Nanofibrous theranostic biosensor for visual detection of infections and triggered drug release: Utilizing ROS-responsive nanoparticles to combat bacterial resistance
Bibliographic record
Abstract
The need for new technologies to treat infections in wound care is increasingly urgent, especially with the rise of antibiotic-resistant bacteria. In this study, we developed a nanofibrous theranostic biosensor that provides both visual infection detection and on-demand drug release to combat bacterial resistance. The biosensor incorporates a hemicyanine dye, citric acid and ciprofloxacin (Cip)-loaded NPs within a nanofibrous matrix. The cleavage of the hemicyanine dye in the presence of bacteria triggers a colorimetric response, while the responsive release of Cip from the NPs upon exposure to elevated ROS enables targeted antibacterial agent delivery. The results of testing biosensors indicated a detection limit of 1.0 × 10 5 CFU/cm 2 , the biosensor provided vivid visual results within 5 h. In direct antibacterial tests, 100 % bacterial reduction for E. coli, MRSA, and P. aeruginosa was achieved within 4 h in the presence of H 2 O 2 (1.0 mM). Citric acid provided untargeted antibacterial action (99.9 % for E. coli and 99.999 % for MRSA and P. aeruginosa ). While ROS-responsive Cip release, in addition to citric acid, ensured sustained triggered bacterial elimination (100.0 % and 8.0 log reduction), significantly enhancing overall efficacy. Cytocompatibility tests with 3-day elution confirmed that the biosensor was non-cytotoxic, maintaining over 90.0 % fibroblast viability even after complete drug release. This theranostic biosensor effectively prevents untargeted antibiotic release, reducing the risk of bacterial resistance while supporting fibroblast health and proliferation. These results suggest that this biosensor is a promising tool for wound care, offering real-time infection monitoring and targeted antibacterial treatment. • A nanofibrous theranostic biosensor was developed for in situ bacterial detection and treatment. • Detection is triggered by bacterial lipase at concentrations producing a visible yellow-to-green color change detectable by the naked eye. • Treatment is achieved through the release of antibiotics from reactive oxygen species (ROS)-responsive nanoparticles. • The system achieved complete bacterial eradication, with an 8-log reduction within 24 h, while detection occurred within 5 h. • 90 % Human Fibroblast viability was achieved using the developed biosensor.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".