Strain-specific differences in perivascular inflammation in lungs in two murine models of allergic airway inflammation
Bibliographic record
Abstract
Histological data show perivascular recruitment of inflammatory cells in lung inflammation. However, the process of perivascular inflammation is yet-to-be characterized in any systematic manner at cell and molecular levels. Therefore, we investigated impact of genetic background on perivascular inflammation in acute or chronic airway inflammation in different strains of mice. Further, to address molecular mechanisms of perivascular inflammation, we examined immunohistochemical expression of vascular adhesion protein-1 (VAP-1) in chronic airway inflammation. Histological scoring revealed time and strain specific differences in perivascular recruitment of inflammatory cells in chronic and acute airway inflammation (P < 0.05). The data show that A/J strain is significantly more susceptible for perivascular inflammation followed by BALB/c and C57BL/6, while C3H/HeJ strain showed no perivascular accumulation of inflammatory cells. Of the two strains examined for perivascular inflammation in acute airway inflammation, BALB/c showed more accumulation of inflammatory cells compared to C57BL/c. VAP-1 expression occurred in the endothelium of pulmonary arteries but not in alveolar septa or airways in the control as well as challenged mice. In the inflamed lungs from A/J mice, the VAP-1 staining in pulmonary arteries was more intense compared to the other strains. VAP-1 staining was generally observed throughout the pulmonary arterial wall in chronic lung inflammation. These data show that periarterial inflammation is influenced by the genetic background, and may be partially regulated by VAP-1.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".