The α4β1 (Very Late Antigen (VLA)-4, CD49d/CD29) and α5β1 (VLA-5, CD49e/CD29) Integrins Mediate β2 (CD11/CD18) Integrin-Independent Neutrophil Recruitment to Endotoxin-Induced Lung Inflammation
Bibliographic record
Abstract
The beta(2) integrin cell adhesion molecules (CAM) mediate polymorphonuclear leukocyte (PMNL) emigration in most inflamed tissues, but, in the lung, other yet to be identified CAMs appear to be involved. In Lewis rats, the intratracheal injection of Escherichia coli-LPS induced acute (6-h) PMNL accumulation in the lung parenchyma (280 x 10(6) by myeloperoxidase assay; PBS control = 35 x 10(6)) and bronchoalveolar lavage fluid (BALF = 27 x 10(6); PBS = 0.1 x 10(6)). Parenchymal accumulation was not inhibited by a blocking Ab to beta(2) integrins and only minimally inhibited (20.5%; p < 0.05) in BALF. We examined the role of alpha(4)beta(1) and alpha(5)beta(1) integrins and of selectins in this PMNL recruitment. Treatment with mAbs to alpha(4)beta(1) or alpha(5)beta(1), even in combination, had no effect on PMNL accumulation induced by intratracheal LPS. However, anti-alpha(4) combined with anti-beta(2) mAbs inhibited PMNL recruitment to the parenchyma by 56% (p < 0.001) and to BALF by 58% (p < 0.01). The addition of anti-alpha(5) mAb to beta(2) plus alpha(4) blockade inhibited PMNL accumulation further (by 79%; p < 0.05). In contrast, blockade of L-, P-, and E-selectins in combination or together with beta(2), alpha(4), and alpha(5) integrins had no effect. LPS-induced BALF protein accumulation was not inhibited by treatment with anti-beta(2) plus alpha(4) mAbs, but was prevented when alpha(5)beta(1) was also blocked. Thus, while selectins appear to play no role, alpha(4)beta(1) and alpha(5)beta(1) function as major alternate CAMs to the beta(2) integrins in mediating PMNL migration to lung and to pulmonary vascular and epithelial permeability.
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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) | 0.001 | 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".