Spatially resolved diffuse reflectance with laser Doppler imaging for the simultaneous in-vivo measurement of tissue perfusion and metabolic state
Bibliographic record
Abstract
Laser Doppler Imaging (LDI) has become an established technique for the two dimensional measurement of tissue perfusion but the uncertainty of photon penetration depth leads to ambiguous interpretation of what fraction of the tissue microcirculation is being sampled. This study investigates a diffuse reflectance technique for measuring tissue optical properties during LDI perfusion measurement for the simultaneous determination of photon penetration depth and tissue metabolic state. LDI and diffuse reflectance spectroscopy measurements were made on surgically exposed ligaments in pregnant and non-pregnant rabbits. Photon penetration depths are reported. It was observed that anisotropic scattering occurs due to the ordered alignment of collagen fibers within ligament. Tissue perfusion in the ligaments of pregnant animals was significantly lower than in non-pregnant animals. Tissue hemoglobin concentration and oxygenation, and percent vascularization are also reported showing no statistical difference between the ligaments in pregnant and non-pregnant rabbits. A significant difference was observed in the photon scattering coefficient between the pregnant and non-pregnant groups suggesting a change in fibril spacing and/or orientation, most likely caused by an increased laxity in the ligaments of the pregnant animals. These investigations compare well with previous biochemical and biomechanical information obtained on ligaments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".