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Chagas disease, caused by Trypanosoma cruzi, stands as the primary cause of dilated cardiomyopathy in the Americas. Macrophages play a crucial role in the heart's response to infection. Given their functional and phenotypic adaptability, manipulating specific macrophage subsets could be vital in aiding essential cardiovascular functions including tissue repair and defense against infection. PPARα are ligand-dependent transcription factors involved in lipid metabolism and inflammation regulation. However, the role of fenofibrate, a PPARα ligand, in the activation profile of cardiac macrophages as well as its effect on the early inflammatory and fibrotic response in the heart remains unexplored. The present study demonstrates that fenofibrate significantly reduces not only the serum activity of tissue damage biomarker enzymes (LDH and GOT) but also the circulating proportions of pro-inflammatory monocytes (CD11b+ LY6Chigh). Furthermore, both CD11b+ Ly6Clow F4/80high macrophages (MΦ) and recently differentiated CD11b+ Ly6Chigh F4/80high monocyte-derived macrophages (MdMΦ) shift toward a resolving phenotype (CD206high) in the hearts of fenofibrate-treated mice. This shift correlates with a reduction in fibrosis, inflammation, and restoration of ventricular function in the early stages of Chagas disease. These findings encourage the repositioning of fenofibrate as a potential ancillary immunotherapy adjunct to antiparasitic drugs, addressing inflammation to mitigate Chagas disease symptoms.

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The COVID-19 pandemic has caused a global health crisis, and wastewater-based epidemiology (WBE) has emerged as an important tool to assist public health decision-making. Recent studies have shown that the SARS-CoV-2 RNA concentration in wastewater samples is a reliable indicator of the severity of the pandemic for large populations. However, few studies have established a strong correlation between the number of infected people and the viral concentration in wastewater due to variations in viral shedding over time, viral decay, infiltration, and inflow. Herein we present the relationship between the number of COVID-19-positive patients and the viral concentration in wastewater samples from three different hospitals (A, B, and C) in the city of Belo Horizonte, Minas Gerais, Brazil. A positive and strong correlation between wastewater SARS-CoV-2 concentration and the number of confirmed cases was observed for Hospital B for both regions of the N gene (R = 0.89 and 0.77 for N1 and N2, respectively), while samples from Hospitals A and C showed low and moderate correlations, respectively. Even though the effects of viral decay and infiltration were minimized in our study, the variability of viral shedding throughout the infection period and feces dilution due to water usage for different activities in the hospitals could have affected the viral concentrations. These effects were prominent in Hospital A, which had the smallest sewershed population size, and where no correlation between the number of defecations from COVID-19 patients and viral concentration in wastewater was observed. Although we could not determine trends in the number of infected patients through SARS-CoV-2 concentrations in hospitals' wastewater samples, our results suggest that wastewater monitoring can be efficient for the detection of infected individuals at a local level, complementing clinical data.

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Coronavirus pandemic started in March 2020 and since then has caused millions of deaths worldwide. Wastewater-based epidemiology (WBE) can be used as an epidemiological surveillance tool to track SARS-CoV-2 dissemination and provide warning of COVID-19 outbreaks. Considering that there are public places that could be potential hotspots of infected people that may reflect the local epidemiological situation, the presence of SARS-CoV-2 RNA was analyzed by RT-qPCR for approximately 16 months in sewage samples from five public places located in the metropolitan area of Belo Horizonte, MG, Brazil: the sewage treatment plant of Confins International Airport (AIR), the main interstate bus terminal (BUS), an upscale shopping centre (SHC1), a popular shopping centre (SHC2) and a university institute (UNI). The results were compared to those of the influent sewage of the two main sewage treatment plants of Belo Horizonte (STP1 and STP2). Viral monitoring in the STPs proved to be an useful regional surveillance tool, reflecting the trends of COVID-19 cases. However, the viral concentrations in the samples from the selected public places were generally much lower than those of the municipal STPs, which may be due to the behaviour of the non-infected or asymptomatic people, who are likely to visit these places relatively more than the symptomatic infected ones. Among these places, the AIR samples presented the highest viral concentrations and concentration peaks were observed previously to local outbreaks. Therefore, airport sewage monitoring can provide an indication of the regional epidemiological situation. For the other places, particularly the UNI, the results suggested a greater potential to detect the infection and trace cases especially among employees and regular attendees. Taken together, the results indicate that for a regular and permanent sentinel sewage surveillance the sewage from STPs, AIR and UNI could be monitored.

