Syndecan-3 regulates the time of transition from cell cycle exit to initial differentiation stage in mouse cerebellar granule cell precursors.

To analyze the role of syndecan-3 (SDC3), a heparan sulfate proteoglycan, in cerebellum development, we examined the effect of SDC3 on the transition from cell cycle exit to the initial differentiation stage of cerebellar granule cell precursors (CGCPs). First, we examined SDC3 localization in the developing cerebellum. SDC3 was mainly localized to the inner external granule layer where the transition from the cell cycle exit to the initial differentiation of CGCPs occurs. To examine how SDC3 regulates the cell cycle exit of CGCPs, we performed SDC3-knockdown (SDC3-KD) and -overexpression (Myc-SDC3) assays using primary CGCPs. SDC3-KD significantly increased the ratio of p27Kip1+ cells to total cells at day 3 in vitro (DIV3) and 4, but Myc-SDC3 reduced that at DIV3. Regarding the cell cycle exit efficiency using 24 h-labelled bromodeoxyuridine (BrdU) and a marker of cell cycling, Ki67, SDC3-KD significantly increased cell cycle exit efficiency (Ki67-; BrdU+ cells/BrdU+ cells) in primary CGCP at DIV4 and 5, but Myc-SDC3 reduced that at DIV4 and 5. However, SDC3-KD and Myc-SDC3 did not affect the efficiency of the final differentiation from CGCPs to granule cells at DIV3-5. Furthermore, the ratio of CGCPs in the cell cycle exiting stage to total cells, identified by initial differentiation markers TAG1 and Ki67 (TAG1+; Ki67+ cells), was considerably decreased by SDC3-KD at DIV4, but increased by Myc-SDC3 at DIV4 and 5. Altogether, these results indicate that SDC3 regulates the timing of the transition from the cell cycle exit stage to the initial differentiation stage of CGCP.

A Deep Learning Model for Cervical Cancer Screening on Liquid-Based Cytology Specimens in Whole Slide Images.

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.