N-3 fatty acids, neuronal activity and energy metabolism in the brain
Bibliographic record
Abstract
The content of docosahexaenoic acid (DHA) in brain membranes is of crucial importance for the optimum development of brain functions. A lack of DHA accretion in the brain is accompanied by deficits in learning behavior linked to impairments in neurotransmission processes, which might result from alteration of brain fuel supply and hence energy metabolism. Experimental data we published support the hypothesis that n-3 fatty acids may modulate brain glucose utilization and metabolism. Indeed rats made deficient in DHA by severe depletion of total n-3 fatty acid intake have 1) a lower brain glucose utilization, 2) a decrease of the glucose transporter protein content GLUT1 both in endothelial cells and in astrocytes, 3) a repression of GLUT1 gene expression in basal state as well as upon neuronal activation. This could be due to the specific action of DHA on the regulation of GLUT1 expression since rat brain endothelial cells cultured with physiological doses of DHA had an increased GLUT1 protein content and glucose transport when compared to non-supplemented cells. These experimental data highlight the impact of n-3 fatty acids on the use of brain glucose, thereby constituting a key factor in the control of synaptic activity. This emerging role suggests that dietary intake of n-3 fatty acids can help to reduce the cognitive deficits in the elderly and possibly symptomatic cerebral metabolic alterations in Alzheimer disease by promoting brain glucose metabolism.
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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".