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ABSTRACTLong-term sequelae of coronavirus disease (COVID)-19 are frequent and of major concern. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection affects the host gut microbiota, which is linked to disease severity in patients with COVID-19. Here, we report that the gut microbiota of post-COVID subjects had a remarkable predominance of Enterobacteriaceae strains with an antibiotic-resistant phenotype compared to healthy controls. Additionally, short-chain fatty acid (SCFA) levels were reduced in feces. Fecal transplantation from post-COVID subjects to germ-free mice led to lung inflammation and worse outcomes during pulmonary infection by multidrug-resistant Klebsiella pneumoniae. transplanted mice also exhibited poor cognitive performance. Overall, we show prolonged impacts of SARS-CoV-2 infection on the gut microbiota that persist after subjects have cleared the virus. Together, these data demonstrate that the gut microbiota can directly contribute to post-COVID sequelae, suggesting that it may be a potential therapeutic target.

The present study describes an efficient analytical method for analyzing apple pesticide residues. This novel approach employs ultrasound-assisted cleaning (UAC), solid-phase microextraction (SPME), and gas chromatography/mass spectrometry (GC-MS). A fiber coating with carbon nanomaterials (CNMs) was used for the extraction process. The methodology underwent extraction process optimization and analytical method validation. Moreover, its applicability for detecting pesticides in apples was evaluated. The validation showed satisfactory results with linearity coefficients greater than 0.99 (R2 > 0.99) and linear working ranges from 0.5 ng·g−1 to 300.0 ng·g−1. The limits of detection and quantification (LODs and LOQs) ranged from 0.1 ng·g−1 to 0.3 ng·g−1 and from 0.5 ng·g−1 to 0.7 ng·g−1, respectively. The calculated recovery values ranged from 79% to 98%. The precision presented a relative standard deviation (RSD) lower than 14%. The validated method was applied to apple samples acquired from different markets and fruit stands in Belo Horizonte, Minas Gerais, Brazil. Organophosphate pesticides typically used in Brazilian apple production areas were detected in some samples. This study demonstrates the utility of UAC and CNM microextraction as an effective and highly efficient approach for analyzing pesticides in apples.

Pesticides constitute a category of chemical products intended specifically for the control and mitigation of pests. With their constant increase in use, the risk to human health and the environment has increased proportionally due to occupational and environmental exposure to these compounds. The use of these chemicals is associated with several toxic effects related to acute and chronic toxicity, such as infertility, hormonal disorders and cancer. The present work aimed to study the metabolic profile of individuals occupationally exposed to pesticides, using a metabolomics tool to identify potential new biomarkers. Metabolomics analysis was carried out on plasma and urine samples from individuals exposed and non-exposed occupationally, using liquid chromatography coupled with mass spectrometry (UPLC-MS). Non-targeted metabolomics analysis, using principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) or partial least squares discriminant orthogonal analysis (OPLS-DA), demonstrated good separation of the samples and identified 21 discriminating metabolites in plasma and 17 in urine. The analysis of the ROC curve indicated the compounds with the greatest potential for biomarkers. Comprehensive analysis of the metabolic pathways influenced by exposure to pesticides revealed alterations, mainly in lipid and amino acid metabolism. This study indicates that the use of metabolomics provides important information about complex biological responses.

Benzene is a human carcinogen whose exposure to concentrations below 1 ppm (3.19 mg·m−3) is associated with myelotoxic effects. The determination of biomarkers such as trans-trans muconic acid (AttM) and S-phenylmercapturic acid (SPMA) show exposure without reflecting the toxic effects of benzene. For this reason, in this study, the urinary metabolome of individuals exposed to low concentrations of benzene was investigated, with the aim of understanding the biological response to exposure to this xenobiotic and identifying metabolites correlated with the toxic effects induced by it. Ultra-efficient liquid chromatography coupled to a quadrupole-time-of-flight mass spectrometer (UHPLC-ESI-Q-ToF-MS) was used to identify metabolites in the urine of environmentally (n = 28) and occupationally exposed (n = 32) to benzene (mean of 22.1 μg·m−3 and 31.8 μg·m−3, respectively). Non-targeted metabolomics analysis by PLS-DA revealed nine urinary metabolites discriminating between groups and statistically correlated with oxidative damage (MDA, thiol) and genetic material (chromosomal aberrations) induced by the hydrocarbon. The analysis of metabolic pathways revealed important alterations in lipid metabolism. These results point to the involvement of alterations in lipid metabolism in the mechanisms of cytotoxic and genotoxic action of benzene. Furthermore, this study proves the potential of metabolomics to provide relevant information to understand the biological response to exposure to xenobiotics and identify early effect biomarkers.