Synergistic anticancer enhancement: Sericin nanogels co-delivery of anthocyanin and cancer drugs irinotecan, paclitaxel, and oxaliplatin
Bibliographic record
Abstract
In this study, we investigated the potential of a food derivative sericin nanogel-anthocyanin (SNG-C3G) nanocomposite to enhance the anticancer effects of various chemotherapeutic drugs, including irinotecan (IRI), paclitaxel (PTX), and oxaliplatin (OXA), on different cell lines such as HeLa, HSEC, and HEK293T. The SNG-C3G nanocomposites exhibited a uniform spherical morphology, and upon co-encapsulation with either IRI or PTX, the particle size and zeta potential remained relatively consistent, ranging from 24.96 to 29.44 nm and −18.37 to −23.67 mV, respectively. Our findings indicate a marked increase in the anticancer effectiveness when these drugs are combined with the SNG-C3G nanocomposite. Focusing on the interaction with PTX, our proteomic analysis revealed significant changes in cell behavior, including cytoprotective mechanisms, ligand interactions, stress response, and pathways associated with oxidative and metabolic detoxification. Specifically, 13 proteins were upregulated and 10 were downregulated in the SNG-C3G-PTX group compared to the PTX-only group. The strong antioxidant properties of C3G might stand out as a key factor in enhancing these effects. This study suggests the potential for developing new cancer treatment strategies that utilize anthocyanins to boost the effectiveness of traditional chemotherapy protocols.
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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) | 0.000 | 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) | 0.000 | 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 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".