Characterization of Endothelial Cilia Distribution During Cerebral-Vascular Development in Zebrafish ( <i>Danio rerio</i> )
Bibliographic record
Abstract
Objective— Endothelial cells (ECs) sense and respond to flow-induced mechanical stress, in part, via microtubule-based projections called primary cilia. However, many critical steps during vascular morphogenesis occur independent of flow. The involvement of cilia in regulating these stages of cranial vascular morphogenesis is poorly understood because cilia have not been visualized in primary head vessels. The objective of this study was to investigate involvement of cilia in regulating the early stages of cranial vascular morphogenesis. Approach and Results— Using high-resolution imaging of the Tg(kdrl:mCherry-CAAX ) y171 ;(bactin::Arl13b:GFP ) zebrafish line, we showed that cilia are enriched in the earliest formed cranial vessels that assemble via vasculogenesis and in angiogenic hindbrain capillaries. Cilia were more prevalent around the boundaries of putative intravascular spaces in primary and angiogenic vessels. Loss of cardiac contractility and blood flow, because of knockdown of cardiac troponin T type 2a ( tnnt2a ) expression, did not affect the distribution of cilia in primary head vasculature. In later stages of development, cilia were detected in retinal vasculature, areas of high curvature, vessel bifurcation points, and during vessel anastomosis. Loss of genes crucial for cilia biogenesis ( ift172 and ift81 ) induced intracerebral hemorrhages in an EC-autonomous manner. Exposure to high shear stress induced premature cilia disassembly in brain ECs and was associated with intracerebral hemorrhages. Conclusions— Our study suggests a functional role for cilia in brain ECs, which is associated with the emergence and remodeling of the primary cranial vasculature. This cilia function is flow-independent, and cilia in ECs are required for cerebral-vascular stability.
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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 | 0.000 |
| 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".