Search Results (7)

Malaria, the deadliest parasitic disease in the world, is sexually dimorphic, inflammatory, and oxidative. Males experience more severe symptoms and mortality than females do; therefore, the roles of 17β-estradiol and testosterone in this phenomenon have been studied. Both hormones affect oxidative stress, the primary mechanism of Plasmodium elimination. Estradiol has antioxidant activity, but the role of testosterone is controversial. Testosterone increases oxidative stress by reducing superoxide dismutase (SOD), glutathione peroxidase (GPx), and catalase (CAT) activities, which increase lipoperoxidation in the testis. However, the antioxidant properties of testosterone in prostate and nervous tissue have also been reported. The discrepancies are probably because when testosterone levels increase, the aromatase enzyme transforms testosterone into estrogens that possess antioxidant activity, which masks the results. Therefore, it is unknown whether testosterone is involved in the sexual dimorphism that occurs in oxidative stress in malaria. In this work, we administered testosterone and simultaneously inhibited aromatase with letrozole to evaluate the role of testosterone in the sexually dimorphic pattern of oxidative stress that occurs in the blood, spleen, and brain of male and female CBA/Ca mice infected with Plasmodium berghei ANKA (P. berghei ANKA). Testosterone triggers parasitemia in males, who also display more oxidative stress than females in the absence of infection, leading to sexually dimorphic patterns. Interestingly, increasing testosterone levels in infected mice reduced oxidative stress in males and increased oxidative stress in females, reversing or eliminating the dimorphic patterns observed. Oxidative stress varies in each tissue; the brain was the most protected, while the blood was the greatest damaged. Our findings highlight the role of testosterone as a regulator of oxidative stress in a tissue and sex-specific manner; therefore, understanding the role of testosterone in malaria may contribute to the development of sex-specific personalized antimalarial therapies. Full article

The symptoms of multiple sclerosis (MS) are determined by the location of demyelinating lesions in the white matter of the brain and spinal cord. Currently, magnetic resonance imaging (MRI) is the most common tool used for diagnosing MS, understanding the course of the disease, and analyzing the effects of treatments. However, undesirable components may appear during the generation of MRI scans, such as noise or intensity variations. Mathematical morphology (MM) is a powerful image analysis technique that helps to filter the image and extract relevant structures. Granulometry is an image measurement tool for measuring MM that determines the size distribution of objects in an image without explicitly segmenting each object. While several methods have been proposed for the automatic segmentation of MS lesions in MRI scans, in some cases, only simple data preprocessing, such as image resizing to standardize the input dimensions, has been performed before the algorithm training. Therefore, this paper proposes an MRI preprocessing algorithm capable of performing elementary morphological transformations in brain images of MS patients and healthy individuals in order to delete undesirable components and extract the relevant structures such as MS lesions. Also, the algorithm computes the granulometry in MRI scans to describe the size qualities of lesions. Using this algorithm, we trained two artificial neural networks (ANNs) to predict MS diagnoses. By computing the differences in granulometry measurements between an image with MS lesions and a reference image (without lesions), we determined the size characterization of the lesions. Then, the ANNs were evaluated with the validation set, and the performance results (test accuracy = 0.9753; cross-entropy loss = 0.0247) show that the proposed algorithm can support specialists in making decisions to diagnose MS and estimating the disease progress based on granulometry values. Full article

The Gulf of California (GC) is the only interior sea in the Eastern Pacific Ocean and is the most important fishing area in the northwestern region of the Mexican Pacific. This study focuses on the oceanographic variability of the GC, including its southern portion, which is an area with a high flow of energy and exchange of properties with the Pacific Ocean (PO), in order to determine its role in physical–biological cycles and climate change. The purpose of this work is to analyze the sea surface temperature (SST) and chlorophyll a concentration (Chl-a) during the period from 1998–2022 as indicators of long-term physical and biological processes, oceanographic variability, and primary production in the GC. In total, 513 subareas in the GC were analyzed, and a cluster analysis was applied to identify similar areas in terms of SST and Chl-a via the K-means method and using the silhouette coefficient (>0.5) as a metric to validate the clusters obtained. The trends of the time series of both variables were analyzed, and a fast Fourier analysis was performed to evaluate cycles in the series. A descriptive analysis of the SST and Chl-a series showed that the SST decreased from south to north. Six bioregions were identified using a combined of both SST and Chl-a data. The spectral analysis of the SST showed that the main frequencies in the six bioregions were annual and interannual (3–7 years), and the frequencies of their variations were associated with basin-level weather events, such as El Niño and La Niña. The SST in the GC showed a heating trend at an annual rate of ~0.036 °C (~0.73 °C in 20 years) and a decrease in Chl-a at an annual rate of ~0.012 mg/m³ (~0.25 mg/m³ in 20 years), with potential consequences for communities and ecosystems. Additionally, cycles of 10–13 and 15–20 years were identified, and the 10–13-year cycle explained almost 40–50% of the signal power in some regions. Moreover, mesoscale features (eddies and filaments) were identified along the GC, and they were mainly associated with the clusters of the SST. All these spatial and temporal variabilities induce conditions that generate different habitats and could explain the high biodiversity of the GC. If the warming trend of the SST and the decreasing trend of the Chl-a continue in the long term, concerns could be raised, as they can have important effects on the dynamics of this important marine ecosystem, including habitat loss for numerous native species, declines in the catches of the main fishery resources, and, consequently, support for the arrival of harmful invasive species. Full article

