Objective To evaluate the efficacy and safety of 450 nm blue laser vaporization enucleation of the prostate in the treatment of benign prostatic hyperplasia(BPH)complicated with urinary retention, so as to promote its clinical application. Methods A retrospective analysis was conducted on the clinical data of 60 patients with BPH and urinary retention who underwent 450 nm blue laser vaporization enucleation of the prostate in our hospital during Feb. 2023 and Apr. 2024. All patients had urinary catheters indwelt preoperatively due to urinary retention. The perioperative indicators and complications were recorded. The maximum urinary flow rate(Qmax), postvoid residual(PVR), international prostate symptom score(IPSS), and quality of life score(QoL)were compared before surgery and 3 months after surgery. Results All procedures were completed successfully without conversion to transurethral resection of the prostate(TURP). No cases of severe intraoperative bleeding or transfusion were reported. The average operation time was(60.5±21.5)min, postoperative hemoglobin reduction(3.2±0.8)g/L, catheterization time(3.1±0.4)days, and hospital stay(3.2±0.4)days. No bladder perforation or urethral injury occurred. At 3 months postoperatively, Qmax [(19.2±4.9)mL/s vs.(3.5±2.3)mL/s], PVR [(50.8±20.1)mL vs.(369.2±163.2)mL], IPSS [(9.7±0.9)vs.(21.8±3.2)], and QoL [(1.4±0.5)vs.(4.9±0.1)]were significantly improved compared with preoperative values(P＜0.001). Urinary retention occurred in 2 patients after catheter removal on postoperative day 3, which was resolved one week after re-catheterization. Conclusion The 450 nm blue laser vaporization enucleation of the prostate is a safe and effective treatment for BPH complicated with urinary retention. It can significantly improve the short-term voiding function and quality of life with a low incidence of postoperative complications, demonstrating promising potential for clinical adoption.

Objective To explore the independent risk factors for early failure of internal mammary artery grafts after coronary artery bypass grafting (CABG), and to construct and preliminarily evaluate a risk prediction model for the decline of internal mammary artery bridges, optimizing postoperative risk stratification and management strategies for patients. Methods Patients who underwent CABG at Fuwai Hospital, Chinese Academy of Medical Sciences from January 2016 to January 2020 were retrospectively enrolled. The primary endpoint was the failure of the internal mammary artery bridge one year after surgery, and the secondary endpoint was major adverse cardiac and cerebrovascular events (MACCE) within five years after surgery, including all-cause death, myocardial infarction, stroke, and revascularization. Patients were divided into a failure group and a non-failure group based on whether there was early failure of the internal mammary artery bridge after surgery. Independent risk factors for the failure of the internal mammary artery bridge were explored through the least absolute shrinkage and selection operator regression and multivariate logistic regression, and a failure risk prediction model was constructed and cross-validated. Patients were stratified for MACCE risk according to the total score of independent risk factors, and the 5-year cumulative MACCE-free rate was drawn using the Kaplan-Meier method. Results A total of 657 patients were included, among whom there were 54 patients in the failure group, including 38 males and 16 females, with an average age of (61.85±8.03) years; there were 603 patients in the non-failure group, including 467 males and 136 females, with an average age of (60.45±8.23) years. Multivariate logistic regression analysis showed that non-left main lesion [OR=3.28, 95%CI (1.41, 7.62), P=0.006], pulsatility index (PI)>3.0 [OR=2.63, 95%CI (1.20, 5.75), P=0.016], quantitative flow ratio (QFR)>0.80 [OR=5.57, 95%CI (2.98, 10.41), P<0.001] and in-hospital complications [OR=4.02, 95%CI (1.59, 10.19), P=0.003] were independent risk factors for the failure of internal mammary artery grafts after CABG. Compared with the prediction model in previous literature [area under receiver operating characteristic curve was 0.632, 95%CI (0.588, 0.688)], the risk prediction model constructed with QFR>0.80, PI>3.0, non-left main lesion and in-hospital complications had a higher predictive ability for early failure of internal mammary artery grafts [area under curve: 0.758, 95%CI (0.707, 0.817); net reclassification index: 0.272, 95%CI (0.180, 0.370); comprehensive discriminative improvement index: 0.109, 95%CI (0.059, 0.158); P<0.05]. The risk score of independent risk factors for internal mammary artery graft failure demonstrated significant MACCE risk stratification efficiency in the 5-year patient follow-up, with the high-risk group showing a significantly higher incidence of MACCE compared to the medium and low-risk groups (P=0.001). Conclusion QFR>0.80, PI>3.0, non-left main lesions, and in-hospital complications are independent risk factors for early failure of internal mammary artery grafts after CABG. The constructed risk prediction model based on this has preliminary capabilities in predicting the risk of internal mammary artery graft failure and MACCE risk stratification, which is beneficial for the postoperative management of CABG patients and improving their mid-long term prognosis.

