In Vivo Resolution of Multiexponential Decays of Multiple Near-Infrared Molecular Probes by Fluorescence Lifetime-Gated Whole-Body Time-Resolved Diffuse Optical Imaging
Bibliographic record
Abstract
The biodistribution of two near-infrared fluorescent agents was assessed in vivo by time-resolved diffuse optical imaging. Bacteriochlorophyll a (BC) and cypate-glysine-arginine-aspartic acid-serine-proline-lysine-OH (Cyp-GRD) were administered separately or combined to mice with subcutaneous xenografts of human breast adenocarcinoma and slow-release estradiol pellets for improved tumor growth. The same excitation (780 nm) and emission (830 nm) wavelengths were used to image the distinct fluorescence lifetime distribution of the fluorescent molecular probes in the mouse cancer model. Fluorescence intensity and lifetime maps were reconstructed after raster-scanning whole-body regions of interest by time-correlated single-photon counting. Each captured temporal point-spread function (TPSF) was deconvolved using both a single and a multiexponental decay model to best determine the measured fluorescence lifetimes. The relative signal from each fluorophore was estimated for any region of interest included in the scanned area. Deconvolution of the individual TPSFs from whole-body fluorescence intensity scans provided corresponding lifetime images for comparing individual component biodistribution. In vivo fluorescence lifetimes were determined to be 0.8 ns (Cyp-GRD) and 2 ns (BC). This study demonstrates that the relative biodistribution of individual fluorophores with similar spectral characteristics can be compartmentalized by using the time-domain fluorescence lifetime gating method.
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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 | 0.000 | 0.001 |
| 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".