Objective To investigate the predictive value of endotracheal tube cuff pressure change(△Pcuff)on extubation outcome in mechanically ventilated patients with acute exacerbation of chronic obstructive pulmonary diseas(AECOPD).Methods A total of 93 AECOPD patients who underwent mechanical ventilation for at least 48 hours and required extubation through spontaneous breathing trial(SBT)from March 2023 to August 2024 in the Department of Critical Care Medicine of Deyang People's Hospital were selected as study subjects.According to the outcome of extubation,they were divided into successful extubation group and failed extubation group.General clinical data,laboratory results,△Pcuff at the start and at 30min of SBT were compared between two groups.Binary Logistic regression was used to analyze the risk factors affecting the outcome of extubation in AECOPD patients.The predictive value of △Pcuff for extubation outcome was evaluated by receiver operating characteristic(ROC)curve.Results Of the 93 patients,81 were successfully extubed and 12 were failed.Univariate analysis showed that △Pcuff(at the beginning of SBT),△Pcuff(SBT 30min),systolic blood pressure,pH,arterial partial pressure of oxygen,arterial partial pressure of carbon dioxide,and oxygenation index were all factors influencing the outcome of extubation(P＜0.05).Binary Logistic regression analysis showed that △Pcuff(at the beginning of SBT)was an independent risk factor for extubation outcome(P＜0.05).The ROC curve showed that △Pcuff(at the beginning of SBT)predicted extubation outcome with an area under the curve of 0.913,sensitivity of 84.0％,specificity of 83.3％,and Yoden index of 0.673,with an optimal cutoff of 34.5cmH2O(1cmH2O=0.098kPa).Conclusion The △Pcuff(at the beginning of SBT)has a good predictive value for the extubation outcome of mechanically ventilated patients with AECOPD,and the probability of successful extubation of a patient is higher when the △Pcuff is ≥34.5 cmH2O.

OBJECTIVE T o analyze the current situation and characteristics of hospital-associated infection in a psy-chiatric hospital,providing references for formulation of control measures for hospital-associated infection.METHODS A retrospective analysis was conducted on hospital-associated infection data from Shanghai Mental Health Center between Jan.1,2019,and Oct.31,2024.The infection rates and infection sites across different years,patient populations,and departments were analyzed.RESULTS A total of 47 051 inpatients were investiga-ted,with 1 798 cases of hospital-associated infections,resulting in an infection rate of 3.82％.The highest inci-dence rate was observed in 2020(4.13％).The top three departments with the highest incidence rates were the in-fectious disease department(12.13％),geriatric psychiatry(7.67％),and the Traditional Chinese Medicine De-partment department(4.90％).The primary infection sites were the lower respiratory tract(57.06％),urinary tract(18.24％),skin and soft tissue(14.29％).The incidence rate was higher in males(5.32％)than in females(2.72％)(P＜0.001).Statistically significant differences were found in the incidence rates among different age groups(P＜0.001),with the highest rate observed in patients aged over 90 years(17.11％).The lowest infection rate was found in patients hospitalized for less than 60 days(0.69％),while the highest was in those hospitalized for more than 1 000 days(49.94％).CONCLUSIONS The patients in psychiatric hospitals are susceptible to infec-tions in the respiratory tract,urinary tract,skin and soft tissue.Targeted prevention and control measures can be implemented based on high-risk factors such as age,length of stay,and gender to reduce the occurrence of in-fections.

