Pre-Diabetes Augments Neuropeptide Y1- and α1-Receptor Control of Basal Hindlimb Vascular Tone in Young ZDF Rats
Bibliographic record
Abstract
BACKGROUND: Peripheral vascular disease in pre-diabetes may involve altered sympathetically-mediated vascular control. Thus, we investigated if pre-diabetes modifies baseline sympathetic Y(1)-receptor (Y(1)R) and α(1)-receptor (α(1)R) control of hindlimb blood flow (Q(fem)) and vascular conductance (VC). METHODS: Q(fem) and VC were measured in pre-diabetic ZDF rats (PD) and lean controls (CTRL) under infusion of BIBP3226 (Y(1)R antagonist), prazosin (α(1)R antagonist) and BIBP3226+prazosin. Neuropeptide Y (NPY) concentration and Y(1)R and α(1)R expression were determined from hindlimb skeletal muscle samples. RESULTS: Baseline Q(fem) and VC were similar between groups. Independent infusions of BIBP3226 and prazosin led to increases in Q(fem) and VC in CTRL and PD, where responses were greater in PD (p<0.05). The percent change in VC following both drugs was also greater in PD compared to CTRL (p<0.05). As well, Q(fem) and VC responses to combined blockade (BIBP3226+prazosin) were greater in PD compared to CTRL (p<0.05). Interestingly, an absence of synergistic effects was observed within groups, as the sum of the VC responses to independent drug infusions was similar to responses following combined blockade. Finally, white and red vastus skeletal muscle NPY concentration, Y(1)R expression and α(1)R expression were greater in PD compared to CTRL. CONCLUSIONS: For the first time, we report heightened baseline Y(1)R and α(1)R sympathetic control of Q(fem) and VC in pre-diabetic ZDF rats. In support, our data suggest that augmented sympathetic ligand and receptor expression in pre-diabetes may contribute to vascular dysregulation.
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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) | 0.001 | 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) | 0.002 | 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".