Co-delivery of miR-181a and melphalan by lipid nanoparticles for treatment of seeded retinoblastoma
Bibliographic record
Abstract
Melphalan is an efficient chemotherapeutic agent that is currently used to treat retinoblastoma (Rb); however, the inherent risk of immunogenicity and the hazardous integration of this drug in healthy cells is inevitable. MicroRNAs are short non-coding single-stranded RNAs that affect a vast range of biological processes. Previously, we focused on the regulatory role of miR-181a during cancer development and progression. In this manuscript, 171 nm switchable lipid nanoparticles (LNP) co-delivered melphalan and miR-181a with encapsulation efficiencies of 93%. Encapsulation of melphalan in LNP significantly improved its therapeutic efficiency. Gene analysis shows that miR-181a decreases the expression of anti-proliferative gene MAPK1 and anti-apoptotic gene Bcl-2, but significantly increased the expression of pro-apoptotic gene BAX. Our results suggest that the two agents have a complementary effect in reducing the viability of cultured Rb cells (primary and cell line) and decreasing Rb cell counts in an in-vivo xenograft Rb model in rats. Our results suggest that the proposed co-delivery technique significantly increases the therapeutic impact, allows for lower administration of melphalan, and consequently, could minimize the cytotoxic side-effects of this drug.
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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".