Insulin Inhibits and Oral Sucrose Increases Neointimal Growth after Arterial Injury in Rats
Bibliographic record
Abstract
BACKGROUND/AIMS: In our previous studies, rats on insulin treatment (5 U/day) and oral glucose to avoid hypoglycemia had reduced neointimal growth after arterial injury. However, plasma glucose in the insulin-treated rats was lower than normal and the effect of oral glucose remained undetermined. In this study, the effects of normoglycemic hyperinsulinemia and oral glucose or sucrose were investigated in the same model. METHODS: Rats were divided into 6 groups: (1) control implants and tap water; (2) insulin implants (5 U/day) and oral glucose + i.p. glucose to avoid any glucose lowering; (3) insulin implants (4 U/day) and oral glucose; (4) insulin implants (4 U/day) and oral sucrose; (5) control implants and oral glucose, and (6) control implants and oral sucrose. RESULTS: Insulin treatment at both doses reduced neointimal area (p < 0.001) 14 days after injury in rats receiving oral glucose but not in those receiving oral sucrose. Oral glucose, without insulin, had no effect on neointimal formation, whereas oral sucrose increased neointimal growth (p < 0.05). Oral sucrose (p < 0.05) but not oral glucose decreased insulin sensitivity measured with hyperinsulinemic clamps. CONCLUSIONS: (1) Insulin decreases neointimal growth after arterial injury independent of glucose-lowering or oral glucose administration and (2) oral sucrose per se affects neointimal growth.
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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".