Characterization of milk-specific immunoglobulins of oral immunotherapy participants
Bibliographic record
Abstract
Abstract Since the standard treatment for food allergies such as cow’s milk allergy is strict avoidance and epinephrine injection upon anaphylaxis, alternative treatments are needed. Allergic reactions including anaphylaxis are presumed to be due to high-affinity immunoglobulin (Ig)E antibodies against cow’s milk proteins (CMP) such as casein. Currently, our laboratory is leading a pan-Canadian randomized controlled trial of milk oral immunotherapy (OIT). Participants in the OIT receive increasing doses of cow’s milk until 200ml of milk is tolerated without allergic reactions, followed by 12 months of follow-up while continuing to ingest milk and dairy at least twice a week. Using enzyme-linked immunosorbent assay (ELISA) to detect antibodies specific for casein, we determined that female participants have higher casein-specific IgE and IgG4 than male participants. We then aimed to assess whether the sex-dependent change in the affinity of immunoglobulins throughout OIT. Using a modified ELISA that assays for relative binding affinity, we found that the affinity of IgE for casein decreases in approximately half of the participants following the 12-months observation period, compared to the baseline. This is associated with an increase in tolerance and significant protection against minor symptoms or adverse reactions following milk ingestion. The results suggest that oral immunotherapy may induce changes in the affinity binding of allergen-specific IgE and contribute to the improvement of symptoms associated with allergy desensitization to milk.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".