Social and clinical impact of COVID-19 on patients with fibrodysplasia ossificans progressiva.

Background: COVID-19, caused by the SARS-CoV-2 virus, is a severe inflammatory condition. Patients with pre-existing conditions including diabetes, hypertension, and cardiovascular disease are at particularly high risk of complications. Fibrodysplasia ossificans progressiva (FOP) is an ultra-rare and debilitating genetic disorder that is characterized by a pro-inflammatory state, which leads to progressive heterotopic ossification and complications after trauma, including intramuscular vaccinations.

Phenotypic Characterization of Chronic Kidney Patients Through Hierarchical Clustering.

Chronic kidney disease is a major public health problem around the world and this disease early diagnosis is still a great challenge as it is asymptomatic in its early stages. Thus, in order to identify variables capable of assisting CKD diagnosis and monitoring, machine learning techniques and statistical analysis use has shown itself to be extremely promising. For this work, unsupervised machine learning, statistical analysis techniques and discriminant analysis were used.

Social and Clinical Impact of COVID-19 on Patients with Fibrodysplasia Ossificans Progressiva.

Background COVID-19, caused by the SARS-CoV-2 virus, is a severe inflammatory condition. Patients with pre-existing conditions including diabetes, hypertension, and cardiovascular disease are at particularly high risk of complications. Fibrodysplasia ossificans progressiva (FOP) is an ultra-rare and debilitating genetic disorder that is characterized by a pro-inflammatory state, which leads to progressive heterotopic ossification and complications after trauma, including intramuscular vaccinations.

Development of a Computational Model to Predict Excess Body Fat in Adolescents through Low Cost Variables.

Background: Excess body fat has been growing alarmingly among adolescents, especially in low income and middle income countries where access to health services is scarce. Currently, the main method for assessing overweight in adolescents is the body mass index, but its use is criticized for its low sensitivity and high specificity, which may lead to a late diagnosis of comorbidities associated with excess body fat, such as cardiovascular diseases. Thus, the aim of this study was to develop a computational model using linear regression to predict obesity in adolescents and compare it with commonly used anthropometric methods.