Blister fluid as a cellular input for ex vivo diagnostics in drug-induced severe cutaneous adverse reactions improves sensitivity and explores immunopathogenesis
Bibliographic record
Abstract
Background: testing methods, such as patch or intradermal testing, are limited by a lack of standardization and poor sensitivity. Modern approaches to testing include measurement of IFN-γ release from patient PBMCs stimulated with the suspected causative drug. Objective: diagnostics for drug-induced SCARs by comparing enzyme-linked immunospot (ELISpot) sensitivities and flow cytometry-based intracellular cytokine staining and determination of the cellular composition of separate samples (PBMCs or blister fluid cells [BFCs]) from the same donor. Methods: ELISpot and flow cytometry analyses of IFN-γ release were performed on donor-matched PBMC and BFC samples from 4 patients with SCARs with distinct drug hypersensitivity. Results: Immune responses to suspected drugs were detected in both the PBMC and BFC samples of 2 donors (donor patient 1 in response to ceftriaxone and case patient 4 in response to oxypurinol), with BFCs eliciting stronger responses. For the other 2 donors, only BFC samples showed a response to meloxicam (case patient 2) or sulfamethoxazole and its 4-nitro metabolite (case patient 3). Consistently, flow cytometry revealed a greater proportion of IFN-γ-secreting cells in the BFCs than in the PBMCs. The BFCs from case patient 3 were also enriched for memory, activation, and/or tissue recruitment markers over the PBMCs. Conclusion: Analysis of BFC samples for drug hypersensitivity diagnostics offers a higher sensitivity for detecting positive responses than does analysis of PBMC samples. 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".