Designing Nanomedicines for Breast Cancer Therapy
Bibliographic record
Abstract
In 2020, breast cancer became the most diagnosed cancer worldwide. Conventional chemotherapies have major side effects due to their non-specific activities. Alternatively, short interfering RNA(siRNA)-carrying nanoparticles (NPs) have a high potential to overcome this non-specificity. Lipid-substituted polyethyleneimine (PEI) polymers (lipopolymers) have been reported as efficient non-viral carriers of siRNA. This study aims to engineer novel siRNA/lipopolymer nanocomplexes by incorporating anionic additives to obtain gene silencing through siRNA activity with minimal nonspecific toxicity. We first optimized our polyplexes in GFP+ MDA-MB-231 cells to effectively silence the GFP gene. Inclusion of phosphate buffer with pH 8.0 as complex preparation media and N-Lauroylsarcosine Sodium Salt as additive, achieved ~80% silencing with the least amount of undesired cytotoxicity, which was persistent for at least 6 days. The survivin gene was then selected as a target in MDA-MB-231 cells since there is no strong drug (i.e., small organic molecule) for inhibition of its oncogenic activity. The qRT-PCR, flow cytometry analysis and MTT assay revealed >80% silencing, ~95% cell uptake and >70% cell killing by the same formulation. We conclude that our lipopolymer can be further investigated as a lead non-viral carrier for breast cancer gene therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".