Development of vascular myogenic responses in zebrafish
Bibliographic record
Abstract
ABSTRACT The vascular system is placed under enormous stress at the onset of cardiac contractility and blood flow. Nascent blood vessel tubes initially consist of a thin endothelial wall and rapidly acquire support from mural cells (pericytes and vascular smooth muscle cells; vSMCs). Following their association with vessels, mural cells acquire vasoactive ability (contraction and relaxation). However, we have little information as to when this vasoactivity first develops, and the extent to which each mural cell type contributes to vascular tone regulation during development. For the first time in an in vivo system, we highlight the dynamic changes in mural cell vasoactivity during development. We assess mural cell vasoactivity in the early zebrafish cerebral vasculature in response to pharmacological agents. We determine that pericyte-covered vessels constrict and dilate at 4 days post fertilization (dpf) but not at 6 dpf. The prostaglandin EP4 receptor contributes to pericyte-covered vessel dilation at 4 dpf. In contrast, vSMC-covered vessels constrict but do not dilate at 4 dpf. At 6 dpf, vSMC-covered vessels continue to constrict but only dilate from a pre-constricted state. Using genetic ablation, we demonstrate that mural cell contraction and relaxation is an active response by pericytes and vSMCs. Thus, we show that both pericytes and vSMCs have the ability to regulate cerebral vascular tone but at different stages of development. Pericytes are involved in regulating vessel diameters prior to the maturation of the vSMCs. Once vSMCs mature, pericytes are no longer active, and only vSMCs regulate vascular tone in the developing embryonic brain of zebrafish. The onset of vasoactivity of vSMCs corresponds to the development of increased neuronal activity and neurovascular coupling.
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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 | 0.001 | 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 | 0.001 |
| 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".