Leukocyte‐Specific Protein 1 (LSP1) regulates neutrophil migration in acute lung inflammation
Bibliographic record
Abstract
LSP1, an F‐actin binding protein, plays a role in neutrophil recruitment in peritoneum. Because mechanisms of excessive migration of activated neutrophils, credited with tissue damage, are not fully understood, we explored the hitherto unknown expression and role of LSP1 in neutrophil migration in acute lung inflammation (ALI). We induced ALI through intranasal E. coli LPS (80μg) in wild type 129/SVJ (WT) and LSP1 deficient (LSP −/− ) mice. WT (n=10) and LSP −/− (n=11) mice showed significant neutrophilia and more neutrophils in broncho‐alveolar lavage (BAL) at 9 hr post‐LPS challenge compared to respective saline‐treated controls (WT=7; LSP −/− =10). BAL neutrophil numbers were higher in LPS‐treated WT mice compared to LSP −/− mice (P<0.001). Lung myeloperoxidase and Gr1+ were higher in LPS‐treated WT compared to the LSP −/− mice (P<0.05). Lung tissue and BAL fluid KC, MCP1, MIP1α and MIP1β concentration and vascular permeability were not different between LPS‐treated WT and LSP −/− mice but TNFα concentration was higher in LPS‐treated WT mice. LSP1 expression was increased in inflamed lungs from LPS‐treated mice, and autopsied lungs from septic humans, compared to their respective controls. H&E staining showed more septal congestion in LPS‐treated WT mice compared to LSP −/− mice. These data show that LSP1 expression is modulated in ALI and that LSP1 deficiency reduces neutrophil migration into ALI. Funding: NSERC
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 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) | 0.001 | 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".