HLA haplotypes and differential regional mortality caused by COVID-19 in Brazil: an ecological study based on a large bone marrow donor bank dataset.

The coronavirus disease 2019 (COVID-19) mortality rates varied among the states of Brazil during the course of the pandemics. The human leukocyte antigen (HLA) is a critical component of the antigen presentation pathway. Individuals with different HLA genotypes may trigger different immune responses against pathogens, which could culminate in different COVID-19 responses. HLA genotypes are variable, especially in the highly admixed Brazilian population. In this ecological study, we aimed to investigate the correlation between HLA haplotypes and the different regional distribution of COVID-19 mortality in Brazil. HLA data was obtained from 4,148,713 individuals registered in The Brazilian Voluntary Bone Marrow Donors Registry. COVID-19 data was retrieved from epidemiological bulletins issued by State Health Secretariats via Brazil's Ministry of Health from February/2020 to July/2022. We found a positive significant correlation between the HLA-A*01~B*08~DRB1*03 haplotype and COVID-19 mortality rates when we analyzed data from 26 states and the Federal District. This result indicates that the HLA-A*01~B*08~DRB1*03 haplotype may represent an additional risk factor for dying due to COVID-19. This haplotype should be further studied in other populations for a better understanding of the variation in COVID-19 outcomes across the world.

Fire association with respiratory disease and COVID-19 complications in the State of Pará, Brazil.

BACKGROUND: Brazil has faced two simultaneous problems related to respiratory health: forest fires and the high mortality rate due to COVID-19 pandemics. The Amazon rain forest is one of the Brazilian biomes that suffers the most with fires caused by droughts and illegal deforestation. These fires can bring respiratory diseases associated with air pollution, and the State of Pará in Brazil is the most affected. COVID-19 pandemics associated with air pollution can potentially increase hospitalizations and deaths related to respiratory diseases. Here, we aimed to evaluate the association of fire occurrences with the COVID-19 mortality rates and general respiratory diseases hospitalizations in the State of Pará, Brazil. METHODS: We employed machine learning technique for clustering k-means accompanied with the elbow method used to identify the ideal quantity of clusters for the k-means algorithm, clustering 10 groups of cities in the State of Pará where we selected the clusters with the highest and lowest fires occurrence from the 2015 to 2019. Next, an Auto-regressive Integrated Moving Average Exogenous (ARIMAX) model was proposed to study the serial correlation of respiratory diseases hospitalizations and their associations with fire occurrences. Regarding the COVID-19 analysis, we computed the mortality risk and its confidence level considering the quarterly incidence rate ratio in clusters with high and low exposure to fires. FINDINGS: Using the k-means algorithm we identified two clusters with similar DHI (Development Human Index) and GDP (Gross Domestic Product) from a group of ten clusters that divided the State of Pará but with diverse behavior considering the hospitalizations and forest fires in the Amazon biome. From the auto-regressive and moving average model (ARIMAX), it was possible to show that besides the serial correlation, the fires occurrences contribute to the respiratory diseases increase, with an observed lag of six months after the fires for the case with high exposure to fires. A highlight that deserves attention concerns the relationship between fire occurrences and deaths. Historically, the risk of mortality by respiratory diseases is higher (about the double) in regions and periods with high exposure to fires than the ones with low exposure to fires. The same pattern remains in the period of the COVID-19 pandemic, where the risk of mortality for COVID-19 was 80% higher in the region and period with high exposure to fires. Regarding the SARS-COV-2 analysis, the risk of mortality related to COVID-19 is higher in the period with high exposure to fires than in the period with low exposure to fires. Another highlight concerns the relationship between fire occurrences and COVID-19 deaths. The results show that regions with high fire occurrences are associated with more cases of COVID deaths. INTERPRETATION: The decision-make process is a critical problem mainly when it involves environmental and health control policies. Environmental policies are often more cost-effective as health measures than the use of public health services. This highlight the importance of data analyses to support the decision making and to identify population in need of better infrastructure due to historical environmental factors and the knowledge of associated health risk. The results suggest that The fires occurrences contribute to the increase of the respiratory diseases hospitalization. The mortality rate related to COVID-19 was higher for the period with high exposure to fires than the period with low exposure to fires. The regions with high fire occurrences is associated with more COVID-19 deaths, mainly in the months with high number of fires. FUNDING: No additional funding source was required for this study.

Respiratory Diseases, Malaria and Leishmaniasis: Temporal and Spatial Association with Fire Occurrences from Knowledge Discovery and Data Mining.

The relationship between the fires occurrences and diseases is an essential issue for making public health policy and environment protecting strategy. Thanks to the Internet, today, we have a huge amount of health data and fire occurrence reports at our disposal. The challenge, therefore, is how to deal with 4 Vs (volume, variety, velocity and veracity) associated with these data. To overcome this problem, in this paper, we propose a method that combines techniques based on Data Mining and Knowledge Discovery from Databases (KDD) to discover spatial and temporal association between diseases and the fire occurrences. Here, the case study was addressed to Malaria, Leishmaniasis and respiratory diseases in Brazil. Instead of losing a lot of time verifying the consistency of the database, the proposed method uses Decision Tree, a machine learning-based supervised classification, to perform a fast management and extract only relevant and strategic information, with the knowledge of how reliable the database is. Namely, States, Biomes and period of the year (months) with the highest rate of fires could be identified with great success rates and in few seconds. Then, the K-means, an unsupervised learning algorithms that solves the well-known clustering problem, is employed to identify the groups of cities where the fire occurrences is more expressive. Finally, the steps associated with KDD is perfomed to extract useful information from mined data. In that case, Spearman's rank correlation coefficient, a nonparametric measure of rank correlation, is computed to infer the statistical dependence between fire occurrences and those diseases. Moreover, maps are also generated to represent the distribution of the mined data. From the results, it was possible to identify that each region showed a susceptible behaviour to some disease as well as some degree of correlation with fire outbreak, mainly in the drought period.

Synthesis and identification of heteroaromatic N-benzyl sulfonamides as potential anticancer agents.

Sulfonamides are a crucial class of bioisosteres that are prevalent in a wide range of pharmaceuticals, however, the available methods for their production directly from heteroaryl aldehyde reagents remains surprisingly limited. A new approach for regioselective incorporation of a sulfonamide unit to heteroarene scaffolds has been developed and is reported within. As a result, a variety of primary benzylic N-alkylsulfonamides have been prepared via a two-step (one pot) formation from the in situ reduction of an intermediate N-sulfonyl imine under mild, practical conditions. The compounds have been screened against a variety of cell lines for cytotoxicity effects using a Cell Titer Blue assay. The cell viability investigation identifies a subset of N-benzylic sulfonamides derived from the indole scaffold to be targeted for further development into novel molecules with potential therapeutic value. The most cytotoxic of the compounds prepared, AAL-030, exhibited higher potency than other well-known anticancer agents Indisulam and ABT-751.