Role and Molecular Mechanisms of Pericytes in Regulation of Leukocyte Diapedesis in Inflamed Tissues
Bibliographic record
Abstract
Leukocyte recruitment is a hallmark of the inflammatory response. Migrating leukocytes breach the endothelium along with the vascular basement membrane and associated pericytes. While much is known about leukocyte-endothelial cell interactions, the mechanisms and role of pericytes in extravasation are poorly understood and the classical paradigm of leukocyte recruitment in the microvasculature seldom adequately discusses the involvement of pericytes. Emerging evidence shows that pericytes are essential players in the regulation of leukocyte extravasation in addition to their functions in blood vessel formation and blood-brain barrier maintenance. Junctions between venular endothelial cells are closely aligned with extracellular matrix protein low expression regions (LERs) in the basement membrane, which in turn are aligned with gaps between pericytes. This forms preferential paths for leukocyte extravasation. Breaching of the layer formed by pericytes and the basement membrane entails remodelling of LERs, leukocyte-pericyte adhesion, crawling of leukocytes on pericyte processes, and enlargement of gaps between pericytes to form channels for migrating leukocytes. Furthermore, inflamed arteriolar and capillary pericytes induce chemotactic migration of leukocytes that exit postcapillary venules, and through direct pericyte-leukocyte contact, they induce efficient interstitial migration to enhance the immunosurveillance capacity of leukocytes. Given their role as regulators of leukocyte extravasation, proper pericyte function is imperative in inflammatory disease contexts such as diabetic retinopathy and sepsis. This review summarizes research on the molecular mechanisms by which pericytes mediate leukocyte diapedesis in inflamed tissues.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".