The long-term photobiomodulation of 810 nm LED light on the IMR-32 human neuroblastoma cell line was investigated. Daily light treatments of 1.26 J/cm2 and 1.755 J/cm2 both increase resistance to doxorubicin, sodium azide, and FCCP toxicity. However, 1.26 J/cm2 increases the mitochondrial DNA copy number, alters cell cycle-related pathways’ RNA expression, and promotes cell division. Operating differently, 1.755 J/cm2 decreases the mitochondrial DNA copy number and activates the oxidative phosphorylation pathway at the RNA, protein, and functional levels. The 810 nm light treatment shows two different long-term dose-dependent effects on cellular and mitochondrial activities.
Convolutional neural networks (CNNs) have shown significant success in image recognition and segmentation. Based on a CNN-like U-Net architecture, such a model can effectively predict subcellular structures from transmitted light (TL) images after learning the relationships between TL images and fluorescent-labeled images. In this paper, we focused on building corresponding models of subcellular mitochondrial structures using the CNN method and compared the prediction results derived from confocal microscopic, Airyscan microscopic, z-stack, and time-series images. With multi-model combined prediction, it is possible to generate integrated images using only TL inputs, which reduces the time required for sample preparation and increases the temporal resolution. This enables visualization, measurement, and understanding of the morphology and dynamics of mitochondria and mitochondrial DNA.
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