Reduction of Inflammatory Hyperplasia in the Intestine in Colon Cancer‐prone Mice by Water‐extract of <i>Cistanche deserticola</i>
Bibliographic record
Abstract
Cistanche deserticola has commonly been used in traditional Chinese medicine to treat many health problems including irritable bowel syndrome or constipation. This study was designed to test the efficacy of a water-extract of C. deserticola in the prevention of colorectal cancer in a mouse model. Polysaccharide-rich water-extract of C. deserticola was prepared by boiling its stem powder in distilled water. Tgfb1Rag2 null mice were used as an experimental model. Here we showed that feeding of water-extract of C. deserticola significantly reduced the number of mucosal hyperplasia and intestinal helicobacter infection in mice. This beneficial effect correlated with significant stimulation of the immune system, evidenced by the enlargement of the spleens with increased number of splenic macrophage and natural killer cells, and with more potent cytotoxicity of splenocytes. In vitro water-extract of C. deserticola enhanced the cytotoxicity of naïve splenocytes against a human colon cancer cell line, and in macrophage cultures up-regulated nitric oxide synthase II expression and stimulated phagocytosis. In conclusion, our data indicate that oral administration of C. deserticola extract reduces inflammatory hyperplastic polyps and helicobacter infection in mice by its immune-stimulatory activity, suggesting that C. deserticola extract may have potential in preventing intestinal inflammation disorders including colorectal cancer.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".