Docosahexaenoic Acid (DHA), an Important Fatty Acid in Aging and the Protection of Neurodegenerative Diseases
Bibliographic record
Abstract
Docosahexaenoic acid (DHA) is a fatty acid essential for the proper development and functioning of the nervous and visual system. DHA is found in significant concentrations in the phospholipids of neuronal membranes. DHA is provided by the mother during the fetal and early infancy life, during pregnancy and through breastfeeding. Given the importance of an adequate supply of the fatty acid to the newborn, it has been suggested DHA supplementation to the mother before and during the pregnancy and also during the breastfeeding period. In the recent years, research from different scientist have established that DHA has an important role in the development of the nervous system, as well as having an important key role in the preservation of this tissue, especially during aging and in some neurodegenerative diseases, such as Alzheimer's disease, Multiple Sclerosis and Parkinson's disease. DHA may preserve the integrity and the neuronal viability against different metabolic insults and/or cytotoxic events, among which inflammation and oxidative stress are the most relevant. The neuroprotective effects of DHA in neural tissue are mediated by a metabolic derivative, known as neuroprotectin D-1. This molecule may respond to aggression having anti-inflammatory, antiapoptotic and even neuroregenerative effects, which may contribute to preserve the proper neuronal viability as well as the health and function of the nervous system. This review discusses different evidences about the neuroprotective effect of DHA, during aging and against some neurodegenerative diseases, highlighting the important role of the proper nutrition in this protection
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".