Fluorine-18-Labeled Fluorescent Dyes for Dual-Mode Molecular Imaging
Bibliographic record
Abstract
Recent progress realized in the development of optical imaging (OPI) probes and devices has made this technique more and more affordable for imaging studies and fluorescence-guided surgery procedures. However, this imaging modality still suffers from a low depth of penetration, thus limiting its use to shallow tissues or endoscopy-based procedures. In contrast, positron emission tomography (PET) presents a high depth of penetration and the resulting signal is less attenuated, allowing for imaging in-depth tissues. Thus, association of these imaging techniques has the potential to push back the limits of each single modality. Recently, several research groups have been involved in the development of radiolabeled fluorophores with the aim of affording dual-mode PET/OPI probes used in preclinical imaging studies of diverse pathological conditions such as cancer, Alzheimer’s disease, or cardiovascular diseases. Among all the available PET-active radionuclides, 18F stands out as the most widely used for clinical imaging thanks to its advantageous characteristics (t1/2 = 109.77 min; 97% β+ emitter). This review focuses on the recent efforts in the synthesis and radiofluorination of fluorescent scaffolds such as 4,4-difluoro-4-bora-diazaindacenes (BODIPYs), cyanines, and xanthene derivatives and their use in preclinical imaging studies using both PET and OPI technologies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".