The idea is to develop an artificial intelligence model capable of simultaneously analyzing data from the Laboratory Information System and from the Hospital Information System. This technology aims to enable the delivery to hospital physicians of a ranked list of antimicrobials that are more suitable to treat infection by multi-resistant microorganism with a focus on newborn and young children.

This project proposes the development of the One Health Brazilian Resistance (OneBR), a curated and integrated genomic database. OneBR will use algorithms based on artificial intelligence to conduct surveillance, diagnosis, management and treatment of antimicrobial resistance (AMR) in the human-animal-environment interface. The goal is for this platform to be used by Brazilian health professionals in diverse settings, particularly within the Unified Healthcare System (SUS).

The project proposes to characterize the resistant determinants of microbial communities from key sources in hospitals, environment and farms to model the dynamics of the flow of antibiotic resistant microorganisms. The goal is to understand how the hospital environment and animal farming affect the ecology of antibiotic resistance movement. The project will rely on a methodology that allows the analysis of genes related to antibiotic resistance in a complex microbial community derived from specific samples instead of culture based methods for AMR identification.

The project aims to monitor AMR in microorganisms of the urinary tract and correlate it with the genetic determinants of resistance in animal enterobacteria. The study results will be disseminated in order to inform potential changes to guidelines regarding selection of the appropriate antimicrobials first-line treatment for urinary tract infections (UTI).

The project will develop a cellulose filter containing immobilized DNA aptamers, molecules that bind to a specific target molecule, that act as specific and high affinity probes for the uptake and retention of antibiotic molecules present in effluents. Nowadays, the removal of antibiotic residues from effluents is mainly based on chemical processes and physical methods that require expensive technologies and costly maintenance. The success of this project will represent a wastewater treatment option that is low-cost and environment-friendly.

The main goal of the project is to develop and explore an innovative measure of gestational age - "potential pregnancy days lost" (PPDL) - to produce evidence of its association with maternal and child health, morbidity and mortality in the short, medium and long term. The indicator also aims to convince women and policy makers about the need to promote less interventions and "harm-free care" during pregnancy.

Seeks to understand the impacts of the Bolsa Família conditional cash transfer on birth outcomes (e.g., birth weight, gestational weeks, etc). The proposed design will disentangle the measured effects into two components: one that is associated to the cash transfer; and another related to prenatal care assistance. Moreover, this strategy will allow the researchers to determine the window of opportunity where CCT interventions exhibit highest impacts on birth outcomes, recognizing heterogeneous impacts according to how early in the pregnancy the CCT intervention starts.

Aims to access all 68.3 million living births certificates from Brazil, from 1994 to 2016, and compare them with breastfeeding policies in all Brazilian hospitals to assess the impact of the initiatives on infant health. The study also plans to estimate the number of avoidable deaths during this time period, if those initiatives were adopted in Brazil.

The study is aimed at evaluating the effectiveness of Mãe Coruja intervention in reducing low birthweight and preterm birth. By using appropriate statistical methods, the study will use the Cidacs dataset combined with the data from Mãe Coruja program to carry out the quasi-experimental study. With the support of machine learning techniques, the project will also Identify social, economic, geographic and environmental conditions that are associated with the outcomes. The researchers will also build an index of perinatal health risk to inform improvements in targeting populations and the deployment of similar strategies and programs elsewhere in Brazil.

Does air pollution affect the rates of stillbirths, congenital malformations and neonatal mortality? This study aims to answer this question by merging the child health data collected within the 100 Million Brazilian Cohort from Cidacs with high-resolved satellite-derived data on air pollution to establish critical ambient air pollution thresholds for preventing adverse birth outcomes and malformations based on concentrations of fine particles, PM 2.5.

Identifying the preventable causes and performing early risk stratification of pregnant women are instrumental to develop strategies to prevent and reduce preterm birth (PTB). The ability to identify at-risk pregnancies and to enroll women in prevention strategies has been difficult due to complexity of associated risk factors. The study aims to combine different national level data sources to understand the main predictors of PTB and develop a machine-learning-based predictive model to conduct automated risk stratification at the point of care level, integrated with advanced data visualization for clinical decision support.

This research aims to analyze the relationship between a conditional cash transfer program and the child's health, considering two generations of the families and using two different approaches: econometric analysis and data mining algorithms. By analyzing the long term impacts of Bolsa Familia program on future generations' health performance, the project will investigate if a child who was born in a family whose grandparents received the cash transfer is in better health conditions than a similar child born in a family whose grandparents did not receive the same benefit.