Bibliographic record
Abstract
In recent years, a significant amount of research has focused on nanoparticle-based “theranostic” agents for the treatment of a wide array of diseases, including cancer. This new paradigm in personalized medicine intends to exploit nanoplatforms that carry both therapeutic and diagnostic (theranostic) modalities. Compared with delivering drugs or imaging agents separately, theranostic agents can simultaneously deliver them to specific sites, enabling detection and treatment of disease in a single procedure. Many theranostic nanoplatforms are triggered by light, however, the vast majority of these are limited in that the ultraviolet or visible excitation light used has minmal applicability in biological applications. Lanthanide doped nanoparticles, on the other can be excited with biologically friendly near-infrared light (in the biological windows) and can emit in the ultraviolet, visible or near-infrared regions through a multiphoton upconversion process while simultaneously emitting in the near-infrared region through a Stokes or downshifted process. Hence, the upconversion luminescence can be used to trigger the therapeutic application (drug delivery, photodynamic therapy, etc.) while the near-infrared luminescence can be used for the diagnostic modality (bioimaging, nanothermometry, etc). In this presentation, we will introduce lanthanide doped nanoparticles and demonstrate their usefulness in theranostics. In particular, we will show complex nanoparticle architectures can endow further functionality to these nanoparticles including the ability to decouple the theranostic processes that are conventionally delivered 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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.018 | 0.009 |
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".