Purpose: To assess the predictive capability of hematological inflammatory markers for urethral stricture recurrence after primary urethroplasty and to compare traditional statistical methods with a machine-learning-based artificial intelligence algorithm. Materials and Methods: Two hundred eighty-seven patients who underwent primary urethroplasty were scanned. Ages, smoking status, comorbidities, hematological inflammatory parameters (neutrophil-lymphocyte ratios, platelet-lymphocyte ratios [PLR], systemic immune-inflammation indexes [SII], and pan-immune-inflammation values [PIV]), stricture characteristics, history of previous direct-visual internal urethrotomy, urethroplasty techniques, and grafts/flaps placements were collected. Patients were followed up for one year for recurrence and grouped accordingly. Univariate and multivariate logistic regression analyses were conducted to create a predictive model. Additionally, a machine-learning-based logistic regression analysis was implemented to compare predictive performances. p<0.05 was considered statistically significant. Results: Comparative analysis between the groups revealed statistically significant differences in stricture length (p=0.003), localization (p=0.027), lymphocyte counts (p=0.008), PLR (p=0.003), SII (p=0.003), and PIV (p=0.001). In multivariate analysis, stricture length (odds ratio [OR] 1.230, 95% confidence interval [CI] 1.142–1.539, p<0.0001) and PIV (OR 1.002, 95% CI 1.000–1.003, p=0.039) were identified as significant predictors of recurrence. Classical logistic regression model exhibited a sensitivity of 0.76, specificity of 0.43 with an area under curve (AUC) of 0.65. However, the machine-learning algorithm outperformed traditional methods achieving a sensitivity of 0.80, specificity of 0.76 with a higher AUC of 0.82. Conclusions PIV and machine-learning algorithms shows promise on predicting urethroplasty outcomes, potentially leading to develop possible nomograms. Evolving machine-learning algorithms will contribute to more personalized and accurate approaches in managing urethral stricture.

Purpose: To assess the predictive capability of hematological inflammatory markers for urethral stricture recurrence after primary urethroplasty and to compare traditional statistical methods with a machine-learning-based artificial intelligence algorithm. Materials and Methods: Two hundred eighty-seven patients who underwent primary urethroplasty were scanned. Ages, smoking status, comorbidities, hematological inflammatory parameters (neutrophil-lymphocyte ratios, platelet-lymphocyte ratios [PLR], systemic immune-inflammation indexes [SII], and pan-immune-inflammation values [PIV]), stricture characteristics, history of previous direct-visual internal urethrotomy, urethroplasty techniques, and grafts/flaps placements were collected. Patients were followed up for one year for recurrence and grouped accordingly. Univariate and multivariate logistic regression analyses were conducted to create a predictive model. Additionally, a machine-learning-based logistic regression analysis was implemented to compare predictive performances. p<0.05 was considered statistically significant. Results: Comparative analysis between the groups revealed statistically significant differences in stricture length (p=0.003), localization (p=0.027), lymphocyte counts (p=0.008), PLR (p=0.003), SII (p=0.003), and PIV (p=0.001). In multivariate analysis, stricture length (odds ratio [OR] 1.230, 95% confidence interval [CI] 1.142–1.539, p<0.0001) and PIV (OR 1.002, 95% CI 1.000–1.003, p=0.039) were identified as significant predictors of recurrence. Classical logistic regression model exhibited a sensitivity of 0.76, specificity of 0.43 with an area under curve (AUC) of 0.65. However, the machine-learning algorithm outperformed traditional methods achieving a sensitivity of 0.80, specificity of 0.76 with a higher AUC of 0.82. Conclusions PIV and machine-learning algorithms shows promise on predicting urethroplasty outcomes, potentially leading to develop possible nomograms. Evolving machine-learning algorithms will contribute to more personalized and accurate approaches in managing urethral stricture.

Purpose: Obesity is defined as the abnormal or excessive accumulation of fat over acceptable limits. Leptin is a metabolic hormone present in the circulation in amounts proportional to fat mass. Leptin reduces food intake and increases energy expenditure, thus regulating body weight and homeostasis. Various polymorphisms are present in the leptin gene and its receptor. These polymorphisms may be associated with obesity. This study aimed to show the association of leptin (+19) AG, leptin (2548) GA, and Gln223Arg leptin receptor polymorphisms with obesity and metabolic syndrome in Turkish children aged 6–17 years, and to conduct further investigations regarding the genetic etiology of obesity. Methods: A total of 174 patients diagnosed with obesity and 150 healthy children who were treated at Tokat Gaziosmanpaşa Medical School Hospital between September 2014 and March 2015 were included in this study. The ages of the children were between 6 and 17 years, and anthropometric and laboratory results were recorded. Genotyping of leptin (+19) AG, leptin (2548) GA, and leptin receptor Gln223Arg polymorphisms was performed by polymerase chain reaction. Results: An association between leptin receptor Gln223Arg gene polymorphism and obesity was detected. Conclusion Further studies are needed to determine the role of genetic etiologies and to indicate the role of leptin signal transmission impairment in the pathogenesis of obesity. We hope that gene therapy can soon provide a solution for obesity.

