Amphiphilic Janus nanoparticles for imaging-guided synergistic chemo-photothermal hepatocellular carcinoma therapy in the second near-infrared window
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) is one of the most common and deadly malignant tumors worldwide. With unsatisfactory effects of traditional systematic chemotherapy for HCC owing to its drug resistance, novel therapeutic strategies based on nanomaterials for HCC treatments are promising solutions. To solve the challenges of nanoparticles (NPs)-based drug delivery systems for potential clinical applications, we designed water soluble amphiphilic oleic acid-NaYF4:Yb,Er/polydopamine Au nanoflower Janus NPs (OA-UCNPs/PDA-AuF JNPs) with discrete multi compartment nanostructures as dual-drug delivery systems (DDDSs). This unique nanostructure meets the requirements for containing hydrophobic hydroxycamptothecin/hydrophilic doxorubicin in divided spaces and releasing each drug from non-interfering channels under pH/near-infrared (NIR) dual-stimuli. The amphiphilic DDDSs were utilized to eradicate the tumor burden on a high-fidelity HCC model of a patient-derived xenograft (PDX), and represented an efficient strategy for defeating HCC using multi-modal imaging-guided dual-drug chemo-photothermal therapy in the second NIR window. In addition, the potential mechanisms of action for the DDDSs were evaluated.
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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".