Preventing Staphylococci Surgical Site Infections with a Nitric Oxide-Releasing Poly(lactic acid- co -glycolic acid) Suture Material.
Lauren GriffinMark Richard Stephen GarrenPatrick MaffeSama GhaleiElizabeth J BrisboisHitesh HandaPublished in: ACS applied bio materials (2024)
Of the 27 million surgeries performed in the United States each year, a reported 2.6% result in a surgical site infection (SSI), and Staphylococci species are commonly the culprit. Alternative therapies, such as nitric oxide (NO)-releasing biomaterials, are being developed to address this issue. NO is a potent antimicrobial agent with several modes of action, including oxidative and nitrosative damage, disruption of bacterial membranes, and dispersion of biofilms. For targeted antibacterial effects, NO is delivered by exogenous donor molecules, like S -nitroso- N -acetylpenicillamine (SNAP). Herein, the impregnation of SNAP into poly(lactic- co -glycolic acid) (PLGA) for SSI prevention is reported for the first time. The NO-releasing PLGA copolymer is fabricated and characterized by donor molecule loading, leaching, and the amount remaining after ethylene oxide sterilization. The swelling ratio, water uptake, static water contact angle, and tensile strength are also investigated. Furthermore, its cytocompatibility is tested against 3T3 mouse fibroblast cells, and its antimicrobial efficacy is assessed against multiple Staphylococci strains. Overall, the NO-releasing PLGA copolymer holds promise as a suture material for eradicating surgical site infections caused by Staphylococci strains. SNAP impregnation affords robust antibacterial properties while maintaining the cytocompatibility and mechanical integrity.
Keyphrases
- surgical site infection
- nitric oxide
- drug release
- drug delivery
- lactic acid
- antimicrobial resistance
- bone regeneration
- escherichia coli
- staphylococcus aureus
- induced apoptosis
- hydrogen peroxide
- cancer therapy
- oxidative stress
- silver nanoparticles
- high resolution
- candida albicans
- cell cycle arrest
- deep learning
- cell death
- artificial intelligence
- machine learning
- pi k akt
- oxide nanoparticles
- tissue engineering