The Predictive Value of the In Vitro Platelet Toxicity Assay (iPTA) for the Diagnosis of Hypersensitivity Reactions to Sulfonamides
Bibliographic record
Abstract
Drug hypersensitivity reactions (DHRs) are rare but potentially fatal adverse drug reactions (ADRs). A reliable test to diagnose DHRs would be a major advance in the clinical care for patients and in the evaluation of ADRs during drug development as well as for mechanistic studies of drug hypersensitivity. Available in vitro tests including the lymphocyte toxicity assay (LTA) have been used but are time-consuming, cumbersome, and expensive. We have developed a novel diagnostic test for DHRs, the in vitro platelet toxicity assay (iPTA). The aim of this study was to evaluate the predictive value of the iPTA in diagnosis of DHRs to sulfonamides. We recruited 66 individuals (36 DHS-sulfa patients and 30 healthy controls) to participate in the study. Blood samples were obtained and LTA and iPTA were performed in parallel. There was concentration-dependent toxicity in the cells of patients when incubated with the reactive hydroxylamine metabolite of sulfamethoxazole for both the LTA and iPTA (P < .05). The iPTA was more sensitive than conventional LTA test in detecting susceptibility of patient cells to in vitro toxicity (P < .05). The novel iPTA has considerable potential as an investigative tool for DHS as it is more sensitive and cheaper, requiring no special reagents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".