The Effects of Fatty Acids on Brain Microglia Immune Responses
Bibliographic record
Abstract
BACKGROUND In 2018, the estimated global cost of health care for patients with dementia is expected to be over 1 trillion dollars (United States currency). Dementia in many cases is caused by Alzheimer's disease (AD), for which no viable treatment currently exists. Some significant risks in the development of AD are preventable, including diet‐related factors. One important diet‐related factor, dietary fats, is involved in key cellular processes. However, there is limited research on the role of dietary fats in the regulation of inflammation within the central nervous system (CNS). Non‐neuronal cells called microglia regulate the immune status of the CNS, and their dysregulated responses can lead to the enhanced release of cytotoxic mediators. These microglia‐released mediators can cause extensive neuron death, which is observed in AD. Metabolites of alpha‐linolenic acid (ALA) and linoleic acid (LA) are well‐known for modulating peripheral and CNS inflammation. However, the effects of ALA and LA on microglia are unknown. OBJECTIVES Assess whether ALA and LA affect microglial immune responses. METHODS BV‐2 microglia cells were treated with ALA or LA for 24 h, followed by pro‐inflammatory stimulation with bacterial lipopolysaccharide (LPS) for 24 h. Microglial supernatants and total protein from lysed BV‐2 cells were collected. The supernatants were used to quantify the secretion of reactive nitrogen species (RNS) by BV‐2 cells. Proteins were used to quantify the production of inducible nitric oxide synthase (iNOS) by western blotting. RESULTS Both ALA and LA significantly reduced RNS secretion by LPS‐stimulated BV‐2 microglia. LA, but not ALA, significantly reduced iNOS levels in LPS‐stimulated microglia. Data were analyzed using the randomized block design analysis of variance (ANOVA), followed by Dunnett's post‐hoc test. CONCLUSIONS Dietary fatty acids can modulate select microglial immune responses, and should be further investigated for their roles in CNS inflammation, which is involved in the progression of AD. Support or Funding Information Natural Sciences and Engineering Research Council, The Jack Brown and Family Alzheimer's Disease Research Foundation This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".