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Reversible-deactivation radical polymerizations are privileged approaches for the synthesis of functional and hybrid materials. A bottleneck for conducting these processes is the need to maintain oxygen free conditions. Herein we report a broadly applicable approach to "polymerize through" oxygen using the synergistic combination of two radical initiators having different rates of homolysis. The in situ monitoring of the concentrations of oxygen and monomer simultaneously provided insight into the function of the two initiators and enabled the identification of conditions to effectively remove dissolved oxygen and control polymerization under open-to-air conditions. By understanding how the surface area to volume ratio of reaction vessels influence open-to-air polymerizations, well-defined polymers were produced using acrylate, styrenic, and methacrylate monomers, which each represent an expansion of scope for the "polymerizing through" oxygen approach. Demonstration of this method in tubular reactors using continuous flow chemistry provided a more complete structure-reactivity understanding of how reaction headspace influences PTO RAFT polymerizations.

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Modern polymer science suffers from the curse of multidimensionality. The large chemical space imposed by including combinations of monomers into a statistical copolymer overwhelms polymer synthesis and characterization technology and limits the ability to systematically study structure-property relationships. To tackle this challenge in the context of 19F magnetic resonance imaging (MRI) agents, we pursued a computer-guided materials discovery approach that combines synergistic innovations in automated flow synthesis and machine learning (ML) method development. A software-controlled, continuous polymer synthesis platform was developed to enable iterative experimental-computational cycles that resulted in the synthesis of 397 unique copolymer compositions within a six-variable compositional space. The nonintuitive design criteria identified by ML, which were accomplished by exploring <0.9% of the overall compositional space, lead to the identification of >10 copolymer compositions that outperformed state-of-the-art materials.

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RESUMO A recente detecção de material genético (RNA) do novo coronavírus em amostras de fezes e no esgoto aponta para a possibilidade de se identificar a circulação do vírus e até mesmo estimar o número de pessoas infectadas em determinada região pelo monitoramento sistemático do esgoto, configurando-se em importante ferramenta epidemiológica de testagem massiva indireta, incluindo portadores sintomáticos e assintomáticos. Nesse sentido, concebeu-se um projeto para a detecção e a quantificação do novo coronavírus em amostras de esgoto coletadas em 15 sub-bacias de esgotamento sanitário dos ribeirões Arrudas e Onça, visando entender a dinâmica de circulação e a prevalência do vírus nas regiões investigadas. Tais sub-bacias esgotam os efluentes gerados por uma população da ordem de 1,5 milhão de pessoas no município de Belo Horizonte e parte de Contagem. O plano de amostragem contemplou 17 pontos (15 sub-bacias + afluente às 2 estações de tratamento de esgoto) de monitoramento semanal, com coletas compostas durante todo o período da manhã. A detecção e a quantificação do RNA viral efetuaram-se em laboratório por meio de ensaios de RT-qPCR. Os resultados obtidos em quatro semanas de monitoramento (semanas epidemiológicas 21 a 24) mostraram um incremento da ocorrência do vírus, atingindo 100% das regiões investigadas na semana epidemiológica 24. A estimativa da população infectada pelo novo coronavírus pelo monitoramento do esgoto em Belo Horizonte apresentou tendência de crescimento exponencial, sendo até 20 vezes maior do que o número de casos confirmados acumulados. Quanto à circulação do vírus, as concentrações do RNA viral têm se mostrado bastante variáveis nas regiões monitoradas, com maiores porcentagens de população infectada estimada ao norte e nordeste da capital mineira.

ABSTRACT The recent detection of SARS-CoV-2 RNA in stool and sewage samples highlights the possibility of mapping the circulation of the virus and even estimating the number of infected people through the systematic monitoring of sewage in a specific region. Therefore, this is an important epidemiological tool for large-scale indirect testing, including symptomatic and asymptomatic carriers. This project was conceived for the detection and quantification of the SARS-CoV-2 in sewage samples collected in 15 watersheds of the Arrudas and Onça streams, aiming to understand the dynamics of spread and the prevalence of the virus in these regions/watersheds. These sub-basins exhaust the effluents generated by a population of approximately 1.5 million people in the municipality of Belo Horizonte and part of Contagem. Weekly composite samples were collected during the morning periods in seventeen monitoring points (15 water sheds + influent to 2 sewage treatment plants). RNA detection and quantification were performed in the laboratory using RT-qPCR. The results obtained in four weeks of monitoring (epidemiological weeks 21 to 24) showed an increase in the occurrence of the virus, reaching 100% of the monitored regions investigated in epidemiological week 24. The infected population, estimated by sewage monitoring in Belo Horizonte, showed exponential growth, being up to 20 times higher than those of accumulated confirmed cases. As for the dynamics of virus spread, RNA concentrations have shown to be quite variable in the monitored regions with higher percentages of the estimated infected population in the northern and north-eastern portions of Belo Horizonte.

