AI-Driven Optimization of PCL/PEG Electrospun Scaffolds for Enhanced In Vivo Wound Healing
Само за регистроване кориснике
2024
Аутори
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
Чланак у часопису (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
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.
Извор:
ACS Applied Materials & Interfaces, 2024Издавач:
- American Chemical Society
Финансирање / пројекти:
- Министарство науке, технолошког развоја и иновација Републике Србије, институционално финансирање - 200107 (Универзитет у Крагујевцу, Факултет инжењерских наука) (RS-MESTD-inst-2020-200107)
- Министарство науке, технолошког развоја и иновација Републике Србије, институционално финансирање - 200378 (Институт за информационе технологије, Крагујевац) (RS-MESTD-inst-2020-200378)
- Junior projects of Faculty of Medical Sciences, University of Kragujevac JP 25/19
- Junior projects of Faculty of Medical Sciences, University of Kragujevac JP 05/ 20
- Junior projects of Faculty of Medical Sciences, University of Kragujevac JP 06/20
- Junior projects of Faculty of Medical Sciences, University of Kragujevac JP 24/20
- European Union’s Horizon 2020 research and innovation programme under grant agreement No 952603 (SGABU)
- Vienna Science and Technology Fund (WWTF) [10.47379/LS19035]
Напомена:
- Supplementary information: https://hdl.handle.net/21.15107/rcub_intor_873
Повезане информације:
- Повезани садржај
https://hdl.handle.net/21.15107/rcub_intor_873
Институција/група
TorlakTY - 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 - http://intor.torlakinstitut.com/handle/123456789/872 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 T2 - ACS Applied Materials & InterfacesACS Appl. Mater. 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, ACS Applied Materials & InterfacesACS Appl. Mater. 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 . .