Study of laser-induced thermoelastic deformation of native and coagulated ex-vivo bovine liver tissues for estimating their optical and thermomechanical properties
Bibliographic record
Abstract
Several studies have explored the potential of optoacoustic imaging for monitoring thermal therapies, yet the origin of the contrast in the images is not well understood. A technique is required to measure the changes in the optical and thermomechanical properties of tissues upon coagulation to better understand this contrast. An interferometric method is presented for measuring simultaneously the optical and thermomechanical properties of native and coagulated ex-vivo bovine tissue samples based on analysis of the surface displacement of irradiated samples. Surface displacements are measured after irradiation by short laser pulses at 750 nm. A 51% decrease in the optical attenuation depth is observed for coagulated liver samples compared to native samples. No significant differences in the Grüneisen coefficient are measured in the native and coagulated tissue samples. A mean value of 0.12 for the Grüneisen coefficient is measured for both native and coagulated liver tissues. The displacement profiles exhibit consistent differences between the two tissue types. To assess the changes in the sample mechanical properties, the experimental data also are compared to numerical solutions of the equation for thermoelastic deformation. The results demonstrate that differences in the tissue expansion dynamics arise from higher values of elastic modulus for coagulated liver samples compared to native ones.
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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) | 0.000 | 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".