High Resolution Fluorescence Imaging of Cancers Using Lanthanide Ion-Doped Upconverting Nanocrystals
Bibliographic record
Abstract
During the last decade inorganic luminescent nanoparticles that emit visible light under near infrared (NIR) excitation (in the biological window) have played a relevant role for high resolution imaging of cancer. Indeed, semiconductor quantum dots (QDs) and metal nanoparticles, mostly gold nanorods (GNRs), are already commercially available for this purpose. In this work we review the role which is being played by a relatively new class of nanoparticles, based on lanthanide ion doped nanocrystals, to target and image cancer cells using upconversion fluorescence microscopy. These nanoparticles are insulating nanocrystals that are usually doped with small percentages of two different rare earth (lanthanide) ions: The excited donor ions (usually Yb3+ ion) that absorb the NIR excitation and the acceptor ions (usually Er3+, Ho3+ or Tm3+), that are responsible for the emitted visible (or also near infrared) radiation. The higher conversion efficiency of these nanoparticles in respect to those based on QDs and GNRs, as well as the almost independent excitation/emission properties from the particle size, make them particularly promising for fluorescence imaging. The different approaches of these novel nanoparticles devoted to "in vitro" and "in vivo" cancer imaging, selective targeting and treatment are examined in this review.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".