RNA Interference Effectors Selectively Silence the Pathogenic Variant GNAO1 c.607 G > A In Vitro.

RNA interference (RNAi)-based therapeutics hold the potential for dominant genetic disorders, enabling sequence-specific inhibition of pathogenic gene products. We aimed to direct RNAi for the selective suppression of the heterozygous GNAO1 c.607 G > A variant causing GNAO1 encephalopathy. By screening short interfering RNA (siRNA), we showed that GNAO1 c.607G>A is a druggable target for RNAi. The si1488 candidate achieved at least twofold allelic discrimination and downregulated mutant protein to 35%. We created vectorized RNAi by incorporating the si1488 sequence into the short hairpin RNA (shRNA) in the adeno-associated virus (AAV) vector. The shRNA stem and loop were modified to improve the transcription, processing, and guide strand selection. All tested shRNA constructs demonstrated selectivity toward mutant GNAO1, while tweaking hairpin structure only marginally affected the silencing efficiency. The selectivity of shRNA-mediated silencing was confirmed in the context of AAV vector transduction. To conclude, RNAi effectors ranging from siRNA to AAV-RNAi achieve suppression of the pathogenic GNAO1 c.607G>A and discriminate alleles by the single-nucleotide substitution. For gene therapy development, it is crucial to demonstrate the benefit of these RNAi effectors in patient-specific neurons and animal models of the GNAO1 encephalopathy.

Artificial Intelligence Assists in the Detection of Blood Vessels in Whole Slide Images: Practical Benefits for Oncological Pathology.

The analysis of the microvasculature and the assessment of angiogenesis have significant prognostic value in various diseases, including cancer. The search for invasion into the blood and lymphatic vessels and the assessment of angiogenesis are important aspects of oncological diagnosis. These features determine the prognosis and aggressiveness of the tumor. Traditional manual evaluation methods are time consuming and subject to inter-observer variability. Blood vessel detection is a perfect task for artificial intelligence, which is capable of rapid analyzing thousands of tissue structures in whole slide images. The development of computer vision solutions requires the segmentation of tissue regions, the extraction of features and the training of machine learning models. In this review, we focus on the methodologies employed by researchers to identify blood vessels and vascular invasion across a range of tumor localizations, including breast, lung, colon, brain, renal, pancreatic, gastric and oral cavity cancers. Contemporary models herald a new era of computational pathology in morphological diagnostics.