Search Results (29)

Biopesticides represent a safe and sustainable strategy for biological pest management, applicable to weed and fungal control. Biotechnological processing offers a promising approach to enhance the bioactivity of natural products for agricultural use. In this study, guishe juice, an agroindustrial residue derived from Agave lechuguilla, was bioprocessed via inoculation with Fusarium chlamydosporum, and its fungicidal and herbicidal potentials were evaluated. The fungal biotransformation led to the accumulation of phytochemicals, including flavonoids and polyphenols, significantly enhancing antioxidant activity to 76% and 96% as measured by DPPH and ABTS assays, respectively. The resulting bioprocessed guishe extract (BGE), particularly at 10% concentration (BGE-10), exhibited strong fungicidal activity, achieving 100% control of phytopathogenic fungi Fusarium spp. and Penicillium spp. Additionally, BGE-10 demonstrated a bioherbicidal effect, with a 77% weed control rate against Verbesina encelioides. These findings emphasize the potential of bioprocessed agave residues as dual-action bioagents, supporting the development of novel, eco-friendly agricultural solutions. Full article

Rotavirus alphagastroenteritidis (R. alphagastroenteritidis) remains the leading cause of pediatric diarrhea. Although Angola introduced Rotarix^®, the human monovalent R. alphagastroenteritidis vaccine since 2014 as part of its routine childhood immunization program, no follow-up study has been conducted. The aim of this study was to evaluate the distribution of R. alphagastroenteritidis genotypes among children under five years of age, hospitalized with acute gastroenteritis (AGE), after the introduction of the rotavirus vaccine. To achieve this goal, stool samples collected between 2021 and 2022 from children under 5 years of age diagnosed with AGE at six hospitals in Luanda Province were analyzed. The R. alphagastroenteritidis-antigen immunochromatographic test (SD Bioline™, Abbott, Chicago, IL, USA) was performed, and 121 positive samples were genotyped. Ten samples were randomly selected for further Sanger sequencing. The results showed that the G9P[6] was the most prevalent genotype (17.3%), followed by G9P[8] (16.5%), G2P[4] (14.9%), G3P[6] (13.2%), G8P[6] (11.5%), and less frequently G12P[8] (9.1%), G1P[6] (4.1%), and G1P[8] (2.5%). The genotype combinations G3P[6], G8P[6], and G12P[8] were detected for the first time in Luanda Province. In conclusion, the emergence of new genotype combinations supports the need for continuous surveillance to identify the trend in R. alphagastroenteritidis infection and the emergence of new strains circulating in Luanda Province in the post-vaccination period. Full article

In agriculture, machine learning (ML) and deep learning (DL) have increased significantly in the last few years. The use of ML and DL for image classification in plant disease has generated significant interest due to their cost, automatization, scalability, and early detection. However, high-quality image datasets are required to train robust classifier models for plant disease detection. In this work, we have created an image dataset of 649 orange leaves divided into two groups: control (n = 379) and huanglongbing (HLB) disease (n = 270). The images were acquired with several smartphone cameras of high resolution and processed to remove the background. The dataset enriches the information on characteristics and symptoms of citrus leaves with HLB and healthy leaves. This enhancement makes the dataset potentially valuable for disease identification through leaf segmentation and abnormality detection, particularly when applying ML and DL models. Full article