ObjectiveTo construct a risk prediction model for carotid artery plaques in high-risk populations of stroke based on five machine learning methods. MethodsThe clinical information of the high-risk population of stroke was collected. Factor analysis and statistical analysis of syndrome elements were conducted on their traditional Chinese medicine （TCM） symptoms and tongue and pulse manifestations. On the basis of the results of factor analysis and variable screening， five machine learning methods-classification and regression tree （CART） decision tree， support vector machine （SVM）， back propagation（BP） neural network， logistic regression， and random forest-were used to construct the risk prediction model for carotid artery plaques. ResultsThe most common TCM syndrome elements in the high-risk population of stroke was Qi deficiency. The scores of Qi deficiency， Yin deficiency， and Yang deficiency in the population with carotid artery plaques were higher than those without carotid artery plaques （P<0.05）. The CART decision tree， SVM， logistic regression， BP neural network， and random forest models showed the areas under the receiver operating characteristic （ROC） curves of 0.71， 0.75， 0.76， 0.76， and 0.75， the accuracy rates of 68.94%， 69.27%， 69.44%， 70.10%， and 69.60%， the precision rates of 68.56%， 68.53%， 68.75%， 69.74%， and 69.42%， the recall rates of 68.91%， 67.93%， 67.92%， 68.10%， and 67.31%， and the F₁ values of 0.69， 0.68， 0.68， 0.68， and 0.68， respectively. ConclusionAmong the high-risk population of stroke， the most frequently distributed TCM syndrome element is Qi deficiency， with the rest mainly being fire heat， Yin deficiency， Yang deficiency， phlegm dampness， blood stasis， and Qi stagnation. Deficiency syndrome may be a major factor leading to carotid artery plaques in the high-risk population of stroke. The BP neural network model demonstrates better performance in predicting the risk of carotid artery plaques in the high-risk population of stroke. People with carotid artery plaques are more likely to present with symptoms such as a heavy head， dizziness， headache， and thready pulse. The primary community benefits more widely when the BP neural network model is adopted to predict the risk of carotid artery plaques in the high-risk population of stroke over 40 years old.

ObjectiveTo construct a risk prediction model for carotid artery plaques in high-risk populations of stroke based on five machine learning methods. MethodsThe clinical information of the high-risk population of stroke was collected. Factor analysis and statistical analysis of syndrome elements were conducted on their traditional Chinese medicine （TCM） symptoms and tongue and pulse manifestations. On the basis of the results of factor analysis and variable screening， five machine learning methods-classification and regression tree （CART） decision tree， support vector machine （SVM）， back propagation（BP） neural network， logistic regression， and random forest-were used to construct the risk prediction model for carotid artery plaques. ResultsThe most common TCM syndrome elements in the high-risk population of stroke was Qi deficiency. The scores of Qi deficiency， Yin deficiency， and Yang deficiency in the population with carotid artery plaques were higher than those without carotid artery plaques （P<0.05）. The CART decision tree， SVM， logistic regression， BP neural network， and random forest models showed the areas under the receiver operating characteristic （ROC） curves of 0.71， 0.75， 0.76， 0.76， and 0.75， the accuracy rates of 68.94%， 69.27%， 69.44%， 70.10%， and 69.60%， the precision rates of 68.56%， 68.53%， 68.75%， 69.74%， and 69.42%， the recall rates of 68.91%， 67.93%， 67.92%， 68.10%， and 67.31%， and the F₁ values of 0.69， 0.68， 0.68， 0.68， and 0.68， respectively. ConclusionAmong the high-risk population of stroke， the most frequently distributed TCM syndrome element is Qi deficiency， with the rest mainly being fire heat， Yin deficiency， Yang deficiency， phlegm dampness， blood stasis， and Qi stagnation. Deficiency syndrome may be a major factor leading to carotid artery plaques in the high-risk population of stroke. The BP neural network model demonstrates better performance in predicting the risk of carotid artery plaques in the high-risk population of stroke. People with carotid artery plaques are more likely to present with symptoms such as a heavy head， dizziness， headache， and thready pulse. The primary community benefits more widely when the BP neural network model is adopted to predict the risk of carotid artery plaques in the high-risk population of stroke over 40 years old.

