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Studies on animal virome have mainly concentrated on chordates and medically significant invertebrates, often overlooking sylvatic mosquitoes, constituting a major part of mosquito species diversity. Despite their potential role in arbovirus transmission, the viromes of sylvatic mosquitoes remain largely unexplored. These mosquitoes may also harbor insect-specific viruses (ISVs), affecting arboviral transmission dynamics. The Cerrado biome, known for rapid deforestation and its status as a biodiversity hotspot, offers an ideal setting for investigating mosquito viromes due to potential zoonotic spillover risks from land use changes. This study aimed to characterize the viromes of sylvatic mosquitoes collected from various locations within Minas Gerais state, Brazil. The total RNA was extracted from mosquito pools of Psorophora albipes , Sabethes albiprivus , Sa. chloropterus , Psorophora ferox , and Coquillettidia venezuelensis species, followed by high-throughput sequencing (HTS). Bioinformatic analysis included quality control, contig assembly, and viral detection. Sequencing data analysis revealed 11 near-complete viral genomes (new viruses are indicated with asterisks) across seven viral families and one unassigned genus. These included: Xinmoviridae (Ferox mosquito mononega-like virus* and Albipes mosquito Gordis-like virus*), Phasmaviridae (Sabethes albiprivus phasmavirus*), Lispiviridae (Pedras lispivirus variant MG), Iflaviridae (Sabethes albiprivus iflavivirus*), Virgaviridae (Buriti virga-like virus variant MG and Sabethes albiprivus virgavirus 1*), Flaviviridae (Psorophora ferox flavivirus*), Mesoniviridae (Alphamesonivirus cavallyense variant MG), and the genus Negevirus (Biggie virus variant MG virus and Coquillettidia venezuelensis negevirus*). Moreover, the presence of ISVs and potential novel arboviruses underscores the need for ongoing surveillance and control strategies to mitigate the risk of emerging infectious diseases.

This study presents fibers based on methacrylic acid-methyl methacrylate (Eudragit L100) as Cu(II) adsorbents, resulting in antimicrobial complexes. Eudragit L100, an anionic copolymer synthesized by radical polymerization, was electrospun in dimethylformamide (DMF) and ethanol (EtOH). The electrospinning process was optimized through a 2 2 -factorial design, with independent variables (copolymer concentration and EtOH/DMF volume ratio) and three repetitions at the central point. The smallest average fiber diameter (259 ± 53 nm) was obtained at 14% w / v Eudragit L100 and 80/20 EtOH/DMF volume ratio. The fibers were characterized using scanning electron microscopy (SEM), infrared spectroscopy in attenuated total reflectance mode (FTIR-ATR), and differential scanning calorimetry (DSC). The pseudo-second-order mechanism explained the kinetic adsorption toward Cu(II). The fibers exhibited a maximum adsorption capacity (q e ) of 43.70 mg/g. The DSC analysis confirmed the Cu(II) absorption, indicating complexation between metallic ions and copolymer networks. The complexed fibers showed a lower degree of swelling than the non-complexed fibers. The complexed fibers exhibited bacteriostatic activity against Gram-negative ( Pseudomonas aeruginosa ) and Gram-positive ( Staphylococcus aureus ) bacteria. This study successfully optimized the electrospinning process to produce thin fibers based on Eudragit L100 for potential applications as adsorbents for Cu(II) ions in aqueous media and for controlling bacterial growth.

The infection of the central nervous system (CNS) with neurotropic viruses induces neuroinflammation and is associated with the development of neuroinflammatory and neurodegenerative diseases, including multiple sclerosis and epilepsy. The activation of the innate and adaptive immune response, including microglial, macrophages, and T and B cells, while required for efficient viral control within the CNS, is also associated with neuropathology. Under healthy conditions, resident microglia play a pivotal role in maintaining CNS homeostasis. However, during pathological events, such as CNS viral infection, microglia become reactive, and immune cells from the periphery infiltrate into the brain, disrupting CNS homeostasis and contributing to disease development. Theiler's murine encephalomyelitis virus (TMEV), a neurotropic picornavirus, is used in two distinct mouse models: TMEV-induced demyelination disease (TMEV-IDD) and TMEV-induced seizures, representing mouse models of multiple sclerosis and epilepsy, respectively. These murine models have contributed substantially to our understanding of the pathophysiology of MS and seizures/epilepsy following viral infection, serving as critical tools for identifying pharmacological targetable pathways to modulate disease development. This review aims to discuss the host-pathogen interaction during a neurotropic picornavirus infection and to shed light on our current understanding of the multifaceted roles played by microglia and macrophages in the context of these two complexes viral-induced disease.

In this work, a computational scheme is proposed to identify the main combinations of handcrafted descriptors and deep-learned features capable of classifying histological images stained with hematoxylin and eosin. The handcrafted descriptors were those representatives of multiscale and multidimensional fractal techniques (fractal dimension, lacunarity and percolation) applied to quantify the histological images with the corresponding representations via explainable artificial intelligence (xAI) approaches. The deep-learned features were obtained from different convolutional neural networks (DenseNet-121, EfficientNet-b2, Inception-V3, ResNet-50 and VGG-19). The descriptors were investigated through different associations. The most relevant combinations, defined through a ranking algorithm, were analyzed via a heterogeneous ensemble of classifiers with the support vector machine, naive Bayes, random forest and K-nearest neighbors algorithms. The proposed scheme was applied to histological samples representative of breast cancer, colorectal cancer, oral dysplasia and liver tissue. The best results were accuracy rates of 94.83% to 100%, with the identification of pattern ensembles for classifying multiple histological images. The computational scheme indicated solutions exploring a reduced number of features (a maximum of 25 descriptors) and with better performance values than those observed in the literature. The presented information in this study is useful to complement and improve the development of computer-aided diagnosis focused on histological images.

