Surface treatment of PET multifilament textile for biomedical applications: roughness modification and fibroblast viability assessment
Abstract
Objectives
The aim of this study was to investigate the potential of tuning the topography of textile surfaces for biomedical applications towards modified cell-substrate interactions.
Methods
For that purpose, a supercritical Nitrogen N2 jet was used to spray glass particles on multi-filament polyethylene terephthalate (PET) yarns and on woven fabrics. The influence of the jet projection parameters such as the jet pressure (P) and the standoff distance (SoD) on the roughness was investigated.
Results
The impact of the particles created local filament ruptures on the treated surfaces towards hairiness increase. The results show that the treatment increases the roughness by up to 17 % at P 300 bars and SoD 300 mm while the strength of the material is slightly decreased. The biological study brings out that proliferation can be slightly limited on a more hairy surface, and is increased when the surface is more flat. After 10 days of fibroblast culture, the cells covered the entire surface of the fabrics and had mainly grown unidirectionally, forming cell clusters oriented along the longitudinal axis of the textile yarns. Clusters were generated at yarn crossings.
Conclusions
This approach revealed that the particle projection technology can help tuning the cell proliferation on a textile surface.
Funding source: Institut carnot ICEEL
Award Identifier / Grant number: BIOSURF
Funding source: Institut Carnot MICA
Award Identifier / Grant number: BIOSURF
Acknowledgments
The authors are also very grateful to the staff of pôle bucco dentaire (Pr. C. Mauprivez), Reims Hospital, for providing gingiva, and to Dr. F. Velard for SEM imaging and PICT platform.
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Ethical approval: No applicable.
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Informed consent: Informed consent was obtained from all individuals included in this study.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Competing interests: Authors state no conflict of interest.
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Research funding: This work was supported by Instituts Carnot MICA and ICEEL within the “BIOSURF” project.
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© 2023 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Review
- Correlation between Periotest value and implant stability quotient: a systematic review
- Research Articles
- Surface characteristics and wettability of novel gingival col designed 3-D printed dental sectional matrices
- Surface treatment of PET multifilament textile for biomedical applications: roughness modification and fibroblast viability assessment
- Influence of the skull bone and brain tissue on the sound field in transcranial extracorporeal shock wave therapy: an ex vivo study
- A numerical study of palatal snoring
- Effects of a full-body electrostimulation garment application in a cohort of subjects with cerebral palsy, multiple sclerosis, and stroke on upper motor neuron syndrome symptoms
- Noise reduction and QRS detection in ECG signal using EEMD with modified sigmoid thresholding
- Machine learning based hybrid anomaly detection technique for automatic diagnosis of cardiovascular diseases using cardiac sympathetic nerve activity and electrocardiogram
Articles in the same Issue
- Frontmatter
- Review
- Correlation between Periotest value and implant stability quotient: a systematic review
- Research Articles
- Surface characteristics and wettability of novel gingival col designed 3-D printed dental sectional matrices
- Surface treatment of PET multifilament textile for biomedical applications: roughness modification and fibroblast viability assessment
- Influence of the skull bone and brain tissue on the sound field in transcranial extracorporeal shock wave therapy: an ex vivo study
- A numerical study of palatal snoring
- Effects of a full-body electrostimulation garment application in a cohort of subjects with cerebral palsy, multiple sclerosis, and stroke on upper motor neuron syndrome symptoms
- Noise reduction and QRS detection in ECG signal using EEMD with modified sigmoid thresholding
- Machine learning based hybrid anomaly detection technique for automatic diagnosis of cardiovascular diseases using cardiac sympathetic nerve activity and electrocardiogram