<em>Lentinula edodes </em>Cultured Extract Treated Mesenchymal-Derived Extracellular Vesicle Modulated Breast Cancer Stem Cells and Reprogramming of microRNAs
Bibliographic record
Abstract
Background: Breast carcinoma represents the most frequent malignancy in women in Canada. The intrinsic or acquired drug resistance significantly contributes to in-creased risk of recurrence and metastasis. Although, front-line therapy is multimodal, chemoresistance remains a major hurdle in treatment and therapy. Intake of natural compounds issued from the fermentation processes are now considered a god strategy to help overcome chemoresistance. Materials and Methods: Extracellular vehicles (EVs) from Mesenchymal Stro-mal/Stem Cells (MSCs) pretreated with Lentinula edodes cultured extract (AHCC) were used to study the effect on reducing chemoresistance and modulating mi-croRNAs in cell lines, MCF-7 and MCF-7/DOX cells by EVs derived from MSCs under AHCC treatment. Characterization of EVs was done by using nanoparticles tracking analysis. MicroRNas and the formation of cancer stem cells were studied. Results: miRNA analysis revealed that AHCC remarkably affected the expression of several microRNAs, amongst which are miR-155, miR-34a, miR-Let7 and miR-200c. In vitro experiments showed inhibition of cancer stem cell proliferation after challenging the cells with EVs pretreated with AHCC. Conclusion: Our data demonstrated that AHCC may contribute to the modulation of tumor microenvironment, thus influenc-ing the development of cancer stem cells.
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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 | 0.001 |
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".