) and of the morphology of vascular endothelial cells (by the transmission electron microscopy). Results:Compared with C group, the concentrations of serum COR, TNF-α, IL-6 and ET-1 were significantly increased in F, L and BL groups, and the incidence of lower limb DVT within 7 days after surgery was significantly increased in F and L groups ( P<0.05). Compared with F group, the incidence of lower limb DVT within 7 days after surgery was significantly decreased in L and BL groups, and the concentrations of serum COR, TNF-α, IL-6 and ET-1 were significantly decreased ( P<0.05), and the pathological damage was significantly reduced in BL group. Compared with L group, the incidence of lower limb DVT and concentration of IL-6 were significantly decreased ( P<0.05), and pathological damage was significantly alleviated in BL group. Conclusions:BZYQT combined with LMWH can prevent the occurrence of lower limb DVT after fracture fixation in aged rabbits.

The objective of this study was to develop a rapid and precise detection technique for por-cine Senecavirus A(SVA)employing reverse transcription recombinase polymerase amplification(RT-RAA)in conjunction with CRISPR Cas13a technology.Additionally,the study aimed to opti-mize the assay's reaction conditions to enhance amplification efficiency.Eight RT-RAA primer sets were designed based on the conserved gene sequence of porcine SVA,and a series of reaction condi-tions were evaluated to refine the RT-RAA reaction system.Subsequently,CRISPR-derived RNA(crRNA)sequences were developed and selected to construct the RT-RAA-CRISPR reaction sys-tem.The method's specificity was determined by examining six prevalent porcine pathogenic nucleic acids,while its sensitivity was assessed using SVA cRNA standards quantified by digital PCR.The method's stability and the consistency of clinical sample analysis were also evaluated.The findings revealed that the optimized RT-RAA and CRISPR reaction systems exhibited the highest amplifi-cation efficiency at a reaction temperature of 37 ℃.Among the eight crRNAs,five were identified as exhibiting the strongest detection signals.The formulated RT-RAA-CRISPR Cas13a method demonstrated exceptional specificity,showing no cross-reactivity with other common porcine disea-ses,including ASFV,PRRSV,PEDV,PCV2,CSFV,and PRV.The method achieved high sensitivi-ty,detecting as low as 0.86 copies/μL of SVA,and exhibited stable fluorescence output,robust re-producibility,and the ability to complete clinical sample analysis within 50 minutes.Consistency e-valuation with six positive and 58 negative samples indicated 100％agreement in outcomes.These results substantiate that the study successfully established a rapid and specific RT-RAA-CRISPR Cas13a detection method for the on-site identification of porcine Senecavirus A,demonstrating high specificity and sensitivity,and holds promise for application in SVA monitoring and control initia-tives.

