Data from St. John's Wort Attenuates Colorectal Carcinogenesis in Mice through Suppression of Inflammatory Signaling
Bibliographic record
Abstract
Abstract Despite widespread use as well as epidemiologic indications, there have been no investigations into the effect of St. John's wort (SJW) extract on colorectal carcinogenesis in vivo. This study reports a systematic evaluation of the impact of dietary supplementation of SJW extract on azoxymethane-induced colorectal carcinogenesis in mice. Mice were fed with either AIN-93G (control) diet or SJW extract–supplemented diet (SJW diet) prior to azoxymethane treatment. SJW diet was found to significantly improve the overall survival of azoxymethane-treated mice. Pretreatment with the SJW diet significantly reduced body weight loss as well as decrease of serum albumin and cholesterol levels associated with azoxymethane-induced colorectal tumorigenesis. SJW diet–fed mice showed a significant decrease in tumor multiplicity along with a decrease in incidence of large tumors and a trend toward decreased total tumor volume in a dose-dependent manner. A short-term study, which examined the effect of SJW prior to rectal bleeding, also showed decrease in colorectal polyps in SJW diet–fed mice. Nuclear factor kappa B (NF-κB) and extracellular signal–regulated kinase (ERK1/2) pathways were attenuated by SJW administration. SJW extract resulted in early and continuous attenuation of these pathways in the colon epithelium of SJW diet–fed mice under both short-term and long-term treatment regimens. In conclusion, this study demonstrated the chemopreventive potential of SJW extract against colorectal cancer through attenuation of proinflammatory processes. Cancer Prev Res; 8(9); 786–95. ©2015 AACR.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".