Anticancer effects of dietary administration of secoisolariciresinol diglucoside in a patient of gastrointestinal stromal tumor: a case report
Bibliographic record
Abstract
Introduction: Gastrointestinal stromal tumor (GIST) is among the leading malignancies of the digestive system. GIST is not susceptible to chemotherapies and is prone to recurrence or metastasis after surgical operation. Secoisolariciresinol diglucoside (SDG) was suggested to have potential anticancer effects, but no clinical evidence had been available. Here we report successful treatment of a postoperative GIST case with dietary SDG. Presentation of Case: The patient was a 58-year-old man. He had GIST and received resection of small intestinal lesions 1 year before. He was diagnosed with postoperation of GIST and presented with high level of serum carbohydrate antigen 72-4 (CA72-4). The patient was then treated with dietary administration of SDG with his voluntary acceptance for 5 months, and underwent 2 serum CA72-4 measurements during this period. CA72-4 level of the patient was restored to normal range after treatment with dietary SDG for 1 month. There was no aberrant CA72-4 level, recurrence or metastasis after the treatment with dietary SDG. Discussion: This is to our knowledge the first report on application of dietary SDG on a postoperative GIST patient with aberrant level of serum CA72-4. SDG can be transformed into active substances with antitumor effects by human gut bacteria. Dietary SDG might inhibit tumorigenicity and malignant behavior of GIST cells. Conclusion: The excellent effects suggest dietary SDG to be a potential therapy for GIST, especially against recurrence or metastasis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".