Targeted delivery of epirubicin to cancerous cell using copper sulphide nanoparticle coated with polyarginine and 5TR1 aptamer
Bibliographic record
Abstract
Chemotherapy has been widely acknowledged as a primary approach for cancer treatment. However, the administration of chemotherapy agents is often limited by their adverse effects that result from an inability to distinguish between healthy and malignant cells. As such, utilising nanocarriers in targeted drug delivery can significantly reduce these side effects while enhancing therapeutic efficacy. Herein, we developed copper sulphide nanoparticles (CuSNPs) loaded with epirubicin (Epi) coated by polyarginine and 5TR1 aptamer (CEPA) to target mucin-1 which is overexpressed on various types of cancer cells. MTT results revealed that CEPA significantly induced cytotoxicity of the drug in desired cell lines (C26 and MCF-7, mucin+) compared to CEPA-treated CHO cells (non-target, mucin−), verifying the targeting ability of CEPA complex. The obtained results from both flow cytometry analysis and cell imaging demonstrated that CEPA complex had successful internalisation in both target cell lines but no internalisation in CHO cell line. The result of in vivo assay showed more tumour inhibition and more accumulation in tumour tissue for CEPA complex in comparison to free Epi. To conclude, the CEPA complex has demonstrated superior efficacy and fewer adverse reactions compared to Epi. This indicates a promising and effective strategy for treating cancer.
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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".