Unlike lithium, anticonvulsants and antidepressants do not alter rat brain myo-inositol
Bibliographic record
Abstract
Lithium is the first-line in bipolar disorder treatment. Lithium's clinical efficacy might be due to its inhibition of myo-inositol turnover in the phosphatidylinositol second messenger system. This study aimed to determine whether this action can extend to antidepressants and anticonvulsants also used to treat bipolar symptoms. Male rats were treated for 2 weeks with an intraperitoneal injection of phenelzine, fluoxetine, desipramine, carbamazepine, lamotrigine, sodium valproate or vehicle. Brains were dissected and myo-inositol concentrations were analyzed using high-field nuclear magnetic resonance spectroscopy at 18.8 T and quantified using Chenomx Profiler software. Brain regions assessed included the prefrontal, temporal and occipital cortical areas as well as the hippocampus. The main finding is that contrary to lithium, the anticonvulsants and antidepressants do not alter brain myo-inositol concentration. This suggests that these agents might work via a mechanism that is not centered on changes in myo-inositol concentration.
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 | 0.000 | 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) | 0.003 | 0.001 |
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".