Volatile organic compounds (VOCs) detection for the identification of bacterial infections in clinical wound samples
Bibliographic record
Abstract
Early detection of wound infections is critical for timely intervention and prevention of possible complications since prompt treatment can help lower pathogen spread and enhance faster healing. Early detection also helps reduce the risk of serious infections requiring extensive medical interventions or life-threatening diseases such as sepsis. Culture-based approaches currently used for bacterial identification have limited sensitivity and specificity. At the same time, they are time-consuming, resulting in delays in therapy and, therefore, having a negative impact on the treatment outcomes. Quantifying the volatile organic compounds (VOCs) released by bacteria residing in wounds is a promising, non-invasive option for detecting infections at early stages. This method allows for continuous monitoring without requiring invasive procedures, thereby reducing patient discomfort and the risk of further complications. Spectroscopy methods and sensors are the primary VOC detection and quantification approaches, but sensors are more rapid, cost-effective, non-invasive, and precise. This review highlights the significance of the early detection of wound infection to enable timely intervention and prevent complications, emphasizing the limitations of culture-based approaches. It also explores the potential of quantifying VOCs using different methods and discusses the correlation between their levels and the rate of bacterial infections in wounds. Additionally, the review evaluates current VOC-based monitoring methods for wound management, identifies gaps in the field, and advocates for further research to advance wound care and enhance patient outcomes. • Early wound infection detection aids prompt treatment and faster healing. • VOC quantification offers a non-invasive, continuous infection monitoring option. • Sensors for VOC detection are rapid, affordable, precise, and non-invasive. • The review covers VOC quantification methods for wound monitoring. • The main focus is on array-based, nano, micro, and point-of-care sensors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".