Mood stabilizer lithium inhibits amphetamine-increased 4-hydroxynonenal-protein adducts in rat frontal cortex
Bibliographic record
Abstract
Recent studies indicate that bipolar disorder is associated with mitochondrial dysfunction and oxidative stress. Previous studies in our laboratory have shown that the mood stabilizer lithium inhibits oxidative stress. The α,β-unsaturated aldehyde 4-hydroxy-2-nonenal (4-HNE), a major product of lipid peroxidation, is able to exert cytotoxicity and disturb cellular function by forming protein adducts. The purpose of this study is to determine whether chronic lithium treatment prevents 4-HNE-protein adduction in an amphetamine-induced hyperactive mania-like model. We found that repeated amphetamine stimulation significantly induced hyperactive behaviour, decreased activities of mitochondrial complexes I and III, and increased 4-HNE-protein adducts in rat frontal cortex, and that chronic lithium treatment inhibited both amphetamine-induced hyperactivity and 4-HNE-protein adduction. Monoamine neurotransmitters are involved in the aetiology and pathology of bipolar disorder and other psychiatric diseases, and also contribute significantly to amphetamine-induced behavioural effects. Vesicular monoamine transporter 2 (VMAT2) is critical in packaging monoamine neurotransmitters. We found that 4-HNE can form protein adducts with VMAT2. Repeated amphetamine stimulation significantly increased 4-HNE-VMAT2 adducts, while chronic lithium treatment reduced amphetamine-increased 4-HNE-VMAT2 adducts in rat frontal cortex. Our findings suggest that chronic lithium treatment may inhibit amphetamine-induced hyperactive mania-like behaviour by preventing 4-HNE-VMAT2 adduction. This finding also indicates that prevention of 4-HNE-VMAT2 adduction may contribute in part to the pharmacological action of lithium for the treatment of bipolar disorder.
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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".