Background/Objectives: The COVID-19 pandemic resulted in 675 million cases and 6.9 million deaths by 2022. Despite substantial declines in case fatalities following widespread vaccination campaigns, the threat of future coronavirus outbreaks remains a concern. Current treatments for COVID-19 have been repurposed from existing therapies for other infectious and non-infectious diseases. Emerging evidence suggests a role for genetic factors in both susceptibility to SARS-CoV-2 infection and response to treatment. However, comprehensive studies correlating clinical outcomes with genetic variants are lacking. The main aim of our study is the identification of host genetic biomarkers that predict the clinical outcome of COVID-19 pharmacological treatments. Methods: In this study, we present findings from GWAS and candidate gene and pathway enrichment analyses leveraging diverse patient samples from the Spanish Coalition to Unlock Research of Host Genetics on COVID-19 (SCOURGE), representing patients treated with immunomodulators (n = 849), corticoids (n = 2202), and the combined cohort of both treatments (n = 2487) who developed different outcomes. We assessed various phenotypes as indicators of treatment response, including survival at 90 days, admission to the intensive care unit (ICU), radiological affectation, and type of ventilation. Results: We identified significant polymorphisms in 16 genes from the GWAS and candidate gene studies (TLR1, TLR6, TLR10, CYP2C19, ACE2, UGT1A1, IL-1α, ZMAT3, TLR4, MIR924HG, IFNG-AS1, ABCG1, RBFOX1, ABCB11, TLR5, and ANK3) that may modulate the response to corticoid and immunomodulator therapies in COVID-19 patients. Enrichment analyses revealed overrepresentation of genes involved in the innate immune system, drug ADME, viral infection, and the programmed cell death pathways associated with the response phenotypes. Conclusions: Our study provides an initial framework for understanding the genetic determinants of treatment response in COVID-19 patients, offering insights that could inform precision medicine approaches for future epidemics. Full article

Mobile robots represent one of the most relevant areas of study within robotics due to their potential for designing and developing new nonlinear control structures that can be implemented in simulations and applications in specific environments. In this work, a fuzzy steering controller with a symmetric distribution of fuzzy numbers is proposed and designed for implementation in the kinematic model of a non-holonomic mobile robot. The symmetry in the distribution of triangular fuzzy numbers contributes to a balanced response to disturbances and minimizes systematic errors in direction estimation. Additionally, it improves the system’s adaptability to various reference paths, ensuring accurate tracking and optimized performance in robot navigation. Furthermore, this fuzzy logic-based controller emulates the behavior of a classic PID controller by offering a robust and flexible alternative to traditional methods. A virtual environment was also developed using the UNITY platform to evaluate the performance of the fuzzy controller. The results were evaluated by considering the average tracking error, maximum error, steady-state error, settling time, and total distance traveled, emphasizing the trajectory error. The circular trajectory showed high accuracy with an average error of 0.0089 m, while the cross trajectory presented 0.01814 m, reflecting slight deviations in the turns. The point-to-point trajectory registered a more significant error of 0.9531 m due to abrupt transitions, although with effective corrections in a steady state. The simulation results validate the robustness of the proposed fuzzy controller, providing quantitative insights into its precision and efficiency in a virtual environment, and demonstrating the effectiveness of the proposal. Full article

The genus Leptospira includes at least 69 Gram-negative, aerobic spirochetes, of which 25 are pathogenic and associated with a diverse range of mammals, including members of the order Chiroptera. On the American continent, there are six confirmed Leptospira species. Among these, the common vampire bat (Desmodus rotundus), which ranges widely from northern Mexico to northern Argentina, has been reported to harbor four pathogenic taxa: Leptospira borgpetersenii, Leptospira interrogans, Leptospira weilii, and Leptospira cf. noguchii. All these species are frequently isolated from beef and dairy cattle, suggesting that contact with urine from infected cattle could serve as a potential source of infection for bats. However, previous studies have been limited by small sample sizes and low geographical representation among the countries where they were conducted. For this reason, the aim of this study was to identify the species of Leptospira associated with D. rotundus populations in five states within the Neotropical region of Mexico. Between 2015 and 2021, 54 bats were collected across five Mexican states. Our analysis identified the exclusive presence of L. interrogans in 13 specimens. The findings are discussed within the framework of a One Health perspective, emphasizing their relevance to understanding interspecies transmission dynamics. Full article

