Levels of sCD163 in women rheumatoid arthritis: Relationship with cardiovascular risk markers.

Aim: The soluble scavenger receptor differentiation antigen 163 (sCD163), a monocyte/macrophage activation marker, is related to cardiovascular mortality in the general population. This study aimed to evaluate their relationship between serum levels of sCD163 with cardiovascular risk indicators in rheumatoid arthritis (RA).

Methods: A cross-sectional study was performed on 80 women diagnosed with RA.

Parkinson's Disease Detection from Voice Recordings Using Associative Memories.

Parkinson's disease (PD) is a neurological condition that is chronic and worsens over time, which presents a challenging diagnosis. An accurate diagnosis is required to recognize PD patients from healthy individuals. Diagnosing PD at early stages can reduce the severity of this disorder and improve the patient's living conditions.

Landslide Susceptibility Assessment Using an AutoML Framework.

The risks associated with landslides are increasing the personal losses and material damages in more and more areas of the world. These natural disasters are related to geological and extreme meteorological phenomena (e.g.

Computer-aided diagnosis based on hand thermal, RGB images, and grip force using artificial intelligence as screening tool for rheumatoid arthritis in women.

Rheumatoid arthritis (RA) is an autoimmune disorder that typically affects people between 23 and 60 years old causing chronic synovial inflammation, symmetrical polyarthritis, destruction of large and small joints, and chronic disability. Clinical diagnosis of RA is stablished by current ACR-EULAR criteria, and it is crucial for starting conventional therapy in order to minimize damage progression. The 2010 ACR-EULAR criteria include the presence of swollen joints, elevated levels of rheumatoid factor or anti-citrullinated protein antibodies (ACPA), elevated acute phase reactant, and duration of symptoms.

An Associative Memory Approach to Healthcare Monitoring and Decision Making.

The rapid proliferation of connectivity, availability of ubiquitous computing, miniaturization of sensors and communication technology, have changed healthcare in all its areas, creating the well-known healthcare paradigm of e-Health. In this paper, an embedded system capable of monitoring, learning and classifying biometric signals is presented. The machine learning model is based on associative memories to predict the presence or absence of coronary artery disease in patients.

Development of a Laparoscopic Box Trainer Based on Open Source Hardware and Artificial Intelligence for Objective Assessment of Surgical Psychomotor Skills.

Background: A trainer for online laparoscopic surgical skills assessment based on the performance of experts and nonexperts is presented. The system uses computer vision, augmented reality, and artificial intelligence algorithms, implemented into a Raspberry Pi board with Python programming language.

Methods: Two training tasks were evaluated by the laparoscopic system: transferring and pattern cutting.

A non-invasive glucose level estimation in a multi-sensing health care monitoring system.

Background And Objective: The treatment and care of patients with chronic diseases depends directly on the evolution of biomedical parameters. It is important to have a monitoring health care system that provides biomedical data at any time and place. Here, a multi-sensing health care monitoring system with a built-in non-invasive blood glucose level estimation method is presented.