Sci‐PM Fri ‐ 03: Potential use of diffusion theory for quantitative <i>in vivo</i> fluorescence and bioluminescence imaging
Bibliographic record
Abstract
In recent years, there has been increasing interest in the potential of optical methods to detect and monitor in vivo minimal residual disease and metastasis in animal cancer models. The overall objective of the present work is to develop analytical methods and instrumentation to construct quantitative bioluminescence and fluorescence images of bone metastases. The diffusion approximation was investigated as a mathematical model of light propagation in tissue. Testing of the model against Monte Carlo simulation data showed that the optical properties could be retrieved from a reflectance curve with an accuracy of better than 2%. The evaluation of the model was also performed on liquid tissue‐simulating phantoms by using the tip of an optical fiber to simulate a point source. A charge‐coupled device (CCD) camera was used to acquire images of the surface of the phantom with the point source inserted at different depths. In addition, a non‐invasive measurement of the optical properties of the phantom was performed. Results showed that, for the depth ranges where diffusion theory was expected to be valid, the depth could be reconstructed with 15% accuracy and the fitted relative intensities of the source were consistent to expected values within 30%.
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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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".