Design, synthesis and photocytotoxicity of upconversion nanoparticles: Potential applications for near‐infrared photodynamic and photothermal therapy
Bibliographic record
Abstract
Abstract Photodynamic therapy (PDT) and photothermal therapy (PTT) are emerging modalities for the treatment of tumors and nonmalignant conditions, based on the use of photosensitizers to generate singlet oxygen or heat, respectively, upon light (laser) irradiation. They have potential advantages over conventional treatments, being minimally invasive with precise spatial‐temporal selectivity and reduced side effects. However, most clinically employed PDT agents are activated at visible (vis) wavelengths for which the tissue penetration and, hence, effective treatment depth are compromised. In addition, the lipophilicity of near‐infrared (NIR) photothermal agents limits their use and efficiency. To achieve combined PDT/PTT effects, both excitation wavelengths need to be tuned into the NIR spectral window of biological tissues. This paper reports the synthesis of neodymium‐doped upconversion nanoparticles (NaYF 4 :Yb,Er,Nd@NaYF 4 :Nd) that convert 800 nm light into vis wavelengths, which can then activate conventional photosensitizers on the nanoparticle surface for PDT. Covalently bonded IR‐780 dyes can readily be activated by 800 nm laser irradiation. The PEGylated nanoplatform exhibited a narrow size distribution, good stability and efficient generation of singlet oxygen under laser irradiation. The in vitro photocytotoxicity of this engineered nanoplatform as either a PDT or PTT agent in HeLa cells is demonstrated, while fluorescence microscopy in nanoplatform‐incubated cells highlights its potential for bioimaging.
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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 | 0.000 | 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 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".