Angiotensin II (ANG II), renal perfusion pressure (RPP) and synchronization of cortical blood flow studied using laser speckle perfusion imaging (LSPI)
Bibliographic record
Abstract
Renal autoregulatory mechanisms, myogenic (MR) and tubuloglomerular feedback (TGF), are synchronized among neighboring nephrons. Using LSP I (Moor FLPI) we examined on a physiologically relevant scale the contribution of this synchronization, or coupling, to regulation and autoregulation of renal blood flow. In particular we explore the roles of RPP and ANG II on synchronization at the surface of the kidney cortex. A circular spatial filter (r=562±87 μm) was applied to each frame. Time‐frequency spectra were computed for each pixel and synchronization was assessed by frequency locking within MR and TGF bands. Five male Long Evans rats were studied during spontaneous pressure (SPN, 110±13 mmHg), reduced RPP (LP, 61±2), captopril (1 mg/kg, CAP, 89±12), and LP+CAP, 61±2 mmHg. Surface flux was 1679+227, 1386+172, 2076+126 and 1807+156 units, respectively. Synchronization occurred in both MR and TGF frequency bands at SPN. As expected power in these bands was markedly reduced at LP as was synchronization. Instead, a single dominant frequency developed and was widely synchronized. LP+CAP also showed reduced MR and TGF power, but neither a single dominant frequency nor wide synchronization. The data show that reduced RPP during ACE inhibition reduces synchronization of MR and TGF and suggest that ANG II is a major regulator of the radius of synchronization. Funded 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) | 0.000 | 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".