Testing the Hypothesis that Selenium Deficiency is a Risk Factor for Clozapine-Induced Agranulocytosis in Rats
Bibliographic record
Abstract
Clozapine is an effective atypical antipsychotic associated with a relatively high incidence of drug-induced agranulocytosis. It forms a reactive nitrenium ion metabolite upon oxidation by peripheral neutrophils and their precursors in the bone marrow. Although the mechanism of this idiosyncratic drug reaction is still unknown, the observation that it does not occur rapidly on rechallenge of patients with a history of clozapine-induced agranulocytosis suggests that it is not immune-mediated. Previous studies by other research groups had found that patients on clozapine had lower plasma and red blood cell levels of selenium. The reactive metabolite of clozapine reacts with glutathione, and therefore, it is likely that it also binds to selenocysteine-containing proteins, such as glutathione peroxidase, thioredoxin reductase, and protein disulfide isomerase. We set out to test the hypothesis that clozapine-induced agranulocytosis is associated with selenium deficiency with rats on a selenium-deficient diet. We studied the effects of clozapine on selenium levels and the effect of selenium deficiency on leukocyte and neutrophil counts and clozapine covalent binding. We did not observe any significant difference between clozapine-treated rats given a selenium-adequate or deficient diet. Therefore, it is unlikely that selenium deficiency is a major risk factor for clozapine-induced agranulocytosis.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".