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Abdul, L.

Publications and source records attributed to Abdul, L..

2 recordsLinked to original sources

Flexible hippocampal representation of abstract boundaries supports memory-guided choice.

Hippocampal cognitive maps encode the relative locations of spatial cues in an environment and adapt their representation when boundaries geometrically change. Hippocampal cognitive maps can represent abstract knowledge, yet its unclear whether the hippocampus is sensitive to changes to the extreme coordinates, boundaries, of abstract spaces. We created a memory-guided choice task to test whether the human hippocampus and medial prefrontal cortex(mPFC) flexibly learn abstract boundary representations in distinct two-dimensional(2D) knowledge spaces. Participants built up a 2D map-like representation of abstract boundaries, where the hippocampus and mPFC represented a decision cues Euclidean distance to the closest boundary. Notably, mPFC distance representations selectively reflected individual performance improvements during the task. Testing for neural sensitivity to boundary-defined contextual changes, only the hippocampus flexibly represented abstract boundaries, which related to choice behavior. These findings suggest that abstract knowledge retrieval within dynamically changing contexts is facilitated by generalized mPFC and flexible hippocampal boundary representations.

neuroscience↗

Deep-LUMEN Assay - Human lung epithelial spheroid classification from brightfield images using deep learning

Three-dimensional (3D) tissue models such as epithelial spheroids or organoids have become popular for pre-clinical drug studies. However, different from 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. Deep-LUMEN is an object detection algorithm that has been fine-tuned to automatically uncover subtle differences in epithelial spheroid morphology from brightfield images. This algorithm can track changes in the luminal structure of tissue spheroids and distinguish between polarized and non-polarized lung epithelial spheroids. The Deep-LUMEN assay was validated by screening for changes in spheroid epithelial architecture in response to different extracellular matrices and drug treatments. Specifically, we found the dose-dependent toxicity of Cyclosporin can be underestimated if the effect of the drug on tissue morphology is not considered. Hence, Deep-LUMEN could be used to assess drug effects and capture morphological changes in 3D spheroid models in a non-invasive manner. Significance of the workDeep learning has been applied for the first time to autonomously detect subtle morphological changes in 3D multi-cellular spheroids, such as spheroid polarity, from brightfield images in a label-free manner. The technique has been validated by detecting changes in spheroid morphology in response to changes in extracellular matrices and drug treatments.

bioengineering↗