TNF-α modulation by natural bioactive molecules in mouse RAW 264.7 macrophage cells
Bibliographic record
Abstract
BACKGROUND: The present study was designed to examine the anti-inflammatory effects of plant-derived products marketed for human health benefits. METHODS: The tumor necrotic factor-α (TNF-α) was used as a proinflammatory biomarker generated by mouse macrophage RAW 264.6 cells. The in vitro tested plant products include Saskatoon berry (SKB), quercetin, purified oat beta-glucan (OBG), curcumin, and turmeric. Quantification of TNF-α in cell culture supernatants was carried out using mouse TNF-α assay kit and the cell proliferation was determined by MTT (3-(4, 5-dimethylthiazolyl-2)-2,5- diphenyltetrazolium bromide) assay. The cells were grown in Dulbecco's modified Eagle's medium supplemented with 10% heat-inactivated fetal bovine serum and 100 U/mL penicillin and 100 µg/mL streptomycin. Bacterial lipopolysaccharide (LPS) at a concentration of 500 ng/mL was employed to stimulate the TNF-α production in mouse macrophage cells. RESULTS: Results showed that curcumin at 10 µM (3.7 µg/mL) level effectively attenuated the LPS-induced inflammatory response, and at 100 µM completely inhibited macrophage RAW cell growth (p<0.05). The aqueous turmeric extract caused inhibitory effect on TNF-α at 25, 50, 100, and 500 µg/mL. SKB inhibited TNF-α production at 50, 100, 500, and 1,000 µg/mL. On the other hand, at 10, 25, 500, and 1,000 µg/mL SKB promoted significant cell growth/proliferation. Quercetin at 10, 25, 50 and 100 µg/mL inhibited TNF-α, but at 500 and 1,000 µg/mL stimulated cell growth. OBG at 10, 25, and 50 µg/mL inhibited TNF-α, but in some cases OBG stimulated TNF-α At 1,000 and 10,000 µg/mL OBG proved to be extremely toxic or lethal to the macrophage cells. CONCLUSIONS: Overall, the plant products showed anti-inflammatory effects as well as cell proliferation or inhibition in the in vitro system used in this investigation. The underlying mechanisms of dualistic actions caused by plant-derived ingredients, viz., macrophage cellular growth stimulation or retardation, remain to be elucidated.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".