Dietary phenolic-type Nrf2-activators: implications in the control of toxin-induced hepatic disorders
Bibliographic record
Abstract
Numerous studies have exemplified the importance of nuclear factor erythroid 2-related factor 2 (Nrf2) activation in the alleviation of toxin-induced hepatic disorders primarily through eliminating oxidative stress. Whereafter, increasingly more efforts have been contributed to finding Nrf2-activators, especially from dietary polyphenols. The present review summarized the phenolic-type Nrf2-activators published in the past few decades, analyzed their effectiveness based on their structural characteristics and outlined their related mechanisms. It turns out that flavonoids are the largest group of phenolic-type Nrf2-activators, followed by nonflavonoids and phenolic acids. When counting on subgroups, the top three types are flavonols, flavones, and hydroxycinnamic acids, with curcuminoids having the highest effective doses. Moreover, most polyphenols work through the phosphorylation of Nrf2. Besides, mitogen-activated protein kinases (MAPKs) and protein kinase B (Akt) are the frequent targets of these Nrf2-activators, which indirectly mediate the behavior of Nrf2. However, current data are not sufficient to conclude any structure-activity relationship.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".