Capitalizing on diversity: an integrative approach towards the multiplicity of cellular mechanisms underlying myogenic responsiveness
Bibliographic record
Abstract
The intrinsic ability of resistance arteries to respond to transmural pressure is the single most important determinant of their function. Despite an ever-growing catalogue of signalling pathways that underlie the myogenic response, it remains an enigmatic mechanism. The myogenic response's mechanistic diversity has largely been attributed to 'hard-wired' differences across species and vascular beds; however, emerging evidence suggests that the mechanistic basis for the myogenic mechanism is, in fact, 'plastic'. This means that the myogenic response can change quantitatively (i.e. change in magnitude) and qualitatively (i.e. change in mechanistic basis) in response to environmental challenges (e.g. disease conditions). Consequently, understanding the dynamics of how the myogenic response capitalizes on its mechanistic diversity is key to unlocking clinically viable interventions. Using myogenic sphingosine-1-phosphate (S1P) signalling as an example, this review illustrates the remarkable plasticity of the myogenic response. We propose that currently unidentified 'organizational programmes' dictate the contribution of individual signalling pathways to the myogenic response and introduce the concept that certain signalling elements act as 'divergence points' (i.e. as the potential higher level regulatory sites). In the context of pressure-induced S1P signalling, the S1P-generating enzyme sphingosine kinase 1 serves as a divergence point, by orchestrating the calcium-dependent and -independent signalling pathways underlying microvascular myogenic responsiveness. By acting on divergence points, the proposed 'organizational programmes' could form the basis for the flexible recruitment and fine-tuning of separate signalling streams that underlie adaptive changes to the myogenic response and its distinctiveness across species and vascular beds.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".