Multifunctional and Stimuli‐Responsive Polydopamine Nanoparticle‐Based Platform for Targeted Antimicrobial Applications
Bibliographic record
Abstract
Abstract The rising threat of antimicrobial resistance is a crisis of a global scale. If not addressed, it can lead to health care system problems worldwide. This warrants alternative therapeutic approaches whose mechanism of action starkly differs from conventional antibiotic‐based therapies. Here, a multifunctional and stimuli‐responsive (NIR laser‐activated) antimicrobial platform is engineered by combining the intrinsic photothermal capability and excellent biocompatibility of polydopamine nanoparticles (PdNPs), with the membrane targeting and lytic activities of an antimicrobial peptide (AMP). The resulting PdNP‐AMP nanosystem can specifically target and destabilize the mechanical integrity of the outer membrane of Escherichia coli, as measured using the atomic force microscope. Furthermore, the laser‐induced nano‐localized heating of PdNP—in close proximity to the already compromised bacterial envelope—induces further membrane damage. This results in a more efficient, laser‐activated, bacterial killing action of PdNP‐AMP. The antimicrobial platform developed in this work is shown to be effective against a drug‐resistant E. coli. Overall, this work highlights the advantage and strength of combining multiple and coordinated biocidal mechanisms, into one nanomaterial‐based system and its promise in treating drug‐resistant pathogens.
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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 | 0.000 | 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".