Duodenal-jejunal bypass protects GK rats from β-cell loss and aggravation of hyperglycemia and increases enteroendocrine cells coexpressing GIP and GLP-1
Bibliographic record
Abstract
Dramatic improvement of type 2 diabetes is commonly observed after bariatric surgery. However, the mechanisms behind the alterations in glucose homeostasis are still elusive. We examined the effect of duodenal-jejunal bypass (DJB), which maintains the gastric volume intact while bypassing the entire duodenum and the proximal jejunum, on glycemic control, β-cell mass, islet morphology, and changes in enteroendocrine cell populations in nonobese diabetic Goto-Kakizaki (GK) rats and nondiabetic control Wistar rats. We performed DJB or sham surgery in GK and Wistar rats. Blood glucose levels and glucose tolerance were monitored, and the plasma insulin, glucagon-like peptide-1 (GLP-1), and glucose-dependent insulinotropic polypeptide (GIP) levels were measured. β-Cell area, islet fibrosis, intestinal morphology, and the density of enteroendocrine cells expressing GLP-1 and/or GIP were quantified. Improved postprandial glycemia was observed from 3 mo after DJB in diabetic GK rats, persisting until 12 mo after surgery. Compared with the sham-GK rats, the DJB-GK rats had an increased β-cell area and a decreased islet fibrosis, increased insulin secretion with increased GLP-1 secretion in response to a mixed meal, and an increased population of cells coexpressing GIP and GLP-1 in the jejunum anastomosed to the stomach. In contrast, DJB impaired glucose tolerance in nondiabetic Wistar rats. In conclusion, although DJB worsens glucose homeostasis in normal nondiabetic Wistar rats, it can prevent long-term aggravation of glucose homeostasis in diabetic GK rats in association with changes in intestinal enteroendocrine cell populations, increased GLP-1 production, and reduced β-cell deterioration.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".