Bacteria-activated macrophage membrane coated ROS-responsive nanoparticle for targeted delivery of antibiotics to infected wounds
Bibliographic record
Abstract
Bacterial infections and antibiotic resistance represent significant global public health challenges, necessitating the development of innovative antibacterial agents with targeted delivery capabilities. Our study utilized macrophages' natural ability to recognize bacteria and the increased reactive oxygen species (ROS) at infection sites to develop a novel nanoparticle for targeted delivery and controlled release. We prepared bacteria-activated macrophage membranes triggered by Staphylococcus aureus (Sa-MMs), which showed significantly higher expression of Toll-like receptors (TLRs), compared to normal macrophage membranes (MMs). These Sa-MMs were then used to coat vancomycin-loaded amphiphilic nanoparticles with ROS responsiveness (Van-NPs), resulting in the novel targeted delivery system Sa-MM@Van-NPs. Studies both In vitro and in vivo demonstrated that biocompatible Sa-MM@Van-NPs efficiently targeted infected sites and released vancomycin to eliminate bacteria, facilitating faster wound healing. By combining targeted delivery to infected sites and ROS-responsive antibiotic release, this approach might represent a robust strategy for precise infection eradication and enhanced wound healing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 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".