Effect of mechanical deformation of neutrophils on their CD18/ICAM-1-dependent adhesion
Bibliographic record
Abstract
Mechanical deformation of polymorphonuclear leukocytes (PMN) changes their expression of the surface adhesion molecule CD11b/CD18. We tested the hypothesis that mechanical deformation of PMN enhances their adhesiveness. Purified human PMN were deformed through either 5- or 3-microm polycarbonate membrane filters and allowed to adhere to 96-well plates coated with human recombinant intercellular adhesion molecule-1 (ICAM-1). Flow cytometric studies showed that deformation of PMN increased CD11b/CD18 expression (P < 0.01). PMN adhesion to ICAM-1-coated plates was dependent on the magnitude of cell deformation (5 microm, 63.8 +/- 8.1%, P < 0.04; 3 microm, 232.4 +/- 20.9%, P < 0.01). Priming of PMN (0.5 nM N-formyl-methionyl-leucyl-phenylalanine) before deformation (5 microm) increased PMN adhesion (63.8 +/- 8.1 vs. 105.3 +/- 16.4%; P < 0.04). Stimulation (5% zymosan-activated plasma) of PMN after deformation resulted in increased adhesion, and the degree of increase was dependent on the magnitude of PMN deformation (stimulation, 50.6 +/- 4%; 5-microm filtration and stimulation, 62.9 +/- 6.6%; 3-microm filtration and stimulation, 249.9 +/- 24.2%; P < 0.01). This study shows that mechanical deformation of PMN causes an increase in PMN adhesiveness to ICAM-1 that was enhanced by both priming of PMN before deformation and stimulation after cell deformation.
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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 | 0.000 | 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 | 0.000 |
| 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".