A Nanocomposite Dynamic Covalent Cross-Linked Hydrogel Loaded with Fusidic Acid for Treating Antibiotic-Resistant Infected Wounds
Why this work is in the frame
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Bibliographic record
Abstract
Methicillin-resistant Staphylococcus aureus (MRSA) is associated with high levels of morbidity and is considered a difficult-to-treat infection, often requiring nonstandard treatment regimens and antibiotics. Since over 40% of the emerging antibiotic compounds have insufficient solubility that limits their bioavailability and thus efficacy through oral or intravenous administration, it is crucial that alternative drug delivery products be developed for wound care applications. Existing effective treatments for soft tissue MRSA infections, such as fusidic acid (FA), which is typically administered orally, could also benefit from alternative routes of administration to improve local efficacy and bioavailability while reducing the required therapeutic dose. Herein, we report an antimicrobial poly(oligoethylene glycol methacrylate) (POEGMA)-based composite hydrogel loaded with fusidic acid-encapsulating self-assembled polylactic acid- b -poly(oligo(ethylene glycol) methyl ether methacrylate) (PLA-POEGMA) nanoparticles for the treatment of MRSA-infected skin wounds. The inclusion of the self-assembled nanoparticles (380 nm diameter when loaded with fusidic acid) does not alter the favorable mechanical properties and stability of the hydrogel in the context of its use as a wound dressing, while fusidic acid (FA) can be released from the hydrogel over ∼10 h via a diffusion-controlled mechanism. The antimicrobial studies demonstrate a clear zone of inhibition in vitro and a 1−2 order of magnitude inhibition of bacterial growth in vivo in an MRSA-infected full-thickness excisional murine wound model even at very low antibiotic doses. Our approach thus can both circumvent challenges in the local delivery of hydrophobic antimicrobial compounds and directly deliver antimicrobials into the wound to effectively combat methicillin-resistant infections using a fraction of the drug dose required using other clinically relevant strategies.
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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