Critical Considerations on the Clinical Translation of Upconversion Nanoparticles (UCNPs): Recommendations from the European Upconversion Network (COST Action CM1403)
Bibliographic record
Abstract
The unique photoluminescent properties of upconversion nanoparticles (UCNPs) have attracted worldwide research interest and inspired many bioanalytical applications. The anti-Stokes emission with long luminescence lifetimes, narrow and multiple absorption and emission bands, and excellent photostability enable background-free and multiplexed detection in deep tissues. So far, however, in vitro and in vivo applications of UCNPs are restricted to the laboratory use due to safety concerns. Possible harmful effects may originate from the chemical composition but also from the small size of UCNPs. Potential end users must rely on well-founded safety data. Thus, a risk to benefit assessment of the envisioned combined therapeutic and diagnostic ("theranostic") applications is fundamentally important to bridge the translational gap between laboratory and clinics. The COST Action CM1403 "The European Upconversion Network-From the Design of Photon-Upconverting Nanomaterials to Biomedical Applications" integrates research on UCNPs ranging from fundamental materials synthesis and research, detection instrumentation, biofunctionalization, and bioassay development to toxicity testing. Such an interdisciplinary approach is necessary for a better and safer theranostic use of UCNPs. Here, the status of nanotoxicity research on UCNPs is compared to other nanomaterials, and routes for the translation of UCNPs into clinical applications are delineated.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".