Interferon-beta is one of the most widely prescribed disease-modifying therapies for multiple sclerosis patients. However, this treatment is only partially effective, and a significant proportion of patients do not respond to this drug. This paper proposes an alternative fuzzy logic system, based on the opinion of a neurology expert, to classify relapsing–remitting multiple sclerosis patients as high, medium, or low responders to interferon-beta. Also, a pipeline prediction model trained with biomarkers associated with interferon-beta responses is proposed, for predicting whether patients are potential candidates to be treated with this drug, in order to avoid ineffective therapies. The classification results showed that the fuzzy system presented 100% efficiency, compared to an unsupervised hierarchical clustering method (52%). So, the performance of the prediction model was evaluated, and 0.8 testing accuracy was achieved. Hence, a pipeline model, including data standardization, data compression, and a learning algorithm, could be a useful tool for getting reliable predictions about responses to interferon-beta. Full article

Coastal zones are important areas for the development of diverse ecosystems. The analysis of chlorophyll a (Chl a), as an indicator of primary production in these regions, is crucial for the quantification of phytoplankton biomass, which is considered the main food chain base in the oceans and an indicator of the trophic state index. This variable is greatly important for the analysis of the oceanographic variability, and it is crucial for determining the tendencies of change in these areas with the objective of determining the effects on the ecosystem and the population dynamics of marine resources. In this study, we analysed the Chl a concentration distribution on the mainland coast of the Gulf of California based on the monthly data from July 2002 to July 2019, obtained from remote sensing (Moderate-Resolution Imaging Spectroradiometer Aqua (MODIS-Aqua) with a 9 km resolution). The results showed a clear distribution pattern of Chl a observed along this area with the maximum levels in March and minimum levels in August. A four-region characterisation on this area was used to make a comparison of the Chl a concentrations during warm and cold periods. The majority of the results were statistically significant. The spectral analysis in each of the four regions analysed in this study determined the following variation frequencies: annual, semi-annual, seasonal, and inter-annual; the last was related to the macroscale climatological phenomena El Niño-La Niña affecting the variability of the Chl a concentration in the study region. Full article

The coastal zones are areas with a high flow of energy and materials where diverse ecosystems are developed. The study of coastal oceanography is important to understand the variability of these ecosystems and determine their role in biogeochemical cycles and climate change. Sea surface temperature (SST) analysis is indispensable for the characterization of physical and biological processes, and it is affected by processes at diverse timescales. The purpose of this work is to analyze the oceanographic variability of the Eastern Coastal Zone of the Gulf of California through the study of the SST from time series analysis of monthly data obtained from remote sensors (AVHRR-Pathfinder Version 5.1 and Version 5 resolution of 4 km, MODIS-Aqua, resolution of 4 km) for the period 1981 to 2016. The descriptive analysis of SST series showed that the values decrease from south to north, as well as the amplitude of the warm period decrease from south to north (cold period increase from south to north). The minimum values occurred during January and February, and ranged between 18 and 20 °C; and maximum values, of about 32 °C, arose in August and September. Cluster analysis allowed to group the data in four regions (south, center, midriff islands and north), the spectral analysis in each region showed frequencies of variation in scales: Annual (the main), seasonal, semiannual, and interannual. The latter is associated with the El Niño and La Niña climatological phenomena. Full article

This study analyzed monthly and inter-annual variability of mesoscale phenomena, including the El Niño Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO) climate indexes and wind intensity considering their influence on sea surface temperature (SST) and chlorophyll a (Chl-a). These analyses were performed to determine the effects, if any, of climate indexes and oceanographic and environmental variability on the central and southern coastal ecosystem of Sonora in the Gulf of California (GC). Monthly satellite images of SST (°C) and Chl-a concentration were used with a 1-km resolution for oceanographic and environmental description, as well as monthly data of the climate indexes and wind intensity from 2002–2015. Significant differences (p > 0.05) were observed while analyzing the monthly variability results of mesoscale phenomena, SST and Chl-a, where the greatest percentage of anti-cyclonic gyres and filaments was correlated with a greater Chl-a concentration in the area of study, low temperatures and, thus, greater productivity. Moreover, the greatest percentage of intrusion was correlated with the increase in temperature and cyclonic gyres and a strong decrease of Chl-a concentration values, causing oligotrophic conditions in the ecosystem and a decrease in upwelling and filament occurrence. As for the analysis of the interannual variability of mesoscales phenomena, SST, Chl-a and winds, the variability between years was not significant (p > 0.05), so no correlation was observed between variabilities or phenomena. The results of the monthly analyses of climate indexes, environmental variables and wind intensity did not show significant differences for the ENSO and PDO indexes (p > 0.05). Nonetheless, an important correlation could be observed between the months of negative anomalies of the ENSO with high Chl-a concentration values and intense winds, as well as with low SST values. The months with positive ENSO anomalies were correlated with high SST values, low Chl-a concentration and moderate winds. Significant inter-annual differences were observed for climate indexes where the years with high SST values were related to the greatest positive anomaly of ENSO, of which 2002 and 2009 stood out, characterized as moderate Niño years, and 2015 as a strong El Niño year. The years with the negative ENSO anomaly were related to the years of lower SST values, of which 2007–2008 and 2010–2011 stood out, characterized as moderate Niñas. Thus, variability associated with mesoscale oceanographic phenomena and seasonal and inter-annual variations of climate indexes had a great influence on the environmental conditions of the coastal ecosystem of Sonora in the Gulf of California. Full article