Changes in optical properties of<i>ex vivo</i>rat prostate due to heating
Bibliographic record
Abstract
This study examines the effectiveness of a single, first-order Arrhenius process in accurately modelling the thermally induced changes in the optical properties, particularly the reduced scattering coefficient, mu(s)', and the absorption coefficient, mu(a), of ex vivo rat prostate. Recent work has shown that mu(s)' can increase as much as five-fold due to thermal coagulation, and the observed change in mu(s)' has been modelled well according to a first-order rate process in albumen. Conversely, optical property measurements conducted using pig liver suggest that this change in mu(s)' cannot suitably be described using a single rate parameter. In canine prostate, measurements have indicated that while the absorption coefficient varies with temperature, it does not do so according to first-order kinetics. A double integrating sphere system was used to measure the reflectance and transmittance of light at 810 nm through a thin sample of prostate. Using prostate samples collected from Sprague Dawley rats, optical properties were measured at a constant elevated temperature. Tissue samples were measured over the range 54-83 degrees C. The optical properties of the sample were determined through comparison with reflectance and transmittance values predicted by a Monte Carlo simulation of light propagation in turbid media. A first order Arrhenius model was applied to the observed change in mu(s)' and mu(a) to determine the rate process parameters for thermal coagulation. The measured rate coefficients were Ea = (7.18 +/- 1.74) x 10(4) J mol(-1) and Afreq = 3.14 x 10(8) s(-1) for mu(s)'. It was determined that the change in mu(s)' is well described by a single first-order rate process. Similar analysis performed on the changes in mu(a) due to increased temperatures yielded Ea = (1.01 +/- 0.35) x 10(5) J mol(-1) and Afreq = 8.92 x 10(12) s(-1). The results for mu(a) suggest that the Arrhenius model may be applicable to the changes in absorption.
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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 | 0.000 |
| 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".