Exploiting the biological windows: current perspectives on fluorescent bioprobes emitting above 1000 nm
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".