767 versus 0.725 for OS and 0.775 versus 0.715 for CSS), with better discrimination than that of the American Joint Committee on Cancer (AJCC) stage model and the calibration was validated in the SEER validation cohort. The model's 3- and 5-year OS and CSS were superior to AJCC and T staging on the analysis decision curve. The prognosis prediction of SRCC established by the prediction model could be evaluated through the web-based survival rate calculator, which plays a guiding role in clinical treatment.
 
  Nomograms and a web-based survival rate calculator predicting the OS and CSS of SRCC patients with better discrimination and calibration were developed.
 Nomograms and a web-based survival rate calculator predicting the OS and CSS of SRCC patients with better discrimination and calibration were developed.
  To shed light on the survival outcomes of prostate cancer (PCa) patients diagnosed after a prior cancer and identify prognostic factors for overall survival (OS) and cancer-specific survival (CSS) in PCa patients.
 
  In the primary group, a total of 1,778 PCa patients with a prior cancer were identified in the Surveillance, Epidemiology, and End Results (SEER) database from 2005 to 2015, retrospectively. Baseline characteristics and causes of death (COD) of these patients were collected and compared. In the second group, a total of 10,296 PCa patients [5,148 patients with PCa as the only malignancy and 5,148 patients with PCa as their second primary malignancy (SPM)] diagnosed between 2010 and 2011 were extracted to investigate the impact of prior cancers on survival outcomes.
 
  In PCa patients with a prior cancer, the most common type of prior cancer was from gastrointestinal system (29.92%), followed by urinary system (21.37%). Patients were more likely to die of the prior caner, and those with prior cancer from respiratory system had the worst survival outcomes. Moreover, the overall ratios in patients with stage (PCa) I-II and III-IV diseases were 0.21 and 1.65, indicating that patients with higher stage diseases were more likely to die of PCa. In the second group, patients with PCa as the SPM had worse OS than those with PCa as the first primary cancer. Lastly, prognostic factors for OS and CSS in PCa patients were explored.
 
  PCa remains to be an important COD for patients with a prior malignancy, especially for those with high-stage diseases. PCa patients with a prior cancer had worse survival outcomes than those without.
 PCa remains to be an important COD for patients with a prior malignancy, especially for those with high-stage diseases. PCa patients with a prior cancer had worse survival outcomes than those without.
  Keratinizing squamous metaplasia (KSM) is a clinically heterogeneous disease that lacks research that provide definitive recurrent risk factors. Therefore, we identified the recurrence factors in patients with KSM of the bladder after transurethral resection (TUR). We also attempted to investigate the association between KSM and bladder cancer.
 
  Clinical information of 257 patients diagnosed with KSM who underwent TUR in Xiangya Hospital from January 2010 to November 2018 were retrospectively collected. Clinical information was available for follow-up of 223 patients. To determine the risk factors for recurrence, we conducted univariate and multivariate cox regression analysis respectively. To explore the association between KSM and bladder cancer, we used clinical follow-up data.
 
  The median follow-up time is 49 (IQR, 12-121) months. Five-year recurrence-free rate (RFR) and 1-year RFR were 86.1% and 91.9%, respectively. Thirty-one patients (13.9%) relapsed of KSM after a median follow-up of 49 months (rat risk factor in patients with KSM recurrence. In cases with bladder atypical urothelial hyperplasia, close follow-ups are necessary. Also, we demonstrated that KSM did not increase the subsequent risk of bladder cancer.
  Erectile dysfunction (ED) shares common risk factors with cardiovascular disease (CVD), such as diabetes mellitus (DM) and dyslipidemia, but the relationship between the risk factors of CVD in biochemical markers and young men with ED age 20-40 years is not fully clarified.
 
  A total of 289 ED outpatients (20-40 years old) were allocated under ED group, based on patients' complaints and physical examinations. According to the frequency matching ratio of 14, 1,155 male individuals (20-40 years old) without ED were set as control group. All participants were tested for lipid profiles including total cholesterol (TC), triglyceride (TG), high density lipoprotein (HDL), low density lipoprotein (LDL), blood glucose (BG), homocysteine (HCY), liver function including alanine aminotransferase (ALT) and aspartate aminotransferase (AST), and renal function including uric acid (UA) and creatinine (CR). The study was designed to compare the two groups using an established binary logistic regression analysis model. The Eors of ED in young men.  https://www.selleckchem.com/products/Furosemide(Lasix).html  Lipid profile was significantly different between young men with ED aged 20-30 and 31-40 years.
  To develop a machine learning (ML)-assisted model capable of accurately identifying patients with calculous pyonephrosis before making treatment decisions by integrating multiple clinical characteristics.
 
  We retrospectively collected data from patients with obstructed hydronephrosis who underwent retrograde ureteral stent insertion, percutaneous nephrostomy (PCN), or percutaneous nephrolithotomy (PCNL). The study cohort was divided into training and testing datasets in a 7030 ratio for further analysis. We developed 5 ML-assisted models from 22 clinical features using logistic regression (LR), LR optimized by least absolute shrinkage and selection operator (Lasso) regularization (Lasso-LR), support vector machine (SVM), extreme gradient boosting (XGBoost), and random forest (RF). The area under the curve (AUC) was applied to determine the model with the highest discrimination. Decision curve analysis (DCA) was used to investigate the clinical net benefit associated with using the predictive models.
 
  A too urologists in treatment planning, patient selection, and decision-making.
 Our ML-based models had good discrimination in predicting patients with obstructed hydronephrosis at high risk of harboring pyonephrosis, and the use of these models may be greatly beneficial to urologists in treatment planning, patient selection, and decision-making.