Chronic treatment with both lithium and sodium valproate may normalize phosphoinositol cycle activity in bipolar patients
Bibliographic record
Abstract
BACKGROUND: It has been proposed that lithium may be clinically effective due to its actions on the phosphoinositol second messenger system (PI-cycle). Studies have also suggested that untreated manic patients may have raised myo-inositol and phosphomonoester (PME) concentrations and also that unmedicated euthymic bipolar patients may have lowered PME concentrations. The objective of the present study was to test the hypothesis that chronic treatment with either lithium or sodium valproate in patients with bipolar mood disorder leads to a normalization in the activity of the PI-cycle. METHODS: This study had two parts each with different MRS methodology. The first part compared healthy controls (n = 19) with euthymic bipolar patients who were taking either lithium (n = 16) or sodium valproate (n = 11) using both (1)H-MRS and (31)P-MRS. In the second part we examined a separate group of euthymic bipolar disorder patients taking sodium valproate (n = 9) and compared these with age and sex-matched healthy controls (n = 11) using (1)H-MRS. RESULTS: Both studies showed that there were no differences in either myo-inositol or phosphomonoester (PME) concentrations between controls and patients taking either medication. CONCLUSIONS: These findings examine two key components of the PI-cycle in treated euthymic bipolar (myo-inositol and PME concentrations). The results from this study are consistent with the suggestion that chronic treatment with either lithium or sodium valproate in bipolar patients may normalize PI-cycle functioning.
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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".