AI-Driven Optimization of PCL/PEG Electrospun Scaffolds for Enhanced In Vivo Wound Healing
Samo za registrovane korisnike
2024
Autori
Virijević, KatarinaŽivanović, Marko N.
Nikolić, Dalibor
Milivojević, Nevena
Pavić, Jelena
Morić, Ivana
Šenerović, Lidija
Dragačević, Luka
Thurner, Philipp J.
Rufin, Manuel
Andriotis, Orestis G.
Ljujić, Biljana
Miletić Kovačević, Marina
Papić, Miloš
Filipović, Nenad
Članak u časopisu (Objavljena verzija)
Metapodaci
Prikaz svih podataka o dokumentuApstrakt
Here, an artificial intelligence (AI)-based approach was employed to optimize the production of electrospun scaffolds for in vivo wound healing applications. By combining polycaprolactone (PCL) and poly(ethylene glycol) (PEG) in various concentration ratios, dissolved in chloroform (CHCl3) and dimethylformamide (DMF), 125 different polymer combinations were created. From these polymer combinations, electrospun nanofiber meshes were produced and characterized structurally and mechanically via microscopic techniques, including chemical composition and fiber diameter determination. Subsequently, these data were used to train a neural network, creating an AI model to predict the optimal scaffold production solution. Guided by the predictions and experimental outcomes of the AI model, the most promising scaffold for further in vitro analyses was identified. Moreover, we enriched this selected polymer combination by incorporating antibiotics, aiming to develop electrospun nanofiber scaffolds... tailored for in vivo wound healing applications. Our study underscores three noteworthy conclusions: (i) the application of AI is pivotal in the fields of material and biomedical sciences, (ii) our methodology provides an effective blueprint for the initial screening of biomedical materials, and (iii) electrospun PCL/PEG antibiotic-bearing scaffolds exhibit outstanding results in promoting neoangiogenesis and facilitating in vivo wound treatment.
Ključne reči:
electrospun scaffolds / artificial intelligence / wound healing / polycaprolactone / polyethylene glycolIzvor:
ACS Applied Materials & Interfaces, 2024Izdavač:
- American Chemical Society
Institucija/grupa
Institut za molekularnu genetiku i genetičko inženjerstvoTY - JOUR AU - Virijević, Katarina AU - Živanović, Marko N. AU - Nikolić, Dalibor AU - Milivojević, Nevena AU - Pavić, Jelena AU - Morić, Ivana AU - Šenerović, Lidija AU - Dragačević, Luka AU - Thurner, Philipp J. AU - Rufin, Manuel AU - Andriotis, Orestis G. AU - Ljujić, Biljana AU - Miletić Kovačević, Marina AU - Papić, Miloš AU - Filipović, Nenad PY - 2024 UR - https://doi.org/10.1021/acsami.4c03266 UR - https://imagine.imgge.bg.ac.rs/handle/123456789/2360 AB - Here, an artificial intelligence (AI)-based approach was employed to optimize the production of electrospun scaffolds for in vivo wound healing applications. By combining polycaprolactone (PCL) and poly(ethylene glycol) (PEG) in various concentration ratios, dissolved in chloroform (CHCl3) and dimethylformamide (DMF), 125 different polymer combinations were created. From these polymer combinations, electrospun nanofiber meshes were produced and characterized structurally and mechanically via microscopic techniques, including chemical composition and fiber diameter determination. Subsequently, these data were used to train a neural network, creating an AI model to predict the optimal scaffold production solution. Guided by the predictions and experimental outcomes of the AI model, the most promising scaffold for further in vitro analyses was identified. Moreover, we enriched this selected polymer combination by incorporating antibiotics, aiming to develop electrospun nanofiber scaffolds tailored for in vivo wound healing applications. Our study underscores three noteworthy conclusions: (i) the application of AI is pivotal in the fields of material and biomedical sciences, (ii) our methodology provides an effective blueprint for the initial screening of biomedical materials, and (iii) electrospun PCL/PEG antibiotic-bearing scaffolds exhibit outstanding results in promoting neoangiogenesis and facilitating in vivo wound treatment. PB - American Chemical Society T2 - ACS Applied Materials & Interfaces T1 - AI-Driven Optimization of PCL/PEG Electrospun Scaffolds for Enhanced In Vivo Wound Healing DO - 10.1021/acsami.4c03266 ER -
