Liquid-based cytology (LBC) for cervical cancer screening is now more common than the conventional smears, which when digitised from glass slides into whole-slide images (WSIs), opens up the possibility of artificial intelligence (AI)-based automated image analysis. Since conventional screening processes by cytoscreeners and cytopathologists using microscopes is limited in terms of human resources, it is important to develop new computational techniques that can automatically and rapidly diagnose a large amount of specimens without delay, which would be of great benefit for clinical laboratories and hospitals. The goal of this study was to investigate the use of a deep learning model for the classification of WSIs of LBC specimens into neoplastic and non-neoplastic. To do so, we used a dataset of 1605 cervical WSIs. We evaluated the model on three test sets with a combined total of 1468 WSIs, achieving ROC AUCs for WSI diagnosis in the range of 0.89–0.96, demonstrating the promising potential use of such models for aiding screening processes.
The hydrolysis of disaccharides was conducted using a microcapillary system under hydrothermal conditions (up to 190°C at 10 MPa and pH 4–11). The hydrolysis reaction showed a sigmoidal progression with time, especially under alkaline conditions. Analysis using a kinetic model yielded the reaction induction period. The specific pH value (pHamb) at the induction time, which is the pH value corresponding to the progression of disaccharide hydrolysis, was peculiar to each disaccharide. Finally, the calculation of the electron density around the oxygen atom of the glycosidic bond between saccharides was found to roughly predict the pHamb value required for the progression of hydrolysis.
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