Chronic dietary intake of α-linolenic acid does not replicate the effects of DHA on passive properties of entorhinal cortex neurons
Bibliographic record
Abstract
n-3 PUFA are receiving growing attention for their therapeutic potential in central nervous system (CNS) disorders. We have recently shown that long-term treatment with DHA alters the physiology of entorhinal cortex (EC) neurons. In the present study, we investigated by patch-clamp the effect of another major dietary n-3 PUFA, α-linolenic acid (LNA), on the intrinsic properties of EC neurons. Mice were chronically exposed to isoenergetic diets deficient in n-3 PUFA or enriched in either DHA or LNA on an equimolar basis. GC analyses revealed an increase in DHA (34%) and a decrease in arachidonic acid (AA, - 23%) in brain fatty acid concentrations after consumption of the DHA-enriched diet. Dietary intake of LNA similarly affected brain fatty acid profiles, but at a lower magnitude (DHA: 23%, AA: - 13%). Compared to the n-3 PUFA-deficient diet, consumption of DHA, but not LNA, induced membrane hyperpolarisation ( -60 to -70 mV), increased cellular capacitance (32%) and spontaneous excitatory postsynaptic current frequency (50%). We propose that the inefficiency of LNA to modulate cellular capacitance was related to its inability to increase the brain DHA:AA ratio over the threshold necessary to up-regulate syntaxin-3 (46%) and translocate drebrin (40% membrane:cytosol ratio). In summary, our present study shows that the increase in brain DHA content following chronic administration of LNA was not sufficient to alter the passive and synaptic properties of EC neurons, compared to direct dietary intake of DHA. These diverging results have important implications for the therapeutic use of n-3 PUFA in CNS disease, favouring the use of preformed DHA.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".