CD43 Deficiency Has No Impact in Competitive In Vivo Assays of Neutrophil or Activated T Cell Recruitment Efficiency
Bibliographic record
Abstract
Using noncompetitive methodologies comparing CD43(+/+) and CD43(-/-) mice, it has been reported that CD43(-/-) leukocytes exhibit reduced recruitment efficiency to sites of inflammation. More recent analyses demonstrate that CD43 on activated T cells can function as an E-selectin ligand (E-SelL) in vitro, suggesting that CD43 might promote rolling interactions during recruitment of leukocytes and account for the reported recruitment deficits in CD43(-/-) T cells and neutrophils in vivo. Internally controlled competitive in vivo methods using fluorescent tracking dyes were applied to compare recruitment efficiency of CD43(+/+) vs CD43(-/-) activated T cells to inflamed skin and of peripheral blood neutrophils to inflamed peritoneum. A simple CFSE perfusion method was developed to distinguish arterial/venous vasculature and confirm appropriate extravasation through venules in a Con A-induced cutaneous inflammation model. In vivo recruitment of peripheral blood neutrophils to inflamed peritoneum was core 2 GlcNAcT-I dependent, but recruitment efficiency was not influenced by absence of CD43. There were also no significant differences in core 2 GlcNAcT-I-dependent, selectin-dependent, cutaneous recruitment of activated T cells from CD43(+/+) and congenic CD43(-/-) mice in either B6 or P-selectin(-/-) recipients despite biochemical confirmation that a CD43-specific E-SelL was present on activated T cells. We conclude that recruitment of neutrophils and activated T cells in these in vivo models is not influenced by CD43 expression and that if CD43 on activated T cells performs an E-SelL function in vivo, it contributes in a limited physiological context.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".