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Introduction: Despite the introduction of robot-assisted surgery in daily clinical practice, complex renal masses are still challenging even for expert surgeon. In this scenario 3D anatomical models and augmented reality represent valuable tools for the surgeon. Materials and methods: We present a challenging case where PN was mandatory to preserve the overall renal function. The patient was 69 years old, with indwelling catheter for BPH and Parkinson disease. After a single episode of hematuria with negative cystoscopy, a cT1N0M0 renal cancer was diagnosed (38 mm maximum diameter). Pre-operative three-dimensional (3D) model was obtained.After multidisciplinary discussion robot-assisted partial nephrectomy was proposed. The surgery was planned according to the anatomical model. Results: Before the procedure a 7Ch single loop ureteral stent was placed. The surgery was carried out in 220 minutes. Selective ischaemia was perfomed for 24 minutes. Estimated blood loss was 400cc. No post-operative complications were observed.Ureteral stent was removed 4 days after the surgery. Definitive histological examination described a pG2-3 T1a Nx R0 clear cell renal carcinoma. Conclusion: In selected cases 3D model result to be a useful tool for the pre-operative planning of the surgery.

Abstract Over 380 host plant species have been known to develop leaf spots as a result of the fungus Alternaria alternata. It is an aspiring pathogen that affects a variety of hosts and causes rots, blights, and leaf spots on different plant sections. In this investigation, the lipopeptides from the B. subtilis strains T3, T4, T5, and T6 were evaluated for their antifungal activities. In the genomic DNA, iturin, surfactin, and fengycin genes were found recovered from B. subtilis bacterium by PCR amplification. From different B. subtilis strains, antifungal Lipopeptides were extracted, identified by HPLC, and quantified with values for T3 (24 g/ml), T4 (32 g/ml), T5 (28 g/ml), and T6 (18 g/ml). To test the antifungal activity, the isolated lipopeptides from the B. subtilis T3, T4, T5, and T6 strains were applied to Alternaria alternata at a concentration of 10 g/ml. Lipopeptides were found to suppress Alternaria alternata at rates of T3 (75.14%), T4 (75.93%), T5 (80.40%), and T6 (85.88%). The T6 strain outperformed the other three by having the highest antifungal activity against Alternaria alternata (85.88%).

Object detection and classification in autonomous vehicles are crucial for ensuring safe and efficient navigation through complex environments. This paper addresses the need for robust detection and classification algorithms tailored specifically for Indian roads, which present unique challenges such as diverse traffic patterns, erratic driving behaviors, and varied weather conditions. Despite significant progress in object detection and classification for autonomous vehicles, existing methods often struggle to generalize effectively to the conditions encountered on Indian roads. This paper proposes a novel approach utilizing the YOLOv8 deep learning model, designed to be lightweight, scalable, and efficient for real-time implementation using onboard cameras. Experimental evaluations were conducted using real-life scenarios encompassing diverse weather and traffic conditions. Videos captured in various environments were utilized to assess the model's performance, with particular emphasis on its accuracy and precision across 35 distinct object classes. The experiments demonstrate a precision of 0.65 for the detection of multiple classes, indicating the model's efficacy in handling a wide range of objects. Moreover, real-time testing revealed an average accuracy exceeding 70% across all scenarios, with a peak accuracy of 95% achieved in optimal conditions. The parameters considered in the evaluation process encompassed not only traditional metrics but also factors pertinent to Indian road conditions, such as low lighting, occlusions, and unpredictable traffic patterns. The proposed method exhibits superiority over existing approaches by offering a balanced trade-off between model complexity and performance. By leveraging the YOLOv8 architecture, this solution achieved high accuracy while minimizing computational resources, making it well suited for deployment in autonomous vehicles operating on Indian roads. [ABSTRACT FROM AUTHOR]

Herlyn-Werner-Wunderlich Syndrome (HWWS) is a rare congenital anomaly of Mullerian and mesonephric ducts characterized by uterus didelphys with obstructed hemivagina and ipsilateral renal agenesis. It commonly presents after menarche with symptoms related to obstructive hemivagina. However in patients with significantly normal pubertal and menstrual history, the diagnosis of Mullerian Duct Anomaly (MDA) like HWWS is often missed. Here we report a case of 28 year old primigravida presenting with acute abdomen pain which was followed by spontaneous abortion. This is the first instance of HWWS coexisting with uterine fibroids with/without cystic degeneration based on our literature review. Early and accurate diagnosis for the correct treatment of this entity is also emphasized.