In recent years the immunomodulatory actions of vitamin D, a steroid hormone, have been extensively studied. In 2020, due to the COVID-19 pandemic, the question arose as to 25(OH)D status would be related to susceptibility to SARS-CoV-2 infection, since several studies pointed out a higher prevalence and severity of the disease in populations with low levels of 25(OH)D. Thus, we investigated the 25(OH)D levels in adults "Detected" positive for SARS CoV-2 by RT-PCR (reverse transcriptase polymerase chain reaction) test, and in negative controls, "not Detected" , using the Fleury Group's examination database, in Sao Paulo, Brazil. Of a total of 14.692 people with recent assessments of 25(OH)D and RT-PCR tests for COVID-19, 2.345 were positive and 11.585 were negative for the infection. The groups did not differ in the percentage of men and women, or in the age distribution. There were no differences in the distribution of 25(OH)D between the two groups (p = 0.08); mean 25(OH)D of 28.8 ± 21.4 ng/mL and 29.6 ± 18.1 ng/mL, respectively. In the specific population studied, clinical, environmental, socioeconomic and cultural factors should have greater relevance than 25(OH)D in determining the susceptibility to COVID-19.

Background The Complete Blood Count (CBC) is a commonly used low-cost test that measures white blood cells, red blood cells, and platelets in a person’s blood. It is a useful tool to support medical decisions, as intrinsic variations of each analyte bring relevant insights regarding potential diseases. In this study, we aimed at developing machine learning models for COVID-19 diagnosis through CBCs, unlocking the predictive power of non-linear relationships between multiple blood analytes. Methods We collected 809,254 CBCs and 1,088,385 RT-PCR tests for SARS-Cov-2, of which 21% (234,466) were positive, from 900,220 unique individuals. To properly screen COVID-19, we also collected 120,807 CBCs of 16,940 individuals who tested positive for other respiratory viruses. We proposed an ensemble procedure that combines machine learning models for different respiratory infections and analyzed the results in both the first and second waves of COVID-19 cases in Brazil. Results We obtain a high-performance AUROC of 90 + % for validations in both scenarios. We show that models built solely of SARS-Cov-2 data are biased, performing poorly in the presence of infections due to other RNA respiratory viruses. Conclusions We demonstrate the potential of a novel machine learning approach for COVID-19 diagnosis based on a CBC and show that aggregating information about other respiratory diseases was essential to guarantee robustness in the results. Given its versatile nature, low cost, and speed, we believe that our tool can be particularly useful in a variety of scenarios—both during the pandemic and after.

Background Treatment with levothyroxine (LT4) that normalize serum TSH is expected to restore lipid metabolism. Methods Here we assessed statin utilization in LT4-treated patients through an observational drug utilization study in 3 sites: #1: 10,723 outpatients placed on LT4 during 2006-2019 identified from the Clinical Research Data Warehouse of the University of Chicago; #2: ∼1.4 million LT4 prescriptions prepared by primary care physicians during January-December 2018, identified from the AQVIA™ database of medical prescriptions in Brazil; #3: ~5.4 million patient interviews during 2009-2019, including ~0.32 million patients on LT4, identified from the Fleury Group database in Brazil. Results On Site #1, initiation of therapy with LT4 increased the frequency of statin utilization (19.1 vs 24.6%), which occurred ~1.5 years later (median 76 wks) and, among those patients that were on statins, increased intensity of treatment by 33%, despite normalization of serum TSH levels; on Site #2, after matching for sex and age, the frequency of statins prescription was higher for those patients using LT4: females: 2.1 vs 3.4% (OR: 1.656 [1.639 - 1.673]); males: 3.1 vs 4.4% (OR: 1.435 [1.409 - 1.462]); and, on Site #3, after matching for sex and age, the frequency of statins utilization was higher in those patients using LT4: females: 10 vs 18% (OR: 2.02 [2.00 - 2.04]); males: 15 vs 25% (OR: 1.92 [1.88 - 1.96]); all P-values were <.0001. Conclusion Prescription and utilization of statins were higher in patients taking LT4. The reasons for this association should be addressed in future studies.

