A novel microbe-based treatment that attenuates the inflammatory profile in a mouse model of allergic airway disease
Bibliographic record
Abstract
Abstract There is an unmet need for effective new and innovative treatments for asthma. It is becoming increasingly evident that bacterial stimulation can have beneficial effects at attenuating allergic airway disease through immune modulation. Our aim was to test the ability of a novel inactivated microbe-derived therapeutic based on Klebsiella (KB) in a model of allergic airway disease in mice. BALB/c mice were exposed intranasally to house dust mite (HDM) for two weeks. Mice were treated prophylactically via subcutaneous route with either KB or placebo for one week prior to HDM exposure and throughout the two week exposure period. 24 hours after the last exposure, lungs were analysed for inflammatory cell infiltrate, gene expression, cytokine levels, goblet cell metaplasia, and serum was analysed for allergen-specific serum IgE levels. HDM exposed mice developed goblet cell hyperplasia, elevated allergen-specific serum IgE, airway eosinophilia, and a concomitant increase in TH2 cytokines including IL-4, IL-13 and IL-5. Treatment with KB attenuated HDM-mediated airway eosinophilia, total bronchoalveolar lavage (BAL) cell numbers, BAL TH2 cytokine production, and goblet cell metaplasia. Our prophylactic intervention study illustrates the potential of subcutaneous treatment with bacterial derived biologics as a promising approach for allergic airway disease treatment.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".