Predicting emergency health care demands due to respiratory diseases.

BACKGROUND: Timely care in the health sector is essential for the recovery of patients, and even more so in the case of a health emergency. In these cases, appropriate management of human and technical resources is essential. These are limited and must be mobilised in an optimal and efficient manner. OBJECTIVE: This paper analyses the use of the health emergency service in a city, Jaén, in the south of Spain. The study is focused on the most recurrent case in this service, respiratory diseases. METHODS: Machine Learning algorithms are used in which the input variables are multisource data and the target attribute is the prediction of the number of health emergency demands that will occur for a selected date. Health, social, economic, environmental, and geospatial data related to each of the emergency demands were integrated and related. Linear and nonlinear regression algorithms were used: support vector machine (SVM) with linear kernel and generated linear model (GLM), and the nonlinear SVM with Gaussian kernel. RESULTS: Predictive models of emergency demand due to respiratory disseases were generated with am absolute error better than 35 %. CONCLUSIONS: This model helps to make decisions on the efficient sizing of emergency health resources to manage and respond in the shortest possible time to patients with respiratory diseases requiring urgent care in the city of Jaén.

Assessment of human lymphocyte proliferation associated with metabolic syndrome.

BACKGROUND: Metabolic syndrome (MetS), a cluster of various metabolic conditions, has become epidemic and causes increased morbidity and mortality. PURPOSE: The aim of this study was to compare lymphocyte proliferation under two different stimuli, Concanavalin A (ConA) and insulin, in a group of patients with MetS (Group 1) and a healthy group (Group 2). METHODS: Group 1 consisted of 53 patients who met the diagnostic criteria for MetS. Group 2 consisted of 63 patients without MetS. All individuals were evaluated for lipid profile and glycemia. Lymphocyte extraction and culture were performed for each subject and lymphocyte proliferation was assessed using the Alamar blue technique. RESULTS: There was no gender difference between both groups, but in terms of age, there was a significant difference. The use of Con A at concentrations of 1 and 5 µg/mL induced a high lymphocyte proliferation in both groups. In contrast, when different concentrations of insulin were added, no significant changes in lymphocyte proliferation were observed. However, the proliferation of lymphocytes was significantly higher in Group 1 compared to Group 2 under insulin stimulus, which did not happen under ConA stimulation. Even after age and gender correction, this difference was maintained. CONCLUSIONS: The increased lymphocyte proliferative response to insulin in patients with MetS found in this study suggests a role of the lymphocyte response to insulin in the pathophysiology of MetS. This response may be used as an immuno-biological marker for MetS, although further studies to evaluate its clinical usefulness need to be conducted.