Effect of increasing intraperitoneal infusion rates on bupropion hydrochloride-induced seizures in mice
Bibliographic record
Abstract
BACKGROUND: It is not known if there is a relationship between input rate and incidence of bupropion-induced seizures. This is important, since different controlled release formulations of bupropion release the active drug at different rates. METHODS: We investigated the effect of varying the intraperitoneal infusion rates of bupropion HCl 120 mg/kg, a known convulsive dose 50 (CD50), on the incidence and severity of bupropion-induced convulsions in the Swiss albino mice. A total of 69 mice, approximately 7 weeks of age, and weighing 21.0 to 29.1 g were randomly assigned to bupropion HCl 120 mg/kg treatment by intraperitoneal (IP) administration in 7 groups (9 to 10 animals per group). Bupropion HCl was infused through a surgically implanted IP dosing catheter with infusions in each group of 0 min, 15 min, 30 min, 60 min, 90 min, 120 min, and 240 min. The number, time of onset, duration and the intensity of the convulsions or absence of convulsions were recorded. RESULTS: The results showed that IP administration of bupropion HCl 120 mg/kg by bolus injection induced convulsions in 6 out of 10 mice (60% of convulsing mice) in group 1. Logistic regression analysis revealed that infusion time was significant (p = 0.0004; odds ratio = 0.974) and increasing the IP infusion time of bupropion HCl 120 mg/kg was associated with a 91% reduced odds of convulsions at infusion times of 15 to 90 min compared to bolus injection. Further increase in infusion time resulted in further reduction in the odds of convulsions to 99.8% reduction at 240 min. CONCLUSION: In conclusion, the demonstration of an inverse relationship between infusion time of a fixed and convulsive dose of bupropion and the risk of convulsions in a prospective study is novel.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".