Renal autoregulation dynamics monitored across the renal surface
Bibliographic record
Abstract
We extended dynamic analysis of renal autoregulation across a spatial surface using laser speckle perfusion imaging (LSPI) of an area of the renal cortex. Anaesthetized (isoflurane) male Long‐Evans rats (N=8) had left kidneys exposed and an occluder placed around the renal artery. Monitoring included LSPI of the renal cortex, renal blood flow (RBF) and blood pressure (BP) under BP forcing during NOS inhibition by intrarenal infusion of L‐NAME (10 μg/min × 20 min then 3 μg/min i.r.a.) followed by rho‐kinase inhibition by intrarenal infusion of Y‐27632 (to achieve 10 μmol/L in RBF). Transfer function analysis was performed with either RBF or LSPI as output signals and BP as the input. Using both pairs of transfer functions, we found a significant increase in coherence (0.05 – 0.08 Hz) and a significant decrease in gain slope (0.05 – 0.15 Hz) and phase (0.1 Hz) from L‐NAME to Y‐27632. These results indicate LSPI detected decreased tubuloglomerular feedback and myogenic activity during rho‐kinase inhibition. Additionally, using LSPI we were able to identify changes at all locations within the imaging window, and using time‐varying analysis we were able to identify them temporally. We conclude that transfer function analysis of LSPI can be used to monitor renal autoregulation across spatial and temporal dimensions. This work was performed under CIHR Grant MOP‐102694, and CS was supported by an AHA predoctoral fellowship.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".