MLACNN: an attention mechanism-based CNN architecture for predicting genome-wide DNA methylation.

Methylation is an important epigenetic regulation of methylation genes that plays a crucial role in regulating biological processes. While traditional methods for detecting methylation in biological experiments are constantly improving, the development of artificial intelligence has led to the emergence of deep learning and machine learning methods as a new trend. However, traditional machine learning-based methods rely heavily on manual feature extraction, and most deep learning methods for studying methylation extract fewer features due to their simple network structures.

Altered expression of the prion gene in rat astrocyte and neuron cultures treated with prion peptide 106-126.

Neuronal degeneration and astrogliosis are hallmarks of prion disease. Synthetic prion protein (PrP) peptide 106-126 (PrP106-126) can induce death of neurons and proliferation of astrocytes in vitro and this neurotoxic effect depends on the expression of cellular PrP (PrPC) and is hence believed to be PrP(C) -mediated. To further elucidate the involvement of PrPC in PrP106-126-induced neurotoxicity, we determined the expression of PrP mRNA in primary culture of rat cortical neuron cells, cerebellar granule cells, and astrocytes following treatment with 50 microM of PrP106-126 scrambled PrP106-126 by quantitative real-time RT-PCR.

Quantification of prion gene expression in brain and peripheral organs of golden hamster by real-time RT-PCR.

Determination of tissue-specific expression of cellular prion protein (PrPc) is essential for understanding its poorly explained role in organisms. Herein we report on quantification of PrP mRNA in golden hamsters, a popular experimental model for studying mechanisms of transmissible spongiform encephalopathies (TSE), by real-time RT-PCR. Total RNA was isolated from four different regions of the brain and six peripheral organs of eight golden hamsters.