Dose Dependency of Adjuvant Activity of Particulate Matter from Five European Sites in Three Seasons in an Ovalbumin–Mouse Model
Bibliographic record
Abstract
Various particulate matter (PM) samples were tested for their adjuvant potency in an animal model of allergy (ovalbumin) in the European Union study entitled Respiratory Allergy and Inflammation Due to Ambient Particles. Coarse and fine ambient particles were collected during spring, summer, and winter in Rome, Oslo, Lodz, Amsterdam, and De Zilk. De Zilk, at the Dutch seaside, has mainly westerly winds and served as a negative pollution control. EHC-93 (Ottawa dust) was used as a positive control. We studied the adjuvant potency of the particle antibody responses to ovalbumin and histopathological changes in the lung. After a sensitization phase by coexposure to EHC-93 and ovalbumin, the antibody response to ovalbumin and inflammatory responses in the lung were huge. There was more adjuvant activity in reaction to 9-mg/ml samples than to 3-mg/ml samples. A best-fit analysis of these samples shows that the ambient coarse and fine particles at these sites, in combination with allergens, have severe to mild adjuvant activity in the order Lodz, Rome, Oslo, and Amsterdam. A high dose of the fine fraction was more potent than a high dose of the coarse fraction, except at De Zilk, where the reverse was true. Spring and winter PM was more potent than summer PM. Depending on the site, either a water-soluble or a water-insoluble fraction was responsible for the adjuvant activity. A concentration of 3 mg/ml is effective for screening high-activity samples, as is a concentration of 9 mg/ml for screening low-activity samples in the ovalbumin-mouse model.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".