Therapeutic and Diagnostic Potential of Folic Acid Receptors and Glycosylphosphatidylinositol (GPI) Transamidase in Prostate Cancer.

Due to the proliferation-induced high demand of cancer cells for folic acid (FA), significant overexpression of folate receptors 1 (FR1) is detected in most cancers. To our knowledge, a detailed characterization of FR1 expression and regulation regarding therapeutic and diagnostic feasibilities in prostate cancer (PCa) has not been described. In the present study, cell cultures, as well as tissue sections, were analyzed using Western blot, qRT-PCR and immunofluorescence. In addition, we utilized FA-functionalized lipoplexes to characterize the potential of FR1-targeted delivery into PCa cells. Interestingly, we detected a high level of FR1-mRNA in healthy prostate epithelial cells and healthy prostate tissue. However, we were able to show that PCa cells in vitro and PCa tissue showed a massively enhanced FR1 membrane localization where the receptor can finally gain its function. We were able to link these changes to the overexpression of GPI-transamidase (GPI-T) by image analysis. PCa cells in vitro and PCa tissue show the strongest overexpression of GPI-T and thereby induce FR1 membrane localization. Finally, we utilized FA-functionalized lipoplexes to selectively transfer pDNA into PCa cells and demonstrate the therapeutic potential of FR1. Thus, FR1 represents a very promising candidate for targeted therapeutic transfer pathways in PCa and in combination with GPI-T, may provide predictive imaging in addition to established diagnostics.

[Urinary retention and postrenal kidney injury]. / Harnverhalt und postrenales Nierenversagen.

In men aged 40-83 years, the overall incidence of urinary retention is 4.5-6.8 cases per 1000 men per year. The incidence increases significantly with age, so that a man in his 70 s has a 10% chance and a man in his 80 s has a more than 30% chance of experiencing an episode of acute urinary retention [1]. The goal of diagnosis is to quickly reach a finding through clinical examination and ultrasound to be able to relieve the bladder. The first maneuver is catheterization, followed by, if necessary, initiation of pharmacological therapy that targets the underlying cause. Despite the high association of urinary retention with benign prostatic hyperplasia (BPH), a comprehensive history and diagnosis are crucial to identify possible rare and complex causes and to enable targeted treatment. The challenge lies in finding the balance between rapid symptomatic treatment and thorough investigation of atypical and rare pathologies to develop individually adapted and effective therapy strategies.

Harnessing Artificial Intelligence for Enhanced Renal Analysis: Automated Detection of Hydronephrosis and Precise Kidney Segmentation.

Background and objective: Hydronephrosis is essential in the diagnosis of renal colic. We automated the detection of hydronephrosis from ultrasound images to standardize the therapy and reduce the misdiagnosis of renal colic. Methods: Anonymously collected ultrasound images of human kidneys, both normal and hydronephrotic, were preprocessed for neural networks. Six "state of the art" models were trained and cross-validated for the detection of hydronephrosis, and two convolutional networks were used for kidney segmentation. In the testing phase, performance metrics included true positives, true negatives, false positives, false negatives, accuracy, and F1 score, while the evaluation of the segmentation task involved accuracy, precision, dice, jaccard, recall, and ASSD. Key findings and limitations: A total of 523 sonographic kidney images (423 nonhydronephrotic and 100 hydronephrotic) were collected from three different ultrasound devices. After training on this dataset, all models were used to evaluate 200 new ultrasound kidney images (142 nonhydronephrotic and 58 hydronephrotic kidneys). The highest validation accuracy (98.5%) was achieved by the AlexNet model (GoogLeNet 97%, AlexNet_v2 96%, ResNet50 96%, ResNet101 97.5%, and ResNet152 95%). The deeplabv3_resnet50 and deeplabv3_resnet101 reached a dice coefficient of 94.74% and 94.48%, respectively, on the task of automated kidney segmentation. The study is limited by analyzing only hydronephrosis, but this specific focus enabled high detection accuracy. Conclusions and clinical implications: We show that our automated ultrasound deep learning model can be trained and used to interpret and segmentate ultrasound images from different sources with high accuracy. This method will serve as an automated tool in the diagnostic algorithm of acute renal failure in the future. Patient summary: Hydronephrosis is crucial in the diagnosis of renal colic. Recent advances in artificial intelligence allow automated detection of hydronephrosis in ultrasound images with high accuracy. These methods will help standardize the diagnosis and treatment renal colic.

[Urothelial carcinoma of the upper urinary tract]. / Urothelkarzinom des oberen Harntrakts.

Urothelial carcinoma of the upper urinary tract (upper tract urothelial carcinoma, UTUC) is less common than bladder carcinoma with nearly identical risk factors and has a poorer prognosis. The standard diagnostic procedure is imaging of the upper urinary tract by computed tomography urography. In cases of diagnostic uncertainty, a diagnostic ureterorenoscopy with biopsy sampling can be performed in addition to urine cytology. Treatment depends primarily on the stage and grading of the tumor. Depending on the extent and localization, organ-preserving treatment or radical nephroureterectomy is indicated. Perioperative systemic treatment in high-risk UTUC can be performed in both neoadjuvant and adjuvant settings, although the current data on neoadjuvant chemo- and immunotherapy do not yet allow standard application. For metastatic disease, a multimodal treatment approach consisting of cisplatin-based or carboplatin-based chemotherapy, immunotherapy, and treatment with enfortumab vedotin can be considered. Salvage surgery, radiotherapy and metastasectomy are available for rare individual cases.

[Artificial intelligence in urology-opportunities and possibilities]. / Künstliche Intelligenz in der Urologie ­ Chancen und Möglichkeiten.

The use of artificial intelligence (AI) in urology can contribute to a significant improvement with regard to individualization of diagnostics and therapy as well as healthcare cost reduction. The potential applications and advantages of AI in medicine are often underestimated or incompletely understood. This makes it difficult to conceptually solve relevant medical problems using AI. With current advances in computer science, multiple, highly complex nonmedical processes have already been studied and optimized in an automated fashion. The development of AI models, if applied correctly, can lead to more effective processing and analysis of patient-related data and correspondingly optimized diagnosis and therapy of urological patients. In this review, the current status on the application of AI in medicine and its opportunities and possibilities in urology are presented from a conceptual perspective using practical examples.