Acid Degradable Cationic Galactose-Based Hyperbranched Polymers as Nanotherapeutic Vehicles for Epidermal Growth Factor Receptor (EGFR) Knockdown in Cervical Carcinoma
Bibliographic record
Abstract
Strong signaling cascades derived from upregulation and overexpression of growth factors such as the EGF-family (epidermal growth factors) have been crucially related to cancer pathogenesis. Gene silencing techniques to modulate the expression of oncogenes and tumor suppresor genes are a strategy that shows great promise for cancer management but still faces some limitations in the design of biocompatible and effective vectors. In this study, we synthesized, by reversible addition-fragmentation chain transfer (RAFT) polymerization, several acid degradable galactose-based hyperbranched cationic polymers with varying molecular weights (10 to 20 kDa) and compositions with 2-lactobioamidoethyl methacrylamide [LAEMA] and 2-aminoethyl methacrylamide hydrochloride [AEMA] at different ratios (2.0, 1.0, and 0.5). These polymers were then evaluated for their ability to enhance Epidermal Growth Factor Receptor (EGFR) knockdown in cervical carcinoma. All the polymer constructs have enhanced capabilities to condensate siRNA (small interfering RNA), showing low toxicity at higher LAEMA:AEMA ratios (1.0 and 2.0). Western blot assays were conducted to quantify the EGFR expression of each treatment group demonstrating superior gene knockdown efficiency for the polymers having a LAEMA:AEMA ratio of 2.0 than the lower ratio counterparts; while maintaining low toxicity levels. Gene silencing of EGFR of up to 60% was achieved with acid degradable polymers having 10 kDa molecular weight and a LAEMA:AEMA ratio of 2.0. The superior stability of the polyplexes under physiological conditions and the low cytotoxicity observed in the 48 h post-transfection demonstrated the high potential of these acid degradable galactose-based hyperbranched cationic polymers for EGFR silencing treatment applications at the clinical level.
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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".