Curcumin ameliorates hepatic fibrosis in type 2 diabetes mellitus – insights into its mechanisms of action
Bibliographic record
Abstract
UNLABELLED: A wide variety of beneficial effects have been attributed to curcumin, a major polyphenol from the golden spice Curcuma longa known as turmeric, including amelioration of severe complications of type 2 diabetes such as hepatic fibrosis, retinopathy, neuropathy and nephropathy. In the present issue of BJP, Lin and colleagues reveal new mechanisms by which curcumin inhibits the activation of hepatic stellate cells in vitro, a hallmark of non-alcoholic steatohepatitis and hepatic fibrogenesis associated with type 2 diabetes mellitus. They demonstrated that curcumin suppresses the advanced glycation end-products (AGEs)-mediated induction of the receptor for AGEs (RAGE) gene expression by increasing PPARγ activity and stimulating de novo synthesis of glutathione. As a result, downstream elements of RAGE-activated pathways are inhibited, which prevents oxidative stress, inflammation and hepatic stellate cell activation. This report suggests that curcumin may have potential as an anti-fibrotic agent in type 2 diabetes and opens the door to the evaluation of curcumin therapeutic effects in liver conditions of different aetiology and in other disorders linked to the impairment of PPARγ activity, such as obesity and atherosclerosis. LINKED ARTICLE: This article is a commentary on Lin et al., pp. 2212-2227 of this issue. To view this paper visit http://dx.doi.org/10.1111/j.1476-5381.2012.01910.x.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".