RESUMEN

Exposure to particulate matter (PM) pollution is a significant health risk, driving the search for innovative metrics that more accurately reflect the potential harm to human health. Among these, oxidative potential (OP) has emerged as a promising health-based metric, yet its application and relevance across different environments remain to be further explored. This study, set in two high-altitude Bolivian cities, aims to identify the most significant sources of PM-induced oxidation in the lungs and assess the utility of OP in assessing PM health impacts. Utilizing two distinct assays, OPDTT and OPDCFH, we measured the OP of PM samples, while also examining the associations between PM mass, OP, and black carbon (BC) concentrations with hospital visits for acute respiratory infections (ARI) and pneumonia over a range of exposure lags (0-2 weeks) using a Poisson regression model adjusted for meteorological conditions. The analysis also leveraged Positive Matrix Factorization (PMF) to link these health outcomes to specific PM sources, building on a prior source apportionment study utilizing the same dataset. Our findings highlight anthropogenic combustion, particularly from traffic and biomass burning, as the primary contributors to OP in these urban sites. Significant correlations were observed between both OPDTT and PM2.5 concentration exposure and ARI hospital visits, alongside a notable association with pneumonia cases and OPDTT levels. Furthermore, PMF analysis demonstrated a clear link between traffic-related pollution and increased hospital admissions for respiratory issues, affirming the health impact of these sources. These results underscore the potential of OPDTT as a valuable metric for assessing the health risks associated with acute PM exposure, showcasing its broader application in environmental health studies.

Asunto(s)

Contaminantes Atmosféricos , Altitud , Ciudades , Material Particulado , Material Particulado/análisis , Bolivia/epidemiología , Humanos , Contaminantes Atmosféricos/análisis , Adulto , Infecciones del Sistema Respiratorio/epidemiología , Oxidación-Reducción , Masculino , Persona de Mediana Edad , Femenino , Neumonía/epidemiología , Neumonía/inducido químicamente , Adulto Joven , Adolescente , Contaminación del Aire/análisis , Contaminación del Aire/efectos adversos , Niño , Monitoreo del Ambiente/métodos , Preescolar

RESUMEN

BACKGROUND: Cancer is a collection of diseases caused by the deregulation of cell processes, which is triggered by somatic mutations. The search for patterns in somatic mutations, known as mutational signatures, is a growing field of study that has already become a useful tool in oncology. Several algorithms have been proposed to perform one or both the following two tasks: (1) de novo estimation of signatures and their exposures, (2) estimation of the exposures of each one of a set of pre-defined signatures. RESULTS: Our group developed signeR, a Bayesian approach to both of these tasks. Here we present a new version of the software, signeR 2.0, which extends the possibilities of previous analyses to explore the relation of signature exposures to other data of clinical relevance. signeR 2.0 includes a user-friendly interface developed using the R-Shiny framework and improvements in performance. This version allows the analysis of submitted data or public TCGA data, which is embedded in the package for easy access. CONCLUSION: signeR 2.0 is a valuable tool to generate and explore exposure data, both from de novo or fitting analyses and is an open-source R package available through the Bioconductor project at ( https://doi.org/10.18129/B9.bioc.signeR ).

Asunto(s)

Neoplasias , Humanos , Teorema de Bayes , Neoplasias/genética , Mutación , Programas Informáticos , Algoritmos

