IMMUNE MODULATION OF ALLERGIC RESPONSES
Bibliographic record
Abstract
Asthma is defined as reversible airflow obstruction and an estimated 1-in-3 Canadians will be diagnosed over their lifetime. Many clinical phenotypes of asthma exist, but allergic asthma is the most common presentation. Despite effective therapies, approximately 65% of Canadian asthmatics have poorly controlled disease. Thus, there remains pressing need to develop disease modifying therapies. Allergen-specific immunotherapy (SIT) is a disease-modifying therapy for allergic disease that consists of repeatedly administering doses of allergen, to an allergic individual; over 100 years of clinical use, SIT has been demonstrated to reduce symptoms of disease both during and after cessation of therapy. Widespread clinical uptake of SIT has been limited by the risk of developing anaphylaxis as a response to therapy. Peptide immunotherapy is a derivation of SIT, that attempts to retain the disease-modifying benefits, while lessening the risk of anaphylaxis, by treating subjects with allergen-derived T-cell peptide epitopes. Peptide immunotherapy has been demonstrated to reduce symptoms of allergic disease in treated subjects; however, it remains unknown how administration of a single (or several) T-cell epitopes can modulate immune responses to entire complex allergens. Additionally, how genetic diversity in peptide epitope presentation effects the development of immune tolerance is unknown. In this thesis, we sought to characterize these mechanisms of peptide immunotherapy; the hypothesis was, “The induction of immunosuppression by peptide immunotherapy involves the infectious spread of tolerance beyond the treatment epitope, and is dependent upon treatment peptide dose and affinity to MHC”. Through the definition of these mechanistic traits we hoped to expedite and inform the design of future peptide based therapeutics. The studies presented within this thesis examine the topic of immune modulation of allergic disease in mouse models, and have focused upon broadly pertinent characteristics of immune modulation, such as the number, dose and affinity of immunomodulatory epitopes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.005 | 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 teacher head, 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".