Acellular filtrate of a microbial-based cleaning product potentiates house dust mite allergic lung inflammation
Bibliographic record
Abstract
Microbial-based cleaning products (MBCPs) contain bacteria and chemical constituents. They are used in consumer applications such as odor reduction, unclogging drains, and surface cleaning. To determine the capacity of a model MBCP to contribute to acute allergic lung inflammation, a two-week repeated exposure regimen was used. Mice were exposed by endotracheal instillation to saline alone, MBCP alone, house dust mites (HDM) alone, or sequentially (i.e., MBCP followed by HDM, HDM followed by MBCP, or HDM + MBCP followed by HDM). Both whole MBCP and acellular MBCP filtrate were investigated, and showed minimal differences in the endpoints examined. HDM exposure caused pulmonary perivascular inflammation, bronchiolar mucous cell metaplasia, elevated bronchoalveolar lavage fluid (BALF) eosinophils, and HDM-specific IgG1. For MBCP, notable changes were associated with sequential exposures. MBCP/HDM caused elevated T H 2 cytokines in BALF, and elevated neutrophils, eosinophils and IL-5 in peripheral blood. Co-administration of MBCP and HDM followed by HDM resulted in elevated blood and BALF eosinophils and HDM-specific IgE and IgG1. These results demonstrated that acellular MBCP filtrate, and not bacteria within MBCPs, potentiated the acute allergic inflammation to HDM. This methodology could be extended to investigate chronic allergic inflammation and inflammatory potential of other MBCPs and biotechnology products with complex compositions.
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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 | 0.000 | 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".