Cuproptosis-based layer-by-layer silk fibroin nanoplatform-loaded PD-L1 siRNA combining photothermal and chemodynamic therapy against metastatic breast cancer
Bibliographic record
Abstract
Cuproptosis is a newly identified form of copper-dependent cell death that differs from other known pathways. This discovery provides a new way to explore copper-based nanomaterial applications in cancer therapy. This study used a layer-by-layer self-assembling method to load Cu 2-x S nanoparticle (NP) cores with the siRNA of the PD-L1 immune escape-related gene and wrap a silk fibroin (SF) shell to form a multifunctional copper-based SF nanoplatform, denoted as CuS-PEI-siRNA-SFNs. CuS-PEI-siRNA-SFNs induced cuproptosis and exerted an antitumor effect via multiple mechanisms, including photothermal therapy (PTT), chemodynamic therapy (CDT), and immune activation. The presence of significant dihydrolipoamide S -acetyltransferase (DLAT) oligomers in 4T1 cells treated with CuS-PEI-siRNA-SFNs indicated the triggering of cuproptosis. Furthermore, in vivo experimental results showed that CuS-PEI-siRNA-SFNs efficiently accumulated in the tumor tissues of 4T1 tumor-bearing mice inhibited primary tumor and lung metastasis, and displayed excellent biosafety and antitumor activity. This study demonstrated that the synergistic effect of cuproptosis, PTT, CDT, and immune activation showed promise for treating metastatic breast cancer. • Core-shell nanoparticles were prepared by layer-by-layer self-assembling method. • Synergistic effect of cuproptosis and other treatment for metastatic breast cancer. • The effect of inhibiting lung metastasis of breast cancer was significant. • The shell structure of silk fibroin improved the biocompatibility and therapeutic effect.
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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".