(1) Objective: To design an artificial intelligence system for prostate cancer prediction using the data obtained by shear wave elastography of the prostate, by comparing it with the histopathological exam of the prostate biopsy specimens. (2) Material and methods: We have conducted a prospective study on 356 patients undergoing transrectal ultrasound-guided prostate biopsy, for suspicion of prostate cancer. All patients were examined using bi-dimensional shear wave ultrasonography, which was followed by standard systematic transrectal prostate biopsy. The mean elasticity of each of the twelve systematic biopsy target zones was recorded and compared with the pathological examination results in all patients. The final dataset has included data from 223 patients with confirmed prostate cancer. Three machine learning classification algorithms (logistic regression, a decision tree classifier and a dense neural network) were implemented and their performance in predicting the positive lesions from the elastographic data measurements was assessed. (3) Results: The area under the curve (AUC) results were as follows: for logistic regression—0.88, for decision tree classifier—0.78 and for the dense neural network—0.94. Further use of an upsampling strategy for the training set of the neural network slightly improved its performance. Using an ensemble learning model, which combined the three machine learning models, we have obtained a final accuracy of 98%. (4) Conclusions: Bi-dimensional shear wave elastography could be very useful in predicting prostate cancer lesions, especially when it benefits from the computational power of artificial intelligence and machine learning algorithms.

Background and Objectives: Over decades, prostate cancer (PCa) has become one of the leading causes of cancer mortality in men. Extensive evidence exists that microRNAs (miRNAs or miRs) are key players in PCa and a new class of non-invasive cancer biomarkers. Materials and Methods: We performed miRNA profiling in plasma and tissues of PCa patients and attempted the validation of candidate individual miRs as biomarkers. Results: The comparison of tissue and plasma profiling results revealed five commonly dysregulated miRs, namely, miR-130a-3p, miR-145-5p, miR-148a-3p, miR-150-5p, and miR-365a-3p, of which only three show concordant changes—miR-130a-3p and miR-150-5p were downregulated and miR-148a-3p was upregulated in both tissue and plasma samples, respectively. MiR-150-5p was validated as significantly downregulated in both plasma and tissue cancer samples, with a fold change of −2.697 (p < 0.001), and −1.693 (p = 0.035), respectively. ROC analysis showed an area under the curve (AUC) of 0.817 (95% CI: 0.680–0.995) for plasma samples and 0.809 (95% CI: 0.616–1.001) for tissue samples. Conclusions: We provide data indicating that miR-150-5p plasma variations in PCa patients are associated with concordant changes in prostate cancer tissues; however, given the heterogeneous nature of previous findings of miR-150-5p expression in PCa cells, additional future studies of a larger sample size are warranted in order to confirm the biomarker potential and role of miRNA-150-5p in PCa biology.

Despite significant progress regarding clinical detection/imaging evaluation modalities and genetic/molecular characterization of pathogenesis, advanced renal cell carcinoma (RCC) remains an incurable disease and overall RCC mortality has been steadily rising for decades. Concomitantly, clinical definitions have been greatly nuanced and refined. RCCs are currently viewed as a heterogeneous series of cancers, with the same anatomical origin, but fundamentally different metabolisms and clinical behaviors. Thus, RCC pathological diagnosis/subtyping guidelines have become increasingly intricate and cumbersome, routinely requiring ancillary studies, mainly immunohistochemistry. Meanwhile, RCC-associated-antigen targeted systemic therapy has been greatly diversified and emerging, novel clinical applications for RCC immunotherapy have already reported significant survival benefits, at least in the adjuvant setting. Even so, systemically disseminated RCCs still associate very poor clinical outcomes, with currently available therapeutic modalities only being able to prolong survival. In lack of a definitive cure for advanced RCCs, integration of the amounting scientific knowledge regarding RCC pathogenesis into RCC clinical management has been paramount for improving patient outcomes. The current review aims to offer an integrative perspective regarding contemporary RCC clinical definitions, proper RCC clinical work-up at initial diagnosis (semiology and multimodal imaging), RCC pathological evaluation, differential diagnosis/subtyping protocols, and novel clinical tools for RCC screening, risk stratification and therapeutic response prediction.

Pregnancy complications associated with thrombophilia represent significant risks for maternal and fetal health, leading to adverse outcomes such as pre-eclampsia, recurrent pregnancy loss, and intra-uterine growth restriction (IUGR). They are caused by disruptions in key physiological processes, including the coagulation cascade, trophoblast invasion, angiogenesis, and immune control. Recent advancements in epigenetics have revealed that non-coding RNAs, especially microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and extracellular vesicles (EVs) carrying these RNAs, play crucial roles in the regulation of these biological processes. This review aims to identify the epigenetic biomarkers that are the best candidates for evaluating thrombophilia-related pregnancy complications and for assessing the efficacy of anticoagulant and antiaggregant therapies. We emphasize their potential integration into personalized treatment plans, aiming to improve the risk assessment and therapy strategies for thrombophilic pregnancies. Future research should focus on validating these epigenetic biomarkers and establishing standardized protocols to enable their integration into clinical practice, paving the way for a precision medicine approach in obstetric care.

This pictorial essay aims to navigate through the complexities and challenges of renal transplantation (RT), by weaving together visual imagery with clinical insights within a comprehensive illustrative surgical guide. Herein, we provide a detailed visual exploration of the intricate anatomy and surgical processes necessary for both renal graft retrieval from the donor and also for an adequate implantation in the recipient. Regarding graft retrieval, after reviewing the relevant retroperitoneal surgical anatomy, and donor nephrectomy techniques, graft preservation and optimal backbench graft dissection principles were meticulously analyzed. Thereafter, the recipient surgical strategy for graft implantation was addressed, focusing on preoperative preparations, the site of implantation selection, exposure, operative bed dissection, graft revascularization, and urinary tract reconstruction. Careful donor and recipient selection, meticulous surgical execution, and rigorous postoperative management clearly hold a pivotal role in optimizing patient outcomes. Fostering a deeper understanding of the surgical nuances and clinical management practices that contribute to successful results post-RT, we hope to provide a useful practical tool for clinicians about to embark on the treacherous road of RT surgery. Innovative technologies and surgical practices that have already significantly improved the safety and effectiveness of RT stand testament to the importance of further scientific inquiry, conceptual developments, and clinical integration. Moving forward, it is essential that the medical community continues to refine these strategies and advocate for equitable access to transplantation, ensuring that advancements in the field translate into real-world benefits for all patients grappling with ESRD. The collaborative efforts of multidisciplinary teams are essential in addressing the complex clinical challenges associated with RT, with the ultimate goal of improving patient survival, enhancing graft longevity, and reducing healthcare disparities.