Ultrasensitive Nanofiber Biosensor: Rapid <i>In Situ</i> Chromatic Detection of Bacteria for Healthcare Innovation
Bibliographic record
Abstract
Rapid detection of bacterial presence in skin wounds is crucial to prevent the transition from acute to chronic wounds and the onset of systemic infections. Current methods for detecting infections, particularly at low concentrations (<1.0 × 10 5 CFU/cm 2 ), often require complex technologies and direct sampling, which can be invasive and time-consuming. Addressing this gap, we introduce a colorimetric nanofibrous biosensor enabling real-time in situ monitoring of bacterial concentrations in wounds. This biosensor employs a colorimetric hemicyanine dye (HCy) probe, which changes color in response to bacterial lipase, a common secretion in infected wounds. To enhance the biosensor’s sensitivity, we incorporated two key materials science strategies: aligning the nanofibers to promote efficient bacterial attachment and localization and integrating Tween 80, a surfactant, within the nanofiber matrix. This combination of physical and chemical cues results in a notable increase in lipase activity. The cross-aligned core–shell nanofibers, embedded with Tween 80 and HCy, demonstrate an immediate and distinct color change when exposed to as low as 3.0 × 10 4 CFU/cm 2 of common pathogens such as Pseudomonas aeruginosa and MRSA. Significantly, the presence of Tween 80 amplifies the colorimetric response, making visual detection more straightforward and four times more pronounced. Our nanobiosensor design facilitates the detection of low-concentration bacterial infections in situ without the need to remove wound dressings. This advancement marks a significant step forward in real-time wound monitoring, offering a practical tool for the early detection of clinical bacterial infections.
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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) | 0.002 | 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".