A novel multimodal framework for early diagnosis and classification of COPD based on CT scan images and multivariate pulmonary respiratory diseases.

Background And Objective: Chronic Obstructive Pulmonary Disease (COPD) is one of the world's worst diseases; its early diagnosis using existing methods like statistical machine learning techniques, medical diagnostic tools, conventional medical procedures, and other methods is challenging due to misclassification results of COPD diagnosis and takes a long time to perform accurate prediction. Due to the severe consequences of COPD, detection and accurate diagnosis of COPD at an early stage is essential. This paper aims to design and develop a multimodal framework for early diagnosis and accurate prediction of COPD patients based on prepared Computerized Tomography (CT) scan images and lung sound/cough (audio) samples using machine learning techniques, which are presented in this study.

transcriptome assembly and mining of EST-SSR markers in .

is an economical source of pharmaceutical colchicine, which is a mitotic poison used to treat gout, cancer and inflammatory diseases. It is important to study the genetic variations in this plant, but the progress is impeded due to limited number of molecular markers. In this study, we developed the expressed sequence tag-derived simple sequence repeat (EST-SSR) markers from the transcriptome sequence of the leaf samples of three different ecotypes of .

Maternal Infection Is a Risk Factor for Early Childhood Infection in Filariasis.

Background: Global Program to Eliminate Lymphatic Filariasis (GPELF) launched by WHO aims to eliminate the disease by 2020. To achieve the goal annual mass drug administration (MDA) with diethylcarbamazine (DEC) plus albendazole (ABZ) has been introduced in all endemic countries. The current policy however excludes pregnant mothers and children below two years of age from MDA.