RESUMEN

In this study, positive matrix factorization method was used for source apportionment of PM10 in the city of São Carlos from 2015 to 2018. The annual mean concentrations of PM10, 15 PAHs, 4 oxy-PAHs, 6 nitro-PAHs, 21 saccharides, and 17 ions in these samples were in the ranges 18.1 ± 6.99 to 25.0 ± 11.3 µg m-3 for PM10, 9.80 × 10-1 ± 2.06 to 2.03 ± 8.54 × 10-1 ng m-3 for ΣPAHs, 83.9 ± 35.7 to 683 ± 521 pg m-3 for Σoxy-PAHs, 1.79 × 10-2 ± 1.23 × 10-1 to 7.12 ± 4.90 ng m-3 for Σnitro-PAHs, 83.3 ± 44.7 to 142 ± 85.9 ng m-3 for Σsaccharides, and 3.80 ± 1.54 to 5.66 ± 4.52 µg m-3 for Σions. For most species, the concentrations were higher in the dry season than in the rainy. This was related not only to the low rainfall and relative humidity characteristic of the dry season but also to an increase in fire spots recorded in the region between April and September every year from 2015 to 2018. A 4-factor solution provided the best description of the dataset, with the four identified sources of PM10 being soil resuspension (28%), biogenic emissions (27%), biomass burning (27%), and vehicle exhaust together with secondary PM (18%). Although the PM10 concentrations were not above the limit established by local legislation, the epidemiological study showed that by reducing PM2.5 concentrations to the level recommended by the WHO, approximately 35 premature deaths per 100,000 population could be avoided annually. The results revealed that biomass burning continues to be one of the main anthropic sources of emissions to the atmosphere in the region, so it needs to be incorporated into the existing guidelines and policies to reduce the concentration of particulate matter to within the limits recommended by the WHO, in order to avoid premature deaths.

Asunto(s)

Contaminantes Atmosféricos , Hidrocarburos Policíclicos Aromáticos , Material Particulado/análisis , Contaminantes Atmosféricos/análisis , Brasil , Evaluación del Impacto en la Salud , Monitoreo del Ambiente , Emisiones de Vehículos/análisis , Estaciones del Año , Hidrocarburos Policíclicos Aromáticos/análisis , China

RESUMEN

Volatile organic compounds (VOCs) data in conjunction with other inorganic pollutants, surface meteorological data and continuous measurement of the Planetary Boundary Layer height (PBLH) at an urban site in Mexico City were performed from 6 to 18 March 2016. Positive Matrix Factorization (PMF) identified four emission source factors of VOCs along with equivalent black carbon (eBC), gaseous pollutants (CO, NO, NO2, SO2, NH3) and ions (Na+, Mg2+, Ca2+, NO3-, NH4+): (1) secondary aerosol precursors, (2) evaporation and non-LPG fuel combustion, (3) geogenic source and (4) vehicle exhaust. Propylene Equivalent and Maximum Incremental Reactivity (MIR) methods identified isoprene and ethylene as the highest oxidant and O3 forming species. Pollutant data normalized to the variation of the PBLH revealed continued production of O3 precursors in the afternoon beyond the typical morning rush hour. In particular this could be observed during the second part of the measurement period (12-15 March) when a strong O3 episode occurred under weak wind and lower PBLH conditions compared to the preceding period (6-11 March) when well mixed conditions due to elevated daytime PBLH and strong advection led to overall reduced pollutant mixing ratios in the afternoon hours.

Asunto(s)

Contaminantes Atmosféricos , Ozono , Compuestos Orgánicos Volátiles , Ozono/análisis , Contaminantes Atmosféricos/análisis , Meteorología , México , Monitoreo del Ambiente/métodos , Emisiones de Vehículos/análisis , Compuestos Orgánicos Volátiles/análisis , China

RESUMEN

We present two machine learning approaches for drug repurposing. While we have developed them for COVID-19, they are disease-agnostic. The two methodologies are complementary, targeting SARS-CoV-2 and host factors, respectively. Our first approach consists of a matrix factorization algorithm to rank broad-spectrum antivirals. Our second approach, based on network medicine, uses graph kernels to rank drugs according to the perturbation they induce on a subnetwork of the human interactome that is crucial for SARS-CoV-2 infection/replication. Our experiments show that our top predicted broad-spectrum antivirals include drugs indicated for compassionate use in COVID-19 patients; and that the ranking obtained by our kernel-based approach aligns with experimental data. Finally, we present the COVID-19 repositioning explorer (CoREx), an interactive online tool to explore the interplay between drugs and SARS-CoV-2 host proteins in the context of biological networks, protein function, drug clinical use, and Connectivity Map. CoREx is freely available at: https://paccanarolab.org/corex/.