STRUCTURED ABSTRACT Context Small adjustments in LT4 dose do not appear to provide clinical benefit despite changes in TSH levels within the reference range. We hypothesize that the accompanying changes in serum T3 levels do not reflect the magnitude of the changes in serum TSH. Objective Characterize the relationships of serum FT4 vs T3, FT4 vs TSH, and FT4 vs the T3/FT4 ratio Design Cross-sectional observational study Setting A large clinical database from January 1, 2009, to December 31, 2019 Participants 9850 participants aged 18 years and older treated with LT4 Exposure Treatment with LT4, subdivided by serum FT4 level Main Outcome Measures: Model fitting of the relationships between serum FT4 vs TSH, FT4 vs T3, and FT4 vs T3/FT4. Mean and median values of TSH, T3, and T3/FT4 were calculated. Results The relationships T3 vs FT4 and TSH vs FT4 were both complex and best represented by distinct, segmented regression models. Increasing FT4 levels were linearly associated with T3 levels until an inflection point at a FT4 level of 0.7 ng/dL, after which a flattening of the slope was observed following a convex quadratic curve. In contrast, increasing FT4 levels were associated with steep declines in TSH following two negative sigmoid curves. The FT4 vs T3/FT4 relationship was fit to an asymptotic regression curve supporting less T4 to T3 activation at higher FT4 levels. Conclusions In LT4-treated patients, the relationships between serum FT4 vs TSH and FT4 vs T3 across a range of FT4 levels are disproportionate. As a result, dose changes in LT4 that robustly modify serum FT4 and TSH values may only minimally affect serum T3 levels and result in no significant clinical benefit.

RESUMODevido à complexa dinâmica apresentada pelos leitos fluidizados se faz necessário o desenvolvimento de novas de técnicas de análise de sinais que representem, com maior fidelidade, as características reais destes processos, principalmente quando técnicas convencionais não são apropriadas para a caracterização de regimes de fluidização. É justamente neste cenário que surge a análise do caos determinístico como alternativa às metodologias clássicas empregadas. O presente trabalho objetiva aplicar a teoria do caos a sinais de variação de pressão, associando os invariantes caóticos, K e D, a regimes fluidodinâmicos de um leito fluidizado gás-partícula. Para tanto, utilizou-se uma coluna de acrílico (0,1 m de diâmetro) e ar na fluidização de partículas de catalisador FCC e microesferas de vidro, ambas pertencentes ao grupo A da classificação Geldart. Na obtenção dos sinais de pressão, foram empregados transdutores diferenciais de pressão a taxas de 1.000 Hz. Para ambas as partículas, notou-se acréscimo nos valores de K e D a medida em que a velocidade superficial do ar aumenta, alcançando valores máximos de tais invariantes na transição do leito fixo para a condição de mínima fluidização. A complexidade do sistema decai quando se atinge o regime pistonado. Como decorrência, constata-se que tanto a entropia de Kolmogorov quanto a dimensão de correlação podem ser empregadas como parâmetros de caracterização de regimes em sistemas fluidizados.

