Evaluation of a Synthetic PEI-based Polymeric Vector for ING4 Gene Delivery to MCF-7 Breast Cancer Cells
Bibliographic record
Abstract
Objectives: Breast cancer is the most common cancer type among women and is the second most common cause of death after lung cancer.INhibitor of Growth (ING) transcript levels is often suppressed in cancer cells, which makes it a promising candidate for cancer therapy.In this study, it was aimed to formulate a polyplex that effectively carry and deliver pING4 to breast cancer cells.Materials and Methods: PEI (Polyethyleneimine)-based non-viral vectors were synthesized and characterized for plasmid DNA delivery.Complexation was carried out by electrostatic interactions between the synthesized polymeric vector and plasmid DNAs.Characterization studies were carried out by testing SDS-induced decomplexation, DNase I protection and serum stability of polyplexes.Subsequently, polyplexes tested on MCF-7 cells for anticancer activity with XTT cell viability assay.Finally, western blot analysis performed against ING4 protein.Results: Polyplexes that carried ING4 gene showed significantly lower cell viability than the control polyplexes.During the 5-day viability assay, lowest cell viability observed in day 4. Approximately 69% cell viability observed with ING4 treatment while control group showing U N C O R R E C T E D P R O O F no cell death at day 4. Which means prepared delivery systems didn't show a toxic effect on MCF-7 cells when treated alone.Moreover, MCF10A normal mammary cell line used as a positive control.For the confirmation of overexpressed ING4 protein in treatment groups, western blot assay conducted.Unlike the control groups, the overexpression of ING4 protein was clear in wells with treatment group.Conclusion: With aforementioned results, our work suggests that ING4 gene delivery with prepared PEI-based non-viral delivery systems is a promising approach for breast cancer treatments.
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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) | 0.000 | 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 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".