Biology subjects

Kong, E. M.

Publications and source records attributed to Kong, E. M..

3 recordsLinked to original sources

bioRxiv2026

The selective OX1R antagonist 1-SORA-51 reduces binge-like feeding behavior in male and female mice without detectable changes in dopamine.

Binge eating disorder (BED) is characterized by episodic overconsumption of palatable food and involves dysregulated motivational and arousal processes. The orexin (hypocretin) system, through its widespread projections to mesolimbic circuits, has been implicated in cue-driven reward seeking and escalated intake, raising the possibility that orexin receptor antagonists may modulate binge-like behavior. Here we evaluated the effects of a dual orexin receptor antagonist (DORA-22) and an OX1R-selective antagonist (1-SORA-51) in a cyclic intermittent high-fat access model that generates robust and reproducible binge-like intake in male and female mice. DORA-22 produced no detectable effect on consumption at either early (2 h) or extended (24 h) binge timepoints. In contrast, 1-SORA-51 significantly reduced high-fat intake during the initial 2 hours of access in both sexes, with no effect on 24-hour consumption, indicating a selective attenuation of the early phase of binge intake. Fiber photometry recordings of GRABDA2m fluorescence in the nucleus accumbens revealed that 1-SORA-51 did not alter baseline dopamine signals or the dopamine increase triggered by high-fat pellet delivery, demonstrating that its behavioral effects occur without detectable modulation of mesolimbic dopamine dynamics. Together, these findings identify OX1R antagonism as a strategy for suppressing the initiation of binge-like feeding and highlight the receptor-level specificity of orexin contributions to maladaptive overconsumption.

neuroscience↗

bioRxiv2020

A 'Cell-Nucleus Segmentation' Script for Non-Invasive Nuclear Dry Mass Measurement

The nucleus is the largest organelle in cells carrying genetic materials that support genetic replication and transcription. It is likely that such genetic activities can influence the nuclear dry mass, but there is a lack of analytical tools enabling us to monitor dynamic changes in this quantity. To this end, this study demonstrates an image analysis script that allows us to quantify these changes in the nuclear dry mass. The script runs the cell-nuclei segmentation using Matlab. By using the fluorescent image as a template for the boundaries of cell nuclei and quantitative phase images for retrieving the dry mass density, the script recognizes nuclei of all cells in an image at a time and quantifies the nuclear dry mass. Using the "the cell-nucleus segmentation" script, this study reveals an interesting correlation between the nuclear dry mass and the filopodia protrusion of cervical epithelial cells. As the filopodia density and protrusion length increase, the nuclear dry mass increases. On the other hand, whenever the nuclear dry mass decreases, cells filopodia retract significantly. Taken together, the imaging script developed here will be useful to quantifying dynamic nuclear activities of a broad array of cells non-invasively.

bioengineering↗

bioRxiv2020

Label-free cell viability assay using phase imaging with computational specificity

Existing approaches to evaluate cell viability involve cell staining with chemical reagents. However, this step of exogenous staining makes these methods undesirable for rapid, nondestructive and long-term investigation. Here, we present instantaneous viability assessment of unlabeled cells using phase imaging with computation specificity (PICS). This new concept utilizes deep learning techniques to compute viability markers associated with the specimen measured by label-free quantitative phase imaging. Demonstrated on different live cell cultures, the proposed method reports approximately 95% accuracy in identifying live and dead cells. The evolution of the cell dry mass and projected area for the labelled and unlabeled populations reveal that the viability reagents decrease viability. The nondestructive approach presented here may find a broad range of applications, from monitoring the production of biopharmaceuticals, to assessing the effectiveness of cancer treatments.

bioengineering↗