Erratum to: Bioactive terpenoids and flavonoids from Daucus littoralis Smith subsp. hyrcanicus Rech.f, an endemic species of Iran
Bibliographic record
Abstract
Adipose tissue is a primary site of obesity-induced inflammation, which has been emerging as an important contributor to obesity associated disorders.The factors influencing adipose tissue-induced inflammation and the resulting pathophysiological events remain poorly understood.However, dietary fiber consumptions appear to be protective.Short-chain fatty acids such as propionic acid (PA) are the principal products of the dietary fiber fermentation by microbiota.Therefore, we aim to investigate the influence of PA on inflammation, lipogenesis and glucose uptake markers from human subcutaneous adipose tissue (SAT).We showed that the treatment of SATwith PA resulted in a significant downregulation of inflammatory parameters (e.g.TNF-α and IP-10) and macrophage markers (e.g.CD163 and MMP-9).The expression levels of PA receptors (i.e.G protein coupled receptor-41 and -43) in human primary adipocytes were very low in comparison with SATand macrophages.Upon PA treatment, no anti-inflammatory effect was observed in human adipocytes.PA significantly upregulated the expression of lipoprotein lipase (LPL), sterol regulatory-element-binding protein-1c (SREBP-1c) and glucose transporter 4 (GLUT-4), which are associated with lipogenesis and glucose uptake.We also showed that the observed anti-inflammatory effects of PA on SATwere partly mediated by Gi/o protein coupled receptor.Our data suggests that PA anti-inflammatory effects on SATare mediated partly via Gi/o proteins, leading to the improved expression of factors associated with lipogenesis and glucose uptake.These responses appeared to be not mediated by adipocytes; but most probably by macrophages.The current study provides new knowledge, which can be used as a potential new avenue for drug development in preventing obesityrelated inflammation and metabolic disorders in future.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".