Proinflammatory effects of arachidonic acid in a lipopolysaccharide-induced inflammatory microenvironment in 3T3-L1 adipocytes in vitro
Bibliographic record
Abstract
Long-chain n-3 polyunsaturated fatty acids (PUFA), eicosapentaenoic acid (20:5n-3, EPA) and docosahexaenoic acid (22:6n-3, DHA), have known anti-inflammatory effects, including the modulation of adipose tissue-derived inflammatory mediators (i.e., adipokines) implicated in obesity-related pathologies, such as insulin resistance. Less is known about the effects of plant-derived n-3 PUFA, α-linolenic acid (ALA, 18:3n-3) and stearidonic acid (SDA 18:4n-3), or n-6 PUFA linoleic acid (LA, 18:2n-6) and arachidonic acid (AA, 20:4n-6), especially in combination with an inflammatory stimulus, such as lipopolysaccharide (LPS), at a dose intended to mimic obesity-associated low-grade inflammation. To study this, 3T3-L1 adipocytes were incubated with 100 μmol/L of various n-3 or n-6 PUFA with or without 10 ng/mL LPS for up to 24 h. AA in the presence of LPS synergistically increased (p < 0.05) pro-inflammatory monocyte chemoattractant protein-1 (MCP)-1 and interleukin (IL)-6 secretion and gene expression, as well as COX-2 and TLR2 gene expression at 6 and/or 24 h, suggesting their potential roles in the synergistic effects of AA and LPS. Plant-derived fatty acids ALA, SDA, and LA did not differentially affect adipokine gene expression or secretion, whereas LPS-induced pro-inflammatory IL-1β expression and MCP-1 secretion was decreased (p < 0.05) by EPA, DHA, and/or EPA+DHA (50 μmol/L each) compared with LPS alone. Only DHA increased (p < 0.05) gene expression of the n-3 PUFA receptor GPR120 and simultaneously decreased LPS-induced nuclear factor-κB activation compared with control. Our findings emphasize that specific fatty acids within the n-3 or n-6 PUFA class warrant consideration in the development of nutritional strategies to improve obesity-associated inflammation.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".