Ultrasensitive Nanofiber Biosensor: Rapid <i>In Situ</i> Chromatic Detection of Bacteria for Healthcare Innovation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 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 it