Frequency modulation of renal myogenic autoregulation by perfusion pressure
Bibliographic record
Abstract
Recent studies of renal autoregulation have shown modulation of the faster myogenic mechanism by the slower tubuloglomerular feedback and that the modulation can be detected in the dynamics of the myogenic mechanism. Conceptual and empirical considerations suggest that perfusion pressure may modulate the myogenic mechanism, although this has not been tested to date. Here we present data showing that the myogenic operating frequency, assessed by transfer-function analysis, varied directly as a function of perfusion pressure in the hydronephrotic kidney perfused in vitro over the range from 80 to 140 mmHg. A similar result was obtained in intact kidneys in vivo when renal perfusion pressure was altered by systemic injection of N(G)-nitro-L-arginine methyl ester (L-NAME). When perfusion pressure was not allowed to increase, L-NAME did not affect the myogenic operating frequency despite equivalent reduction of renal vascular conductance. Blood-flow dynamics were assessed in the superior mesenteric artery before and after L-NAME. In this vascular bed, the operating frequency of the myogenic mechanism was not affected by perfusion pressure. Thus the operating frequency of the renal myogenic mechanism is modulated by perfusion pressure independently of tubuloglomerular feedback, and the data suggest some degree of renal specificity of this response.
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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 | 0.000 | 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".