Elderberry (Sambucus nigra L.) Fruit Extract Alleviates Oxidative Stress, Insulin Resistance, and Inflammation in Hypertrophied 3T3-L1 Adipocytes and Activated RAW 264.7 Macrophages
Bibliographic record
Abstract
Oxidative stress and inflammation in hypertrophied adipose tissue with excessive fat accumulation play a crucial role in the development of obesity and accompanying metabolic dysfunctions. This study demonstrated the capacity of elderberry fruit (EDB) extract to decrease the elevated production of reactive oxygen species in hypertrophied 3T3-L1 adipocytes. Treatment with the EDB extract resulted in modulation of mRNA expression and protein secretion of key adipokines in hypertrophied adipocytes. Expression of leptin and adiponectin was, respectively, down- and up-regulated. Moreover, glucose uptake stimulation was noticed in mature adipocytes, both sensitive to insulin and insulin resistant. This may suggest a positive effect of EDB extract on insulin resistance status. The extract was also found to alleviate the inflammatory response in activated RAW 264.7 macrophages by down-regulating the expression of proinflammatory genes (TNF-α, IL-6, COX-2, iNOS) and suppressing the enhanced production of inflammatory mediators (TNF-α, IL-6, PGE2, NO). In vitro experiments showed that the EDB extract could inhibit digestive enzymes, including α-amylase, α-glucosidase, and pancreatic lipase, leading to reduced intestinal absorption of dietary lipids and carbohydrates. Further in vivo studies could be postulated to support EDB as a functional food component for the prevention and treatment of obesity and metabolic-immune comorbidities.
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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) | 0.001 | 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) | 0.001 | 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".