Klebsiella oxytoca toxigenic strains represent a critical health threat, mainly due to their link to antibiotic-associated hemorrhagic colitis. This serious condition results from the bacteria’s ability to produce tilimycin and tilivalline cytotoxins. Our research highlights the pivotal role of OmpR, a key regulator within the EnvZ/OmpR two-component system, in controlling the virulence factors associated with K. oxytoca. Our findings strongly indicate that OmpR is a repressor of the aroX and npsA genes, the first genes of aroX and NRPS operons, respectively, which are indispensable for producing these enterotoxins. Notably, in the absence of OmpR, we observe a significant increase in cytotoxic effects on Caco-2 cells. These observations identify OmpR as a crucial negative transcription regulator for both operons, effectively managing the release of these cytotoxins. This research deepens our understanding of the mechanisms of toxigenic K. oxytoca and opens promising avenues for targeting OmpR for new therapeutic interventions. By focusing on this innovative approach, we can develop more effective solutions to combat this pressing health challenge, ultimately improving patient outcomes against this pathogen. Full article

The gut microbiota plays a vital role in various physical and physiological processes, including immune system regulation, neurotransmitter production, inflammatory response modulation, and the inhibition of pathogenic organisms. An imbalance in the microbial community, known as dysbiosis, has been associated with numerous health issues. Biological influences, health behaviors, socioeconomic determinants, and nutritional status can disrupt this balance. Objective: To evaluate the differences in the gut microbiota composition in medical students according to fiber intake, ultra-processed food (UPF) consumption, sex, body mass index, and socioeconomic status. Methods: A cross-sectional study was conducted with 91 medical students, and 82 fecal samples were analyzed. Sociodemographic and dietary data were collected via questionnaires, UPF consumption was assessed using the NOVA classification, and trained nutritionists performed anthropometry. DNA extraction and 16S rRNA sequencing were performed for the microbial analysis. Bioinformatics and statistical tests included the Dunn and Kruskal–Wallis tests, a PCoA analysis, PERMANOVA, ANOVA, Spearman’s rank correlation, and alpha and beta diversity metrics. Results: Dietary fiber intake strongly influences gut microbiota composition. Lower fiber intake was associated with a higher prevalence of Parabacteroides and Muribaculaceae. Prevotella was more prevalent in individuals with lower UPF intake, while Phascolarctobacterium was prevalent in those with higher UPF consumption. Significant differences were associated with sex and UPF consumption but not BMI or SES. Women consumed more UPF, which correlated with distinct gut microbiota profiles. Conclusions: This study highlights the significant impact of diet, particularly fiber intake and UPF, on gut microbiota composition, emphasizing the importance of dietary habits in maintaining gut health. Full article

Lightweight materials that combine high mechanical strength, insulation, and fire resistance are of great interest to many industries. This work explores the properties of environmentally friendly alginate aerogel composites as potential sustainable alternatives to petroleum-based materials. This study analyzes the effects of two additives (tannic acid and montmorillonite clay), the orientation that results during casting, and the crosslinking of the biopolymer with glutaraldehyde on the properties of the aerogel composites. The prepared aerogels exhibited high porosities between 90% and 97% and densities in the range of 0.059–0.191 g/cm³. Crosslinking increased the density and resulted in excellent performance under loading conditions. In combination with axial orientation, Young’s modulus and yield strength reached values as high as 305 MPa·cm³/g and 7 MPa·cm³/g, respectively. Moreover, the alginate-based aerogels exhibited very low thermal conductivities, ranging from 0.038 W/m·K to 0.053 W/m·K. Compared to pristine alginate, the aerogel composites’ thermal degradation rate decreased substantially, enhancing thermal stability. Although glutaraldehyde promoted combustion, the non-crosslinked aerogel composites demonstrated high fire resistance. No flame was observed in these samples under cone calorimeter radiation, and a minuscule peak of heat release of 21 kW/m² was emitted as a result of their highly efficient graphitization and fire suppression. The combination of properties of these bio-based aerogels demonstrates their potential as substituents for their fossil-based counterparts. Full article

