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Article Abstract

Awareness of the intestinal microflora involved in insulin resistance and type 2 diabetes (T2DM) has now become more evident. However, direct mechanical insight is required to illustrate the contribution of intestinal microflora in the disease progression. In this study, we aimed to precisely assess the changes in intestinal bacteria translocation (IBT) from gut to the pancreas using combinations of FISH, 16S rRNA amplicon sequencing, and deep learning-assisted methods to track IBT in diet-induced obese (DIO) and antibiotic-induced microbiota disruption (AIMD)-DIO mouse models. Our analysis showed deep learning-assisted quantification enhanced the accuracy and objectivity of bacterial tracking. The DIO mice exhibited increased IBT, likely due to excessive intestinal lipid accumulation and compromised intestinal barrier integrity. Elevated bacterial loads in the pancreas were associated with worsened pancreatic function, indicated by higher fasting blood glucose, impaired glucose tolerance, and dysfunctional insulin secretion. In contrast, the AIMD-DIO group lowered the IBT, maintained the islet structure and improved glucose homeostasis. Comparative study between DIO and AIMD-DIO models revealed a strong correlation between number of translocated bacteria and T2DM severity. These findings provide objective evidence of bacterial migration from the intestine to the pancreas and establishes its pathological relationship with pancreatic impairment and dysfunction and underscores to utilize AI techniques more successfully in the future for evaluating IBT.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12374998PMC
http://dx.doi.org/10.1038/s41598-025-15244-wDOI Listing

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