Molecules from American Ginseng Suppress Colitis through Nuclear Factor Erythroid-2-Related Factor 2
Bibliographic record
Abstract
Ulcerative colitis (UC) is a chronic inflammatory bowel disease that affects millions of people worldwide and increases the risk of colorectal cancer (CRC) development. We have previously shown that American ginseng (AG) can treat colitis and prevent colon cancer in mice. We further fractionated AG and identified the most potent fraction, hexane fraction (HAG), and the most potent compound in this fraction, panaxynol (PA). Because (1) oxidative stress plays a significant role in the pathogenesis of colitis and associated CRC and (2) nuclear factor erythroid-2-related factor 2 (Nrf2) is the master regulator of antioxidant responses, we examined the role of Nrf2 as a mechanism by which AG suppresses colitis. Through a series of in vitro and in vivo Nrf2 knockout mouse experiments, we found that AG and its components activate the Nrf2 pathway and decrease the oxidative stress in macrophages (mΦ) and colon epithelial cells in vitro. Consistent with these in vitro results, the Nrf2 pathway is activated by AG and its components in vivo, and Nrf2-/- mice are resistant to the suppressive effects of AG, HAG and PA on colitis. Results from this study establish Nrf2 as a mediator of AG and its components in the treatment of colitis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".