Human Genome Polymorphisms and Computational Intelligence Approach Revealed a Complex Genomic Signature for COVID-19 Severity in Brazilian Patients.

We present a genome polymorphisms/machine learning approach for severe COVID-19 prognosis. Ninety-six Brazilian severe COVID-19 patients and controls were genotyped for 296 innate immunity loci. Our model used a feature selection algorithm, namely recursive feature elimination coupled with a support vector machine, to find the optimal loci classification subset, followed by a support vector machine with the linear kernel (SVM-LK) to classify patients into the severe COVID-19 group. The best features that were selected by the SVM-RFE method included 12 SNPs in 12 genes: PD-L1, PD-L2, IL10RA, JAK2, STAT1, IFIT1, IFIH1, DC-SIGNR, IFNB1, IRAK4, IRF1, and IL10. During the COVID-19 prognosis step by SVM-LK, the metrics were: 85% accuracy, 80% sensitivity, and 90% specificity. In comparison, univariate analysis under the 12 selected SNPs showed some highlights for individual variant alleles that represented risk (PD-L1 and IFIT1) or protection (JAK2 and IFIH1). Variant genotypes carrying risk effects were represented by PD-L2 and IFIT1 genes. The proposed complex classification method can be used to identify individuals who are at a high risk of developing severe COVID-19 outcomes even in uninfected conditions, which is a disruptive concept in COVID-19 prognosis. Our results suggest that the genetic context is an important factor in the development of severe COVID-19.

Risk of SARS-CoV-2 infection among front-line healthcare workers in Northeast Brazil: a respondent-driven sampling approach.

OBJECTIVES: We assessed the prevalence of SARS-CoV-2 infection, personal protective equipment (PPE) shortages and occurrence of biological accidents among front-line healthcare workers (HCW). DESIGN, SETTING AND PARTICIPANTS: Using respondent-driven sampling, the study recruited distinct categories of HCW attending suspected or confirmed patients with COVID-19 from May 2020 to February 2021, in the Recife metropolitan area, Northeast Brazil. OUTCOME MEASURES: The criterion to assess SARS-CoV-2 infection among HCW was a positive self-reported PCR test. RESULTS: We analysed 1525 HCW: 527 physicians, 471 registered nurses, 263 nursing assistants and 264 physical therapists. Women predominated in all categories (81.1%; 95% CI: 77.8% to 84.1%). Nurses were older with more comorbidities (hypertension and overweight/obesity) than the other staff. The overall prevalence of SARS-CoV-2 infection was 61.8% (95% CI: 55.7% to 67.5%) after adjustment for the cluster random effect, weighted by network, and the reference population size. Risk factors for a positive RT-PCR test were being a nursing assistant (OR adjusted: 2.56; 95% CI: 1.42 to 4.61), not always using all recommended PPE while assisting patients with COVID-19 (OR adj: 2.15; 95% CI: 1.02 to 4.53) and reporting a splash of biological fluid/respiratory secretion in the eyes (OR adj: 3.37; 95% CI: 1.10 to 10.34). CONCLUSIONS: This study shows the high frequency of SARS-CoV2 infection among HCW presumably due to workplace exposures. In our setting, nursing assistant comprised the most vulnerable category. Our findings highlight the need for improving healthcare facility environments, specific training and supervision to cope with public health emergencies.

Clinical Characteristics and Mortality Profile of COVID-19 Patients Aged less than 20 years Old in Pernambuco - Brazil.

COVID-19 in children and adolescents has low frequency, severity, and fatality rate all over the world. A cross-sectional study was conducted to assess the epidemiological and clinical aspects of COVID-19 in patients younger than 20 years in Pernambuco (Brazil), with cases confirmed by reverse-transcriptase-PCR SARS-CoV-2 between 13 February and June 19, 2020, reported on information systems. Data regarding age (< 30 days, 1-11 months, 1-4 years, 5-9 years, 10-14 years, and 15-19 years), gender, color/race, symptoms, pregnancy or puerperium, comorbidities, hospitalization, and death were investigated. Fatality rate and mortality coefficient were calculated, and a multiple logistic regression analysis was performed to determine if gender, age, and comorbidities were factors associated with death. Of 682 pediatric cases, 52.8% were female, with a mean age of 9 ± 7.2 years. The most frequent symptoms were fever (64.4%), cough (52.4%), and respiratory distress (32.4%). Hospitalization was reported in 46.2% of cases, mainly among neonates (80.3%) and infants (73.8%). Thirty-eight deaths were notified, and a fatality rate of 5.6% (95% CI: 3.9-7.3) was found, with higher fatality rates among neonates 11.5% (7 of 61) and 9.5% (8 of 84) infants. The mortality coefficient was 10.9 per 100,000 inhabitants < 1 year of age, whereas comorbidities (Odds ratio [OR] = 14.13, 95% CI: 6.35-31.44), age < 30 days (OR = 5.17, 95% CI: 1.81-14.77), and age 1-11 months (OR = 3.28, 95% CI: 1.21-8.91) were independent factors associated with death. The results demonstrate the vulnerability of neonates and infants with severe conditions, need hospitalization, and high fatality rate, indicating the necessity to adapt public health policies for these age-groups.