RESUMEN
Coronavirus disease 2019 (COVID-19) is caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Recent research has demonstrated how epigenetic mechanisms regulate the host-virus interactions in COVID-19. It has also shown that microRNAs (miRNAs) are one of the three fundamental mechanisms of the epigenetic regulation of gene expression and play an important role in viral infections. A pilot study published by our research group identified, through next-generation sequencing (NGS), that miR-4433b-5p, miR-320b, and miR-16-2-3p are differentially expressed between patients with COVID-19 and controls. Thus, the objectives of this study were to validate the expression of these miRNAs using quantitative real-time polymerase chain reaction (qRT-PCR) and to perform in silico analyses. Patients with COVID-19 (n = 90) and healthy volunteers (n = 40) were recruited. MiRNAs were extracted from plasma samples and validated using qRT-PCR. In addition, in silico analyses were performed using mirPath v.3 software. MiR-320b was the only miRNA upregulated in the case group com-pared to the control group. The in silico analyses indicated the role of miR-320b in the regulation of the KITLG gene and consequently in the inflammatory process. This study confirmed that miR-320b can distinguish patients with COVID-19 from control participants; however, further research is needed to determine whether this miRNA can be used as a target or a biomarker.
Asunto(s)
COVID-19 , MicroARNs , SARS-CoV-2 , Humanos , COVID-19/genética , COVID-19/sangre , COVID-19/virología , MicroARNs/sangre , MicroARNs/genética , Masculino , Femenino , SARS-CoV-2/genética , Persona de Mediana Edad , Adulto , Anciano , Estudios de Casos y ControlesRESUMEN
Several statistical models have been proposed in recent years, among them is the semiparametric regression. In medicine, there are several situations in which it is impracticable to consider a linear regression for statistical modeling, especially when the data contain explanatory variables that present a nonlinear relationship with the response variable. Another common situation is when the response variable does not have a unimodal shape, and it is not possible to adopt distributions belonging to the symmetric or asymmetric classes. In this context, a semiparametric heteroskedastic regression is proposed based on an extension of the normal distribution. Then, we show the usefulness of this model to analyze the cost of prostate cancer surgery. The predictor variables refer to two groups of patients such that one group receives a multimodal local anesthetic solution (Preemptive Target Anesthetic Solution) and the second group is treated with neuraxial blockade (spinal anesthesia/traditional standard). The other relevant predictor variables are also evaluated, thus allowing for the in-depth interpretation of the predictor variables with a nonlinear effect on the dependent variable cost. The penalized maximum likelihood method is adopted to estimate the model parameters. The new regression is a useful statistical tool for analyzing medical data.
RESUMEN
Testicular tumors represent the most common type of solid neoplasia in men aged between 18 and 35 years. Its cure rate is approximately 90% 1,2. In some cases, tumoral vascular invasion can occur and demands surgical resection to stop disease progression and prevent thromboembolic events. That is the only valuable therapeutic choice although it is a high risk procedure. We present a case report of a patient who underwent successful chemotherapy and surgery for a right-sided testicular tumor associated with an inferior vena cava tumor thrombus extending from the renal vein to the right atrium and extensive retroperitoneal lymph node disease.