RESUMEN

Scientists often embed cells into a lower-dimensional space when studying single-cell RNA-seq data for improved downstream analyses such as developmental trajectory analyses, but the statistical properties of such nonlinear embedding methods are often not well understood. In this article, we develop the exponential-family SVD (eSVD), a nonlinear embedding method for both cells and genes jointly with respect to a random dot product model using exponential-family distributions. Our estimator uses alternating minimization, which enables us to have a computationally efficient method, prove the identifiability conditions and consistency of our method, and provide statistically principled procedures to tune our method. All these qualities help advance the single-cell embedding literature, and we provide extensive simulations to demonstrate that the eSVD is competitive compared to other embedding methods. We apply the eSVD via Gaussian distributions where the standard deviations are proportional to the means to analyze a single-cell dataset of oligodendrocytes in mouse brains. Using the eSVD estimated embedding, we then investigate the cell developmental trajectories of the oligodendrocytes. While previous results are not able to distinguish the trajectories among the mature oligodendrocyte cell types, our diagnostics and results demonstrate there are two major developmental trajectories that diverge at mature oligodendrocytes. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplementary materials.

RESUMEN

This study aimed to assess the chemical composition of the rainwater in three areas of different environmental impact gradients in Southern Brazil using the receptor model EPA Positive Matrix Factorization (EPA PMF 5.0). The samples were collected in a bulk sampler, from October 2012 to August 2014, in three sampling sites along with the Sinos River Basin: Caraá, Taquara, and Campo Bom. The major ions NH4+, Na+, K+, Ca2+, Mg2+, F-, Cl-, NO3-, SO42-, and pH were analyzed, as well as identify the main emission sources. The most abundant cations and anions were Ca2+, Na+, Cl-, and SO42-, respectively. The mean pH value in the Sinos River Basin during the study period was 6.07 ± 0.49 (5.13-7.05), which suggests inputs of alkaline species into the atmosphere. The most important neutralizing agents of sulfuric and nitric acids in the Sinos River Basin are Ca2+ (NF = 1.36) and NH4+ (NF = 0.57). The source apportionment provided by the EPA PMF 5.0 resulted in four factors, which demonstrate the influence of anthropogenic and natural sources, in the form of (a) industry/combustion of fossil fuels (F- and SO42-), (b) marine contribution (Na+ and Cl-), (c) crustal contribution (K+, Ca2+, and NO3-), and (d) agriculture/livestock (NH4+). Therefore, this study allows a more appropriate understanding of factors that contribute to rainwater chemical composition and also to possible changes in air quality.

Asunto(s)

Lluvia/química , Contaminación del Aire , Aniones/análisis , Atmósfera , Brasil , Cationes/análisis , Monitoreo del Ambiente/métodos , Concentración de Iones de Hidrógeno , Modelos Teóricos , Nitratos/análisis , Sodio/análisis , Sulfatos/análisis

RESUMEN

Histopathological images are an important resource for clinical diagnosis and biomedical research. From an image understanding point of view, the automatic annotation of these images is a challenging problem. This paper presents a new method for automatic histopathological image annotation based on three complementary strategies, first, a part-based image representation, called the bag of features, which takes advantage of the natural redundancy of histopathological images for capturing the fundamental patterns of biological structures, second, a latent topic model, based on non-negative matrix factorization, which captures the high-level visual patterns hidden in the image, and, third, a probabilistic annotation model that links visual appearance of morphological and architectural features associated to 10 histopathological image annotations. The method was evaluated using 1,604 annotated images of skin tissues, which included normal and pathological architectural and morphological features, obtaining a recall of 74% and a precision of 50%, which improved a baseline annotation method based on support vector machines in a 64% and 24%, respectively.