Aerosolization of cat dander using robotic vacuum cleaners
Bibliographic record
Abstract
Introduction: RMT has developed an automated dander aerosolization system using modified robotic vacuum cleaners to evaluate the allergic response to cat. This system aerosolizes dander shed on a carpet by live cats in our Fixed NEC (Naturalistic Exposure Chamber). Milled cat hair from a cannister on the exhaust of the vacuum cleaner and loaded on the carpet was aerosolized in our Mobile NEC. We report the validation of the stability and homogeneity of Fel d 1 levels in the two chambers. Method: Temporal stability and spatial distribution of allergen were characterized by measuring airborne Fel d 1 during multiple 2-hour tests. Airborne Fel d 1 was collected using sampling pumps at 3 locations and quantified by ELISA. The sizes and concentrations of aerosolized particulate were measured with an airborne particle counter (Lighthouse, Handheld 3016). Results: In the Fixed NEC, average room Fel d 1 concentration during the 2-hour tests was 55 ng/m3 ± 20% and it was evenly distributed across the room, with no statistical differences between three sampling locations. In the mobile NEC, the time-average concentration was 100 ± 24 ng/m3 (mean ± SD) and was dependent on the amount of cat hair on the carpet. There was no difference in the concentrations of particles smaller than 10 μm (aerodynamic particle diameter) but higher concentrations of particles larger than 10 μm occurred in the Mobile NEC than in the Fixed NEC. Conclusion: This automated method of dander aerosolization was found to provide stable and homogeneous Fel d 1 levels in both chambers. The system used in a Naturalistic Exposure Chamber provides a controlled but naturalistic means of exposure to pet dander for clinical allergy research.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".