Detection of 3rd mechanism in renal blood flow via high‐resolution, time‐varying spectral analysis of records subjected to chirp forcing of blood pressure
Bibliographic record
Abstract
It is generally agreed that renal autoregulation is mediated by myogenic (0.01–0.3 Hz) and tubuloglomerular feedback (0.02–0.05 Hz) mechanisms. Recently a potential 3rd mechanism that operates at ~0.01 Hz has been reported based upon step response experiments. To begin its characterization, 6 anesthetized, normotensive rats were instrumented for blood pressure (BP) and renal blood flow (RBF) measurements. We forced BP with linearly increasing frequencies (frequency range 0.001 – 0.02 Hz, amplitude ± 7 mmHg). The motivation for using linearly increasing frequencies in BP forcing is that if such a third mechanism exists, it should resonate at its characteristic frequency in the resultant RBF recordings and that RBF power at that frequency should be amplified beyond that in BP. Comparison between the time‐varying spectral amplitudes of BP versus RBF shows that when the BP forcing frequency passed ~0.01 Hz, there was greater spectral amplitude at the same frequency in RBF than in BP. The significance of this increase of spectral amplitude in RBF was verified by testing against white noise signals. Any spectral power above white noise spectral amplitude suggests that such oscillation does exist and not a consequence of some random occurrence. We conclude that a third mechanism contributes to autoregulation of RBF and that it resonates at ~0.01 Hz. Funded by CIHR & NHLBI
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".