miR-429 Suppresses Endometrial Cancer Progression and Drug Resistance via DDX53.

(1) Background: To examine miR-429-meditated DEAD (Asp-Glu-Ala-Asp) box polypeptide 53 (DDX53) function in endometrial cancer (EC). (2) Methods: DDX53 and miR-429 levels were measured using quantitative real-time polymerase chain reaction and western blotting assays, cell invasion and migration using Transwell invasion and wound healing assays, and cell proliferation using colony-forming/proliferation assays. A murine xenograft model was also generated to examine miR-429 and DDX53 functions in vivo.

The Role of ROR1 in Chemoresistance and EMT in Endometrial Cancer Cells.

: Receptor tyrosine kinase-like orphan receptor type 1 (ROR1) plays a critical role in embryogenesis and is overexpressed in many malignant cells. These characteristics allow ROR1 to be a potential new target for cancer treatment. The aim of this study was to investigate the role of ROR1 through in vitro experiments in endometrial cancer cell lines.

The role of vessel wall imaging in determining the best treatment approach for coexisting aneurysms and subarachnoid hemorrhage.

Purpose: The purpose of this study was to investigate the utilization of gadolinium enhancement on vessel wall imaging (VWI) in treatment decision-making for patients with two intracranial aneurysms presenting as a subarachnoid hemorrhage (SAH).

Materials And Methods: We prospectively performed VWI using 3.0-Tesla (3T) magnetic resonance imaging (MRI) before treatment with endovascular coiling or surgical clipping in patients with one or two intracranial aneurysms.

Analysis of depression in social media texts through the Patient Health Questionnaire-9 and natural language processing.

Objective: Although depression in modern people is emerging as a major social problem, it shows a low rate of use of mental health services. The purpose of this study was to classify sentences written by social media users based on the nine symptoms of depression in the Patient Health Questionnaire-9, using natural language processing to assess naturally users' depression based on their results.

Methods: First, train two sentence classifiers: the Y/N sentence classifier, which categorizes whether a user's sentence is related to depression, and the 0-9 sentence classifier, which further categorizes the user sentence based on the depression symptomology of the Patient Health Questionnaire-9.