Renal autoregulation is not impaired during early streptozotocin diabetes mellitus (DM) in Long‐Evans rats
Bibliographic record
Abstract
Diabetes is a leading cause of chronic kidney disease and increases susceptibility to blood pressure induced glomerular injury. Autoregulation is the only mechanism that protects the glomeruli when blood pressure fluctuates. Impaired autoregulation has been reported in early DM and attributed to increased nitric oxide (NO) but intact autoregulation has been demonstrated in established DM. We hypothesized that intrarenal NO increases in early DM with corresponding impairment of autoregulation and that neuronal NOS (nNOS) contributes to the increased NO. Male Long‐Evans rats were divided into two groups: one week diabetic (N=5) and one week intact (N=3). Diabetes was induced with streptozotocin (60mg/kg i.v.) and depot insulin was used to keep blood glucose ~20–24 mmol/L. Rats were anaesthetized (isoflurane) and their left kidneys were exposed. Renal perfusion pressure was forced with a servo‐controlled aortic occluder and the resulting renal blood flow was measured with an ultrasound flow probe during control, nNOS inhibition (L‐VNIO) and non selective NOS inhibition (L‐NAME). Renal autoregulation was intact in all animals except one intact animal. nNOS protein distribution was similar and was little response to either NOS inhibitor in all rats. We conclude that renal autoregulation is not impaired, and that intrarenal NO does not play a role in early diabetes in these rats. Supported by CIHR MOP‐102694.
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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 | 0.002 |
| 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".