Effects of INGAP on islet neoformation in diabetes‐prone BioBreeding rats fed a protective hydrolyzed casein diet
Bibliographic record
Abstract
Type 1 diabetes results from autoimmune destruction of pancreatic β‐cells. β‐cell neoformation has been shown to increase in response to islet neogenesis‐associated protein (INGAP) in hamsters. Using immunohistochemistry and morphometry, INGAP effects in diabetes‐prone BioBreeding (BBdp) rats were investigated. At 23 d, rats were fed a hydrolyzed casein (HC) diet or a cereal‐based NTP‐2000 diet. Rats were administered saline intraperitoneally (n=7) or 250 μg of INGAP‐peptide (n=7) twice daily for two weeks and killed at 45 d. In HC‐fed rats, the number of small and total islets was greater in INGAP‐treated rats compared with controls (59 ± 17 vs. 37 ± 8 islets/cm 2 , mean ± SD, p=0.01 and 143 ± 19 vs. 114 ± 24 islets/cm 2 , p<0.03). In rats given INGAP, insulin + extra‐islet clusters, markers of islet neoformation, were more frequent in those fed HC compared with NTP (82 ± 20 vs. 46 ± 8 clusters/cm 2 , p<0.01). HC‐fed rats given INGAP had a higher percentage of BrdU + /insulin + clusters compared with controls, indicating enhanced proliferation (26.0 ± 7.6 vs. 13.1 ± 2.7%, p<0.001). INGAP‐treated rats fed either an HC or NTP diet had a higher percentage of insulin + cells in small ducts compared with saline controls. These findings demonstrate the presence of INGAP‐stimulated β‐cell neoformation, some aspects of which were further enhanced in BBdp rats fed a protective HC diet. (Supported by CDA and CIHR; CP and GSW contributed equally).
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".