Pericyte-mediated regulation of angiogenesis during cutaneous wound healing in adult zebrafish
Bibliographic record
Abstract
Pericytes are mural cells that wrap small caliber vessels, playing a crucial role in stabilizing vascular structure. Upon induction of angiogenesis, pericytes were thought to detach from the vessel wall, thereby facilitating the sprouting of endothelial cells (ECs). However, the precise roles of pericytes in regulating angiogenesis still remain elusive. Here, we demonstrate, by performing live-imaging of adult zebrafish, that pericytes actively regulate angiogenesis during wound healing. We generated a zebrafish line which enables the conditional ablation of mural cells, including pericytes, and analyzed cutaneous wound angiogenesis. Loss of pericytes significantly increased the number of sprouting events of ECs and promoted their proliferation, resulting in the formation of dense and disorganized blood vessel networks. Furthermore, in the absence of pericytes, the injured vessels showed abnormal vessel elongation, thereby generating ectopic vascular networks. These results suggest that pericytes play an active role for generating functional blood vessels during wound angiogenesis. Live imaging in adult zebrafish reveals that pericytes actively regulate wound angiogenesis by restricting endothelial cell proliferation, sprouting, and directional migration, thereby ensuring the formation of a well-organized vascular network.
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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 | 0.000 | 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".