<i>In Vitro </i>Effects of Oil’s Fatty Acids on T-Cell Function of Obese Men
Bibliographic record
Abstract
Background: Dietary fatty acids have important homeostatic functions in regulating the immune response and may exert beneficial effects on immune alterations during obesity. Objective: To assess the in vitro effects of oil fatty acids, different oils (olive, linseed, Nigel, sunflower) were tested on T-lymphocyte proliferation, Th1- and Th2-type cytokine production, and intracellular oxidant/antioxidant status in obese patients. Methods: Peripheral blood lymphocytes were isolated using Histopaque and were in vitro cultured and stimulated by Con A in the presence or absence of the oils. Cell proliferation, interleukin-2, interleukin-4 and interferon-γ (INFγ) secretions and intracellular oxidative status (glutathione (GSH), malondialdehyde (MDA), carbonyl protein levels, catalase activity and micronuclei frequency) were investigated. Results: Abnormalities in lymphocyte function and intracellular oxidative stress were observed in obesity. Linseed oil induced a reduction in T-lymphocyte proliferation and IL-2 production while Nigel oil increased them in both obese and control groups. In addition, Nigel oil enhanced IFNγ and IL-4 secretion. Olive and sunflower oils had no effect on lymphocyte proliferation and cytokine secretion in both groups. Linseed and Nigel oils induced an increase in T cell GSH concentrations and catalase activity with a concomitant decrease in MDA, carbonyl protein contents and micronuclei frequency especially in obese patients. Conclusion: Linseed and Nigel oils had beneficial effects on lymphocyte proliferation, cytokines secretions and redox status, while olive and sunflower oils had no effects on immune cell function in obesity.
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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) | 0.003 | 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".