Quantification of fluorophore concentration in vivo using two simple fluorescence-based measurement techniques
Bibliographic record
Abstract
The effect of photodynamic therapy treatments depends on the concentration of photosensitizer at the treatment site; thus a simple method to quantify concentration is desirable. This study compares the concentration of a fluorophore and sensitizer, aluminum phthalocyanine tetrasulfonate (AlPcS4), measured by two simple fluorescence-based techniques in vivo to post mortem chemical extraction and fluorometric assay of those tissues: skin, muscle, fascia, liver, and kidney (cortex and medulla). Fluorescence was excited and detected by a single optical fiber, or by an instrument that measured the ratio of the fluorescence and excitation reflectance. The in vivo measurements were compared to calibration measurements made in tissue-simulating phantoms to estimate the tissue concentrations. Reasonable agreement was observed between the concentration estimates of the two instruments in the lighter colored tissues (skin, muscle, and fascia). The in vivo measurements also agreed with the chemical extractions at low (< 0.6 microg/g) tissue concentrations, but underestimated higher tissue concentrations. Measurements of fluorescence lifetime in vivo demonstrated that AlPcS4 retains its mono-exponential decay in skin, muscle, and fascia tissues with a lifetime similar to that measured in aqueous tissue-simulating phantoms. In liver and kidney an additional short lifetime component was evident.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".