Next‐Generation Wound Care: Aptamer‐Conjugated Polydiacetylene/Polyurethane Nanofibrous Biosensors for Selective In Situ Colorimetric Detection of <i>Pseudomonas</i>
Bibliographic record
Abstract
Abstract Biosensors for wound dressings can enable point‐of‐care monitoring of wound bed health by exhibiting a color change visible to the naked eye, to alert healthcare providers of the presence of pathogenic bacteria. Here, a polydiacetylene‐based electrospun nanofibrous wound dressing for the detection of Pseudomonas aeruginosa is reported. Using conventional blend electrospinning, two diacetylene monomers—10,12‐pentacosadiynoic acid (PCDA) and 10,12‐tricosadiynoic acid (TCDA)—are separately electrospun alongside polyurethane as a supporting matrix polymer. The differences in side‐chain length impact the sensitivity of the nanofibers in detecting P. aeruginosa. Furthermore, two DNA aptamers are conjugated to the polydiacetylenes to achieve targeted detection of P. aeruginosa. The aptamer‐modified dressings show improved sensitivity of detection toward eight strains of P. aeruginosa compared to the unmodified membranes. Furthermore, the aptamer‐modified membranes do not respond to non‐target bacteria methicillin‐resistant Staphylococcus aureus (MRSA), Staphylococcus aureus, and Escherichia coli within 3 h of direct contact. Reducing the chain‐length of the diacetylene monomer by substituting PCDA with TCDA boosts the colorimetric response by a factor of >2x compared to the aptamer‐modified PCDA membranes, at the cost of reduced specificity. The aptamer‐conjugated polydiacetylene membranes show promise for application in point‐of‐care wound dressings for improved specificity of detection of 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 | 0.000 | 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 | 0.001 | 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".