A bacterial immune-prophylactic approach against asthma for infants and children
Bibliographic record
Abstract
Objective/purpose The prevalence of asthma in Canada (and worldwide) has been increasing over the last 20 years with currently over 3 million Canadians suffering from it. Asthma appears to result from environmental influences directing a genetically predisposed host towards a pro-allergic, Th2-dominated immune response. Studies in humans and animals identify the time around birth and early infancy as a period during which the decision of proallergic versus non-allergic immune responses to environmental stimuli appears to be made. Presumably this is the reason why the incidence of first diagnosis is highest in infants and children, although asthma can occur at any age. The very fact that all three components (environmental, genetic and developmental) are necessary for asthma to occur also offers the opportunity to intervene early in life through e.g. vaccination against allergies. Vaccination as a strategy to prevent or cure asthma is a tremendous opportunity. An ideal vaccine would be one that prevents or cures asthma after only one dose and protects for life. It is well established that the whole heat-killed bacterium Listeria monocytogenes (Lm) given as an adjuvant along with model allergens effectively prevent allergic sensitization and/or allergic inflammation in adult animals following local allergen challenge. We have successfully developed a novel, live, but highly attenuated neonatal vaccine platform based on the bacterium Listeria monocytogenes (Lm). Our published data suggest that our Lm-based vaccine platform is capable of inducing strong anti-allergic immune responses for an entire life only after one dose given to newborn mice. Now we have investigated whether our Lm-based vaccine platform will provide protection from allergic reactions upon challenge with the allergen, after only one immunization given around birth. Our specific aim addressed the following objective: Do our Listeria monocytogenes vaccine strains producing model allergen ovalbumin (OVA) and inducing a strong Th1 response, prevent allergic reactions upon challenge with the allergen in a neonatal 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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".