Synergistic breast cancer therapy with RGD-decorated liposomes co-delivering mir-34a and cisplatin
Bibliographic record
Abstract
To overcome the drug resistance in MCF7 cancer cells and enhance their sensitivity to cisplatin, a liposomal (Lip) nanocarrier modified by arginine-glycine-aspartic acid (RGD) to co-deliver cisplatin (Cis) and miRNA biomolecule (miR-34a) was investigated. The efficiency of this nanocarrier was evaluated through in vitro and in vivo assays against MCF7 and 4T1 cells, respectively. The in vitro results demonstrated that the miR34a-Cis-Lip-RGD formulation had significantly higher efficiency and also a higher apoptotic effect compared to both miR34a-Cis and miR34a-Cis-Lip (76.24%, 58.29%, and 56.2%, respectively). Additionally, miR34a-Cis-Lip exhibited an overall CI value below 1, indicating a synergistic effect of Cis and miR-34a within the Lip system. The miR34a-Cis-Lip-RGD increased the Bax gene expressions compared to both miR34a-Cis-Lip and Cis-miR34a, possibly due to the integrin receptors on the cells, leading to higher uptake. The efficiency of miR34a-Cis-Lip-RGD in reducing tumor size was significantly higher than Cis-miR34a and miR34a-Cis-Lip. The lower volume of the tumor in the group treated with Cis-miR34a-Lip-RGD is presumed to be attributed to improved cellular uptake facilitated by the RGD modification, which enhances the targeted delivery of the therapeutic payload to cancer cells. The overall weight of the mice in all the groups did not exhibit significant changes. This consistent weight maintenance implies the safety of the designed delivery system for vital organs, indicating that the designed delivery system may offer a promising solution to minimize unwanted side effects associated with conventional 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) | 0.001 | 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) | 0.001 | 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".