The presence of LPS in OVA inhalations affects airway inflammation and AHR but not remodeling in a rodent model of asthma
Bibliographic record
Abstract
Ovalbumin (OVA) is the most frequently used allergen in animal models of asthma. Lipopolysaccharide (LPS) contaminating commercial OVA may modulate the evoked airway inflammatory response to OVA. However, the effect of LPS in OVA on airway remodeling, especially airway smooth muscle (ASM) has not been evaluated. We hypothesized that LPS in commercial OVA may enhance allergen-induced airway inflammation and remodeling. Brown Norway rats were sensitized with OVA on day 0. PBS, OVA, or endotoxin-free OVA (Ef-OVA) was instilled intratracheally on days 14, 19, 24. Bronchoalveolar lavage (BAL) fluid, lung, and intrathoracic lymph node tissues were collected 48 h after the last challenge. Immunohistochemistry for α-smooth muscle actin, Periodic-Acid-Schiff staining, and real-time qPCR were performed. Airway hyperresponsiveness (AHR) was also measured. BAL fluid macrophages, eosinophils, neutrophils, and lymphocytes were increased in OVA-challenged animals, and macrophages and neutrophils were significantly lower in Ef-OVA-challenged animals. The ASM area in larger airways was significantly increased in both OVA and Ef-OVA compared with PBS-challenged animals. The mRNA expression of IFN-γ and IL-13 in lung tissues and IL-4 in lymph nodes was significantly increased by both OVA and Ef-OVA compared with PBS and were not significantly different between OVA and Ef-OVA. Monocyte chemoattractant protein (MCP)-1 in BAL fluid and AHR were significantly increased in OVA but not in Ef-OVA. LPS contamination in OVA contributes to the influx of macrophages and MCP-1 increase in the airways and to AHR after OVA challenges but does not affect OVA-induced Th1 and Th2 cytokine expression, goblet cell hyperplasia, and ASM remodeling.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".