Ex vivo diagnostics using varied cellular inputs in drug-induced severe cutaneous adverse reactions
Bibliographic record
Abstract
Background Drug-induced severe cutaneous adverse reactions (SCARs) are presumed T-cell-mediated hypersensitivities associated with significant morbidity and mortality. Traditional in-vivo testing methods, such as patch or intradermal testing, are limited by a lack of standardisation and poor sensitivity. Modern approaches to testing include measurement of IFN-γ release from patient peripheral blood mononuclear cells (PBMC) stimulated with the suspected causative drug. Objective We sought to improve ex-vivo diagnostics for drug-induced SCAR by comparing enzyme-linked immunospot (ELISpot) sensitivities and flow cytometry-based intracellular cytokine staining (ICS) and cellular composition of separate samples (PBMC or blister fluid cells (BFC)) from the same donor. Methods IFN-γ release ELISpot and flow cytometry analyses were performed on donor-matched PBMC and BFC samples from four SCAR patients with distinct drug-allergies. Results Immune responses to suspected drugs were detected in both PBMC and BFC samples of two donors (Case 1 in response to ceftriaxone and Case 4 to oxypurinol), with BFC eliciting stronger responses. For two other donors, only BFC samples showed a response to meloxicam(Case 2) or sulfamethoxazole and its 4-Nitro metabolite (Case 3). Consistently, flow cytometry revealed a greater proportion of IFN-γ-secreting cells in the BFC compared to PBMC. BFC cells from Case 3 were also enriched for memory/activation/tissue-recruitment markers over PBMC. Conclusion Analysis of BFC samples for drug-allergy diagnostics offers a higher sensitivity for detecting positive responses compared to PBMC. This is consistent with recruitment (and enrichment) of cytokine-secreting cells with a memory/activated phenotype into blisters.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".