(<i>Invited</i>) Rare Earth Nanoparticles: Advancing Light-Driven Theranostic Applications
Bibliographic record
Abstract
In recent years, nanoparticle-based "theranostic" agents have garnered significant attention for the treatment of various diseases, including cancer. This emerging paradigm in personalized medicine leverages nanoplatforms that integrate both therapeutic and diagnostic (theranostic) functionalities. Unlike separate delivery of drugs and imaging agents, theranostic agents enable simultaneous delivery to specific sites, facilitating disease detection and treatment in a single procedure. Many theranostic nanoplatforms are activated by light; however, most rely on UV or visible excitation light, which has limited utility in biological applications. In contrast, rare earth doped nanoparticles (RENPs) can be excited using biologically compatible near-infrared (NIR) light that exploits the biological windows. RENPs exhibit unique luminescence properties, including multiphoton upconversion luminescence in the UV, visible, or NIR regions and simultaneous single-photon luminescence in the NIR region. This dual emission capability allows upconversion luminescence to trigger therapeutic applications (e.g., drug delivery, photodynamic therapy) while the near-infrared luminescence serves as a diagnostic tool (e.g., bioimaging, nanothermometry). In this presentation, we will introduce RENPs and highlight their potential in theranostics. Specifically, we will demonstrate how complex RENP architectures can enhance functionality, including the ability to decouple theranostic processes that are traditionally performed simultaneously.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.008 |
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".