Publications by authors named "Lyan Abdul"

Stem cell-derived organoids are a promising tool to model native human tissues as they resemble human organs functionally and structurally compared to traditional monolayer cell-based assays. For instance, colon organoids can spontaneously develop crypt-like structures similar to those found in the native colon. While analyzing the structural development of organoids can be a valuable readout, using traditional image analysis tools makes it challenging because of the heterogeneities and the abstract nature of organoid morphologies.

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Correction for 'Deep-LUMEN assay - human lung epithelial spheroid classification from brightfield images using deep learning' by Lyan Abdul et al., Lab Chip, 2020, DOI: .

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Three-dimensional (3D) tissue models such as epithelial spheroids or organoids have become popular for pre-clinical drug studies. In contrast to 2D monolayer culture, the characterization of 3D tissue models from non-invasive brightfield images is a significant challenge. To address this issue, here we report a deep-learning uncovered measurement of epithelial networks (Deep-LUMEN) assay.

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Despite the complexity and structural sophistication that 3D organoid models provide, their lack of vascularization and perfusion limit the capability of these models to recapitulate organ physiology effectively. A microfluidic platform named IFlowPlate is engineered, which can be used to culture up to 128 independently perfused and vascularized colon organoids in vitro. Unlike traditional microfluidic devices, the vascularized organoid-on-chip device with an "open-well" design does not require any external pumping systems and allows tissue extraction for downstream analyses, such as histochemistry or even in vivo transplantation.

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