Cholesterol grafted cationic lipopolymers: Potential siRNA carriers for selective chronic myeloid leukemia therapy
Bibliographic record
Abstract
Synthetic siRNA technology has emerged as a promising approach for molecular therapy of cancer but, despite its potential for post-transcriptional gene silencing, there is an urgent need to develop efficient delivery systems particularly for difficult-to-transfect, anchorage-independent cells. In this study, we designed highly hydrophobic cationic lipopolymers by grafting cholesterol (Chol) onto low-molecular weight (0.6, 1.2, and 2.0 kDa) polyethylenimines (PEIs) to enable specific siRNA therapy to chronic myeloid leukemia (CML) cells. The siRNA binding by PEI-Chol led to nano-sized (100-200 nm diameter) polyplexes with enhanced ζ-potential (+20 to +35 mV) and ability to protect the loaded siRNA completely in fresh serum. The siRNA delivery to CML (K562) cells was proportional to degree of substitution and, unexpectedly, inversely proportional to molecular size of the polymeric backbone. Chol grafting with as little as ~1.0 Chol/PEI on 0.6 and 1.2 kDa PEIs enabled silencing of the reporter Green Fluorescent Protein gene as well as the endogenous BCR-Abl oncogene in K562 cells. The PEI-Chol mediated delivery of siRNAs specific for BCR-Abl and KSP genes significantly arrested the growth the cells which was significantly reflected in colony formation potency of K562 cells. BCR-Able siRNA mediated therapeutic efficacy was also observed in significantly increased caspase activity and apoptosis of K562 cells. Thus, Chol-grafted low-molecular weight PEIs appear to be unique siRNA carriers to realize the molecular therapy in CML cells.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".