The relationship between blood pressure and sciatic nerve blood flow velocity in rats with insulin-treated experimental diabetes
Bibliographic record
Abstract
Peripheral nerve blood flow (NBF) does not autoregulate but, instead, responds passively to changes in mean arterial pressure (MAP). How this relationship is impacted by insulin-treated experimental diabetes (ITED) is unknown. We tested the hypothesis that ITED will reduce NBF across a range of MAP in Sprague Dawley rats. Following 10 weeks of control or ITED conditions, conscious MAP (tail-cuff) was measured, and under anaesthesia, the MAP (carotid artery catheter, pressure transducer) and NBF (Doppler ultrasound, 40 MHz) responses to sodium nitroprusside (60 µg/kg) and phenylephrine (12 µg/kg) infusion were recorded (regression equations for MAP vs NBF were created for each rodent). Thereafter, motor nerve conduction velocity (MNCV) and nerve vascularization (haematoxylin and eosin stain) were determined. Conscious MAP was higher and MNCV was lower in the ITED group (p < 0.01). In response to drug infusions, the ΔMAP and ΔNBF were similar between groups (p ≥ 0.18). Estimated conscious NBF (based on substituting conscious MAP values into each individual regression equation) was greater in the ITED group (p < 0.01). Sciatic nerve vascularization was similar between groups (p ≥ 0.50). In contrast to the hypothesis, NBF was not reduced across a range of MAP. In spite of increased estimated conscious NBF values, MNCV was reduced in rats with ITED.
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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) | 0.000 | 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".