BACKGROUND: The mechanisms distinguishing metabolically healthy from unhealthy phenotypes within the same BMI categories remain unclear. This study aimed to investigate the associations between regional fat distribution and metabolically unhealthy phenotypes in Chinese adults across different BMI categories. METHODS: This cross-sectional study involving 11833 Chinese adults aged 20 years and older. Covariance analysis, adjusted for age, compared the percentage of regional fat (trunk, leg, or arm fat divided by whole-body fat) between metabolically healthy and unhealthy participants. Trends in regional fat percentage with the number of metabolic abnormalities were assessed by the Jonckheere-Terpstra test. Odds ratios (ORs) and their 95% confidence intervals (CIs) were estimated by logistic regression models. All analyses were performed separately by sex. RESULTS: In non-obese individuals, metabolically unhealthy participants exhibited higher percent trunk fat and lower percent leg fat compared to healthy participants. Additionally, percent trunk fat increased and percent leg fat decreased with the number of metabolic abnormalities. After adjustment for demographic and lifestyle factors, as well as BMI, higher percent trunk fat was associated with increased odds of being metabolically unhealthy [highest vs. lowest quartile: ORs (95%CI) of 1.64 (1.35, 2.00) for men and 2.00 (1.63, 2.46) for women]. Conversely, compared with the lowest quartile, the ORs (95%CI) of metabolically unhealthy phenotype in the highest quartile for percent arm and leg fat were 0.64 (0.53, 0.78) and 0.60 (0.49, 0.74) for men, and 0.72 (0.56, 0.93) and 0.46 (0.36, 0.59) for women, respectively. Significant interactions between BMI and percentage of trunk and leg fat were observed in both sexes, with stronger associations found in individuals with normal weight and overweight. CONCLUSIONS Trunk fat is associated with a higher risk of metabolically unhealthy phenotype, while leg and arm fat are protective factors. Regional fat distribution assessments are crucial for identifying metabolically unhealthy phenotypes, particularly in non-obese individuals.
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Young Adult ; Adipose Tissue ; Body Fat Distribution ; Body Mass Index ; China/epidemiology* ; Cross-Sectional Studies ; Health Surveys ; Phenotype

Objective To explore the alteration and function of TIMM8A during gefitinib-induced growth inhibition of lung cancer cell line PC-9.Methods TIMM8A siRNA transfection experiment was used to inhibit the expression of TIMM8A in PC-9 and gefitinib resistant PC-9 cells(PC-9-MTAC).CCK-8 assay was carried out to assess PC-9 and PC-9-MTAC cell viability after gefitinib treatment or TIMM8A siRNA transfection.qPCR was used to determine TIMM8A expression in PC-9 or PC-9-MTAC cells.Results Gefitinib inhibited PC-9 cell growth in a dose-depend-ent manner.The expression of TIMM8 A was inhibited during this process,and more than 50％TIMM8 A expression had been inhibited by 25 nmol/L gefitinib,while knockdown of TIMM8 A enhanced inhibitory effects of gefitinib on PC-9 cell proliferation(P＜0.05).Compared with the sensitive cells,treatment of the gefitinib-resistant PC-9-MTAC with 100 nmol/L gefitinib for 48 h only inhibited approximately 30％of TIMM8A expression.Furthermore,inhibition of TIMM8A expression enhanced the sensitivity of PC-9-MTAC cells to gefitinib(P＜0.05).Conclusions Low expression of TIMM8A improves anti-tumor effects of gefitinib in lung cancer cell line PC-9.

