One step synthesis of ultrafine PHF@AuNPs nanocomposite and its application in NIR triggered photodynamic therapy
Bibliographic record
Abstract
Photodynamic therapy (PDT) is a rapid advancing treatment for cancer therapy. The main challenges in PDT include poor absorption in the "tissue optical window" and aggregation tendency of photosensitizers (PS) such as fullerene in aqueous solutions. Herein, we developed a potent nano PS: fullerene hybrid gold nanoparticles (AuNPs) composites which were ultrafine and well-dispersed with a absorption in near infrared (NIR) region. The composites could be facilely prepared by mixing the reducing and capping agent polyhydroxyl fullerene (2 mg/mL) with HAuCl4 (2.425 mM) at equal volume for 2 h. The obtained composites were negatively charged (-26.3 mv) with the particle size of 14.3 nm. A thin layer of fullerene (~1.6 nm) was coated on the AuNPs core. AuNPs in the composites acted as the light collector, absorbing the NIR light and transferring electrons or energy to the fullerene. Consequently, the composites can be efficiently internalized by tumor cells and activated to produce reactive oxygen species (ROS) intracellularly by 808 nm laser. Enhanced PDT efficacy was observed with the IC50 value (50 μg/mL) of the light-activated cytotoxicity and a negligible dark toxicity in vitro. This research provides new insights and methods for developing NIR light-triggered fullerene@AuNPs in PDT.
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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".