Altered hemodynamics and vascular reactivity in a mouse model with severe pericyte deficiency
Bibliographic record
Abstract
Pericytes are the mural cells of the microvascular network that are in close contact with underlying endothelial cells. Endothelial-secreted PDGFB leads to recruitment of pericytes to the vessel wall, but this is disrupted in Pdgfb ret/ret mice when the PDGFB retention motif is deleted. This results in severely reduced pericyte coverage on blood vessels. In this study, we investigated vascular abnormalities and hemodynamics in Pdgfb ret/ret mice throughout the cerebrovascular network and in different cortical layers by in vivo two-photon microscopy. We confirmed that Pdgfb ret/ret mice are severely deficient in pericytes throughout the vascular network, with enlarged brain blood vessels and a reduced number of vessel branches. Red blood cell velocity, linear density, and tube hematocrit were reduced in Pdgfb ret/ret mice, which may impair oxygen delivery to the tissue. We also measured intravascular PO 2 and found that concentrations were higher in cortical Layer 2/3 in Pdgfb ret/ret mice, indicative of reduced blood oxygen extraction. Finally, we found that Pdgfb ret/ret mice had a reduced capacity for vasodilation in response to an acetazolamide challenge during functional MRI imaging. Taken together, these results suggest that severe pericyte deficiency can lead to vascular abnormalities and altered cerebral blood flow, reminiscent of pathologies such as arteriovenous malformations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".