Are upconverting Ln<sup>3+</sup>based nanoparticles any good for deep tissue imaging with retention of optical sectioning?
Bibliographic record
Abstract
An effective strategy is presented to make spherical Ln3+ doped NaYF4 nanoparticles that show upconversion, with the aim of deep-tissue optical imaging. Upconversion is the conversion of two or more low-energy photons into one of higher energy, e.g. 980 nm to 545 and 680 nm and 980 nm to 800 nm. In order to avoid the formation of nanoparticles with an aspect ratio, we developed a strategy in which subsequent shells were grown on spherical seed nanoparticles. The last shell is undoped in order to improve the optical properties. In addition, a simple intercalation strategy involving the oleate ligands on the surface has been developed to make the nanoparticles dispersible in aqueous solutions and physiological buffers. Two-photon upconversion laser scanning microscopy (TPULSM) and two-photon upconversion wide-field microscopy (TPUWFM) have been tested for their suitability in deep-tissue imaging with retention of lateral and depth resolution (also called optical sectioning). TPULSM can be used up to ~ 600 μm deep, but takes inordinately long times to acquire, which is due to the fact that the absorption cross section of Yb3+ is low, the quantum yield of the upconversion process are << 1%, and the Ln3+ excited states are up to several hundreds of μs. Hence UCNPs in general are not very bright (i.e. large emitted photon flux). The TPUWFM seems more promising because acquisition times are only several minutes, with depth profiling up to 400 μm. We show the first optical sectioning with this technique in the brain of a mouse, through a thin shaved skull.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".