Machine Learning Models for the Early Real-Time Prediction of Deterioration in Intensive Care Units-A Novel Approach to the Early Identification of High-Risk Patients.

Predictive machine learning models have made use of a variety of scoring systems to identify clinical deterioration in ICU patients. However, most of these scores include variables that are dependent on medical staff examining the patient. We present the development of a real-time prediction model using clinical variables that are digital and automatically generated for the early detection of patients at risk of deterioration.

Application of genetic testing criteria for hereditary breast cancer in South Africa.

Purpose: Breast cancer (BC) is the commonest cancer in South African women. A proportion are associated with a pathogenic or likely pathogenic (P/LP) variant in a BC susceptibility gene. Clinical guidelines for genetic testing are used to optimise variant detection while containing costs.

Interaction Between the Matrix Protein and the Polymerase Complex of Respiratory Syncytial Virus.

The global burden of respiratory syncytial virus (RSV) and severe associated disease is prodigious. RSV-specific vaccines have been launched recently but there is no antiviral medicine commercially available. RSV polymerase (L) protein is one of the promising antiviral targets, along with fusion and nucleocapsid proteins.

Artificial intelligence in respiratory care.

The evolution of artificial intelligence (AI) has revolutionised numerous aspects of our daily lives, with profound implications across various sectors, including healthcare. Although the concept of AI in healthcare was introduced in the early 1970s, the integration of this technology in healthcare is still in the evolution phase. Despite barriers, the current decade is witnessing an increased utility of AI into diverse specialities of the medical field to enhance precision medicine, predict diagnosis, therapeutic results, and prognosis; this includes respiratory medicine, critical care, and in their allied specialties.

Seroprevalence and risk factors of brucellosis in pastoralists and their livestock in Central Equatoria State, South Sudan.

Background: Brucellosis poses serious public health implications and substantial economic losses in pastoral rural settings in South Sudan. In humans, brucellosis is almost always originating from animals. Current literature provides scant data regarding the seroprevalence of brucellosis in South Sudan.