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Brazil has become one of the epicentres of the COVID-19 pandemic, with cases heavily concentrated in large cities. Testing data is extremely limited and unreliable, which restricts health authorities' ability to deal with the pandemic. Given the stark demographic, social and economic heterogeneities within Brazilian cities, it is important to identify hotspots so that the limited resources available can have the greatest impact. This study shows that decentralised monitoring of SARS-CoV-2 RNA in sewage can be used to assess the distribution of COVID-19 prevalence in the city. The methodology developed in this study allowed the identification of hotspots by comprehensively monitoring sewers distributed through Belo Horizonte, Brazil's third largest city. Our results show that the most vulnerable neighbourhoods in the city were the hardest hit by the pandemic, indicating that, for many Brazilians, the situation is much worse than reported by official figures.

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COVID-19 patients can excrete viable SARS-CoV-2 virus via urine and faeces, which has raised concerns over the possibility of COVID-19 transmission via aerosolized contaminated water or via the faecal-oral route. These concerns are especially exacerbated in many low- and middle-income countries, where untreated sewage is frequently discharged to surface waters. SARS-CoV-2 RNA has been detected in river water (RW) and raw wastewater (WW) samples. However, little is known about SARS-CoV-2 viability in these environmental matrices. Determining the persistence of SARS-CoV-2 in water under different environmental conditions is of great importance for basic assumptions in quantitative microbial risk assessment (QMRA). In this study, the persistence of SARS-CoV-2 was assessed using plaque assays following spiking of RW and WW samples with infectious SARS-CoV-2 that was previously isolated from a COVID-19 patient. These assays were carried out on autoclaved RW and WW samples, filtered (0.22 µm) and unfiltered, at 4 °C and 24 °C. Linear and nonlinear regression models were adjusted to the data. The Weibull regression model achieved the lowest root mean square error (RMSE) and was hence chosen to estimate T90 and T99 (time required for 1 log and 2 log reductions, respectively). SARS-CoV-2 remained viable longer in filtered compared with unfiltered samples. RW and WW showed T90 values of 1.9 and 1.2 day and T99 values of 6.4 and 4.0 days, respectively. When samples were filtered through 0.22 µm pore size membranes, T90 values increased to 3.3 and 1.5 days, and T99 increased to 8.5 and 4.5 days, for RW and WW samples, respectively. Remarkable increases in SARS-CoV-2 persistence were observed in assays at 4 °C, which showed T90 values of 7.7 and 5.5 days, and T99 values of 18.7 and 17.5 days for RW and WW, respectively. These results highlight the variability of SARS-CoV-2 persistence in water and wastewater matrices and can be highly relevant to efforts aimed at quantifying water-related risks, which could be valuable for understanding and controlling the pandemic.

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The number of reports using continuous flow technology in tubular reactors to perform precision polymerizations has grown enormously in recent years. Flow polymerizations allow highly efficient preparation of polymers exhibiting well-defined molecular characteristics, and has been applied to a slew of monomers and various polymerization mechanisms, including anionic, cationic, radical, and ring-opening. Polymerization conducted in continuous flow offers several distinct advantages, including improved efficiency, reproducibility, and enhanced safety for exothermic polymerizations using highly toxic components, high pressures, and high temperatures. The further development of this technology is thus of relevance for many industrial polymerization processes. While much progress has been demonstrated in recent years, opportunities remain for increasing the compositional and architectural complexity of polymeric materials synthesized in a continuous fashion. Extending the reactor processing principles that have heretofore been focused on optimizing homopolymerization to include multisegment block copolymers, particularly from monomers that propagate via incompatible mechanisms, represents a major challenge and coveted target for continuous flow polymerization. Likewise, the spatial and temporal control of reactivity afforded by flow chemistry has and will continue to enable the production of complex polymeric architectures. This Viewpoint offers a brief background of continuous flow polymerization focused primarily on tubular (micro)reactors and includes selected examples that are relevant to these specific developments.

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An alternative methodology based on artificial neural networks is proposed to be a complementary tool to other conventional methods to study controlled drug release. Two systems are used to test the approach; namely, hydrocortisone in a biodegradable matrix and rhodium (II) butyrate complexes in a bioceramic matrix. Two well-established mathematical models are used to simulate different release profiles as a function of fundamental properties; namely, diffusion coefficient (D), saturation solubility (C(s)), drug loading (A), and the height of the device (h). The models were tested, and the results show that these fundamental properties can be predicted after learning the experimental or model data for controlled drug release systems. The neural network results obtained after the learning stage can be considered to quantitatively predict ideal experimental conditions. Overall, the proposed methodology was shown to be efficient for ideal experiments, with a relative average error of <1% in both tests. This approach can be useful for the experimental analysis to simulate and design efficient controlled drug-release systems.

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