Decoupling Theranostics with Rare Earth Doped Nanoparticles
Bibliographic record
Abstract
Abstract Theranostic nanoagents targeted for personalized medicine provide a unified platform for therapeutics and diagnostics. To be able to discretely control each individually, allows for safer, more precise, and truly multifunctional theranostics. Rare earth doped nanoparticles can be rationally tailored to best match this condition with the aid of core/shell engineering. In such nanoparticles, the light‐mediated theranostic approach is functionally decoupled—therapeutics or diagnostics are prompted on‐demand, by wavelength‐specific excitation. These decoupled rare earth nanoparticles (dNPs) operate entirely under near‐infrared (NIR) excitation, for minimized light interference with the target and extended tissue depth action. Under heating‐free 806 nm irradiation, dNPs behave solely as high‐contrast NIR‐to‐NIR optical markers and nanothermometers, visualizing and probing the area of interest without prompting the therapeutic effect beforehand. On the contrary, 980 nm NIR irradiation is upconverted by the dNPs to UV/visible light, which triggers secondary photochemical processes, e.g., generation of reactive oxygen species by photosensitizers coupled to the dNPs, causing damage to cancer cells. Additionally, integration of NIR nanothermometry helps to control the temperature in the vicinity of the dNPs avoiding possible overheating and quenching of upconversion (UC) emission, harnessed for photodynamic therapy. Overall, a new direction is outlined in the development of state‐of‐the‐art rare earth based theranostic nanoplatforms.
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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".