No abstract

Hepatocellular carcinoma (HCC) has become the 4th leading cause of cancer-related deaths, with high social, economical and health implications. Imaging techniques such as multiphase computed tomography (CT) have been successfully used for diagnosis of liver tumors such as HCC in a feasible and accurate way and its interpretation relies mainly on comparing the appearance of the lesions in the different contrast phases of the exam. Recently, some researchers have been dedicated to the development of tools based on machine learning (ML) algorithms, especially by deep learning techniques, to improve the diagnosis of liver lesions in imaging exams. However, the lack of standardization in the naming of the CT contrast phases in the DICOM metadata is a problem for real-life deployment of machine learning tools. Therefore, it is important to correctly identify the exam phase based only on the image and not on the exam metadata, which is unreliable. Motivated by this problem, we successfully created an annotation platform and implemented a convolutional neural network (CNN) to automatically identify the CT scan phases in the HCFMUSP database in the city of São Paulo, Brazil. We improved this algorithm with hyperparameter tuning and evaluated it with cross validation methods. Comparing its predictions with the radiologists annotation, it achieved an accuracy of 94.6%, 98% and 100% in the testing dataset for the slice, volume and exam evaluation, respectively.

Introduction-The engagement in sports or habitual physical activity (PA) has shown an extensive protective role against multiple diseases such as cancer, obesity, and many others. Additionally, PA has also a significant impact on life quality, since it aids with managing stress, preserving cognitive function and memory, and preventing fractures in the elderly. Objective-Considering there has been multiple evidence showing that genetic variation underpins variation of PA-related traits, we aimed to estimate the heritability (h 2) of these phenotypes in a sample from the Brazilian population and assess whether males and females differ in relation to those estimates. Methods-2,027 participants from a highly admixed population from Baependi, MG, Brazil, had information regarding their PA and sedentary behavior (SB) phenotypes collected through a questionnaire (IPAQ-SF). After data cleaning and transformation procedures, we obtained four variables to be evaluated: total PA (TPA MET), walking time, (WK MET), moderate-vigorous PA (MVPA MET), and SB. A model selection procedure was performed using a singlestep covariate inclusion approach. We tested for BMI, waist, hip and neck circumferences, smoking, and depression separately, and performed correlation tests among covariates. Linear mixed models, selection procedure, and the variance components approach to estimate h 2 were implemented using SOLAR-Eclipse 8.3.1. Results-We obtained estimates of 0.221, 0.109, 0.226, and 0 for TPA MET, WK MET, MVPA MET, and SB, respectively. We found evidence for gene-sex interactions, with males having higher

Social isolation is extremely important to minimize the effects of a pandemic. Latin American countries have similar socioeconomic characteristics and health system infrastructures. These countries face difficulties in dealing with the COVID-19 pandemic, and some of them have very high death rates. The government stringency index (GSI) of 12 Latin American countries was gathered from the Oxford COVID-19 Government Response Tracker project. The GSI is calculated by considering nine social distancing and isolation measures. Population data from the United Nations Population Fund and number-of-deaths data were collected from the dashboard of the WHO. We performed an analysis of the data collected from March through December 2020 using a mixed linear model. Peru, Brazil, Chile, Bolivia, Colombia, Argentina, and Ecuador had the highest death rates, with an increasing trend over time. Suriname, Venezuela, Uruguay, Paraguay, and Guyana had the lowest death rates, and these rates remained steady. The GSI in most countries followed the same pattern during the months analyzed. In other words, high indices at the beginning of the pandemic and lower indices in the latter months, whereas the number of deaths increased during the entire period. Almost no country kept its GSI high for a long time, especially from October to December. Time and GSI, as well as their interaction, were highly significant. As their interaction increases, the death rate decreases. In conclusion, a greater GSI at the start of the COVID-19 pandemic was associated with a decrease in the number of deaths over time in Latin American countries.

Breast cancer (BC) is the main cause of cancer-related deaths of the world's female population as well as, particularly the Brazilian women (1). The National Cancer Institute in Brazil (INCA) estimated 66,280 new BC cases in 2020, comprising 29.7% of all tumors with a stratified primary location; this estimate is much higher than that for the cancer of the colon and rectum (9.2% of all cases) and cervical cancer (7.4%) in women. BC tumors can be categorized into five main subtypes that have been widely discussed in the literature according to the PAM50 classification: Basal (B), Luminal A (LA), Luminal B (LB), human epidermal growth factor receptor-2+ (HER2+), and normal breast-like (N). Another important classification encompasses triple-negative (TN) and non-TN (nTN) breast tumors, which are identified based on the immunohistochemistry outcomes for the hormone estrogen receptor (ER) and progesterone receptor (PR), and by the amplification of the HER2 (2, 3). The lack of expression of these three important membrane receptors classify them as TN (4). Approximately 80% of all basal tumors can be classified as TN, with similar expression profiles between these two classes (5, 6).