Optimization of Route of Administration for Coexposure to Ovalbumin and Particle Matter to Induce Adjuvant Activity in Respiratory Allergy in the Mouse
Bibliographic record
Abstract
Epidemiological and experimental studies have not only shown that air pollution induces increased pulmonary morbidity, and mortality, but also that air pollution components may potentiate allergic responses. The respiratory allergy model to ovalbumin in the mouse has been shown a useful tool to characterize the adjuvant potency of air pollution components. However, the choice for the most effective route of administration for testing small amounts of air pollution component is hampered by the diversity of routes of administration used. To test the adjuvant activity of airborne particles (Ottawa dust EHC-93), we studied the optimal route of respiratory administration: intranasally (in) and aerosol (aero) in comparison with responses observed by intraperitoneal (ip) with diesel exhaust particles (DEP) as a positive control. Our results show that the combination of in/aero with ovalbumin caused almost similar immunoglobulin (Ig)E and inflammatory responses compared to the ip/aero. In/in application induced less responses for IgE, less inflammation in the lung, and less increased numbers of eosinophils in the bronchoalveolar lavage (BAL). This response increased dramatically when ovalbumin was coadministered with DEP. Subsequently, EHC-93, which is made up of airborne particles, was tested via the in/in route of administration. EHC-93 induced similar IgE responses, inflammation, and eosinophilic response in BAL compared to DEP. In addition, EHC-93 increased the airway responsiveness of the ovalbumin-sensitized mice measured in unrestrained condition and not in nonsensitized control mice. It is concluded that intranasal sensitization with intranasal challenge with airborne particles (EHC-93) is an effective route of administration to show potency of adjuvant activity of airborne particles.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".