Objective:To investigate the expression,function,and clinical significance of CD24 in testicular germ cell tumors(TGCT).Methods:This study included 204 testicular germ cell tumor(TGCT)patients from the TCGA database and the TGCT-Changhai testicular germ cell tumor cohort(Changhai Hospital cohort).Prognostic analysis and multivariate analysis were employed to evaluate the association between CD24 expression and clinical characteristics.Immunohistochemistry(IHC)staining of tumor tissues was used to elucidate the mechanism by which CD24 regulated the tumor immune microenvironment(TIME)in TGCT.Finally,by analyzing the diffrences in PD-L1 expression levels and tumor-associated macrophage(TAM)M2-type cell infiltration rates between CD24 high-expression and low-expression groups,and employing the TIDE algorithm,we investigated the correlation between CD24 expression levels and immune escape scores as well as immunotherapy response rates.Results:Analysis of the TCGA database revealed that CD24 expression was significantly upregulated in TGCT with high clinical staging and M-stage(P<0.05).Compared to adjacent normal tissues,CD24 expression was significantly elevated in both primary and metastatic TGCT tissues(P<0.05).Significant differences in CD24 expression levels were observed across TNM stages and tumor progression statuses(all P<0.05).Univariate logistic regression analysis identified CD24 as a predictive factor for clinical outcomes in TGCT patients(OR=0.135,95%CI[0.035,0.516],P=0.003),and multivariate analysis further confirmed its role as an independent predictor(OR=0.057,95%CI[0.005,0.624],P=0.019).In TGCT tissues,CD24 mRNA levels correlated with immune cell markers CD206 and CD70(all P<0.05).Additionally,CD24 expression levels demonstrated significant predictive value in immune escape scoring and immunotherapy response rate assessments.Conclusions:CD24 is highly expressed in TGCT tissues and its expression is significantly correlated with the prognosis of TGCT patients,which makes it a potential new target point of biotherapy for testicular germ cell tumor patients.

Objective:To investigate the value of habitat radiomic features based on gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI in establishing a predictive model for cytokeratin 19 (CK19) expression in hepatocellular carcinoma (HCC) and to evaluate its role in prognostic risk stratification.Methods:This multicenter case-control study retrospectively enrolled 489 patients with pathologically confirmed HCC who underwent Gd-EOB-DTPA-enhanced MRI between June 2016 and June 2024. Among them, 346 patients from the First Affiliated Hospital of Soochow University were divided into a training cohort ( n=245) and an internal test cohort ( n=101) via stratified sampling at a 7∶3 ratio. And 143 patients from Nantong Third Hospital Affiliated to Nantong University served as an external validation cohort. The training cohort included 53 CK19-positive and 192 CK19-negative patients. The internal test cohort included 21 CK19-positive and 80 CK19-negative patients. The external validation cohort included 30 CK19-positive and 113 CK19-negative patients. Univariate logistic regression analysis was performed to identify potential factors associated with CK19 expression, and a clinical-radiologic model was constructed. The k-means clustering algorithm was applied to segment target HCC lesions into 3 subregions. Radiomic features were extracted and selected from these habitat subregions. Habitat radiomics models were constructed for the arterial phase (AP), portal venous phase, hepatobiliary phase (HBP), and combined phases (CP). Multivariate logistic regression analysis identified independent clinical and radiologic predictors of CK19 expression, and the optimal habitat model score was integrated to build a clinical-radiologic-habitat combined model. The area under the receiver operating characteristic curve (AUC) was used to evaluate model predictive performance. Recurrence-free survival (RFS) was analyzed using the Kaplan-Meier method and the differences in survival curves were compared with the log-rank