Angiostatin inhibits acute lung injury in a mouse model
Bibliographic record
Abstract
Acute lung injury is marked by profound influx of activated neutrophils, which have delayed apoptosis, along with fluid accumulation that impairs lung function and causes high mortality. Inflammatory and antimicrobial molecules, such as reactive oxygen species from activated neutrophils with prolonged lifespan, cause tissue damage and contribute to lung dysfunction. Angiostatin, an endogenous antiangiogenic molecule, is expressed in the lavage fluid of patients with acute respiratory distress syndrome and modifies neutrophil infiltration in a mouse model of peritonitis. Our aim was to investigate the therapeutic role of angiostatin in acute lung injury. We analyzed bronchoalveolar lavage and lung tissues from C57BL/6 mouse model of Escherichia coli LPS-induced acute lung injury to assess the effects of angiostatin treatment. Subcutaneous angiostatin administered at 5 h after LPS treatment reduces histological signs of inflammation, protein accumulation, lung Gr1+ neutrophils, myeloperoxidase activity, and expression of phosphorylated p38 MAPK in lung tissues and peripheral blood neutrophils, while increasing the number of apoptotic cells in the lungs without affecting the levels of macrophage inflammatory protein-1 α, IL-1β, keratinocyte chemoattractant, and monocyte chemoattractant protein-1 in lavage and lung homogenates at 9 and 24 h after LPS treatment. In contrast, angiostatin administered intravenously 5 h after LPS treatment did not reduce histological sign of inflammation, BAL cell recruitment, and protein concentration at 9 h of LPS treatment. We conclude that angiostatin administered subcutaneously after LPS challenge inhibits acute lung inflammation up to 24 h after LPS treatment.
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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) | 0.001 | 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".