Myosin heavy chain expression in renal afferent and efferent arterioles: relationship to contractile kinetics and function
Bibliographic record
Abstract
The physiological role of smooth muscle myosin heavy chain (MHC) isoform diversity is poorly understood. The expression of MHC-B, which contains an insert at the ATP binding pocket, has been linked to enhanced contractile kinetics. We recently reported that the renal afferent arteriole exhibits an unusually rapid myogenic response and that its kinetic features allow this vessel to modulate tone in response to alterations in systolic blood pressure. In the present study, we examined MHC expression patterns in renal afferent and efferent arterioles. These two vessels regulate glomerular inflow and outflow resistances and control the pressure within the intervening glomerular capillaries (PGC). Whereas the afferent arteriole must respond rapidly to increases in blood pressure, the efferent arteriole plays a distinctly different role, maintaining a tonic elevation in outflow resistance to preserve function when renal perfusion is compromised. Using RT-PCR, Western analysis, and immunofluorescence imaging of intact isolated arterioles, we found that the afferent arteriole predominantly expresses the MHC-B isoform, whereas the efferent arteriole expresses only the slower-cycling MHC-A isoform. We examined the kinetics of angiotensin II- and norepinephrine-induced vasoconstriction and found that the afferent arteriole responds approximately 3-fold faster than the efferent arteriole. Our findings thus point to the renal microcirculation as a unique and important example of smooth muscle adaptation in regard to MHC isoform expression and physiological function.
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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".