test. Results:Univariate logistic regression analysis revealed that alpha-fetoprotein (AFP) ( OR=2.629, 95% CI 1.412-4.896, P=0.002), AP enhancement ( OR=3.636, 95% CI 1.642-8.052, P=0.001), AP peritumoral enhancement ( OR=2.219, 95% CI 1.084-4.542, P=0.029), and HBP peritumoral hypointensity ( OR=2.010, 95% CI 1.004-4.021, P=0.049) were potential factors associated with CK19 expression, which were incorporated into the clinical-radiologic model. In the internal and external validation cohorts, the AUC of the clinical-radiologic model was 0.690 (95% CI 0.590-0.778) and 0.650 (95% CI 0.565-0.727), respectively. The habitat radiomics model based on CP images demonstrated the highest performance. It achieved AUC of 0.729 (95% CI 0.622-0.836) and 0.725 (95% CI 0.607-0.842) in the internal and external validation cohorts, respectively. Multivariate analysis identified AFP ( OR=2.494, 95% CI 1.163-5.348, P=0.019), AP enhancement ( OR=5.230, 95% CI 1.868-14.643, P=0.002) and habitat radiomics model score ( OR=4.105, 95% CI 2.643-6.368, P<0.001) as independent predictors of CK19 positivity. Based on these factors, a combined clinical-radiologic-habitat combined model was established. The clinical-radiologic-habitat combined model achieved AUCs of 0.767 (95% CI 0.671-0.846) and 0.730 (95% CI 0.649-0.801) in the internal and external validation cohorts, respectively. Significant differences in RFS were observed between the CK19-positive group (25.1 month) and CK19-negative group (51.0 month) as predicted by the clinical-radiologic-habitat model ( χ2=4.17, P=0.041). Conclusion:The clinical-radiologic-habitat combined model based on Gd-EOB-DTPA-enhanced MRI habitat radiomics demonstrates good predictive performance for CK19 expression in HCC and offers valuable prognostic stratification for clinical practice.

Objective:To investigate the value of habitat radiomic features based on gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI in establishing a predictive model for cytokeratin 19 (CK19) expression in hepatocellular carcinoma (HCC) and to evaluate its role in prognostic risk stratification.Methods:This multicenter case-control study retrospectively enrolled 489 patients with pathologically confirmed HCC who underwent Gd-EOB-DTPA-enhanced MRI between June 2016 and June 2024. Among them, 346 patients from the First Affiliated Hospital of Soochow University were divided into a training cohort ( n=245) and an internal test cohort ( n=101) via stratified sampling at a 7∶3 ratio. And 143 patients from Nantong Third Hospital Affiliated to Nantong University served as an external validation cohort. The training cohort included 53 CK19-positive and 192 CK19-negative patients. The internal test cohort included 21 CK19-positive and 80 CK19-negative patients. The external validation cohort included 30 CK19-positive and 113 CK19-negative patients. Univariate logistic regression analysis was performed to identify potential factors associated with CK19 expression, and a clinical-radiologic model was constructed. The k-means clustering algorithm was applied to segment target HCC lesions into 3 subregions. Radiomic features were extracted and selected from these habitat subregions. Habitat radiomics models were constructed for the arterial phase (AP), portal venous phase, hepatobiliary phase (HBP), and combined phases (CP). Multivariate logistic regression analysis identified independent clinical and radiologic predictors of CK19 expression, and the optimal habitat model score was integrated to build a clinical-radiologic-habitat combined model. The area under the receiver operating characteristic curve (AUC) was used to evaluate model predictive performance. Recurrence-free survival (RFS) was analyzed using the Kaplan-Meier method and the differences in survival curves were compared with the log-rank test. Results:Univariate logistic regression analysis revealed that alpha-fetoprotein (AFP) ( OR=2.629, 95% CI 1.412-4.896, P=0.002), AP enhancement ( OR=3.636, 95% CI 1.642-8.052, P=0.001), AP peritumoral enhancement ( OR=2.219, 95% CI 1.084-4.542, P=0.029), and HBP