Smart co-delivery of Erlotinib and Camptothecin using silica-coated gold nanorods functionalized with recombinant anti-bone morphogenetic protein receptor type I (BMPR-AI)
Bibliographic record
Abstract
Non-small cell lung carcinoma is a highly aggressive cancer with a poor prognosis. Although Erlotinib (ELT) and Camptothecin (CPT) are commonly used together in chemotherapy, their effectiveness is limited when given as free drugs. To improve their efficacy, we developed a novel nanomedicine consisting of gold nanorods (Au-NRs) coated with a functionalized silica network to deliver both drugs simultaneously. This approach aims to enhance cancer cell targeting, inhibit cell proliferation, and induce apoptosis. The nanomedicine was further engineered with a recombinant anti-BMP receptor AI (BMPR-AI) single-chain variable fragment (scFv) fused with maltose-binding protein for targeted delivery. Successful coating and functionalization were confirmed through various analyses, including HR-TEM, EDS/EDAX, zeta potential measurements, and FT-IR. The resulting CPT/ELT/scFv@Au-NR nanomedicine effectively targeted BMPR-AI-overexpressing cancer cells (A549) while showing minimal cytotoxicity toward normal lung fibroblasts (MRC-5), significantly inhibiting growth and inducing apoptosis more efficiently than the free drugs. This promising strategy demonstrates enhanced cytotoxic effects and holds potential for more effective chemotherapy and future advances in cancer treatment.
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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".