Oxygen-Sensing Biomaterial Construct for Clinical Monitoring of Wound Healing
Bibliographic record
Abstract
OBJECTIVE: Oxygen is essential to wound healing; therefore, accurate monitoring can guide clinical decisions. Clinical wound assessment is often subjective, and tools to monitor wound oxygen are typically expensive, indirect, and highly variable. This study demonstrates the utility of a novel, low-cost oxygen-sensing thin film for serial assessment of wound oxygenation. DESIGN: Dual-layer films were fabricated with boron oxygen-sensing nanoparticles (BNPs) impregnated into a chitosan-polycaprolactone layer for direct wound bed contact with a relatively oxygen impermeable calcium alginate surface layer. The BNPs are a dual-emissive difluoroboron β-diketonate dye incorporated into poly(lactic acid) nanoparticles. Under UV excitation, the BNPs emit fluorescence based on concentration and oxygen-sensitive phosphorescence. The fluorescence/phosphorescence ratio is directly proportional to oxygen concentration. METHODS: A series of in vitro oxygen challenges and in vivo murine and porcine wound healing models were used to validate the utility of the film in sensing wound oxygenation. MAIN RESULTS: In vitro testing demonstrated the oxygen-sensing capability of the BNP film and its ability to shield ambient oxygen to isolate wound oxygen. In vivo testing demonstrated the ability of the film to accurately monitor relative oxygen changes in a murine wound over time, measuring a 22% fluorescence/phosphorescence increase during acute healing. CONCLUSIONS: This study presents a low-cost, noninvasive, direct, and serial oxygen mapping technology to detect spatial differences in wound oxygenation. Clinical use of the films has the potential to monitor wound healing trajectories and guide wound care decisions.
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How this classification was reachedexpand
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 | 0.000 |
| 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 teacher head, 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".