peritumoral hypointensity ( OR=2.010, 95% CI 1.004-4.021, P=0.049) were potential factors associated with CK19 expression, which were incorporated into the clinical-radiologic model. In the internal and external validation cohorts, the AUC of the clinical-radiologic model was 0.690 (95% CI 0.590-0.778) and 0.650 (95% CI 0.565-0.727), respectively. The habitat radiomics model based on CP images demonstrated the highest performance. It achieved AUC of 0.729 (95% CI 0.622-0.836) and 0.725 (95% CI 0.607-0.842) in the internal and external validation cohorts, respectively. Multivariate analysis identified AFP ( OR=2.494, 95% CI 1.163-5.348, P=0.019), AP enhancement ( OR=5.230, 95% CI 1.868-14.643, P=0.002) and habitat radiomics model score ( OR=4.105, 95% CI 2.643-6.368, P<0.001) as independent predictors of CK19 positivity. Based on these factors, a combined clinical-radiologic-habitat combined model was established. The clinical-radiologic-habitat combined model achieved AUCs of 0.767 (95% CI 0.671-0.846) and 0.730 (95% CI 0.649-0.801) in the internal and external validation cohorts, respectively. Significant differences in RFS were observed between the CK19-positive group (25.1 month) and CK19-negative group (51.0 month) as predicted by the clinical-radiologic-habitat model ( χ2=4.17, P=0.041). Conclusion:The clinical-radiologic-habitat combined model based on Gd-EOB-DTPA-enhanced MRI habitat radiomics demonstrates good predictive performance for CK19 expression in HCC and offers valuable prognostic stratification for clinical practice.

Objective To investigate the effect of triptolide(TPL)on renal interstitial fibrosis in mice with unilateral ureteral obstruction(UUO)and its mechanism.Methods Six male C57BL/6J mice were randomly selected as the sham group,and 12 mice with UUO modeling were randomly divided into the model group(UUO)and the triptolide group(TPL).Changes in serum creatinine(SCr),blood urea nitrogen(BUN),and body weight were compared among the groups.Renal tissue specimens were collected at 14 d after UUO for HE and Masson staining.Immunohistochemistry staining was performed to observe the expression of α-smooth muscle actin(α-SMA)and fibronectin(Fn)in kidney tissues.Western blotting analysis was used to detect the protein expression levels of nucleotide combined with structure of oligomerization domain receptor protein 3(NLRP3),GSDMD,cGSDMD,IL-1β and IL-18.Results At week 1,the body weight of mice in the UUO and TPL groups significantly decreased compared with that in the sham group(P＜0.05).Body weight reduced in the TPL group compared with that in the Sham group at week 2(P＜0.01).There was no significant difference in body weight between the TPL and UUO groups.BUN levels did not differ significantly between the three groups.Compared with the sham group,the SCr level in the UUO group significantly increased[(15.680±1.508)μmol/L](P＜0.01).A reduction in SCr level was observed following TPL administration[(12.550±3.004)μmol/L](P＜0.05).HE staining showed that the renal tubules of mice in the UUO group were significantly dilated and atrophic,with interstitial edema and increased inflammatory cell infiltration,while the pathological damage of renal tissues was significantly alleviated after TPL treatment.Masson staining revealed that interstitial collagen deposition significantly increased in the UUO group(36.350±5.183)％(P＜0.01)and reduced after TPL administration(20.820±3.290)％(P＜0.01).Immunohistochemistry results demonstrated that the IOD levels of α-SMA(1.233±0.045)and Fn(1.337±0.045)were higher in UUO group mice than in the sham group,while the IOD levels of α-SMA(1.047±0.025)and Fn(1.113±0.021)were lower in the TPL group than in the UUO group(P＜0.05).Western blotting analysis indicated that the expression levels of NLRP3,cGSDMD,IL-1β and IL-18 in the UUO group mice were higher than those in the sham group,while the protein expression levels of the above-mentioned indicators significantly decreased after TPL treatment(P＜0.05).Conclusion TPL ameliorates renal interstitial fibrosis in mice with UUO by inhibiting NLRP3-mediated pyroptosis.