Difference in mortality rates in hospitalized COVID-19 patients identified by cytokine profile clustering using a machine learning approach: An outcome prediction alternative.

COVID-19 is a disease caused by the novel Coronavirus SARS-CoV-2 causing an acute respiratory disease that can eventually lead to severe acute respiratory syndrome (SARS). An exacerbated inflammatory response is characteristic of SARS-CoV-2 infection, which leads to a cytokine release syndrome also known as cytokine storm associated with the severity of the disease. Considering the importance of this event in the immunopathology of COVID-19, this study analyses cytokine levels of hospitalized patients to identify cytokine profiles associated with severity and mortality. Using a machine learning approach, 3 clusters of COVID-19 hospitalized patients were created based on their cytokine profile. Significant differences in the mortality rate were found among the clusters, associated to different CXCL10/IL-38 ratio. The balance of a CXCL10 induced inflammation with an appropriate immune regulation mediated by the anti-inflammatory cytokine IL-38 appears to generate the adequate immune context to overrule SARS-CoV-2 infection without creating a harmful inflammatory reaction. This study supports the concept that analyzing a single cytokine is insufficient to determine the outcome of a complex disease such as COVID-19, and different strategies incorporating bioinformatic analyses considering a broader immune profile represent a more robust alternative to predict the outcome of hospitalized patients with SARS-CoV-2 infection.

La doble heterocigosis por la mutación común en el codón 39 (C>T) (β0) y la deleción siciliana (δβ0) 13.4 kb causa una beta-talasemia transfusión dependiente en dos pacientes costarricenses

Las talasemias son desórdenes autosómicos recesivos de las cadenas de hemoglobina que poseen expresión clínica variable según el tipo de mutación o deleción. Presentamos el caso de dos jóvenes mujeres costarricenses no relacionadas entre sí y ambas diagnosticadas con la mutación común en el codón 39 (C>T) (β0) en combinación con la deleción siciliana (δβ0) 13.4 kb. La caracterización de doble heterocigota no había sido descrita antes en la literatura médica, y discutimos el significado de este genotipo que causa un defecto tipo β0 talasemia transfusión dependiente.

Thalassemia are autosomal recessive disorders of hemoglobin chains with variable clinical expression depending on the type of mutation or deletion present. We present the common codon 39(C>T) (β0) in combination with the δβ0 13.4 kb Sicilian deletion in two non-related young women from Costa Rica. We report the characterization of the compound heterozygous not previously described phenotype, and discuss the significance of this genotype combination with a transfusion dependent β0 defect Thalassemia.

Alpha-thalassemia. Case report alpha-thalassemia in a Costa Rican family, A case report.

This case report highlights the importance for health care providers to be aware of the αlpha-thalassemia syndromes, their relevance to clinical care and family counseling, appropriate diagnostic algorithm for definitive diagnosis.

Set-theory based benchmarking of three different variant callers for targeted sequencing.

BACKGROUND: Next generation sequencing (NGS) technologies have improved the study of hereditary diseases. Since the evaluation of bioinformatics pipelines is not straightforward, NGS demands effective strategies to analyze data that is of paramount relevance for decision making under a clinical scenario. According to the benchmarking framework of the Global Alliance for Genomics and Health (GA4GH), we implemented a new simple and user-friendly set-theory based method to assess variant callers using a gold standard variant set and high confidence regions. As model, we used TruSight Cardio kit sequencing data of the reference genome NA12878. This targeted sequencing kit is used to identify variants in key genes related to Inherited Cardiac Conditions (ICCs), a group of cardiovascular diseases with high rates of morbidity and mortality. RESULTS: We implemented and compared three variant calling pipelines (Isaac, Freebayes, and VarScan). Performance metrics using our set-theory approach showed high-resolution pipelines and revealed: (1) a perfect recall of 1.000 for all three pipelines, (2) very high precision values, i.e. 0.987 for Freebayes, 0.928 for VarScan, and 1.000 for Isaac, when compared with the reference material, and (3) a ROC curve analysis with AUC > 0.94 for all cases. Moreover, significant differences were obtained between the three pipelines. In general, results indicate that the three pipelines were able to recognize the expected variants in the gold standard data set. CONCLUSIONS: Our set-theory approach to calculate metrics was able to identify the expected ICCs related variants by the three selected pipelines, but results were completely dependent on the algorithms. We emphasize the importance to assess pipelines using gold standard materials to achieve the most reliable results for clinical application.