Association of myeloid cell reactivity patterns with safe food predictions in FPIES patients
Bibliographic record
Abstract
BACKGROUND: Food protein-induced enterocolitis syndrome (FPIES) is an understudied non-IgE-mediated food allergy, which is distinct from and lacks diagnostic testing akin to IgE testing. FPIES affects infants and toddlers but can persist into adulthood. As there are no extant methods to identify safe foods for FPIES patients, food ingestion trials are performed at home and often lead to reactions and development of food aversions, which may lead to failure-to-thrive and gastric feeding tube requirements. We hypothesized that foods that fail to elicit responses in immune cells of FPIES patients would be safe to ingest, which could support development of a diagnostic method to headstart safe food identification in patients. METHODS: We developed an ex vivo model of FPIES using food-stimulated white blood cells (WBCs) from pediatric FPIES patients and controls by defining a 9-gene panel representative of FPIES ex vivo responses and conducted a single-arm pilot clinical trial. RESULTS: Myeloid cells of FPIES patients displayed variable individual-specific myeloid cell reactivity patterns (iMCRPs) to different foods. Foods that failed to elicit repsonses in patients' immune cells were safe to ingest with a negative predictive value of 98.5%. This, when utilized in prospective predictions, reduced newly introduced food reaction rates from 19.5 to 0% while increasing food repertoire diversity. CONCLUSIONS: iMCRPs represent a novel and potentially useful tool that associates with safe food ingestion in FPIES patients for foods that fail to elicit immune cell reactions. Trial Registration The trial has been registered at registered at ClinicalTrials.gov # NCT04644783.
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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 | 0.001 |
| 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) | 0.001 | 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".