Fungal Proteases in the Preventative Treatment of Peanut Allergies: A Research Protocol
Bibliographic record
Abstract
Peanut allergies are a common autoimmune disorder that impacts millions of people worldwide. Currently, there are no known treatments to prevent allergic reactions to peanuts besides avoiding the allergen. To combat this, previous studies have found that fungal proteases can prevent an allergic response; the fungal proteases bind a peanut-specific IgE immunoglobulin, blocking the allergic response. We propose to orally administer these previously identified fungal proteases isolated from the fungus Aspergillus Niger before exposure to peanut antigens. If the fungal proteases successfully bind the peanut-specific IgE before exposure, peanut allergenicity will be reduced. To test our experimental drug, we will utilize peanut-sensitized mice strains, with one control group receiving a placebo and two treatment groups receiving either a high dose of 2.5mg or regular dose of 1.25mg of the drug. All three groups will then be exposed to the peanut allergen, and allergy responses will be monitored through body temperature measurements, blood histamine tests and enzyme-linked immunosorbent assay (ELISA) testing the presence of peanut-specific IgE. We anticipate that the fungal proteases will prevent all allergic reaction responses from occurring such that body temperature will remain stable, blood histamine levels will not increase, and the presence of peanut-specific IgE will be lessened. This novel oral drug will be used as a pre-exposure preventative treatment, unlike the current post-exposure treatments, such as an Epi-pen, filling a key gap in knowledge of preventative treatment for peanut allergies.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.054 | 0.010 |
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".