Development and initial validation of a modified lymphocyte transformation test (LTT) assay in patients with DRESS and AGEP
Bibliographic record
Abstract
BACKGROUND: The lymphocyte transformation test (LTT) is an in vitro assay used to diagnose drug induced hypersensitivity reactions by detecting the activation and expansion of drug-specific memory T cells to the suspected implicated drug. Traditionally radiolabelled thymidine (3H-thymidine) has been used but requires the handling and disposal of radioactive materials. OBJECTIVE: To examine safe alternatives to 3H-thymidine, test assay modifications for improved assay sensitivity and evaluate the modified LTT in patients with DRESS and AGEP. METHODS: Four proliferation detection assays (BRDU, CyQUANT™, MTT and XTT) were screened for LTT sensitivity. XTT the most sensitive and practical was selected for further evaluation Modifications like autologous serum (AS) and regulatory T cell depletion (T-REG) were tested for improved assay sensitivity. Finally, an initial evaluation of the XTT-LTT was performed in 8 patients with DRESS and 2 with AGEP including cytokine testing. RESULTS: Of the non-radioactive alternatives we tested, XTT a colorimetric assay was the most sensitive and practical to move to evaluation. The addition of AS increased background signal. Depletion of T-REGs improved sensitivity but cell sorting time and risk of contamination limited benefit. Of eight patients diagnosed with DRESS and 2 with AGEP tested with XTT-LTT assay results showed our assay matched clinical findings of implicated drugs in 8/10 patients when using a stimulation index (SI) ≥ 2 and 8/10 with analysis by ANOVA. All ten patients were correctly diagnosed by either analysis. CONCLUSION: XTT appears to be a safe, viable alternative to 3H-thymidine, with high sensitivity and allowing direct cytokine quantification on specific patient cells.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".