Molecular regulators of leucocyte chemotaxis during inflammation
Bibliographic record
Abstract
A fundamental feature of any immune response is the movement of leucocytes from one site in the body to another to provide effector functions. Therefore, elucidating the molecular mechanisms underlying the migration of leucocytes from the blood to tissues is critical to our understanding of immune function during inflammation. The classic steps of leucocyte trafficking involve leucocyte tethering and rolling on vessel walls of the vasculature, followed by firm adhesion to the endothelium. Recent evidence suggests that upon adhering, leucocytes crawl within the vessels before transmigrating across vessel walls and crawling into targeted tissues. The directed nature of the crawling events is orchestrated by a complex array of soluble factors and molecular regulators in combination with the local intravascular and extracellular environment. In fact, this process is known as chemotaxis and orientates cell movement in relation to the ligand gradient. Several signalling pathways have been proposed to be involved in this gradient-sensing and amplification process, but the best studied, discussed in detail here, is the phosphatidylinositol 3-kinase pathway. Substantial progress has been made in understanding how cells roll and adhere in blood vessels; however, how cells crawl in blood vessels, emigrate, and then crawl in tissues has received much less attention. Therefore, the focus of this review is to provide recent insights into molecular mechanisms and cellular processes that mediate leucocyte crawling in blood vessels and tissues during the inflammatory response.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".