Type 2 diabetes mellitus (T2DM) is one of the most common metabolic diseases in the world and poses a significant public health challenge. Early detection and management of this metabolic disorder is crucial to prevent complications and improve outcomes. This paper aims to find core differences in male and female markers to detect T2DM by their clinic and anthropometric features, seeking out ranges in potential biomarkers identified to provide useful information as a pre-diagnostic tool whie excluding glucose-related biomarkers using machine learning (ML) models. We used a dataset containing clinical and anthropometric variables from patients diagnosed with T2DM and patients without TD2M as control. We applied feature selection with three different techniques to identify relevant biomarker models: an improved recursive feature elimination (RFE) evaluating each set from all the features to one feature with the Akaike information criterion (AIC) to find optimal outputs; Least Absolute Shrinkage and Selection Operator (LASSO) with glmnet; and Genetic Algorithms (GA) with GALGO and forward selection (FS) applied to GALGO output. We then used these for comparison with the AIC to measure the performance of each technique and collect the optimal set of global features. Then, an implementation and comparison of five different ML models was carried out to identify the most accurate and interpretable one, considering the following models: logistic regression (LR), artificial neural network (ANN), support vector machine (SVM), k-nearest neighbors (KNN), and nearest centroid (Nearcent). The models were then combined in an ensemble to provide a more robust approximation. The results showed that potential biomarkers such as systolic blood pressure (SBP) and triglycerides are together significantly associated with T2DM. This approach also identified triglycerides, cholesterol, and diastolic blood pressure as biomarkers with differences between male and female actors that have not been previously reported in the literature. The most accurate ML model was selection with RFE and random forest (RF) as the estimator improved with the AIC, which achieved an accuracy of 0.8820. In conclusion, this study demonstrates the potential of ML models in identifying potential biomarkers for early detection of T2DM, excluding glucose-related biomarkers as well as differences between male and female anthropometric and clinic profiles. These findings may help to improve early detection and management of the T2DM by accounting for differences between male and female subjects in terms of anthropometric and clinic profiles, potentially reducing healthcare costs and improving personalized patient attention. Further research is needed to validate these potential biomarkers ranges in other populations and clinical settings. Full article

Coffee (Coffea arabica), produced and marketed in Ecuador and worldwide, can be organoleptically improved by means of microorganisms such as well-characterized yeasts. This study aimed to isolate and characterize yeasts from three postharvest fermentation processes (i.e., Natural aerobic at room temperature; Carbonic maceration with a CO₂ atmosphere at room temperature; and Carbonic refrigerated maceration with a CO₂ atmosphere to 10 °C) of coffee fruits in Ecuador. Phenotypic and molecular analyses were conducted on 329 yeast isolates obtained from coffee farms in Loja, Olmedo, and Gonzanamá. Three universal media were used for yeast isolation diversity, and phenotypic characterization included morphology, sugar fermentation, salt tolerance, and ethanol resistance. Molecular characterization involved DNA analysis. The isolated diversity was classified into 12 morphotypes, nine distinct biochemical groups and nine genetic species. Only six species (i.e., Kurtzmaniella quercitrusa, Hanseniaspora opuntiae, Pichia. kluyveri, Torulaspora delbrueckii, T. quercuum, and Wickerhamomyces anomalus) identified phylogenetically corresponded to the designated morphotypes. But surprisingly, nine genetic species matched with the nine biochemical groups determined phenotypically analyzed using principal component analysis (PCA). Most of this diversity was found in the coffee plantation located in Gonzanamá, in contrast to Olmedo and Loja, without statistical significance (p value: 0.08295). On the other hand, the richness is not similar statistically (p value: 0.02991) between postharvest fermentation treatments. The findings suggest that the application of biochemical tests is useful for species determination, although morphological data may be ambiguous. Notably, Pichia kluyveri, detected in this study, holds potential for biotechnological evaluation in coffee fermentation processes. Full article

Ceramic production from ancient Mesoamerican civilizations is related with cultural and technological evolution processes. Studying ritual objects also provides information on ancient traditions and allows researchers to determine the importance of certain materials employed in its manufacture. In this work, a set of 72 of Mayapán’s effigy censers’ sherds was analyzed in situ by using a combination of non-invasive, non-destructive spectroscopic and imaging techniques for material characterization; colorimetry established an initial classification of the pigments present in the objects, XRF provided elemental information, FORS allowed us to describe the molecular characteristics, and hyperspectral imaging established compositional contrasts or similitudes between large regions of the different objects. Pigments were characterized, allowing us to describe the materials used in the decorations of such ritual objects. The pottery matrix was also characterized, leading to a detailed description of the clays and mixtures of minerals employed in the construction of the effigy censers. Full article

