Renz, Bernhard Willibald

ABSTRACT Background Living kidney donation is a crucial option for addressing the global organ shortage and providing kidney transplantation for patients suffering from end‐stage kidney disease. Ensuring donor safety necessitates a comprehensive preoperative assessment of kidney anatomy and function. This study evaluates the relationship between kidney volumes derived from deep learning‐based MRI volumetry, intraoperative kidney volume measurements, split renal function measured by renal scintigraphy, and post‐donation eGFR. Deep learning‐based MRI volumetry is hypothesized to be a reliable method with good correlation. Methods This retrospective study analyzed 178 living kidney donors. Deep learning MRI volumetry‐based kidney volumes were compared with intraoperative volumes of the explanted donor kidneys obtained using the water displacement method. Additionally, MRI‐based volume ratios were compared with scintigraphy‐based split renal function ratios to determine their ability to predict the kidney with poorer renal function and post‐donation eGFR. Results Deep learning‐based MRI volumetry strongly correlated with intraoperatively measured kidney volumes (Pearson's correlation; r = 0.7671; p < 0.0001), confirming its precision in volume estimation. Although MRI‐based kidney volume ratios demonstrated only a moderate correlation with scintigraphy‐based split renal function ratios ( r = 0.4798), MRI volumetry correlated with 1‐year post‐donation eGFR. It tended to be better than renal scintigraphy ( r = 0.6829 versus r = 0.6191). Conclusion Deep learning‐based MRI volumetry is a reliable, non‐invasive tool for estimating kidney volumes in living donors, offering a radiation‐free alternative for preoperative assessment. While it differs from renal scintigraphy in evaluating split renal function ratios, its correlation with post‐donation eGFR tends to be better, supporting its potential role in living kidney donor assessment.

Abstract Background: The neurokinin-1 receptor (NK1R) complex, activated by Substance P (SP), has emerged as a promising target. SP promotes cell growth, whereas its antagonist, Aprepitant (AP), used clinically for chemotherapy-induced nausea, has demonstrated anti-tumor activity. However, further insight on the anti-tumoral mechanisms after antagonistic targeting of the complex are required. Methods: We conducted bioinformatical analysis of public as well as own RNAseq data. Assays to assess cell proliferation and cell viability (MTT, scratch assays, colony formation) as well as apoptosis and autophagy were carried out. Metabolic changes were detected by SeaHorse analysis, ROS, NAD+/NADH, and MitoTracker. Results: Patient-derived organoid cultures showed reduced spheroid formation and altered morphology following AP treatment, while SP enhanced spheroid formation. SP promoted proliferation via Rho-kinase-dependent mechanisms and activated the ERK signaling pathway, with AP slightly reducing pERK levels. Further analyses revealed that AP induces apoptosis signaling without subsequent DNA fragmentation. Annexin V/PI staining indicated apoptosis induction after 24 hours, while subG1 analysis suggested a blockade in the apoptosis cascade. Prolonged AP exposure led to necrosis, which was mitigated by co-treatment with N-acetylcysteine. Western blot analysis confirmed caspase-9 and -3 activation but showed no PARP cleavage, likely due to high expression of inhibitors of apoptosis proteins (IAPs), such as XIAP. Conclusion: In conclusion, AP exhibits antiproliferative and cytotoxic effects in pancreatic tumor cells, potentially disrupting an autocrine/paracrine SP-NK1R feedback loop and altering metabolic resilience. Future studies should explore metabolic stress responses and investigate whether co-treatment could overcome incomplete apoptosis activation. Citation Format: Matthias Ilmer, Maximilian Viessmann, Bernhard Renz, Frank Braun, Jens Werner. Repurposing of the SP/NK1R-complex inhibitor aprepitant in hepatopancreatic malignancies [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research—Emerging Science Driving Transformative Solutions; Boston, MA; 2025 Sep 28-Oct 1; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(18_Suppl_3):Abstract nr B023.

Objectives: Every year, around 300 million surgeries are conducted worldwide, with an estimated 4.2 million deaths occurring within 30 days after surgery. Adequate patient education is crucial, but often falls short due to the stress patients experience before surgery. Large language models (LLMs) can significantly enhance this process by delivering thorough information and addressing patient concerns that might otherwise go unnoticed. Material and methods: This cross-sectional study evaluated Chat Generative Pretrained Transformer-4o’s audio-based responses to frequently asked questions (FAQs) regarding six general surgical procedures. Three experienced surgeons and two senior residents formulated seven general and three procedure-specific FAQs for both preoperative and postoperative situations, covering six surgical scenarios (major: pancreatic head resection, rectal resection, total gastrectomy; minor: cholecystectomy, Lichtenstein procedure, hemithyroidectomy). In total, 120 audio responses were generated, transcribed, and assessed by 11 surgeons from 6 different German university hospitals. Results: ChatGPT-4o demonstrated strong performance, achieving an average score of 4.12/5 for accuracy, 4.46/5 for relevance, and 0.22/5 for potential harm across 120 questions. Postoperative responses surpassed preoperative ones in both accuracy and relevance, while also exhibiting lower potential for harm. Additionally, responses related to minor surgeries were minimal, but significantly more accurate compared to those for major surgeries. Conclusions: This study underscores GPT-4o’s potential to enhance patient education both before and after surgery by delivering accurate and relevant responses to FAQs about various surgical procedures. Responses regarding the postoperative course proved to be more accurate and less harmful than those addressing preoperative ones. Although a few responses carried moderate risks, the overall performance was robust, indicating GPT-4o’s value in patient education. The study suggests the development of hospital-specific applications or the integration of GPT-4o into interactive robotic systems to provide patients with reliable, immediate answers, thereby improving patient satisfaction and informed decision-making.