Predictors of nutritional recovery time in children aged 6-59 months with severe acute malnutrition in Sofala Province, Mozambique: survival analysis approach.

BACKGROUND: Malnutrition is a public health problem that affects physical and psychosocial well-being. It manifests as a rapid deterioration in nutritional status and bilateral edema due to inadequate food intake or illness. METHODS: This study is a retrospective cohort of 1208 children with severe acute malnutrition (SAM) in Sofala Province from 2018 to 2022. It includes hospitalized children aged 6-59 months with SAM and related complications. The dependent variable is recovery, and the independent variables include age, sex of the child, vomiting, dehydration, hypoglycemia, nutritional edema and anthropometry. Survival curves were plotted using the Kaplan-Meier method, and bivariable and multivariable Cox regression analyses were performed. RESULTS: The crude analysis revealed significant factors for nutritional recovery in children with SAM, including age, weight, height, malaria, diarrhea and dehydration. Children under 24 months had a 28% lower likelihood of recovery. Weight below 6.16 kg decreased the likelihood by 2%, and height above 71.1 cm decreased it by 20%. Conversely, malaria, diarrhea and dehydration increased the likelihood of recovery. However, after adjustment, only diarrhea remained a significant predictor of nutritional recovery. CONCLUSION: This study found that diarrhea is a predictor of nutritional recovery in children with SAM.

Social Inequalities in Child Development: Analysis of Low-Birth-Weight Trends in Brazil, 2010-2020.

INTRODUCTION: Low birth weight (LBW) is a global issue prevalent in low-income countries. Economic assessments of interventions to reduce this burden are crucial to guide health policies. However, there is a relative scarcity of research that illustrates the magnitude of LBW by country and region to support the design of public policies. OBJECTIVE: This study aimed to analyze the temporal trend of fetal growth in newborns in Brazil between 2010 and 2020. METHODS: A time series study was conducted using data from the Live Births Information System (SINASC), which is managed by the Department of Information and Informatics of the Unified Health System (DATASUS) of the Brazilian Ministry of Health. The Prais-Winsten linear model was applied to analyze the annual proportions of LBW. The annual percentage changes (APC) and their respective 95% confidence intervals (95%CI) were calculated. Prevalence rate averages of LBW were calculated and displayed on thematic maps to visualize the evolution dynamics in each Federation Unit (FU). RESULTS: A total of 31,887,329 women from all Federative Units of Brazil were included in the study from 2010 to 2020. The Southeast region had the largest proportion of participants, with records from 2015 accounting for 9.5% of the total. Among the women in the study, 49.6% were between the ages of 20 and 29, and the majority (75.5%) had between 8 and 12 years of schooling. The newborns of these women were predominantly male (58.8%) and non-white (59.5%). The study found that there was a trend towards stabilization of increasing proportions of LBW in the North, Northeast, and Centre-West regions between 2010 and 2020. In Brazil and other regions, these tendencies remained stable. CONCLUSION: To improve living conditions and reduce social inequalities and health inequities, public policies and actions are necessary. Strengthening the Unified Health System (SUS), income transfer programs, quota policies for vulnerable groups, and gender equality measures such as improving access to education for women and the labor sector are among the suggested approaches.

Novel COVID-19 biomarkers identified through multi-omics data analysis: N-acetyl-4-O-acetylneuraminic acid, N-acetyl-L-alanine, N-acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate.

This study aims to apply machine learning models to identify new biomarkers associated with the early diagnosis and prognosis of SARS-CoV-2 infection.Plasma and serum samples from COVID-19 patients (mild, moderate, and severe), patients with other pneumonia (but with negative COVID-19 RT-PCR), and healthy volunteers (control) from hospitals in four different countries (China, Spain, France, and Italy) were analyzed by GC-MS, LC-MS, and NMR. Machine learning models (PCA and PLS-DA) were developed to predict the diagnosis and prognosis of COVID-19 and identify biomarkers associated with these outcomes.A total of 1410 patient samples were analyzed. The PLS-DA model presented a diagnostic and prognostic accuracy of around 95% of all analyzed data. A total of 23 biomarkers (e.g., spermidine, taurine, L-aspartic, L-glutamic, L-phenylalanine and xanthine, ornithine, and ribothimidine) have been identified as being associated with the diagnosis and prognosis of COVID-19. Additionally, we also identified for the first time five new biomarkers (N-Acetyl-4-O-acetylneuraminic acid, N-Acetyl-L-Alanine, N-Acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate) that are also associated with the severity and diagnosis of COVID-19. These five new biomarkers were elevated in severe COVID-19 patients compared to patients with mild disease or healthy volunteers.The PLS-DA model was able to predict the diagnosis and prognosis of COVID-19 around 95%. Additionally, our investigation pinpointed five novel potential biomarkers linked to the diagnosis and prognosis of COVID-19: N-Acetyl-4-O-acetylneuraminic acid, N-Acetyl-L-Alanine, N-Acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate. These biomarkers exhibited heightened levels in severe COVID-19 patients compared to those with mild COVID-19 or healthy volunteers.

Association between food environments and fetal growth in pregnant Brazilian women.

INTRODUCTION: Birth weight is described as one of the main determinants of newborns' chances of survival. Among the associated causes, or risk factors, the mother's nutritional status strongly influences fetal growth and birth weight outcomes of the concept. This study evaluates the association between food deserts, small for gestational age (SGA), large for gestational age (LGA) and low birth weight (LBW) newborns. DESIGN: This is a cross-sectional population study, resulting from individual data from the Live Birth Information System (SINASC), and commune data from mapping food deserts (CAISAN) in Brazil. The newborn's size was defined as follows: appropriate for gestational age (between 10 and 90th percentile), SGA (< 10th percentile), LGA (> 90th percentile), and low birth weight < 2,500 g. To characterize food environments, we used tertiles of the density of establishments which sell in natura and ultra-processed foods. Logistic regression modeling was conducted to investigate the associations of interest. RESULTS: We analyzed 2,632,314 live births in Brazil in 2016, after appropriate adjustments, women living in municipalities with limited availability of fresh foods had a higher chance of having newborns with SGA [OR2nd tertile: 1.06 (1.05-1.07)] and LBW [OR2nd tertile: 1.11 (1.09-1.12)]. Conversely, municipalities with greater availability of ultra-processed foods had a higher chance of having newborns with SGA [OR3rd tertile: 1.04 (1.02-1.06)] and LBW [OR2nd tertile: 1.13 (1.11-1.16)]. Stratification by race showed that Black and Mixed/Brown women had a higher chance of having newborns with SGA [OR3rd tertile: 1.09 (1.01-1.18)] and [OR3rd tertile: 1.06 (1.04-1.09)], respectively, while Mixed-race women also had a higher chance of having newborns with LBW [OR3rd tertile: 1.17 (1.14-1.20)]. Indigenous women were associated with LGA [OR3rd tertile: 1.20 (1.01-1.45)]. CONCLUSION: The study found that living in areas with limited access to healthy foods was associated with an increased risk of SGA and low birth weight among newborns, particularly among Black and Mixed/Brown women. Therefore, urgent initiatives aimed at reducing social inequalities and mitigating the impact of poor food environments are needed in Brazil.