α‐4/β‐1 and α‐L/β‐2 integrins mediate cytokine induced lung leukocyte‐epithelial adhesion and injury
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Injury to the alveolar epithelium is a critical feature of acute lung injury (ALI). Using a cytokine model of ALI we demonstrated previously that newly recruited mononuclear phagocytes (MNP) contributed to lung inflammation and injury. We hypothesized that cytokines delivered into the alveolar airspace would have multiple effects on the lung that may contribute to lung injury. EXPERIMENTAL APPROACH: Intratracheal cytokine insufflation and leukocyte adoptive transfer in vivo were combined with in vitro analyses of lung epithelial cell-MNP adhesion and injury. Lung inflammatory injury was assessed by histology, leukocyte infiltration, and release of LDH and RAGE. KEY RESULTS: Cytokine insufflation was associated with apparent MNP-epithelial adhesion, up-regulation of alveolar ICAM-1 and VCAM-1, and the release of LDH and RAGE into the bronchoalveolar lavage. Insufflation of small molecule integrin antagonists suppressed adhesion of MNP and modulated release of LDH and RAGE. Adoptive transfer of MNP purified from cytokine insufflated lungs into leukopenic rats demonstrated the requirement of MNP for release of LDH that was not induced by cytokine alone. Corroboration that disrupting the ICAM/LFA1 interaction or the VCAM/VLA4 interaction blocked MNP-epithelial cell interaction and injury was obtained in vitro using both blocking monoclonal antibodies and the small molecule integrin antagonists, BIO5192 and XVA143. CONCLUSIONS AND IMPLICATIONS: MNP recruited following cytokine insufflation contributed to lung injury. Further, integrin antagonists reduced alveolar epithelial cell injury induced during lung inflammation. Intratracheal delivery of small molecule antagonsists of leukocyte-epithelial adhesion that prevent lung injury may have significant clinical utility.
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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 | 0.001 |
| 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".