Objective:To develop a deep learning-based system for real-time recognition and classification of portal hypertensive gastropathy (PHG) and evaluate its ability to assist junior endoscopists.Methods:A total of 2 848 gastroscopy images from 832 patients with liver cirrhosis were selected from Digestive Endoscopy Center databases of Renmin Hospital of Wuhan University, Wuhan Hospital of Traditional Chinese and Western Medicine, and the Second Hospital of Jingzhou from January 2015 to October 2023. This system referred to 3 endoscopic features of Baveno Ⅱ scoring system. Three models were developed respectively for gastric antral vascular ectasia (GAVE), mosaic-like pattern (MLP), and red marks (RM). The specific classification references were as follows: (1) GAVE model: 0 no, 1 yes; (2) MLP model: 0 no, 1 mild, 2 severe; (3) RM model: 0 no, 1 isolated, 2 fused. The classification results for endoscopic characteristics of PHG of 3 endoscopy experts were taken as the gold standard. The yolov8-m model was used for training. The training dataset, validation dataset, and test dataset were allocated at a ratio of 8∶1∶1. The test dataset was used to evaluate the performance of models and their auxiliary effects on endoscopists. The accuracy, recall, precision, specificity and Kappa coefficient were calculated. Results:The accuracy, recall, specificity of GAVE model were 96.0% (48/50), 87.5% (7/8) and 97.6% (41/42). There was no significant difference between its accuracy and the gold standard ( χ2=316.226, P=1.000). The precision of GAVE1 and GAVE0 were 87.5% (7/8) and 97.6% (41/42) respectively. The accuracy of MLP model was 84.1% (132/157), and there was no significant difference compared with the gold standard ( χ2=3.286, P=0.193). The precision and recall of MLP2 were 88.2% (15/17) and 75.0% (15/20). The precision and recall of MLP1 were 77.9% (60/77) and 88.2% (60/68). The precision and recall of MLP0 were 90.5% (57/63) and 82.6% (57/69). The accuracy of RM model was 87.9% (123/140), and there was no significant difference compared with the gold standard ( χ2=2.891, P=0.409). The precision and recall of RM2 were 94.7% (18/19) and 78.3% (18/23). The precision and recall of RM1 were 72.2% (26/36) and 81.3% (26/32). The precision and recall of RM0 were 92.9% (79/85) and 92.9% (79/85). The mean accuracy of the three junior endoscopists, with and without the assistance of the GAVE model, MLP model, and RM model, respectively increased from 95.3% to 99.3%, from 83.9% to 91.9%, and from 81.9% to 83.1%. The overall consistency analysis of the 3 junior endoscopists with the gold standard indicated that the consistency of the GAVE model before and after assistance was extremely strong (both an overall Kappa of 1.000); the consistency before assistance of the MLP model was moderate (with an overall Kappa of 0.601), which increased to extremely strong after assistance (with an overall Kappa of 0.964); and the consistency of the RM model before and after assistance was also relatively strong (with an overall Kappa of 0.792 before and 0.798 after). Conclusion:The deep learning system accurately identifies and classifies PHG features and significantly enhances diagnostic performance of junior endoscopists.

Objective:To evaluate the item properties and applicability of the eHealth Literacy Scale in Web 3.0 context (eHLS-Web 3.0) among diabetic patients by applying classical test theory (CTT) and item response theory (IRT) to provide a new effective tool for assessing the level of eHealth literacy among diabetic patients.Methods:A cross-sectional study design was adopted and 230 diabetic patients from the inpatient endocrinology department of the Affiliated Yantai Yuhuangding Hospital of Qingdao University were selected by convenience sampling from November 2022 to May 2023 and were surveyed by using the self-general information questionnaire and eHLS-Web 3.0. The correlation coefficient method, Cronbach α coefficient, Guttman split-half reliability coefficient, and structural validity in CCT were used, and discriminant validity, convergent validity, and Samejima Graded Response model in IRT were used to evaluate the scale items. Results:The CTT results showed that Cronbach α coefficient for the eHLS-Web 3.0 was 0.973, the Guttman split-half reliability coefficient was 0.894, and the correlation coefficient between the items and the total score of the scale ranged from 0.708 to 0.845 (all P<0.01). Exploratory factor analysis extracted three common factors with a cumulative variance contribution rate of 76.75%, and the confirmatory factor analysis model had a good fit. The reliability of each dimension combination was >0.60, and the average variance extracted was >0.50. The square root of the average variance extracted from each dimension (0.817, 0.875, 0.845) was greater than the correlation coefficient between this dimension and other dimensions (0.742, 0.763, 0.715). The IRT results showed that the discrimination parameter of all the items ranged from 2.174 to 4.054, the difficulty coefficients increased with the increase of the level (- 2.471 - 2.187), and the average information content of all the items ranged from 1.217 to 2.609. Conclusions:The eHLS-Web 3.0 has good characteristics among diabetic patients as tested by CTT and IRT, and can be used as an effective tool to assess the eHealth literacy level of diabetic patients in China.