Currently, Coronavirus disease (COVID-19), one of the most infectious diseases in the 21st century, is diagnosed using RT-PCR testing, CT scans and/or Chest X-Ray (CXR) images. CT (Computed Tomography) scanners and RT-PCR testing are not available in most medical centers and hence in many cases CXR images become the most time/cost effective tool for assisting clinicians in making decisions. Deep learning neural networks have a great potential for building COVID-19 triage systems and detecting COVID-19 patients, especially patients with low severity. Unfortunately, current databases do not allow building such systems as they are highly heterogeneous and biased towards severe cases. This article is threefold: (i) we demystify the high sensitivities achieved by most recent COVID-19 classification models, (ii) under a close collaboration with Hospital Universitario Clínico San Cecilio, Granada, Spain, we built COVIDGR-1.0, a homogeneous and balanced database that includes all levels Manuscript

This study shows that the SHRI is a valid, simple, reliable, and cost-effective screening tool for the identification, assessment, and quantification of hepatic steatosis in the general population.

AimsInsulin resistance is the pathophysiological precursor of type 2 diabetes mellitus (DM-2), and its relationship with non-alcoholic fatty liver disease (NAFLD) has been widely studied in patients with obesity or metabolic syndrome using not only ultrasound but also liver biopsies or proton magnetic resonance spectroscopy (H1-MRS) to assess liver fat content. In contrast, there are no studies on insulin resistance and NAFLD in lean or overweight Caucasian individuals using H1-MRS or liver biopsies for the quantification of hepatic triglyceride content. Our objectives were to study the presence of insulin resistance in lean and overweight Caucasian adults and investigate its possible relationship with liver triglyceride content, waist circumference (as proxy of visceral adiposity), BMI, and cardiometabolic risk factors.MethodsA cross-sectional study was conducted in 113 non-obese, non-diabetic individuals classified as overweight (BMI 25–29.9 kg/m2) or lean (BMI 19.5–24.9 kg/m2). Hepatic triglyceride content was quantified by 3T H1-MRS. NAFLD was defined as hepatic triglyceride content >5.56%. Insulin resistance (HOMA-IR), serum adiponectin, and tumor necrosis factor (TNF) were determined.ResultsHOMA-IR was significantly correlated with hepatic triglyceride content (r:0.76; p<0.0001). The lean-with-NAFLD group had significantly higher HOMA-IR (p<0.001) and lower serum adiponectin (p<0.05) than the overweight-without-NAFLD group. Insulin resistance was independently associated with NAFLD but not with waist circumference or BMI. Regression analysis showed hepatic triglyceride content to be the most important determinant of insulin resistance (p<0.01).ConclusionsOur findings suggest that NAFLD, once established, seems to be involved in insulin resistance and cardio-metabolic risk factors above and beyond waist circumference and BMI in non-obese, non-diabetic Caucasian individuals.