ANRIL (Antisense Noncoding RNA in the INK4 Locus), also named CDKN2B-AS1, is a long non-coding RNA with outstanding functions that regulates genes involved in atherosclerosis development. ANRIL genotypes and the expression of linear and circular isoforms have been associated with coronary artery disease (CAD). The CDKN2A and the CDKN2B genes at the CDKN2A/B locus encode the Cyclin-Dependent Kinase inhibitor protein (CDKI) p16INK4a and the p53 regulatory protein p14ARF, which are involved in cell cycle regulation, aging, senescence, and apoptosis. Abnormal ANRIL expression regulates vascular endothelial growth factor (VEGF) gene expression, and upregulated Vascular Endothelial Growth Factor (VEGF) promotes angiogenesis by activating the NF-κB signaling pathway. Here, we explored associations between determinations of the linear, circular, and linear-to-circular ANRIL gene expression ratio, CDKN2A, VEGF and its receptor kinase insert domain-containing receptor (KDR) and cardiovascular risk factors and all-cause mortality in high-risk coronary patients before they undergo coronary artery bypass grafting surgery (CABG). We found that the expression of ANRIL isoforms may help in the prediction of CAD outcomes. Linear isoforms were correlated with a worse cardiovascular risk profile while the expression of circular isoforms of ANRIL correlated with a decrease in oxidative stress. However, the determination of the linear versus circular ratio of ANRIL did not report additional information to that determined by the evaluation of individual isoforms. Although the expressions of the VEFG and KDR genes correlated with a decrease in oxidative stress, in binary logistic regression analysis it was observed that only the expression of linear isoforms of ANRIL and VEGF significantly contributed to the prediction of the number of surgical revascularizations.

Insulin is a powerful pleiotropic hormone that affects processes such as cell growth, energy expenditure, and carbohydrate, lipid, and protein metabolism. The molecular mechanisms by which insulin regulates muscle metabolism and the underlying defects that cause insulin resistance have not been fully elucidated. This study aimed to perform a microarray data analysis to find differentially expressed genes. The analysis has been based on the data of a study deposited in Gene Expression Omnibus (GEO) with the identifier “GSE22309”. The selected data contain samples from three types of patients after taking insulin treatment: patients with diabetes (DB), patients with insulin sensitivity (IS), and patients with insulin resistance (IR). Through an analysis of omics data, 20 genes were found to be differentially expressed (DEG) between the three possible comparisons obtained (DB vs. IS, DB vs. IR, and IS vs. IR); these data sets have been used to develop predictive models through machine learning (ML) techniques to classify patients with respect to the three categories mentioned previously. All the ML techniques present an accuracy superior to 80%, reaching almost 90% when unifying IR and DB categories.

(1) Background: Severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) continues to cause profound health, economic, and social problems worldwide. The management and disinfection of materials used daily in health centers and common working environments have prompted concerns about the control of coronavirus disease 2019 (COVID-19) infection risk. Ozone is a powerful oxidizing agent that has been widely used in disinfection processes for decades. The aim of this study was to assess the optimal conditions of ozone treatment for the elimination of heat-inactivated SARS-CoV-2 from office supplies (personal computer monitors, keyboards, and computer mice) and clinical equipment (continuous positive airway pressure tubes and personal protective equipment) that are difficult to clean. (2) Methods: The office supplies and clinical equipment were contaminated in an area of 1 cm² with 1 × 10⁴ viral units of a heat-inactivated SARS-CoV-2 strain, then treated with ozone using two different ozone devices: a specifically designed ozonation chamber (for low–medium ozone concentrations over large volumes) and a clinical ozone generator (for high ozone concentrations over small volumes). SARS-CoV-2 gene detection was carried out using quantitative real-time polymerase chain reaction (RT-qPCR). (3) Results: At high ozone concentrations over small surfaces, the ozone eliminated SARS-CoV-2 RNA in short time periods—i.e., 10 min (at 4000 ppm) or less. The optimum ozone concentration over large volumes was 90 ppm for 120 min in ambient conditions (24 °C and 60–75% relative humidity). (4) Conclusions: This study showed that the appropriate ozone concentration and exposure time eliminated heat-inactivated SARS-CoV-2 RNA from the surfaces of different widely used clinical and office supplies, decreasing their risk of transmission, and improving their reutilization. Ozone may provide an additional tool to control the spread of the COVID-19 pandemic.

The current COVID-19 pandemic is causing profound health, economic, and social problems worldwide. The global shortage of medical and personal protective equipment (PPE) in specialized centers during the outbreak demonstrated the need for efficient methods to disinfect and recycle them in times of emergency. We have previously described that high ozone concentrations destroyed viral RNA in an inactivated SARS-CoV-2 strain within a few minutes. However, the efficient ozone dosages for active SARS-CoV-2 are still unknown. The present study aimed to evaluate the systematic effects of ozone exposure on face masks from hospitalized patients infected with SARS-CoV-2. Face masks from COVID-19 patients were collected and treated with a clinical ozone generator at high ozone concentrations in small volumes for short periods. The study focused on SARS-CoV-2 gene detection (assessed by real-time quantitative polymerase chain reaction (RT-qPCR)) and on the virus inactivation by in vitro studies. We assessed the effects of different high ozone concentrations and exposure times on decontamination efficiency. We showed that high ozone concentrations (10,000, 2,000, and 4,000 ppm) and short exposure times (10, 10, and 2 minutes, respectively), inactivated both the original strain and the B.1.1.7 strain of SARS-CoV-2 from 24 contaminated face masks from COVID-19 patients. The validation results showed that the best condition for SARS-CoV-2 inactivation was a treatment of 4,000 ppm of ozone for 2 minutes. Further studies are in progress to advance the potential applications of these findings.