The objective of this study was to analyze adherence and side effects of three iron supplement regimens (ferrous sulfate) on anemic pregnant women. The clinical trial involved 150 women between the 16th and 20th gestational weeks, at low obstetric risk and with hemoglobin concentration of between 8.0 and 11.0g/dL. Treatment was provided by ferrous sulfate with 60mg of elemental iron during 16 (± 1) weeks, in three regimens: single tablet a week (n = 48); single tablet twice a week (n = 53) or single tablet a day (n = 49). The outcomes were adherence, assessed through interviews and by counting tablets, and side effects, according to patient information. The adherence showed a declining trend (92%, 83% and 71%; p = 0.010) and the side effects revealed a growing trend (40%, 45% and 71%; p = 0.002) as the dosage increased. Diarrhea and epigastric pain were significantly associated with the dose administered (p = 0.002). These results suggest that in anemic pregnant women, complaints are directly proportional and the compliance is inversely proportional to the amount of medicinal iron.

Pregnancy anemia remains as a public health problem, since the official reports in the 70’s. To guide the treatment of iron-deficiency anemia in pregnancy, the haemoglobin concentration is the most used test in spite of its low accuracy, and serum ferritin is the most reliable test, although its cutoff point remains an issue.

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.

OBJETIVOS: descrever as freqüências de anemia e ferropenia em gestantes de baixo risco, de acordo com a ferritina sérica e diferentes pontos de corte da concentração de hemoglobina (Hb). MÉTODOS: estudo transversal, utilizando-se banco de dados com 318 gestantes atendidas no período de maio de 2000 a junho de 2001, no Instituto Materno Infantil Prof. Fernando Figueira-IMIP, em Recife, Pernambuco. Determinou-se a freqüência de anemia e/ou ferropenia em função da ferritina e dois diferentes pontos de corte da Hb (11,0 e 10,5g/dL). Os dados foram analisados nos programas Epi-Info 6.04b e Minitab v.14.2. O projeto foi aprovado pelo Comitê de Ética em Pesquisa do IMIP. RESULTADOS: as freqüências de anemia (Hb

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.