Colorimetric detection methods of pH-sensing wound dressing for point-of-care wound diagnostics
Bibliographic record
Abstract
Abstract Chronic lower extremity wounds like diabetic foot ulcers (DFUs) are a major complication of diabetes and the leading cause of lower limb amputations worldwide. Currently, DFUs are diagnosed through macroscopic evaluation, and molecular diagnostics are lacking for the disease staging, treatment selection, and evaluation of treatment success. There is a need for new diagnostic technologies combined with detection methods for point-of-care use. Preclinical and clinical data support the importance of wound pH as a biomarker in chronic wound healing, with DFUs typically exhibiting more alkaline pH values when compared to normal healing wounds. In a previous study, we developed a pH-sensing fluorescent bandage based on pyranine-loaded microparticles that were physically immobilized in an alginate hydrogel. Here, we present a second-generation pH-sensing bandage for colorimetric wound diagnostics. Pyranine was adsorbed at high concentrations onto microparticles to enable colorimetric signal detection. This approach leverages pyranine’s ability to change color in response to pH variations through proton exchange properties with the wound fluid. The colorimetric properties of our bandage enable signal detection by two methods suited for point-of-care use: a smartphone camera and a cost-effective homebuilt RGB detector. Analyzing the color intensity of the bandage with Red, Green and Blue absorbance values, it is possible to correlate the RGB absorbance to a pH value in the clinically-relevant range to 6.0 to 9.0 in vitro and ex vivo , as the B values decreased with the increase in pH levels, as associated with DFUs. These findings indicate the potential of colorimetric detection using smartphone cameras or home-built absorbance detectors for rapid wound diagnostics at the point-of-care. Graphical Abstract A pH-sensing bandage enables colorimetric detection of chronic wounds. The bandage absorbs wound exudate, triggering proton exchange with dye-loaded microparticles in an alginate matrix. Chronic wounds with high pH values induce strong fluorescence and yellow coloration. The signal is quantified through RGB colorimetry analysis using a smartphone camera or a homebuilt RGB detector, enabling point-of-care diagnostics for chronic wounds.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".