Human fractalkine mediates leukocyte adhesion but not capture under physiological shear conditions; a mechanism for selective monocyte recruitment
Bibliographic record
Abstract
Fractalkine is a unique chemokine possessing a long mucin-like stalk and a transmembrane region that has been proposed to act as an adhesion molecule. We investigated the ability of fractalkine to recruit leukocytes from whole blood, using an immobilized fractalkine fusion protein in the parallel-plate flow-chamber assay. Significant adhesion of leukocytes to fractalkine peaked at 2 dynes/cm(2) but was minimal at 10 dynes/cm(2). In contrast, VCAM-1 could recruit cells from whole blood at 10 dynes/cm(2). Co-immobilization of fractalkine and VCAM-1 at 10 dynes/cm(2) resulted in a twofold increase in adherent cells compared with VCAM-1 alone, suggesting that fractalkine can mediate adhesion at high shear if combined with a molecule that can mediate leukocyte tethering. Pretreatment of blood with pertussis toxin eliminated this increase in adhesion, implicating intracellular signaling in fractalkine-mediated mechanisms of adhesion to co-immobilized fractalkine/VCAM-1. Analysis of the cell types recruited to fractalkine alone at low shear, or to fractalkine and VCAM-1 at 10 dynes/cm(2), revealed that monocytes were recruited to fractalkine with the highest specificity. In conclusion, fractalkine is unlikely to act alone at shear forces found in most vascular beds where it most likely co-operates with tethering molecules, e.g. VCAM-1, in the recruitment of monocytes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".