A review of the possible relevance of inositol and the phosphatidylinositol second messenger system (PI‐cycle) to psychiatric disorders—focus on magnetic resonance spectroscopy (MRS) studies
Bibliographic record
Abstract
Myo-inositol is an important part of the phosphatidylinositol second messenger system (PI-cycle). Abnormalities in nerve cell myo-inositol levels and/or PI-cycle regulation has been suggested as being involved in the pathophysiology and/or treatment of many psychiatric disorders including bipolar disorder, major depressive disorder, panic disorder, obsessive-compulsive disorder, eating disorders and schizophrenia. This review examines the metabolism and biochemical importance of myo-inositol and the PI-cycle. It relates this to the current in vivo evidence for myo-inositol and PI-cycle involvement in these psychiatric disorders, particularly focusing upon the magnetic resonance spectroscopy (MRS) findings in patient studies to date. From this review it is concluded that while the evidence suggests probable relevance to the pathophysiology and/or treatment of bipolar disorder, there is much less support for a significant role for the PI-cycle or myo-inositol in any other psychiatric disorder. More definitive investigation is required before PI-cycle dysfunction can be considered specific to bipolar disorder.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".