Objective To investigate the predictive value of endotracheal tube cuff pressure change(△Pcuff)on extubation outcome in mechanically ventilated patients with acute exacerbation of chronic obstructive pulmonary diseas(AECOPD).Methods A total of 93 AECOPD patients who underwent mechanical ventilation for at least 48 hours and required extubation through spontaneous breathing trial(SBT)from March 2023 to August 2024 in the Department of Critical Care Medicine of Deyang People's Hospital were selected as study subjects.According to the outcome of extubation,they were divided into successful extubation group and failed extubation group.General clinical data,laboratory results,△Pcuff at the start and at 30min of SBT were compared between two groups.Binary Logistic regression was used to analyze the risk factors affecting the outcome of extubation in AECOPD patients.The predictive value of △Pcuff for extubation outcome was evaluated by receiver operating characteristic(ROC)curve.Results Of the 93 patients,81 were successfully extubed and 12 were failed.Univariate analysis showed that △Pcuff(at the beginning of SBT),△Pcuff(SBT 30min),systolic blood pressure,pH,arterial partial pressure of oxygen,arterial partial pressure of carbon dioxide,and oxygenation index were all factors influencing the outcome of extubation(P＜0.05).Binary Logistic regression analysis showed that △Pcuff(at the beginning of SBT)was an independent risk factor for extubation outcome(P＜0.05).The ROC curve showed that △Pcuff(at the beginning of SBT)predicted extubation outcome with an area under the curve of 0.913,sensitivity of 84.0％,specificity of 83.3％,and Yoden index of 0.673,with an optimal cutoff of 34.5cmH2O(1cmH2O=0.098kPa).Conclusion The △Pcuff(at the beginning of SBT)has a good predictive value for the extubation outcome of mechanically ventilated patients with AECOPD,and the probability of successful extubation of a patient is higher when the △Pcuff is ≥34.5 cmH2O.

Background: The genetic basis for hyperglycaemia in pregnancy remain unclear. This study aimed to uncover the genetic determinants of gestational diabetes mellitus (GDM) and investigate their applications. Methods: We performed a meta-analysis of genome-wide association studies (GWAS) for GDM in Chinese women (464 cases and 1,217 controls), followed by de novo replications in an independent Chinese cohort (564 cases and 572 controls) and in silico replication in European (12,332 cases and 131,109 controls) and multi-ethnic populations (5,485 cases and 347,856 controls). A polygenic risk score (PRS) was derived based on the identified variants. Results: Using the genome-wide scan and candidate gene approaches, we identified four susceptibility loci for GDM. These included three previously reported loci for GDM and type 2 diabetes mellitus (T2DM) at MTNR1B (rs7945617, odds ratio [OR], 1.64; 95% confidence interval [CI],1.38 to 1.96]), CDKAL1 (rs7754840, OR, 1.33; 95% CI, 1.13 to 1.58), and INS-IGF2-KCNQ1 (rs2237897, OR, 1.48; 95% CI, 1.23 to 1.79), as well as a novel genome-wide significant locus near TBR1-SLC4A10 (rs117781972, OR, 2.05; 95% CI, 1.61 to 2.62; Pmeta=7.6×10-9), which has not been previously reported in GWAS for T2DM or glycaemic traits. Moreover, we found that women with a high PRS (top quintile) had over threefold (95% CI, 2.30 to 4.09; Pmeta=3.1×10-14) and 71% (95% CI, 1.08 to 2.71; P=0.0220) higher risk for GDM and abnormal glucose tolerance post-pregnancy, respectively, compared to other individuals. Conclusion Our results indicate that the genetic architecture of glucose metabolism exhibits both similarities and differences between the pregnant and non-pregnant states. Integrating genetic information can facilitate identification of pregnant women at a higher risk of developing GDM or later diabetes.

Background: The genetic basis for hyperglycaemia in pregnancy remain unclear. This study aimed to uncover the genetic determinants of gestational diabetes mellitus (GDM) and investigate their applications. Methods: We performed a meta-analysis of genome-wide association studies (GWAS) for GDM in Chinese women (464 cases and 1,217 controls), followed by de novo replications in an independent Chinese cohort (564 cases and 572 controls) and in silico replication in European (12,332 cases and 131,109 controls) and multi-ethnic populations (5,485 cases and 347,856 controls). A polygenic risk score (PRS) was derived based on the identified variants. Results: Using the genome-wide scan and candidate gene approaches, we identified four susceptibility loci for GDM. These included three previously reported loci for GDM and type 2 diabetes mellitus (T2DM) at MTNR1B (rs7945617, odds ratio [OR], 1.64; 95% confidence interval [CI],1.38 to 1.96]), CDKAL1 (rs7754840, OR, 1.33; 95% CI, 1.13 to 1.58), and INS-IGF2-KCNQ1 (rs2237897, OR, 1.48; 95% CI, 1.23 to 1.79), as well as a novel genome-wide significant locus near TBR1-SLC4A10 (rs117781972, OR, 2.05; 95% CI, 1.61 to 2.62; Pmeta=7.6×10-9), which has not been previously reported in GWAS for T2DM or glycaemic traits. Moreover, we found that women with a high PRS (top quintile) had over threefold (95% CI, 2.30 to 4.09; Pmeta=3.1×10-14) and 71% (95% CI, 1.08 to 2.71; P=0.0220) higher risk for GDM and abnormal glucose tolerance post-pregnancy, respectively, compared to other individuals. Conclusion Our results indicate that the genetic architecture of glucose metabolism exhibits both similarities and differences between the pregnant and non-pregnant states. Integrating genetic information can facilitate identification of pregnant women at a higher risk of developing GDM or later diabetes.

