Artificial Intelligence-Based Comparison of Duodenojejunostomy and Sleeve Gastrectomy on Pancreatic Morphology in Zucker Diabetic Fatty Rats | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Artificial Intelligence-Based Comparison of Duodenojejunostomy and Sleeve Gastrectomy on Pancreatic Morphology in Zucker Diabetic Fatty Rats Luisa Schaefer, Ambrus Malyi, Gabriel Seifert, Mira Fink, Stephan Herrmann, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7021785/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction Obesity and type 2 diabetes mellitus (T2D) are interrelated metabolic disorders with increasing global incidence. Bariatric surgeries such as sleeve gastrectomy (SG) and Roux-en Y gastric bypass (RYGB) improve glycemic control, yet their effects on pancreatic morphology and function remain incompletely understood. This study compared the effects of SG, duodenojejunostomy (DJOS), and their combination (DJOS + SG) on glucose regulation and pancreatic histomorphology in Zucker diabetic fatty (ZDF) rats using artificial intelligence (AI)-assisted tissue analysis. Methods Forty-five male ZDF rats were randomized into three surgical groups (SG, DJOS, DJOS + SG). Oral glucose tolerance tests (OGTT) and insulin levels were assessed at 1, 3 and 6 months post-surgery. Pancreatic tissue was analyzed histologically and immunohistochemically for beta cell mass, PCNA and PDX-1 expression. QuPath software enabled AI-based quantification of acinar, adipose, and fibrotic tissue. Results DJOS and DJOS + SG improved glucose tolerance and increased fasting insulin compared to SG. Both bypass groups demonstrated greater beta cell mass and clustering, elevated 6 PCNA and PDX-1 expression, and more acinar tissue. SG was associated with reduced beta cell presence and increased pancreatic adiposity. Conclusions Malabsorptive (DJOS) or combination bariatric procedures (DJOS + SG) significantly enhance glycemic control in the rat model. Concomitantly, beta cell apoptosis is reduced, leading to increased beta cell numbers and clustering, along with enhanced beta cell proliferation and differentiation. Acinar glandular tissue is increased with a reduction in pancreatic adiposity following bypass surgery. Health sciences/Diseases Health sciences/Endocrinology Health sciences/Gastroenterology Health sciences/Medical research Duodenojejunostomy Sleeve Gastrectomy beta cell mass pancreatic morphology AIbased tissue quantification Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7021785","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":512997741,"identity":"e7afbef7-9494-4f58-a772-04e2104671b5","order_by":0,"name":"Luisa Schaefer","email":"","orcid":"","institution":"University of Freiburg, University of Freiburg","correspondingAuthor":false,"prefix":"","firstName":"Luisa","middleName":"","lastName":"Schaefer","suffix":""},{"id":512997742,"identity":"626213d7-eb61-4d9d-928d-f530315daeef","order_by":1,"name":"Ambrus Malyi","email":"","orcid":"","institution":"Semmelweis 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