Computational development of mushroom-6-glucan/paclitaxel as a synergistic complementary medicine for breast cancer therapy
Bibliographic record
Abstract
Abstract Background Breast cancer is chemo-resistant and highly metastatic, often resulting in patient mortality. One of the primary factors contributing to the metastasis and chemotherapy resistance is the presence of cancer stem-like cells. We posited that the natural polysaccharide known as 6-glucans, derived from Pleurotus ostreatus, could effectively counteract the chemotherapy resistance associated with cancer stem-like cells in breast cancer. Methods We computationally developed a specific dual combinatorial therapy involving 6-glucans and Paclitaxel (PTX) and tested on preclinical 3D mammosphere human tumor models representing receptor-positive and receptor-negative breast cancer. Using this preclinical 3D spheroid technology, we tested the anti-cancer properties of these predicted treatment combinations on mammospheres containing human breast cancer stem cells. Results Among the 40 distinct combinations examined, computational prediction revealed that the addition of 2.0 mg/mL of 6-glucans to a low dose of 3.0 µg/mL PTX was the sole combination demonstrating a synergistic effect. This optimized synergistic combination therapy displayed a significant inhibitory impact on human cancer epithelial and stem cell migration, evasion, and colony formation. The inclusion of 6-glucans also augmented apoptosis in both breast cancer cells and stem cells, leading to a six-fold reduction in BrdU labeled cells and an increased arrest of cells in the sub-G0 phase. These effects were mediated through mitochondrial dysfunction and the downregulation of associated oncogenes. Conclusion Our study revealed that the computationally predicted 6-glucans-based binary complementary medicine exhibited sequence- and concentration-dependent anticancer synergistic effects.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".