Brain docosahexaenoic acid uptake and metabolism
Bibliographic record
Abstract
Docosahexaenoic acid (DHA) is the most abundant n-3 polyunsaturated fatty acid in the brain where it serves to regulate several important processes and, in addition, serves as a precursor to bioactive mediators. Given that the capacity of the brain to synthesize DHA locally is appreciably low, the uptake of DHA from circulating lipid pools is essential to maintaining homeostatic levels. Although, several plasma pools have been proposed to supply the brain with DHA, recent evidence suggests non-esterified-DHA and lysophosphatidylcholine-DHA are the primary sources. The uptake of DHA into the brain appears to be regulated by a number of complementary pathways associated with the activation and metabolism of DHA, and may provide mechanisms for enrichment of DHA within the brain. Following entry into the brain, DHA is esterified into and recycled amongst membrane phospholipids contributing the distribution of DHA in brain phospholipids. During neurotransmission and following brain injury, DHA is released from membrane phospholipids and converted to bioactive mediators which regulate signaling pathways important to synaptogenesis, cell survival, and neuroinflammation, and may be relevant to treating neurological diseases. In the present review, we provide a comprehensive overview of brain DHA metabolism, encompassing many of the pathways and key enzymatic regulators governing brain DHA uptake and metabolism. In addition, we focus on the release of non-esterified DHA and subsequent production of bioactive mediators and the evidence of their proposed activity within the brain. We also provide a brief review of the evidence from post-mortem brain analyses investigating DHA levels in the context of neurological disease and mood disorder, highlighting the current disparities within the field.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".