Exploiting the biological windows: current perspectives on fluorescent bioprobes emitting above 1000 nm
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With the goal of developing more accurate, efficient, non-invasive and fast diagnostic tools, the use of near-infrared (NIR) light in the range of the second and third biological windows (NIR-II: 1000-1350 nm, NIR-III: 1550-1870 nm) is growing remarkably as it provides the advantages of deeper penetration depth into biological tissues, better image contrast, reduced phototoxicity and photobleaching. Consequently, NIR-based bioimaging has become a quickly emerging field and manifold new NIR-emitting bioprobes have been reported. Classes of materials suggested as potential probes for NIR-to-NIR bioimaging (using NIR light for the excitation and emission) are quite diverse. These include rare-earth based nanoparticles, Group-IV nanostructures (single-walled carbon nanotubes, carbon nanoparticles and more recently Si- or Ge-based nanostructures) as well as Ag, In and Pb chalcogenide quantum dots. This review summarizes and discusses current trends, material merits, and latest developments in NIR-to-NIR bioimaging taking advantage of the region above 1000 nm (i.e. the second and third biological windows). Further consideration will be given to upcoming probe materials emitting in the NIR-I region (700-950 nm), thus do not possess emissions in these two windows, but have high expectations. Overall, the focus is placed on recent discussions concerning the optimal choice of excitation and emission wavelengths for deep-tissue high-resolution optical bioimaging and on fluorescent bioprobes that have successfully been implemented in in vitro and in vivo applications.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it