Improved Delivery of Mcl-1 and survivin siRNA combination in breast cancer cells with additive siRNA complexes
Bibliographic record
Abstract
Abstract This study aimed at investigating the influence of commercial transfection reagents (Prime-Fect, Leu-Fect A, and Leu-Fect C) complexed with different siRNAs (CDC20, HSP90, Mcl-1 and Survivin) in MDA-MB-436 breast cancer cells and the impact of incorporating an anionic additive, Trans-Booster, for improving in vitro gene silencing and delivery efficiency. Gene silencing was quantitatively analyzed by real-time RT-PCR while cell proliferation and siRNA uptake were evaluated by the MTT assay and flow cytometry, respectively. Amongst the investigated siRNAs and transfection reagents, Mcl-1/Prime-Fect complexes showed the highest inhibition of cell viability and most effective siRNA delivery. The effect of various formulations on transfection efficiency showed that a anionic additive with 1:1 ratio with siRNA was optimal achieving the lowest cell viability compared to untreated cells and negative control siRNA treatment (p<0.05). Furthermore, the combination of Mcl-1 and survivin siRNA suppressed the growth of MDA-MB-436 cells more effectively than treatment with the single siRNAs and resulted in a cell viability as low as ~20% (vs. non-treated cells). This aligned well with the induction of apoptosis as analyzed by flow cytometry, which revealed higher apoptotic cells with the combination treatment group. We conclude that commercial transfection reagents formulated with Mcl-1/Survivin siRNA combination could serve as a potent anti-proliferation agent in treatment of breast cancers.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".