Sub 20 nm Upconversion Photosensitizers for Near‐Infrared Photodynamic Theranostics
Bibliographic record
Abstract
Abstract Efficient type II photodynamic therapy (PDT) requires stable and biocompatible photosensitizers (PS) that present low dark cytotoxicity, are photo‐excitable in deep tissue regions, and can efficiently penetrate and kill cells via in situ singlet oxygen production. Here, heavy‐metal‐free organic PS are combined with near‐infrared (NIR)‐excitable small (<20 nm) upconversion nanoparticles (UCNPs) into UCNP‐PS nanohybrids for accomplishing such advanced PDT conditions. UCNP‐to‐PS energy transfer efficiencies between 11% and 42% and 1O2 generation quantum yields between 74% and 86% resulted in efficient NIR‐sensitized PDT. HeLa cells incubated with UCNP‐PS can be efficiently destroyed via 808 nm laser irradiance at 140 mW cm−2 for 3 min (<30% cell viability) or 3.2 W cm−2 for 6 min (<10% cell viability). Theranostic functionality of UCNP‐PS is demonstrated via live cell in situ imaging of intracellular UCNP‐PS‐mediated 1O2 production, which resulted in cell death, most probably via apoptosis. Preliminary in vivo experiments are also performed and the consequences for a detailed in vivo study toward clinical translation are discussed. The combined PDT and deep‐tissue imaging properties of the nanomolecular PS present a large potential for future implementation into advanced in vivo photodynamic theranostics.
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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) | 0.002 | 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".