Successful Outcome of a Patient with Concomitant Pancreatic and Renal Carcinoma Receiving Secoisolariciresinol Diglucoside Therapy Alone: A Case Report
Bibliographic record
Abstract
Introduction: Pancreatic cancer (PC) is among the deadliest malignancies. Kidney cancer (KC) is a common malignancy globally. Chemo- or radio-therapies are not very effective to control PC or KC, and overdoses often cause severe site reactions to the patients. As a result, novel treatment strategies with high efficacy but without toxic side effects are urgently desired. Secoisolariciresinol diglucoside (SDG) belongs to plant lignans with potential anticancer activities, but clinical evidence is not available in PC or KC treatment. Patient Concerns: We report a rare case of an 83-year-old female patient with pancreatic and kidney occupying lesions that lacked the conditions to receive surgery or chemo- or radiotherapy. Diagnosis: Pancreatic and kidney cancers. Interventions: We gave dietary SDG to the patient as the only therapeutics. Outcomes: SDG effectively halted progression of both PC and KC. All clinical manifestations, including bad insomnia, loss of appetite, stomach symptoms, and skin itching over the whole body, all disappeared. The initial massive macroscopic hematuria became microscopic and infrequent, and other laboratory results also gradually returned to normal. Most of the cancer biomarkers, initially high such as CEA, CA199, CA724, CA125, came down rapidly, among which CA199 changed most radically. This patient has had progression-free survival of one year so far. Conclusion: These results demonstrate the potent inhibitory effects of SDG on PC and KC of this patient and provide promising novel therapeutics for refractory malignant tumors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.005 |
| 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".