Adverse drug reactions (ADR) are important information for verifying the view of the patient on a particular drug. Regular user comments and reviews have been considered during the data collection process to extract ADR mentions, when the user reported a side effect after taking a specific medication. In the literature, most researchers focused on machine learning techniques to detect ADR. These methods train the classification model using annotated medical review data. Yet, there are still many challenging issues that face ADR extraction, especially the accuracy of detection. The main aim of this study is to propose LSA with ANN classifiers for ADR detection. The findings show the effectiveness of utilizing LSA with ANN in extracting AD

The study aimed to assess the level of ANG‑2 in MM patients at diagnosis and in remission state and elaborate on its correlation with interleukin‑6 (IL‑6) and beta‑2 microglobulin (B2M) levels. Sixty MM patients; 20 newly diagnosed (ND), and 40 patients in remission were included. Twenty healthy individuals were included as a control group. Plasma levels of ANG‑2, B2M, and IL‑6 were tested by enzyme‑lin ked immunosorbent assay. There are significant statistical differences between ND patients and those in remission in hemoglobin, neutrophil count, blood urea, serum creatinine, glomerular filtration rate, B2M, IL6, and ANG‑2 (P = 0.001, 0.033, 0.005, 0.001, 0.001, 0.001, 0.004, and 0.001, respectively). ANG‑2 showed signifi

Human interaction technology based on motion capture (MoCap) systems is a vital tool for human kinematics analysis, with applications in clinical settings, animations, and video games. We introduce a new method for analyzing and estimating dorsal spine movement using a MoCap system. The captured data by the MoCap system are processed and analyzed to estimate the motion kinematics of three primary regions; the shoulders, spine, and hips. This work contributes a non-invasive and anatomically guided framework that enables region-specific analysis of spinal motion which could be used as a clinical alternative to invasive measurement techniques. The hierarchy of our model consists of five main levels; motion capture system settings, marker data

Back ground: Diabetic nephropathy is rapidly becoming the leading cause of end-stage renal disease (ESRD). The onset and course of DN can be ameliorated to a very significant degree if intervention institutes at a point very early in the course of the development of this complication.
Objective: The aim of this study was to characterize risk factors associated with nephropathy in type I diabetes and construct a module for early prediction of diabetic nephropathy (DN) by analyzing their risk factors.
Methods: Case control design of 400 patients with type I diabetes mellitus (IDDM), aged 19-45 years. The cases were 200 diabetic patients with overt protein urea while the controls were 200 diabetic patients with no protein urea or micr