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Purpose/Objectives: Retrospective analysis suggests that dose escalation to a biologically effective dose of more than 70 Gy may improve overall survival in patients with pancreatic ductal adenocarcinoma (PDAC), but such treatments in practice are limited by proximity of organs at risk (OARs). We hypothesized that CT-guided online adaptive radiotherapy (OART) can account for interfraction movement of OARs and allow for safe delivery of ablative doses. Materials/Methods: This is a single institution retrospective analysis of patients with PDAC treated with OART on the Ethos platform (Varian Medical Systems, a Siemens Healthineers Company, Palo Alto). All patients were treated to 40 Gy in 5 fractions. PTV overlapping with a 5 mm planning risk volume expansion on the stomach, duodenum and bowel received 25 Gy. Initial treatment plans were created conventionally. For each fraction, PTV and OAR volumes were recontoured with AI assistance after initial cone beam CT (CBCT). The adapted plan was calculated, underwent QA, and then compared to the scheduled plan. A second CBCT was obtained prior to delivery of the selected plan. Total treatment time (first CBCT to end of radiation delivery) and active physician time (first to second CBCT) were recorded. PTV_4000 V95 %, PTV_2500 V9 5%, and D0.03 cc to stomach, duodenum and bowel were reported for scheduled (S) and adapted (A) plans. CTCAEv5.0 toxicities were recorded. Statistical analysis was performed using a two-sided T test and α of 0.05. Results: 21 patients with unresectable or locally-recurrent PDAC were analyzed, with a total of 105 fractions. Average total time was 29 min and 16 s (16:36–49:40) and average active physician time was 19:41 min (9:25–39:34). All fractions were treated with adapted plans. 97 % of adapted plans met PTV_4000 V95.0 % >95.0 % coverage goal and 100 % of adapted plans met OAR dose constraints. Median follow up was 6.6 months. Only 1 patient experienced acute grade 3+ toxicity directly attributable to radiation. Only 1 patient experienced late grade 3+ toxicity directly attributable to radiation. Conclusions: Daily CT-based OART was associated with significantly reduced dose OARs while achieving superior PTV coverage. Given the relatively quick total treatment time, radiation delivery was generally well tolerated and easily incorporated into the clinic workflow. Our initial clinical experience demonstrates OART allows for safe dose escalation in the treatment of PDAC.

Purpose: The emerging online adaptive radiation therapy (OART) treatment strategy based on cone beam computed tomography allows for real-time replanning according to a patient's current anatomy. However, implementing this procedure requires a new approach across the patient's care path and monitoring of the “black box” adaptation process. This study identifies high-risk failure modes (FMs) associated with AI-driven OART and proposes an interdisciplinary workflow to mitigate potential medical errors from highly automated processes, enhance treatment efficiency, and reduce the burden on clinicians. Methods and Materials: An interdisciplinary working group was formed to identify safety concerns in each process step using failure mode and effects analysis (FMEA). Based on the FMEA results, the team designed standardized procedures and safety checklists to prevent errors and ensure successful task completion. The Risk Priority Numbers (RPNs) for the top twenty FMs were calculated before and after implementing the proposed workflow to evaluate its effectiveness. Three hundred seventy-four adaptive sessions across 5 treatment sites were performed, and each session was evaluated for treatment safety and FMEA assessment. Results: The OART workflow has 4 components, each with 4, 8, 13, and 4 sequentially executed tasks and safety checklists. Site-specific template preparation, which includes disease-specific physician directives and Intelligent Optimization Engine template testing, is one of the new procedures introduced. The interdisciplinary workflow significantly reduced the RPNs of the high-risk FMs, with an average decrease of 110 (maximum reduction of 305.5 and minimum reduction of 27.4). Conclusions: This study underscores the importance of addressing high-risk FMs associated with AI-driven OART and emphasizes the significance of safety measures in its implementation. By proposing a structured interdisciplinary workflow and integrated checklists, the study provides valuable insights into ensuring the safe and efficient delivery of OART while facilitating its effective integration into clinical practice.

Introduction: The 2019 Kidney Disease Outcome Quality Initiative (K/DOQI) guideline recommended evaluating arteriovenous fistula (AVF) malfunction risks primarily based on clinical monitoring, which can be assisted with the value of vascular access flow (Qa). Nevertheless, Qa thresholds recommended by different guidelines vary, ranging from 300 to 500 ml/min. This study investigated the optimal Qa threshold to predict future functional patency in AVFs with Qa

Objective: β-cell dedifferentiation has been revealed as a pathological mechanism underlying pancreatic dysfunction in diabetes. We previously showed that increased miR-7 levels trigger β-cell dedifferentiation and diabetes. We used β-cell-specific miR-7 overexpressing mice (Tg7) to test the hypothesis that loss of β-cell identity triggered by miR-7 overexpression alters islet gene expression and islet microenvironment in diabetes. Methods: We performed bulk and single-cell RNA sequencing (RNA-seq) in islets obtained from β-cell-specific miR-7 overexpressing mice (Tg7). We carried out loss- and gain-of-function experiments in MIN6 and EndoC-bH1 cell lines. We analysed previously published mouse and human T2D data sets. Results: Bulk RNA-seq revealed that β-cell dedifferentiation is associated with the induction of genes associated with epithelial-to-mesenchymal transition (EMT) in prediabetic (2-week-old) and diabetic (12-week-old) Tg7 mice. Single-cell RNA-seq (scRNA-seq) indicated that this EMT signature is enriched specifically in β-cells. These molecular changes are associated with a weakening of β-cell: β-cell contacts, increased extracellular matrix (ECM) deposition, and TGFβ-dependent islet fibrosis. We found that the mesenchymal reprogramming of β-cells is explained in part by the downregulation of Pdx1 and its inability to regulate a myriad of epithelial-specific genes expressed in β-cells. Notable among genes transactivated by Pdx1 is Ovol2, which encodes a transcriptional repressor of the EMT transcription factor Zeb2. Following compromised β-cell identity, the reduction in Pdx1 gene expression causes a decrease in Ovol2 protein, triggering mesenchymal reprogramming of β-cells through the induction of Zeb2. We provided evidence that EMT signalling associated with the upregulation of Zeb2 expression is a molecular feature of islets in T2D subjects. Conclusions: Our study indicates that miR-7-mediated β-cell dedifferentiation induces EMT signalling and a chronic response to tissue injury, which alters the islet microenvironment and predisposes to fibrosis. This research suggests that regulators of EMT signalling may represent novel therapeutic targets for treating β-cell dysfunction and fibrosis in T2D.

Diabetes can be associated with a reduction in functional β cell mass, which must be restored if the disease is to be cured or progress is to be arrested. To study the cell count, it is also necessary to determine the number of nuclei within the insulin stained area. It can take a single experimentalist several months to complete a single study of this kind, results of which may still be quite subjective. In this paper, we propose a framework based on a novel measure of local symmetry for detection of cells. The local isotropic phase symmetry measure (LIPSyM) is designed to give high values at or near the cell centers. We demonstrate the effectiveness of our algorithm for detection of two types of specific cells in histology images, cells in mouse pancreatic sections and lymphocytes in human breast tissue. Experimental results for these two problems show that our algorithm performs better than human experts for the former problem, and outperforms the best reported results for the latter.