In Situ Proapoptotic Peptide-Generating Rapeseed Protein-Based Nanocomplexes Synergize Chemotherapy for Cathepsin-B Overexpressing Breast Cancer
Bibliographic record
Abstract
Intracellular activation of nanomaterials within cancer cells presents a powerful means to enhance anticancer specificity and efficacy. In light of upregulated lysosomal protease cathepsin-B (CathB) in many types of invasive cancer cells, herein, we exploit CathB-catalyzed biodegradation of acetylated rapeseed protein isolate (ARPI) to design polymer-drug nanocomplexes that can produce proapoptotic peptides in situ and synergize chemotherapy. ARPI forms nanocomplexes with chitosan (CS) and anticancer drug doxorubicin (DOX) [DOX-ARPI/CS nanoparticles (NPs)] by ionic self-assembly. The dual acidic pH- and CathB-responsive properties of the nanocomplexes and CathB-catalyzed biodegradation of ARPI enable efficient lysosomal escape and nuclei trafficking of released DOX, resulting in elevated cytotoxicity in CathB-overexpressing breast cancer cells. The ARPI-derived bioactive peptides exhibit synergistic anticancer effect with DOX by regulating pro- and antiapoptotic-relevant proteins ( p53, Bax, Bcl-2, pro-caspase-3) at mitochondria. In an orthotopic breast tumor model of CathB-overexpressing breast cancer, DOX-ARPI/CS NPs remarkably inhibit tumor growth, enhance tumor cell apoptosis and prolong host survival without eliciting any systemic toxicity. These results suggest that exploitation of multifunctional biomaterials to specifically produce anticancer agents inside cancer cells and trigger drug release to the subcellular target sites is a promising strategy for designing effective synergistic nanomedicines with minimal off-target toxicity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".