Angiostatin (ANG) Inhibits Acute Lung Inflammation in Mice
Bibliographic record
Abstract
ALI is characterized by the migration of activated neutrophils into lungs and causes significant mortality. Although necessary, activated neutrophils cause significant tissue damage and delay repair of inflamed lungs. Because the mechanisms of neutrophil migration and pathogenesis of ALI are not fully understood, we investigated effect of ANG in a mouse model of E. coli lipopolysaccharide (LPS) induced ALI. ANG expression was increased in inflamed lung extracts as well as BAL supernatant (P<0.007). ANG treatment of the LPS-treated mice decreased total cell counts, and protein concentration (P<0.05) but increased (P<0.05) apoptotic neutrophils in BAL and decreased (P<0.05) myeloperoxidase in lung extracts. The protein and mRNA expression of IL-1β, KC, MCP-1 and MIP-1α was not altered in LPS+ANG mice. Immunohistochemistry of lungs from LPS+ANG mice revealed a lack of staining for phosphorylated p38 MAPK. Time dependent phase contrast as well as diffraction enhanced imaging of live mice using synchrotron radiation (performed at Canadian Light Source facility) shows reduction in lung fluid accumulation in LPS+ANG treated mice when compared to LPS treated mice. We conclude that ANG expression is increased in inflamed lungs and exogenous ANG treatment inhibits neutrophil migration into the inflamed lungs, induces apoptosis in neutrophils and inhibits lung inflammation. ANG treatment may hasten repair processes in inflamed lungs. Grant Funding Source: NSERC Discovery Grant, CIHR, NRC
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 | 0.001 | 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 | 0.001 |
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".