Browsing by Author "Chan, Vincent (7202654913)"

Aim: The escalating demand for corneal transplants significantly surpasses the available supply. To bridge this gap, we concentrated on ethical and sustainable corneal grafting sources. Our objective was to create viable corneal scaffolds from preserved slaughterhouse waste. Materials & methods: Corneas were extracted and decellularized from eyeballs that had been refrigerated for several days. These scaffolds underwent evaluation through DNA quantification, histological analysis, surface tension measurement, light propagation testing, and tensile strength assessment. Results: Both the native and acellular corneas (with ~90% DNA removed using a cost-effective and environmentally friendly surfactant) maintained essential optical and biomechanical properties for potential clinical use. Conclusion: Our method of repurposing slaughterhouse waste, stored at 4°C for several days, to develop corneal scaffolds offers a sustainable and economical alternative xenograft model. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Aim: The escalating demand for corneal transplants significantly surpasses the available supply. To bridge this gap, we concentrated on ethical and sustainable corneal grafting sources. Our objective was to create viable corneal scaffolds from preserved slaughterhouse waste. Materials & methods: Corneas were extracted and decellularized from eyeballs that had been refrigerated for several days. These scaffolds underwent evaluation through DNA quantification, histological analysis, surface tension measurement, light propagation testing, and tensile strength assessment. Results: Both the native and acellular corneas (with ~90% DNA removed using a cost-effective and environmentally friendly surfactant) maintained essential optical and biomechanical properties for potential clinical use. Conclusion: Our method of repurposing slaughterhouse waste, stored at 4°C for several days, to develop corneal scaffolds offers a sustainable and economical alternative xenograft model. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Decellularized corneas offer a promising and sustainable source of replacement grafts, mimicking native tissue and reducing the risk of immune rejection post-transplantation. Despite great success in achieving acellular scaffolds, little consensus exists regarding the quality of the decellularized extracellular matrix. Metrics used to evaluate extracellular matrix performance are study-specific, subjective, and semi-quantitative. Thus, this work focused on developing a computational method to examine the effectiveness of corneal decellularization. We combined conventional semi-quantitative histological assessments and automated scaffold evaluations based on textual image analyses to assess decellularization efficiency. Our study highlights that it is possible to develop contemporary machine learning (ML) models based on random forests and support vector machine algorithms, which can identify regions of interest in acellularized corneal stromal tissue with relatively high accuracy. These results provide a platform for developing machine learning biosensing systems for evaluating subtle morphological changes in decellularized scaffolds, which are crucial for assessing their functionality. Copyright © 2023 Pantic, Cumic, Valjarevic, Shakeel, Wang, Vurivi, Daoud, Chan, Petroianu, Shibru, Ali, Nesic, Salih, Butt and Corridon.

Decellularized corneas offer a promising and sustainable source of replacement grafts, mimicking native tissue and reducing the risk of immune rejection post-transplantation. Despite great success in achieving acellular scaffolds, little consensus exists regarding the quality of the decellularized extracellular matrix. Metrics used to evaluate extracellular matrix performance are study-specific, subjective, and semi-quantitative. Thus, this work focused on developing a computational method to examine the effectiveness of corneal decellularization. We combined conventional semi-quantitative histological assessments and automated scaffold evaluations based on textual image analyses to assess decellularization efficiency. Our study highlights that it is possible to develop contemporary machine learning (ML) models based on random forests and support vector machine algorithms, which can identify regions of interest in acellularized corneal stromal tissue with relatively high accuracy. These results provide a platform for developing machine learning biosensing systems for evaluating subtle morphological changes in decellularized scaffolds, which are crucial for assessing their functionality. Copyright © 2023 Pantic, Cumic, Valjarevic, Shakeel, Wang, Vurivi, Daoud, Chan, Petroianu, Shibru, Ali, Nesic, Salih, Butt and Corridon.