Background: The genetic basis for hyperglycaemia in pregnancy remain unclear. This study aimed to uncover the genetic determinants of gestational diabetes mellitus (GDM) and investigate their applications. Methods: We performed a meta-analysis of genome-wide association studies (GWAS) for GDM in Chinese women (464 cases and 1,217 controls), followed by de novo replications in an independent Chinese cohort (564 cases and 572 controls) and in silico replication in European (12,332 cases and 131,109 controls) and multi-ethnic populations (5,485 cases and 347,856 controls). A polygenic risk score (PRS) was derived based on the identified variants. Results: Using the genome-wide scan and candidate gene approaches, we identified four susceptibility loci for GDM. These included three previously reported loci for GDM and type 2 diabetes mellitus (T2DM) at MTNR1B (rs7945617, odds ratio [OR], 1.64; 95% confidence interval [CI],1.38 to 1.96]), CDKAL1 (rs7754840, OR, 1.33; 95% CI, 1.13 to 1.58), and INS-IGF2-KCNQ1 (rs2237897, OR, 1.48; 95% CI, 1.23 to 1.79), as well as a novel genome-wide significant locus near TBR1-SLC4A10 (rs117781972, OR, 2.05; 95% CI, 1.61 to 2.62; Pmeta=7.6×10-9), which has not been previously reported in GWAS for T2DM or glycaemic traits. Moreover, we found that women with a high PRS (top quintile) had over threefold (95% CI, 2.30 to 4.09; Pmeta=3.1×10-14) and 71% (95% CI, 1.08 to 2.71; P=0.0220) higher risk for GDM and abnormal glucose tolerance post-pregnancy, respectively, compared to other individuals. Conclusion Our results indicate that the genetic architecture of glucose metabolism exhibits both similarities and differences between the pregnant and non-pregnant states. Integrating genetic information can facilitate identification of pregnant women at a higher risk of developing GDM or later diabetes.

Background: The genetic basis for hyperglycaemia in pregnancy remain unclear. This study aimed to uncover the genetic determinants of gestational diabetes mellitus (GDM) and investigate their applications. Methods: We performed a meta-analysis of genome-wide association studies (GWAS) for GDM in Chinese women (464 cases and 1,217 controls), followed by de novo replications in an independent Chinese cohort (564 cases and 572 controls) and in silico replication in European (12,332 cases and 131,109 controls) and multi-ethnic populations (5,485 cases and 347,856 controls). A polygenic risk score (PRS) was derived based on the identified variants. Results: Using the genome-wide scan and candidate gene approaches, we identified four susceptibility loci for GDM. These included three previously reported loci for GDM and type 2 diabetes mellitus (T2DM) at MTNR1B (rs7945617, odds ratio [OR], 1.64; 95% confidence interval [CI],1.38 to 1.96]), CDKAL1 (rs7754840, OR, 1.33; 95% CI, 1.13 to 1.58), and INS-IGF2-KCNQ1 (rs2237897, OR, 1.48; 95% CI, 1.23 to 1.79), as well as a novel genome-wide significant locus near TBR1-SLC4A10 (rs117781972, OR, 2.05; 95% CI, 1.61 to 2.62; Pmeta=7.6×10-9), which has not been previously reported in GWAS for T2DM or glycaemic traits. Moreover, we found that women with a high PRS (top quintile) had over threefold (95% CI, 2.30 to 4.09; Pmeta=3.1×10-14) and 71% (95% CI, 1.08 to 2.71; P=0.0220) higher risk for GDM and abnormal glucose tolerance post-pregnancy, respectively, compared to other individuals. Conclusion Our results indicate that the genetic architecture of glucose metabolism exhibits both similarities and differences between the pregnant and non-pregnant states. Integrating genetic information can facilitate identification of pregnant women at a higher risk of developing GDM or later diabetes.