Background: first trimester glycemia ≥ 92 mg/dL is a valid criterion for diagnosis of gestational diabetes mellitus (GDM), excluding the requirement of Oral Glucose Tolerant Test (OGTT) later in pregnancy. Glycated Hemoglobin (A1C), is well stablished for diagnosis of diabetes mellitus, however its utility in the screening of GDM is not certain. The aim of this study was to associate A1C and glycemia values in the first trimester of pregnancy. Methods: this was an observational retrospective study that included Brazilian women screened with A1C until 20 weeks of gestation between January 2009 and March 2019. Data were collected from a laboratory center. A1C was estimated by high-performance liquid chromatography. Exclusion criteria were hemoglobin < 11.0 and use of antidiabetic drugs or insulin. Women were divided into groups according to first trimester glycemia: <70; 70-85; 85-92; 92-126; ≥ 126 mg/dL. Primary outcome was association between A1C and glycemia values. Results: a total of 17.764 women were included. The majority was in the 70-85 mg/dL group (9.689), followed by 85-92 mg/dL (5.420), 92-126 mg/dL (2.178), < 70 mg/dL (436) and ≥ 126 mg/dL (41). Means’ age were: 32.5 ± 4.79 (<70 mg/dL); 33.8 ± 4.14 (70-85 mg/dL); 34.2 ± 3.9 (85-92 mg/dL); 34.7 ± 4.03 (92-126 mg/dL) and 31.4 ± 3.46 years (≥ 126 mg/dL). Means’ Body Mass Index (BMI): 24.5 ± 2.68 (<70 mg/dL); 25.2 ± 3.88 (70-85 mg/dL); 25.8 ± 4.5 (85-92 mg/dL); 27.6 ± 4.83 (92-126 mg/dL) and 27.3 ± 2.59 years (≥ 126 mg/dL). A1C means were very similar in the first four groups: 4.92% ± 0.41 (<70 mg/dL); 5.01% ± 0.32 (70-85 mg/dL); 5.15% ± 0.32 (85-92 mg/dL) and 5.33% ± 0.42 (92-126 mg/dL). The group with glycemia ≥ 126 mg/dL presented A1C mean of 7.08% ± 1.49, significantly higher than all the other groups (p < 0.01 by Kruskal-Wallis test). Conclusion: in this study, A1C values were not accurate to the screening of GDM in the first trimester of pregnancy. The main difference was in the group with glycemia ≥ 126 mg/dL, already defined as overt diabetes. Disclosure F. Faro: None. R.F. Ramalho: None. M. Pereira: None. J.E. Salles: Board Member; Self; AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Lilly Diabetes, Novo Nordisk Inc. P.S. Rosa: None. M.G. Teles: None. P. de Sá Tavares Russo: None.

Background: diagnostic criteria for gestational diabetes mellitus (GDM) have been controversial. Although Oral Glucose Tolerant Test (OGTT) is generally proposed, it requires an extensive preparation, is uncomfortable and lacks reproducibility. In contrast, Glycated Hemoglobin test (A1C) is more convenient and accurate, however, its use for GDM diagnosis has not been recommended yet. The aim of this study was to evaluate whether a first trimester A1C predicts an abnormal second trimester OGTT. Methods: This was an observational retrospective study that included Brazilian women screened with A1C until 14 weeks of gestation between January 2009 and March 2019. Data were collected from a laboratory center. A1C was measured by high-performance liquid chromatography. Exclusion criteria were first trimester glycemia ≥ 92 mg/dL or A1C ≥ 6.5% and use of antidiabetic drugs or insulin. Primary outcome was association between A1C value and diagnosis of GDM, according to 75g or 100g OGTT, as defined by American Diabetes Association (ADA). Results: a total of 373 women were included, 106 were diagnosed with GDM by OGTT. General characteristics of the groups with and without GDM were, respectively: mean age 35.1 ±3.5 and 34.1 ± 3.5 years, mean Body Mass Index (BMI) 24.2 ± 4.8 and 25.4 ± 3.7 Kg/m², mean first trimester glycemia 81.8 ± 5.1 and 81.2 ± 5.2 mg/dL and mean A1C 5.1 ± 0.3 and 5.0 ± 0.3%. OGTT values in the GDM group were: fasting 80.1 ± 6.6, 1st hour 171.6 ± 25.7 and 2nd hour 152 ± 27.1. For women without GDM, the results were: fasting 78.1 ± 5.7, 1st h 143.4 ± 29.5 and 2nd h 123.1 ± 28.3. There was no significant statistical difference of A1C values between groups with or without GDM (P=0.26, Wilcoxon test). Conclusion: A1C could not predict women who would develop GDM in this population. Ethnicity and different criteria for OGTT interpretation may have contributed for these findings. The limitations of this study were lack of data about previous history of GDM, family history of diabetes and weight gain during pregnancy. Disclosure F. Faro: None. R.F. Ramalho: None. W.H. Prieto: None. M. Pereira: None. J.E. Salles: Board Member; Self; AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Lilly Diabetes, Novo Nordisk Inc. P.S. Rosa: None. M.G. Teles: None. P. de Sá Tavares Russo: None.

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