Improvement of Motor Nerve Conduction Velocity in Diabetic Rats Requires Normalization of the Polyol Pathway Metabolites Flux
Bibliographic record
Abstract
Using ranirestat, an aldose reductase (AR) inhibitor, we investigated the relationship between sorbitol and fructose levels in the sciatic nerve and motor nerve conduction velocity (MNCV) in streptozotocin (STZ)-treated diabetic rats. Ranirestat inhibited rat and recombinant human AR with similar IC50 values and equipotently prevented sorbitol accumulation in rat erythrocytes and sciatic nerves in vitro. One week after STZ administration, sorbitol levels in rat erythrocytes and sciatic nerves significantly increased while MNCV decreased. Oral administration of ranirestat (0.03-1.0 mg/kg per day) for 3 weeks dose-dependently decreased the elevated sorbitol and fructose levels in the rat sciatic nerves without affecting blood glucose level. Particularly, at doses of 0.1 mg/kg per day or higher, ranirestat normalized both sorbitol and fructose levels in the sciatic nerves of STZ-treated rats. Ranirestat (0.1-1.0 mg/kg per day) also improved the STZ-induced decrease in MNCV in a dose-dependent manner. This improvement correlated with the decrease of sorbitol and fructose levels in the rat sciatic nerves. These findings indicate that ranirestat improves MNCV via normalization of sorbitol and fructose accumulation in the sciatic nerve.
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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 | 0.000 | 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".