Differential Gene Expression in Healing and Non‐Healing Diabetic Foot Ulcers and Discovery of Novel Ratiometric Biomarker to Predict Healing Outcome
Bibliographic record
Abstract
Diabetic foot ulcers (DFUs) are common and difficult to treat because the mechanisms behind unsuccessful responses to treatment are poorly understood. The goals of this study were to identify differences in healing and non-healing human DFUs using debrided tissue samples and to identify possible biomarkers of non-healing. First, DFU tissue samples collected over 12 weeks of treatment from 27 subjects (n = 12 healing and n = 15 non-healing) were analysed using a focused panel of 227 inflammation and wound healing-related human genes and 16S ribosomal RNA amplicon sequence to identify microbial species. Gene expression and correlation with microbial species differed between healing and non-healing DFUs. While no individual genes analysed at the initial time point could accurately predict healing outcome 12 weeks later, several 2-gene ratios were highly accurate. The ratio of C3AR1/CCL22 predicted healing outcome in the discovery cohort with an area under the receiver operator characteristic (ROC) curve (AUC) of 0.96. The AUC was 0.80 when tested on 74 unique samples collected at later time points from the discovery cohort, and the AUC was 0.69 when validated in a completely independent cohort of n = 51 subjects and using quantitative reverse transcription polymerase chain reaction (qRTPCR) as a more translational method of detection. The AUC increased to 0.75 when initial wound area was included. Overall, the results suggest that differences in inflammation contribute to differential healing outcomes in human chronic DFUs, and associated biomarkers may be used to predict healing outcome to guide treatment 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 | 0.001 | 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".