@article{ author = "Virijević, Katarina and Živanović, Marko N. and Nikolić, Dalibor and Milivojević, Nevena and Pavić, Jelena and Morić, Ivana and Šenerović, Lidija and Dragačević, Luka and Thurner, Philipp J. and Rufin, Manuel and Andriotis, Orestis G. and Ljujić, Biljana and Miletić Kovačević, Marina and Papić, Miloš and Filipović, Nenad", year = "2024", abstract = "Here, an artificial intelligence (AI)-based approach was employed to optimize the production of electrospun scaffolds for in vivo wound healing applications. By combining polycaprolactone (PCL) and poly(ethylene glycol) (PEG) in various concentration ratios, dissolved in chloroform (CHCl3) and dimethylformamide (DMF), 125 different polymer combinations were created. From these polymer combinations, electrospun nanofiber meshes were produced and characterized structurally and mechanically via microscopic techniques, including chemical composition and fiber diameter determination. Subsequently, these data were used to train a neural network, creating an AI model to predict the optimal scaffold production solution. Guided by the predictions and experimental outcomes of the AI model, the most promising scaffold for further in vitro analyses was identified. Moreover, we enriched this selected polymer combination by incorporating antibiotics, aiming to develop electrospun nanofiber scaffolds tailored for in vivo wound healing applications. Our study underscores three noteworthy conclusions: (i) the application of AI is pivotal in the fields of material and biomedical sciences, (ii) our methodology provides an effective blueprint for the initial screening of biomedical materials, and (iii) electrospun PCL/PEG antibiotic-bearing scaffolds exhibit outstanding results in promoting neoangiogenesis and facilitating in vivo wound treatment.", publisher = "American Chemical Society", journal = "ACS Applied Materials & Interfaces", title = "AI-Driven Optimization of PCL/PEG Electrospun Scaffolds for Enhanced In Vivo Wound Healing", doi = "10.1021/acsami.4c03266" }
Virijević, K., Živanović, M. N., Nikolić, D., Milivojević, N., Pavić, J., Morić, I., Šenerović, L., Dragačević, L., Thurner, P. J., Rufin, M., Andriotis, O. G., Ljujić, B., Miletić Kovačević, M., Papić, M.,& Filipović, N.. (2024). AI-Driven Optimization of PCL/PEG Electrospun Scaffolds for Enhanced In Vivo Wound Healing. in ACS Applied Materials & Interfaces American Chemical Society.. https://doi.org/10.1021/acsami.4c03266
Virijević K, Živanović MN, Nikolić D, Milivojević N, Pavić J, Morić I, Šenerović L, Dragačević L, Thurner PJ, Rufin M, Andriotis OG, Ljujić B, Miletić Kovačević M, Papić M, Filipović N. AI-Driven Optimization of PCL/PEG Electrospun Scaffolds for Enhanced In Vivo Wound Healing. in ACS Applied Materials & Interfaces. 2024;. doi:10.1021/acsami.4c03266 .
Virijević, Katarina, Živanović, Marko N., Nikolić, Dalibor, Milivojević, Nevena, Pavić, Jelena, Morić, Ivana, Šenerović, Lidija, Dragačević, Luka, Thurner, Philipp J., Rufin, Manuel, Andriotis, Orestis G., Ljujić, Biljana, Miletić Kovačević, Marina, Papić, Miloš, Filipović, Nenad, "AI-Driven Optimization of PCL/PEG Electrospun Scaffolds for Enhanced In Vivo Wound Healing" in ACS Applied Materials & Interfaces (2024), https://doi.org/10.1021/acsami.4c03266 . .