Inflammation and oxidative stress are common aspects of most neurodegenerative diseases in the central nervous system. In this context, microglia and astrocytes are central to mediating the balance between neuroprotective and neurodestructive mechanisms. Flavonoids have potent anti-inflammatory and antioxidant properties. Here, we have examined the anti-inflammatory and neuroprotective potential of the flavonoid agathisflavone (FAB), which is derived from the Brazilian plant Poincianella pyramidalis , in in vitro models of neuroinflammation. Cocultures of neurons/glial cells were exposed to lipopolysaccharide (LPS, 1 µg/mL) or interleukin (IL)-1β (10 ng/mL) for 24 h and treated with FAB (0.1 and 1 µM, 24 h). FAB displayed a significant neuroprotective effect, as measured by nitric oxide (NO) production, Fluoro-Jade B (FJ-B) staining, and immunocytochemistry (ICC) for the neuronal marker β-tubulin and the cell death marker caspase-3, preserving neuronal soma and increasing neurite outgrowth. FAB significantly decreased the LPS-induced microglial proliferation, identified by ICC for Iba-1/bromodeoxyuridine (BrdU) and CD68 (microglia M1 profile marker). In contrast, FAB had no apparent effect on astrocytes, as determined by ICC for glial fibrillary acidic protein (GFAP). Furthermore, FAB protected against the cytodestructive and proinflammatory effects of IL-1β, a key cytokine that is released by activated microglia and astrocytes, and ICC showed that combined treatment of FAB with α and β estrogen receptor antagonists did not affect NF-κB expression. In addition, qPCR analysis demonstrated that FAB decreased the expression of proinflammatory molecules TNF-α, IL-1β, and connexins CCL5 and CCL2, as well as increased the expression of the regulatory molecule IL-10. Together, these findings indicate that FAB has a significant neuroprotective and anti-inflammatory effect in vitro, which may be considered as an adjuvant for the treatment of neurodegenerative diseases.

Bioactive compounds present in plant-based foods, and their metabolites derived from gut microbiota and endogenous metabolism, represent thousands of chemical structures of potential interest for human nutrition and health. State-of-the-art analytical methodologies, including untargeted metabolomics based on high-resolution mass spectrometry, are required for the profiling of these compounds in complex matrices, including plant food materials and biofluids. The aim of this project was to compare the analytical coverage of untargeted metabolomics methods independently developed and employed in various European platforms. In total, 56 chemical standards representing the most common classes of bioactive compounds spread over a wide chemical space were selected and analyzed by the participating platforms ( n = 13) using their preferred untargeted method. The results were used to define analytical criteria for a successful analysis of plant food bioactives. Furthermore, they will serve as a basis for an optimized consensus method., Competing Interests: The authors declare no conflict of interest.

Chemometric pattern recognition techniques were employed in order to obtain Structure-Activity Relationship (SAR) models relating the structures of a series of adenosine compounds to the affinity for glyceraldehyde 3-phosphate dehydrogenase of Leishmania mexicana (LmGAPDH). A training set of 49 compounds was used to build the models and the best ones were obtained with one geometrical and four electronic descriptors. Classification models were externally validated by predictions for a test set of 14 compounds not used in the model building process. Results of good quality were obtained, as verified by the correct classifications achieved. Moreover, the results are in good agreement with previous SAR studies on these molecules, to such an extent that we can suggest that these findings may help in further investigations on ligands of LmGAPDH capable of improving treatment of leishmaniasis.

An antioxidant mechanism of tetrahydrocannabinol (THC) and cannabidiol (CBD) were compared with a simplified model of α-tocopherol, butylhydroxytoluene and hydroxytoluene in order to understand the antioxidant nature of THC and CBD molecules using DFT. The following electronic properties were evaluated: frontier orbitals nature, ionization potential, O-H bond dissociation energy (BDEOH), stabilization energy, and spin density distribution. An important factor that shows an influence in the antioxidant property of THC is the electron abstraction at the phenol position. Our data indicate that the decrease of the HOMO values and the highest ionization potential values are related to phenol, ether, and alkyl moieties. On the other hand, BDEOH in molecules with the cyclohexenyl group at ortho position of phenol are formed from lower energies than the molecules with an ether group at the meta position. In the light of our results, the properties calculated here predict that THC has a sightly higher antioxidant potential than CBD.

Quantitative structure-activity relationship (QSAR) studies were performed in order to identify molecular features responsible for the antileishmanial activity of 61 adenosine analogues acting as inhibitors of the enzyme glyceraldehyde 3-phosphate dehydrogenase of Leishmania mexicana (LmGAPDH). Density functional theory (DFT) was employed to calculate quantum-chemical descriptors, while several structural descriptors were generated with Dragon 5.4. Variable selection was undertaken with the ordered predictor selection (OPS) algorithm, which provided a set with the most relevant descriptors to perform PLS, PCR and MLR regressions. Reliable and predictive models were obtained, as attested by their high correlation coefficients, as well as the agreement between predicted and experimental values for an external test set. Additional validation procedures were carried out, demonstrating that robust models were developed, providing helpful tools for the optimization of the antileishmanial activity of adenosine compounds.