Brain arachidonic acid uptake and turnover: implications for signaling and bipolar disorder
Bibliographic record
Abstract
PURPOSE OF REVIEW: Arachidonic acid was first detected in the brain in 1922. Although earlier work examined the role of arachidonic acid in growth and development, more recent advancements have elucidated roles for arachidonic acid in brain health and disease. RECENT FINDINGS: In this review, we summarize evidence demonstrating that unesterified arachidonic acid in the plasma pool, which is supplied in part from adipose, is readily taken up and incorporated into brain phospholipids. By labeling plasma unesterified arachidonic acid, it is possible to trace the subsequent release of arachidonic acid from brain phospholipids upon neuroreceptor-mediated release by phospholipase A2 in response to drugs and neuroinflammation in rodents. With the synthesis of 11C labeled fatty acids, brain arachidonic acid signaling can now be measured in humans with position emission tomography. Arachidonic acid signals are known to regulate important biological functions, including neuroinflammation and excitotoxicity, and we focus on how the brain arachidonic acid cascade is a common target of drugs used to treat bipolar disorder (e.g. lithium, carbamazepine and valproate). SUMMARY: A better understanding of the regulation of arachidonic acid uptake into the brain and the brain arachidonic acid cascade could lead to new imaging techniques and the identification of novel therapeutic targets in excitotoxicity, neuroinflammation and 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".