Kinetics of blood flow during healing of excisional full‐thickness skin wounds in pigs as monitored by laser speckle perfusion imaging
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND/PURPOSE: The laser speckle perfusion imaging (LSPI) system is a new, non-invasive technique for rapidly and reproducibly measuring tissue perfusion. The high resolution and frame rate of the LSPI overcome many of the limitations of traditional laser Doppler imaging techniques. Therefore, LSPI is a useful means for evaluating blood flow in a variety of situations. The present study investigates the ability of the LSPI system to detect temporal changes in blood flow during the healing of cutaneous wounds in a well-characterized animal model. METHODS: Full-thickness excisional skin wounds (2 x 2 cm) were created on the backs of juvenile female red Duroc pigs. Every week post-injury, the wounds were measured and photographed, and normalized blood flow values were determined using the LSPI system. RESULTS: Tissue perfusion values were available after complete re-epithelialization and removal of the eschar, at day 21. At this point, wound blood flow was significantly elevated as compared with the surrounding, uninvolved skin. Wound blood flow declined steadily during healing, and approached normal values by day 35 post-injury. CONCLUSION: The kinetics of blood flow during excisional wound healing in the red Duroc model are comparable with that previously observed in laser Doppler imaging of healing human skin wounds and hypertrophic scars. These results therefore confirm that the red Duroc is a good model of human wound healing, and further indicates that the LSPI is an excellent technique for evaluating angiogenesis and neovascularization during healing in this and other models.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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 | 0.001 |
| 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 it