1.3 μm emitting SrF2:Nd3+ nanoparticles for high contrast in vivo imaging in the second biological window
Bibliographic record
Abstract
Novel approaches for high contrast, deep tissue, in vivo fluorescence biomedical imaging are based on infrared-emitting nanoparticles working in the so-called second biological window (1,000–1,400 nm). This allows for the acquisition of high resolution, deep tissue images due to the partial transparency of tissues in this particular spectral range. In addition, the optical excitation with low energy (infrared) photons also leads to a drastic reduction in the contribution of autofluorescence to the in vivo image. Nevertheless, as is demonstrated here, working solely in this biological window does not ensure a complete removal of autofluorescence as the specimen’s diet shows a remarkable infrared fluorescence that extends up to 1,100 nm. In this work, we show how the 1,340 nm emission band of Nd 3+ ions embedded in SrF 2 nanoparticles can be used to produce autofluorescence free, high contrast in vivo fluorescence images. It is also demonstrated that the complete removal of the food-related infrared autofluorescence is imperative for the development of reliable biodistribution studies.
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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 | 0.001 | 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".