Twenty years have passed since the emergence of hantavirus zoonosis in Panama at the beginning of this millennium. We provide an overview of epidemiological surveillance of hantavirus disease (hantavirus pulmonary syndrome and hantavirus fever) during the period 1999–2019 by including all reported and confirmed cases according to the case definition established by the health authority. Our findings reveal that hantavirus disease is a low-frequency disease, affecting primarily young people, with a relatively low case-fatality rate compared to other hantaviruses in the Americas (e.g., ANDV and SNV). It presents an annual variation with peaks every 4–5 years and an interannual variation influenced by agricultural activities. Hantavirus disease is endemic in about 27% of Panama, which corresponds to agroecological conditions that favor the population dynamics of the rodent host, Oligoryzomys costaricensis and the virus (Choclo orthohantavirus) responsible for hantavirus disease. However, this does not rule out the existence of other endemic areas to be characterized. Undoubtedly, decentralization of the laboratory test and dissemination of evidence-based surveillance guidelines and regulations have standardized and improved diagnosis, notification at the level of the primary care system, and management in intensive care units nationwide. Full article

Camellia japonica is an underexplored medicinal plant with associated bioactivities. Innovative approaches are proposed in regard to the large-scale application of C. japonica, being one of the main routes for the extraction of phenolic compounds. The optimum conditions for the extraction of phenolic compounds from the flowers of C. japonica var. Eugenia de Montijo were determined using the response surface methodology (RSM). A five-level experimental design was carried out and analyzed via RSM using, as variables, temperature (T), time (t) and solvent (S), in the case of microwave-assisted extraction (MAE), and power (P), t and S in the case of ultrasound-assisted extraction (UAE). The compounds were identified using HPLC–MS–MS. Two responses were studied: the extraction yield and concentration of phenolic compounds. The results showed that the maximum yields (80%) were obtained at high temperatures and low times (180 °C, 5 min) when using MAE. Lower yields (56%) were obtained using UAE (optimal conditions 62% amplitude, 8 min, 39% acidified ethanol). The main family of phenolic compounds were flavonols. Moreover, the present study contributes to the valorization of underused flower species commonly present in the North-West region of Spain, by obtaining extracts rich in phenolic compounds that can be potentially applied as ingredients in different industrial fields. Full article

In agriculture, weed management is a significant concern because their uncontrolled proliferation decreases soil quality for food crops. Allelopathy is a natural phenomenon in which the activity of allelochemical compounds inhibits the germination and growth of invasive plants as a defense mechanism. Among allelochemicals are polyphenols, which may affect genetic material or crucial enzyme activities for proper physiological function. Agroindustrial residues are a vast source of polyphenolic compounds with allelochemical activity. The bagasse of Agave Lechuguilla, known as guishe, is an abundant residue in México. The guishe has been characterized before by its polyphenolic content. Based on that, a fungal bioconversion process was developed to increase the availability of the allelochemicals in the guishe juice. First, guishe juice was obtained by mechanical pressed and characterized by spectrophotometric analysis. Results showed (g/L): 5.62 flavonoids, 0.64 of hydrolyzable polyphenols, 12.67 of reducing sugars, and 23.3 total sugars. The compounds detected by HPLC-ESI-MS were pterostilbene, hydroxycaffeic, caffeoyltartaric, and 4-O-glucoside coumaric acids, considered allelopathic. After the fungal bioprocess, (+)-gallocatechin and 3,7-Dimethyl quercetin were detected as additional compounds of interest. The flavonoid and hydrolyzable polyphenol content were modified to the highest accumulation of 1.57 and 14.9 g/L at 72 h, meaning a 2.45- and 2.22-fold increase. A bioprocess guishe juice (BGJ) was obtained at the compound accumulation peak of 72 h and evaluated in an allelopathic assay on model seeds (tomato and corn). Results show that BGJ inhibits up to 96.67% of corn seeds and up to 76.6% of tomato seeds compared to positive control. Full article