Influence of Chemotherapy on Allergen-Specific IgE
Bibliographic record
Abstract
BACKGROUND: Atopy is defined as excess allergen-specific IgE (A-IgE). IgE is produced by plasma cells that differentiate from allergen-specific B cells. B cells are known to be killed by chemotherapy; however, it is not known whether A-IgE-secreting plasma cells are killed or inhibited by chemotherapy. If yes, serum A-IgE levels would be expected to decrease after chemotherapy. OBJECTIVES: We aimed to determine whether A-IgE levels in atopic individuals (serum A-IgE ≥0.35 kUA/L) decrease into the nonatopic range (< 0.35 kUA/L) after chemotherapy. METHODS: In 105 patients undergoing chemotherapy for acute leukemia, we measured serum A-IgE before and after chemotherapy. In a subset of these patients, we also measured B cell counts before and after chemotherapy. RESULTS: Of the 105 patients, 36 were atopic. In these patients, median A-IgE level before chemotherapy was 1.6 kUA/L whereas the median level after chemotherapy was 0.6 kUA/L (p < 0.001). In 12/36 (33%) patients, A-IgE levels decreased into the nonatopic range. In nonatopic patients (n = 69), the median A-IgE level also dropped: from 0.04 kUA/L before to 0.03 kUA/L after chemotherapy (p = 0.001). Among the total patients (n = 105), the median pre:post-chemotherapy A-IgE ratio was 1.8 (2.6 in atopic and 1.5 in nonatopic patients). In contrast, the median ratio of pre:post-chemotherapy B cell counts was 87.6. CONCLUSIONS: A-IgE levels decrease after chemotherapy but markedly less than B cell counts. Thus, at least some A-IgE plasma cells appear to survive chemotherapy.
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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) | 0.002 | 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".