Purpose: Obesity is defined as the abnormal or excessive accumulation of fat over acceptable limits. Leptin is a metabolic hormone present in the circulation in amounts proportional to fat mass. Leptin reduces food intake and increases energy expenditure, thus regulating body weight and homeostasis. Various polymorphisms are present in the leptin gene and its receptor. These polymorphisms may be associated with obesity. This study aimed to show the association of leptin (+19) AG, leptin (2548) GA, and Gln223Arg leptin receptor polymorphisms with obesity and metabolic syndrome in Turkish children aged 6–17 years, and to conduct further investigations regarding the genetic etiology of obesity. Methods: A total of 174 patients diagnosed with obesity and 150 healthy children who were treated at Tokat Gaziosmanpaşa Medical School Hospital between September 2014 and March 2015 were included in this study. The ages of the children were between 6 and 17 years, and anthropometric and laboratory results were recorded. Genotyping of leptin (+19) AG, leptin (2548) GA, and leptin receptor Gln223Arg polymorphisms was performed by polymerase chain reaction. Results: An association between leptin receptor Gln223Arg gene polymorphism and obesity was detected. Conclusion Further studies are needed to determine the role of genetic etiologies and to indicate the role of leptin signal transmission impairment in the pathogenesis of obesity. We hope that gene therapy can soon provide a solution for obesity.

Purpose: To assess the predictive capability of hematological inflammatory markers for urethral stricture recurrence after primary urethroplasty and to compare traditional statistical methods with a machine-learning-based artificial intelligence algorithm. Materials and Methods: Two hundred eighty-seven patients who underwent primary urethroplasty were scanned. Ages, smoking status, comorbidities, hematological inflammatory parameters (neutrophil-lymphocyte ratios, platelet-lymphocyte ratios [PLR], systemic immune-inflammation indexes [SII], and pan-immune-inflammation values [PIV]), stricture characteristics, history of previous direct-visual internal urethrotomy, urethroplasty techniques, and grafts/flaps placements were collected. Patients were followed up for one year for recurrence and grouped accordingly. Univariate and multivariate logistic regression analyses were conducted to create a predictive model. Additionally, a machine-learning-based logistic regression analysis was implemented to compare predictive performances. p<0.05 was considered statistically significant. Results: Comparative analysis between the groups revealed statistically significant differences in stricture length (p=0.003), localization (p=0.027), lymphocyte counts (p=0.008), PLR (p=0.003), SII (p=0.003), and PIV (p=0.001). In multivariate analysis, stricture length (odds ratio [OR] 1.230, 95% confidence interval [CI] 1.142–1.539, p<0.0001) and PIV (OR 1.002, 95% CI 1.000–1.003, p=0.039) were identified as significant predictors of recurrence. Classical logistic regression model exhibited a sensitivity of 0.76, specificity of 0.43 with an area under curve (AUC) of 0.65. However, the machine-learning algorithm outperformed traditional methods achieving a sensitivity of 0.80, specificity of 0.76 with a higher AUC of 0.82. Conclusions PIV and machine-learning algorithms shows promise on predicting urethroplasty outcomes, potentially leading to develop possible nomograms. Evolving machine-learning algorithms will contribute to more personalized and accurate approaches in managing urethral stricture.

Purpose: To assess the predictive capability of hematological inflammatory markers for urethral stricture recurrence after primary urethroplasty and to compare traditional statistical methods with a machine-learning-based artificial intelligence algorithm. Materials and Methods: Two hundred eighty-seven patients who underwent primary urethroplasty were scanned. Ages, smoking status, comorbidities, hematological inflammatory parameters (neutrophil-lymphocyte ratios, platelet-lymphocyte ratios [PLR], systemic immune-inflammation indexes [SII], and pan-immune-inflammation values [PIV]), stricture characteristics, history of previous direct-visual internal urethrotomy, urethroplasty techniques, and grafts/flaps placements were collected. Patients were followed up for one year for recurrence and grouped accordingly. Univariate and multivariate logistic regression analyses were conducted to create a predictive model. Additionally, a machine-learning-based logistic regression analysis was implemented to compare predictive performances. p<0.05 was considered statistically significant. Results: Comparative analysis between the groups revealed statistically significant differences in stricture length (p=0.003), localization (p=0.027), lymphocyte counts (p=0.008), PLR (p=0.003), SII (p=0.003), and PIV (p=0.001). In multivariate analysis, stricture length (odds ratio [OR] 1.230, 95% confidence interval [CI] 1.142–1.539, p<0.0001) and PIV (OR 1.002, 95% CI 1.000–1.003, p=0.039) were identified as significant predictors of recurrence. Classical logistic regression model exhibited a sensitivity of 0.76, specificity of 0.43 with an area under curve (AUC) of 0.65. However, the machine-learning algorithm outperformed traditional methods achieving a sensitivity of 0.80, specificity of 0.76 with a higher AUC of 0.82. Conclusions PIV and machine-learning algorithms shows promise on predicting urethroplasty outcomes, potentially leading to develop possible nomograms. Evolving machine-learning algorithms will contribute to more personalized and accurate approaches in managing urethral stricture.