Laser‐speckle analysis of renal cortical perfusion – regional perfusion dynamics
Bibliographic record
Abstract
Tubuloglomerular feedback and a myogenic mechanism act on afferent arterioles to mediate autoregulation of renal blood flow (RBF) and their actions may extend beyond single nephrons. We tested for physiologically significant coupling among neighboring nephrons. Cortical surface perfusion was assessed by laser‐speckle perfusion imaging at 1 Hz. Time series from 5 regions of interest (ROI, ~0.5 × 0.5 mm) were extracted. RBF (Transonic 400) was systematically altered by manipulation of perfusion pressure, by captopril (−/+ ANG II), and by L‐NAME. Perfusion in the ROIs (units) was linear in RBF (slope = 1120 ± 63 units × min/mL, r2 = 0.94 ± 0.03, n=5). Power spectra of the ROI time series contain the same information as RBF spectra and spectra of all ROIs are very similar. Comparing adjacent to remotely located ROIs by spectra of difference series, by linear cross spectra, and by nonlinear cross bi‐spectra showed that coupling decreased with separation between ROIs. The cross bi‐spectral coupling index declined with separation of ROIs: adjacent = 0.269, medium = 0.170, remote = 0 during control (greater index = greater coupling). ANG II infusion increased the spread of coupling: adjacent = 0.133, medium = 0.034, remote = 0.014. Local coupling suggests that autoregulation may involve coordinate action of clusters of nephrons. Coupling may be regulated by ANG II. Funded by Heart & Stroke Fdn of BC & Yukon, & Victoria Fdn
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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) | 0.001 | 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".