Predicting Gestational Diabetes Mellitus in the first trimester using machine learning algorithms: a cross-sectional study at a hospital fertility health center in Iran.

Background: Gestational Diabetes Mellitus (GDM) is a common complication during pregnancy. Late diagnosis can have significant implications for both the mother and the fetus. This research aims to create an early prediction model for GDM in the first trimester of pregnancy.

Prediction of ovarian cancer using artificial intelligence tools.

Purpose: Ovarian cancer is a common type of cancer and a leading cause of death in women. Therefore, accurate and fast prediction of ovarian tumors is crucial. One of the appropriate and precise methods for predicting and diagnosing this cancer is to build a model based on artificial intelligence methods.

Both T and B cells are indispensable for the development of a PBMC transfer-induced humanized mouse model for SSc.

Background: Recently, a novel humanized mouse model for systemic sclerosis (SSc) was established by transferring peripheral blood mononuclear cells (PBMC) from patients with SSc to Rag2Il2rg immunodeficient mice. Here, we aimed to investigate the role of T and B cells in this humanized mouse model.

Methods: T and B cells were depleted in vitro from freshly isolated PBMC using anti-CD3 and anti-CD19 magnetic microbeads, respectively.

Transfer of PBMC From SSc Patients Induces Autoantibodies and Systemic Inflammation in Rag2-/-/IL2rg-/- Mice.

Objective: The contribution of sustained autologous autoantibody production by B cells to the pathogenesis of systemic sclerosis (SSc) and granulomatosis with polyangiitis (GPA) is not fully understood. To investigate this, a humanized mouse model was generated by transferring patient-derived peripheral blood mononuclear cells (PBMC) into immunocompromised mice.

Methods: PBMC derived from patients with SSc and GPA as well as healthy controls (HD) were isolated, characterized by flow cytometry, and infused into mice.

Medical student and patient perspectives on bedside teaching.

Objective: To evaluate the perspectives of medical students and patients on bedside teaching (BST).

Methods: A cross-sectional study was undertaken to elicit patients and learners opinions on BST in Hazrat Rasool Hospital, a university teaching hospital in Tehran, Iran. From June 2008 to September 2008, 100 fourth-year medical students and 100 adult patients admitted to the general medical service of a teaching hospital were chosen randomly.