Co-delivery of paclitaxel and cetuximab by nanodiamond enhances mitotic catastrophe and tumor inhibition
Bibliographic record
Abstract
The poor intracellular uptake and non-specific binding of anticancer drugs into cancer cells are the bottlenecks in cancer therapy. Nanocarrier platforms provide the opportunities to improve the drug efficacy. Here we show a carbon-based nanomaterial nanodiamond (ND) that carried paclitaxel (PTX), a microtubule inhibitor, and cetuximab (Cet), a specific monoclonal antibody against epidermal growth factor receptor (EGFR), inducing mitotic catastrophe and tumor inhibition in human colorectal cancer (CRC). ND-PTX blocked the mitotic progression, chromosomal separation, and induced apoptosis in the CRC cells; however, NDs did not induce these effects. Conjugation of ND-PTX with Cet (ND-PTX-Cet) was specifically binding to the EGFR-positive CRC cells and enhanced the mitotic catastrophe and apoptosis induction. Besides, ND-PTX-Cet markedly decreased tumor size in the xenograft EGFR-expressed human CRC tumors of nude mice. Moreover, ND-PTX-Cet induced the mitotic marker protein phospho-histone 3 (Ser10) and apoptotic protein active-caspase 3 for mitotic catastrophe and apoptosis. Taken together, this study demonstrated that the co-delivery of PTX and Cet by ND enhanced the effects of mitotic catastrophe and apoptosis in vitro and in vivo, which may be applied in the human CRC therapy.
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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) | 0.001 | 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".