Bistratal Au@Bi2S3 nanobones for excellent NIR-triggered/multimodal imaging-guided synergistic therapy for liver cancer
Bibliographic record
Abstract
To fabricate a highly biocompatible nanoplatform enabling synergistic therapy and real-time imaging, novel [email protected]2S3 core shell nanobones (NBs) ([email protected]2S3 NBs) with Au nanorods as cores were synthesized. The combination of Au nanorods with Bi2S3 film made the [email protected]2S3 NBs exhibit ultrahigh photothermal (PT) conversion efficiency, remarkable photoacoustic (PA) imaging and high computed tomography (CT) performance; these [email protected]2S3 NBs thus are a promising nanotheranostic agent for PT/PA/CT imaging. Subsequently, poly(N-vinylpyrrolidone)-modified [email protected]2S3 NBs ([email protected]2S3-PVP NBs) were successfully loaded with the anticancer drug doxorubicin (DOX), and a satisfactory pH sensitive release profile was achieved, thus revealing the great potential of [email protected]2S3-PVP NBs in chemotherapy as a drug carrier to deliver DOX into cancer cells. Both in vitro and in vivo investigations demonstrated that the [email protected]2S3-PVP NBs possessed multiple desired features for cancer therapy, including extremely low toxicity, good biocompatibility, high drug loading ability, precise tumor targeting and effective accumulation. Highly efficient ablation of the human liver cancer cell HepG2 was achieved through [email protected]2S3-PVP NB-mediated photothermal therapy (PTT). As both a contrast enhancement probe and therapeutic agent, [email protected]2S3-PVP NBs provided outstanding NIR-triggered multi-modal PT/PA/CT imaging-guided PTT and effectively inhibited the growth of HepG2 liver cancer cells via synergistic chemo/PT 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".