Pathogenesis of Beta-lactam-induced Serum Sickness-Like Reaction: The potential role of Reactive Drug Metabolites
Bibliographic record
Abstract
Aims: Drug-induced serum sickness-like reactions (SSLRs) are idiosyncratic drug-induced hypersensitivity reactions that occur in susceptible patients 1-3 weeks after exposure to the culprit drug. The pathophysiology of this type of reactions is not well understood and its diagnosis is difficult due to the lack of safe and reliable diagnostic tests for identifying the culprit drug. The lymphocyte toxicity assay (LTA) is an in vitro test used as a diagnostic and investigative tool for drug hypersensitivity reactions (DHRs). In this pilot study, we investigated the pathogenesis of SSLR using the LTA test to evaluate the potential role of reactive drug metabolites in the pathogenesis of SSLR. Methods: Nineteen patients (14 males and 5 females) were recruited to this study. Demographic data was collected form the patents and blood samples were withdrawn from all patients and from 19 healthy controls. The LTA test was performed on all subjects and data is expressed as percentage increase in cell death compared to control (vehicle without the drug). Results: There was a significant (p<0.05) concentration-related increase in cell death in cells isolated from patients as compared to cells from healthy controls when incubated with the drug in the presence of phenobarbitone-induced rat liver microsomes (MICs). Conclusion: This data suggests the initial bioactivation of the drug to a reactive metabolite followed by a toxic response is a key first step in -lactam antibiotic-induced SSLRs. Further research is needed to explore the implications of this data as to the pathogenesis of -lactam antibiotic induced SSLR.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".