Recent advances in biosensors for real time monitoring of pH, temperature, and oxygen in chronic wounds
Bibliographic record
Abstract
Chronic wounds are among the major healthcare issues affecting millions of people worldwide with high rates of morbidity, losses of limbs and mortality. Microbial infection in wounds is a severe problem that can impede healing of chronic wounds. Accurate, timely and early detection of infections, and real time monitoring of various wound healing biomarkers related to infection can be significantly helpful in the treatment and care of chronic wounds. However, clinical methodologies of periodic assessment and care of wounds require physical visit to wound care clinics or hospitals and time-consuming frequent replacement of wound dressing patches, which also often adversely affect the healing process. Besides, frequent replacements of wound dressings are highly expensive, causing a huge amount of burden on the national health care systems. Smart bandages have emerged to provide in situ physiochemical surveillance in real time at the wound site. These bandages integrate smart sensors to detect the condition of wound infection based on various parameters, such as pH, temperature and oxygen level in the wound which reduces the frequency of changing the wound dressings and its associated complications. These devices can continually monitor the healing process, paving the way for tailored therapy and improved quality of patient's life. In this review, we present an overview of recent advances in biosensors for real time monitoring of pH, temperature, and oxygen in chronic wounds in order to assess infection status. We have elaborated the recent progress in quantitative monitoring of several biomarkers important for assessing wounds infection status and its detection using smart biosensors. The review shows that real-time monitoring of wound status by quantifying specific biomarkers, such as pH, temperature and tissue oxygenation to significantly aid the treatment and care of chronic infected 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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".