Basic biochemical and hematological parameters of structural hemoglobin variants in the postpartum women and their respective newborn from Manaus, Amazonas, Brazil.

The present study aimed at estimating the prevalence of structural hemoglobinopathies in newborn and describing the hematological and biochemical characteristics between postpartum women (PW) and their respective newborns (NB) at a public maternity hospital in Manaus, Amazonas state, Brazil. In total, 825 NB and 820 PW were included in the study. Hematological and biochemical analysis and screening of structural hemoglobinopathies were performed and compared in groups of individuals (NICU or not; hemoglobin genotypes; gestational age and prenatal). The age of PW ranged from 13 to 44 years old (mean of 23.7 ± 6.6 years), with 45.9% pregnant for the first time and 54.1% multiparous. Reported receiving prenatal care 88% and regarding the type of delivery, 47.7% had delivered by cesarean section. Among the births, 19.4% were born premature and 8.3% were admission to the neonatal intensive care unit (NICU). The male NB represented 53.4% of the total. Sickle cell trait (FAS) was found in 16 (1.94%) and heterozygous for D hemoglobin (FAD) in 6 (0.73%) newborns. A statistically significant values was found between the previous history of miscarriage and increase of Mean corpuscular volume (MCV) (p < .001), Red blood cell distribution width (RDW) (p = .003), total and indirect bilirubin concentration (p < .001) and LDL cholesterol (p = .004). Hemoglobin levels below 13.5 g/dL was found in 66% black newborns, compared with 15% of Afro-Brazilian and 5% of whites. The frequency of structural hemoglobinopathies was higher in African-Brazilian newborn babies (78%) and those who with low birth weight had a higher frequency of NICU (35.7%). Interestingly, underage mothers had a higher frequency of NB with low birth weight and premature birth. Postpartum women who had children carriers of FAS and FAD had a higher frequency of urinary tract infection (65.2%) and moderate anemia (23.8%). This study estimated for the first time the prevalence of structural hemoglobinopathies in NB in Manaus, Amazonas, Brazil. Despite the small prevalence of, we highlight the importance of early diagnosis of hemoglobin variants, contributing to the improvement of the quality of life of PW and your NB, reinforce the need to implement educational and prevention programs to raise awareness among the population and in order to counsel parents regarding the probability of having a child with abnormal hemoglobins homozygous as HbSS or HbCC.

Covid-19 Automated Diagnosis and Risk Assessment through Metabolomics and Machine Learning.

COVID-19 is still placing a heavy health and financial burden worldwide. Impairment in patient screening and risk management plays a fundamental role on how governments and authorities are directing resources, planning reopening, as well as sanitary countermeasures, especially in regions where poverty is a major component in the equation. An efficient diagnostic method must be highly accurate, while having a cost-effective profile. We combined a machine learning-based algorithm with mass spectrometry to create an expeditious platform that discriminate COVID-19 in plasma samples within minutes, while also providing tools for risk assessment, to assist healthcare professionals in patient management and decision-making. A cross-sectional study enrolled 815 patients (442 COVID-19, 350 controls and 23 COVID-19 suspicious) from three Brazilian epicenters from April to July 2020. We were able to elect and identify 19 molecules related to the disease's pathophysiology and several discriminating features to patient's health-related outcomes. The method applied for COVID-19 diagnosis showed specificity >96% and sensitivity >83%, and specificity >80% and sensitivity >85% during risk assessment, both from blinded data. Our method introduced a new approach for COVID-19 screening, providing the indirect detection of infection through metabolites and contextualizing the findings with the disease's pathophysiology. The pairwise analysis of biomarkers brought robustness to the model developed using machine learning algorithms, transforming this screening approach in a tool with great potential for real-world application.