A prognostic system for epithelial ovarian carcinomas using machine learning.

Introduction: Integrating additional factors into the International Federation of Gynecology and Obstetrics (FIGO) staging system is needed for accurate patient classification and survival prediction. In this study, we tested machine learning as a novel tool for incorporating additional prognostic parameters into the conventional FIGO staging system for stratifying patients with epithelial ovarian carcinomas and evaluating their survival.

Material And Methods: Cancer-specific survival data for epithelial ovarian carcinomas were extracted from the Surveillance, Epidemiology, and End Results (SEER) program.

Qualitative age interactions between low-grade and high-grade serous ovarian carcinomas.

Purpose: Ovarian epithelial carcinomas, including the predominant serous ovarian carcinoma (SOC) type, are heterogeneous malignancies. Even though invasive SOCs of low and high grade can be distinguished by morphology and molecular or immunohistochemical profiles, age-specific risks relevant to their separate carcinogenic pathways and clinical features have not been fully explored.

Methods: In search of further clues to the etiology/pathogenesis of low-grade and high-grade SOCs, we analyzed incidence rate patterns.

Abeta(1-42) stimulated T cells express P-PKC-delta and P-PKC-zeta in Alzheimer disease.

The protein kinase C (PKC) family of enzymes is a regulator of transmembrane signal transduction, and involvement of some PKC isoforms in T-cell activation has been demonstrated. Nevertheless, very little is known about their involvement in the Amyloid beta (Abeta)-dependent molecular signals in the T lymphocytes of Alzheimer disease (AD) patients. Therefore, the aim of this study was to investigate the involvement of PKC-alpha, PKC-delta and PKC-zeta expression and activity in the signaling machinery activated in Abeta-reactive T cells, in adult healthy individuals, elderly healthy subjects, and from patients with AD.

Population-based analysis of pathologic data: a new approach to the investigation of uterine endometrial and ovarian endometrioid carcinomas.

Context: Population-based analysis of the histopathology of endometrioid adenocarcinoma of the endometrium and ovary combined with epidemiologic techniques offer a new approach to exploring the relationship of tumors that share a similar range of morphologic phenotypes.

Objective: To evaluate the contribution of the Surveillance, Epidemiology, and End Results database to our understanding of gynecologic pathology. Specifically, to test and compare whether the etiology/pathogenesis of ovarian endometrioid cancer is as dependent upon the reproductive environment as uterine endometrial carcinoma.