OBJECTIVE T o analyze the current situation and characteristics of hospital-associated infection in a psy-chiatric hospital,providing references for formulation of control measures for hospital-associated infection.METHODS A retrospective analysis was conducted on hospital-associated infection data from Shanghai Mental Health Center between Jan.1,2019,and Oct.31,2024.The infection rates and infection sites across different years,patient populations,and departments were analyzed.RESULTS A total of 47 051 inpatients were investiga-ted,with 1 798 cases of hospital-associated infections,resulting in an infection rate of 3.82％.The highest inci-dence rate was observed in 2020(4.13％).The top three departments with the highest incidence rates were the in-fectious disease department(12.13％),geriatric psychiatry(7.67％),and the Traditional Chinese Medicine De-partment department(4.90％).The primary infection sites were the lower respiratory tract(57.06％),urinary tract(18.24％),skin and soft tissue(14.29％).The incidence rate was higher in males(5.32％)than in females(2.72％)(P＜0.001).Statistically significant differences were found in the incidence rates among different age groups(P＜0.001),with the highest rate observed in patients aged over 90 years(17.11％).The lowest infection rate was found in patients hospitalized for less than 60 days(0.69％),while the highest was in those hospitalized for more than 1 000 days(49.94％).CONCLUSIONS The patients in psychiatric hospitals are susceptible to infec-tions in the respiratory tract,urinary tract,skin and soft tissue.Targeted prevention and control measures can be implemented based on high-risk factors such as age,length of stay,and gender to reduce the occurrence of in-fections.

Objective:To investigate the diagnostic value of different conventional ultrasound-based radiomics models and their combination with clinical ultrasound features in the pathological subtyping of renal cell carcinoma.Methods:Retrospective data from 286 patients diagnosed with renal cell carcinoma by pathology at the Tenth Affiliated Hospital of Southern Medical University between May 1，2017 and June 7，2024 were collected. Among the 286 patients，203 were clear cell carcinoma，44 were papillary renal cell carcinoma，and 39 were chromophobe renal cell carcinoma. The patients were randomly divided into a training group（201 cases）and a validation group（85 cases）in a ratio of 7 to 3. Regions of interest（ROI）were delineated on conventional ultrasound images，and the radiomics features were extracted. Feature selection was performed using Student's t-test，Pearson correlation，and the least absolute shrinkage and selection operator（LASSO）. Six different machine learning methods included category gradient boosting（CatBoost），light gradient boosting machine（LightGBM），Logistic regression（LR），random forest（RF），support vector machine（SVM）and extreme gradient boosting（XGBoost）were used to establish radiomics models. Weight balancing was applied to correct for sample imbalance，and an imaging genomics model was constructed after balancing the samples. Independent predictors of renal cell carcinoma subtyping were selected from clinical ultrasound features using univariate and multivariate logistic regression analyses，and a clinical imaging model was constructed. The best-performing radiomics model was combined with the clinical independent predictors to construct a combined model. Receiver operating characteristic curves and the obuchowski index were plotted to evaluate model performance. Results:Among the radiomics models，the model constructed using Random Forest（RS RF）after balancing the samples exhibited the best predictive performance，with area under the curve（AUCs）of 0.918（micro-average ROC）and 0.903（macro-average ROC），and the obuchowski index was 0.885 in the validation group. The long and short axes of ultrasound image tumor masses were used as imaging independent predictors to construct a clinical imaging model. In the validation group，the AUCs of the clinical model were 0.886（micro-average ROC）and 0.606（macro-average ROC），and the obuchowski index was 0.569. The combined model achieved AUCs of 0.888（micro-average ROC）and 0.967（macro-average ROC），with an obuchowski index of 0.933，outperforming any single model. Conclusions:The combination of conventional ultrasound-based radiomics models with clinical ultrasound features demonstrates high diagnostic value in differentiating clear cell carcinoma，papillary renal cell carcinoma，and chromophobe renal cell carcinoma. It may serve as an auxiliary tool for providing timely and effective clinical guidance.