An unusual complication of clean intermittent self-catheterization; urethral, glandular cavernosal tissue loss

Temiz aralıklı kateterizaston (TAK), çesitli mesane disfonksiyonlarında, bireylere özgü bir programla mesanenin bosaltılması için rutin olarak uzun yıllardır kullanılan bir tedavi metodudur. Üriner sistem enfeksiyonu TAK yapan hastalarda en sık görülen komplikasyondur. Daimi kateterizasyona göre bariz avantajları olmasına ragmen bu yöntemde, tekrarlayan üretral kateterizasyonlar sebebi ile üretra darlıgı ve üretral yanlıs yol gibi bir takım üretral komplikasyonlar gelisebilmektedir. Bu komplikasyonların (üretra darlıgı ve üretral yanlıs yol)prevelansı TAK'un uzun süre yapılmasıyla artmaktadır. Daha önce literatürde karsılasılmayan bir TAK komplikasyonuna sahip, parsiyel penil doku ve penil üretra kaybı olan bir hastanın olgu sunumu yapıldı ve TAK'a baglı gelisebilecek olan üretral patolojiler gözden geçirildi.Clean intermittent self -catheterization (CISC) is a treatment method that is used routinely in individualized programmes for bladder evacuation in various bladder disorders. Urinary tract infection is the most frequent complication in patients performing CISC Although this method has significant advantages to indwelling catheters (IDC), because of repeatedly urethral catheterization, some complications like urethral stricture and urethral false passage may develop and the prevalence of urethral strictures and false passages increases with longer use of CISC. A case report of a patient, who lost urethral tissue and penile urethra due to CISC, a complication not reported previously in the literature is presented and CISC dependent urethral pathologies are reviewed

Cancer detection rates of different prostate biopsy regimens in patients with renal failure

We aimed to evaluate the cancer detection rates of 6-, 10-, 12-core biopsy regimens and the optimal biopsy protocol for prostate cancer diagnosis in patients with renal failure. A total of 122 consecutive patients with renal failure underwent biopsy with age-specific prostate-specific antigen (PSA) levels up to 20 ng/mL. The 12-core biopsy technique (sextant biopsy + lateral base, lateral mid-zone, lateral apex, bilaterally) performed to all patients. Pathology results were examined separately for each sextant, 10-core that exclude parasagittal mid-zones from 12-cores (10a), 10-core that exclude apex zones from 12-cores (10b) and 12-core biopsy regimens. Of 122 patients, 37 (30.3%) were positive for prostate cancer. The cancer detection rates for sextant, 10a, 10b and 12 cores were 17.2%, 29%, 23.7% and 30.7%, respectively. Biopsy techniques of 10a, 10b and 12 cores increased the cancer detection rates by 40%, 27.5% and 43.2% among the sextant technique, respectively. Biopsy techniques of 10a and 12 cores increased the cancer detection rates by 17.1% and 21.6% among 10b biopsy technique, respectively. There were no statistical differences between 12 core and 10a core about cancer detection rate. Adding lateral cores to sextant biopsy improves the cancer detection rates. In our study, 12-core biopsy technique increases the cancer detection rate by 5.4% among 10a core but that was not statistically different. On the other hand, 12-core biopsy technique includes all biopsy regimens. We therefore suggest 12-core biopsy or minimum 10-core strategy incorporating six peripheral biopsies with elevated age-specific PSA levels up to 20 ng/mL in patients with renal failure

Artificial intelligence for prediction of endometrial intraepithelial neoplasia and endometrial cancer risks in pre- and postmenopausal womenAJOG Global Reports at a Glance

BACKGROUND: The current approach to endometrial cancer screening requires that all patients be able to recognize symptoms, report them, and carry out appropriate interventions. The current approach to endometrial cancer screening could become a problem in the future, especially for Black women and women from minority groups, and could lead to disparities in receiving proper care. Moreover, there is a lack of literature on artificial intelligence in the prediction and diagnosis of endometrial intraepithelial neoplasia and endometrial cancer. OBJECTIVE: This study analyzed different artificial intelligence methods to help in clinical decision-making and the prediction of endometrial intraepithelial neoplasia and endometrial cancer risks in pre- and postmenopausal women. This study aimed to investigate whether artificial intelligence may help to overcome the challenges that statistical and diagnostic tests could not. STUDY DESIGN: This study included 564 patients. The features that were collected included age, menopause status, premenopausal abnormal bleeding and postmenopausal bleeding, obesity, hypertension, diabetes mellitus, smoking, endometrial thickness, and history of breast cancer. Endometrial sampling was performed on all women with postmenopausal bleeding and asymptomatic postmenopausal women with an endometrial thickness of at least 3 mm. Endometrial biopsy was performed on premenopausal women with abnormal uterine bleeding and asymptomatic premenopausal women with suspected endometrial lesions. Python was used to model machine learning algorithms. Random forest, logistic regression, multilayer perceptron, Catboost, Xgboost, and Naive Bayes methods were used for classification. The synthetic minority oversampling technique was used to correct the class imbalance in the training sets. In addition, tuning and boosting were used to increase the performance of the models with a 5-fold cross-validation approach using a training set. Accuracy, sensitivity, specificity, positive predictive value, and F1 score were calculated. RESULTS: The prevalence of endometrial or preuterine cancer was 7.9%. Data from 451 patients were randomly assigned to the training group, and data from another 113 patients were used for internal validation. Of note, 3 of 9 features were selected by the Boruta algorithm for use in the final modeling. Age, body mass index, and endometrial thickness were all associated with a high risk of developing precancerous and cancerous diseases, after fine-tuning for the multilayer computer to have the highest area below the receiver operating characteristic curve (area under the curve, 0.938) to predict a precancerous disease. The accuracy was 0.94 for predicting a precancerous disease. Precision, recall, and F1 scores for the test group were 0.71, 0.50, and 0.59, respectively. CONCLUSION: Our study found that artificial intelligence can be used to identify women at risk of endometrial intraepithelial neoplasia and endometrial cancer. The model is not contingent on menopausal status or symptoms. This may be an advantage over the traditional methodology because many women, especially Black women and women from minority groups, could not recognize them. We have proposed to include patients to provide age and body mass index, and measurement of endometrial thickness by either sonography or artificial intelligence may help improve healthcare for women in rural or minority communities