L-Arginine Enriched Diet Protects BB/OK Rats from Developing Type 1 Diabetes
Bibliographic record
Abstract
L-arginine is the source of all forms of nitric oxide (NO), which can be an extremely relevant factor in the treatment and reversal of important diseases. This observation prompted us to use L-arginine in BB/OK (Bio Breeding/Ottawa Karlsburg) rats developing insulin-dependent type 1 diabetes to evaluate the effect of L-arginine on the prevention of this disease. BB/OK rats were given L-arginine in drinking water (2%) during pregnancy and to the progeny (group 1), to newborn (group 2) and not given (group 3) up to an age of 30 weeks. Diabetes frequency and age at onset of diabetes were recorded in all BB/OK rats. The mRNA expression of genes (Nfkb2, Il10, Il1b, Rarres 2, Pparg, Adipoq, Lep and Slc2a4) was measured in subcutaneous and visceral adipose tissue in BB/OK rats which did not develop diabetes up to an age of 30 weeks. Diabetes frequency was reduced in the L-arginine supplemented BB/OK compared to the untreated BB/OK rats (group 1: p<0.001 and group 2: p<0.05 vs. control group 3). Group 2 showed gender specific differences, because more females than males developed diabetes (94/59%; p<0.05). Gene expression in subcutaneous and visceral adipose tissue was reduced in the L-arginine drinking BB/OK rats compared to the control group. L-arginine in drinking water can protect from type 1 diabetes development in a sex specific manner. Because L-arginine is a precursor of NO, it may be concluded that this manipulation normalized NO activity in ? cells, partially preventing type 1 diabetes development.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".