Advances in technology have facilitated the common use of small-bowel imaging. Intraoperative enteroscopy was the gold standard method for small-bowel imaging. However, noninvasive capsule endoscopy and invasive balloon enteroscopy are currently the main endoscopic procedures that are routinely used for small-bowel pathologies, and the indications for both techniques are similar. Although obstruction is a contraindication for capsule endoscopy, it is not considered to be problematic for double-balloon enteroscopy. The most important advantage of double-balloon enteroscopy is the applicability of therapeutic interventions during the procedure; however, double-balloon enteroscopy has certain advantages as well as disadvantages.
Capsule Endoscopy ; Double-Balloon Enteroscopy* ; Pathology

Idiopathic thrombocytopenic purpura (ITP) is an immune thrombocytopenia with a usually benign clinical course. Bleedings are mostly of the mucocutaneous type with mild symptoms. Massive bleedings requiring transfusion are rarely seen, unless the number of platelets decreases to extremely low levels. In this case, bilateral perirenal hematoma and bilateral distal ureteral stones were detected on a non-contrast computed tomography scan of a 57-year-old male patient who developed macroscopic hematuria during his treatment in the clinics of internal medicine because of left flank pain and diffuse petechial rashes all over his body. The patient, who had been receiving chronic ITP treatment for 1 year, had a very low platelet count (4,000/mm3). The patient was prescribed bed rest, and his platelet count increased to a safe level for surgical intervention of above 50,000/mm3 with administration of prednisolone, intravenous immune globulin, and platelet suspension. A stone-free state was achieved after bilateral ureterorenoscopy and pneumatic lithotripsy. A conservative approach was followed for the perirenal hematoma. Upon regression of the perirenal hematoma, the patient was discharged at 9 weeks postoperatively.
Bed Rest ; Blood Platelets ; Exanthema ; Flank Pain ; Hematoma ; Hematuria ; Humans ; Immunoglobulins, Intravenous ; Internal Medicine ; Lithotripsy ; Male ; Middle Aged ; Platelet Count ; Prednisolone ; Purpura ; Purpura, Thrombocytopenic, Idiopathic ; Thrombocytopenia ; Ureter ; Ureteral Calculi

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: 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: To evaluate the predictive role of the neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), mean platelet volume (MPV), and platelet count (PLT) in the diagnosis of testicular torsion (TT) and testicular viability following TT. MATERIALS AND METHODS: We analyzed two study groups in this retrospective study: 75 patients with a diagnosis of TT (group 1) and 56 age-matched healthy subjects (group 2). We performed a complete blood count as a part of the diagnostic procedure, and NLR, PLR, MPV, and PLT values were recorded. We compared the patient and control groups in terms of these parameters. Then, TT patients were divided into two subgroups according to the time elapsed since the onset of symptoms. Subsequently, we evaluated the relationship between the duration of symptoms and these parameters. RESULTS: There were significant differences between groups 1 and 2 in NLR, PLR, and PLT (p<0.001 for all). There was no predictive role of MPV in the diagnosis of TT (p=0.328). We determined significantly high sensitivity and specificity levels for NLR in the prediction of TT diagnosis (84% and 92%, respectively). Furthermore, NLR was significantly related to the duration of symptoms in TT patients (p=0.01). CONCLUSIONS: NLR may be a useful parameter in the diagnosis of TT. Furthermore, NLR may be used as a predictive factor for testicular viability following TT.
Adolescent ; Humans ; Lymphocyte Count/*methods ; Male ; Neutrophils/*pathology ; Platelet Count/methods ; Predictive Value of Tests ; Prognosis ; Retrospective Studies ; Sensitivity and Specificity ; *Spermatic Cord Torsion/blood/diagnosis/physiopathology ; Symptom Assessment/methods ; *Testis/pathology/physiopathology ; Tissue Survival ; Turkey

BACKGROUND/AIMS: It has been reported that proton pump inhibitors induce relaxation in different types of smooth muscles. The aim of this study is to investigate in vitro effects of proton pump inhibitors on human pylorus muscle. METHODS: Pyloric sphincters were studied in 10 patients who were operated for stomach cancer. In isolated organ bath, control and response to rabeprazole were recorded following contraction with carbachol. During the treatment experiment, while distilled water was applied during the control experiment in every 5 minutes, rabeprazole was administered in every 5 minutes at doses of 10-6, 10-5, 10-4, and 10-3 M respectively. Contraction frequencies, maximum contraction values and muscle tones were measured. RESULTS: The contraction frequencies in the control group were greater than the rabeprazole group in the second, third and fourth intervals while the maximum contraction values in the rabeprazole group were lower in the fourth interval. Even though muscles tones were not different in both groups during all intervals, it was remarkable that the muscle tone was significantly decreased in the rabeprazole group during the fourth interval compared to the first and second intervals. CONCLUSIONS: In the present study, high doses of rabeprazole reduced contraction frequencies, maximum contraction values, and muscle tone of human pylorus.
Baths ; Carbachol ; Humans ; Muscle Tonus ; Muscle, Smooth ; Muscles ; Proton Pump Inhibitors ; Pylorus* ; Rabeprazole* ; Relaxation ; Stomach Neoplasms ; Water