https://repositorio.ufu.br/handle/123456789/20866 2026-10-09T13:32:38Z 2026-10-09T13:32:38Z https://repositorio.ufu.br/handle/123456789/50557 2026-10-08T06:19:14Z 2026-09-09T00:00:00Z

Title: Avaliação dos efeitos de meio condicionado derivado de células de câncer de mama triplo negativo tratadas com ecdisterona sobre fibroblastos: um foco na sinalização purinérgica Abstract: Triple-negative breast cancer (TNBC) is the most aggressive BC subtype, that presents an enrichment of fibroblasts in its immunosuppressive microenvironment that contributes to tumor cell progression and resistance. The mechanisms involved in this interaction remain poorly understood, especially when natural compounds are used. In the present study, the effects of Ecdysterone on modulating TNBC cell migration and how its conditioned medium alters the behavior of human fibroblasts were investigated, highlighting the role of purinergic signaling in this context. To this end, MDA-MB231 (TNBC) and HFF-1 (fibroblasts) cells were treated with Ecdysterone (20E), and horizontal and vertical migration assays were conducted with non-cytotoxic concentrations. Interestingly, Ecdysterone influenced the migratory behavior of MDA-MB-231 cells, especially vertically, suggesting its potential to contain the invasion of these cells. Furthermore, its conditioned medium inhibited horizontal fibroblast migration, which did not occur when HFF cells were treated directly with the compound. Computational analyses demonstrated the potential of 20E to interact with CD73 and the A2A and A2B adenosine receptors. For CD73, 20E exhibited a higher binding affinity than the endogenous ligand and comparable affinity to synthetic inhibitors. In fibroblasts treated with conditioned medium, ADORA2B (gene encoding A2B) transcript levels were significantly reduced, whereas NT5E (gene encoding CD73) transcript levels were increased. Taken together, the results demonstrate that 20E, even at non-cytotoxic concentrations, alters the behavior of TNBC cells and interferes with their communication with cells within the tumor microenvironment, suggesting the involvement of purinergic signaling and its potential role in modulating tumor–stroma interactions.

2026-09-09T00:00:00Z https://repositorio.ufu.br/handle/123456789/50295 2026-09-17T06:21:22Z 2026-06-02T00:00:00Z

Title: Micrornas em alface (Lactuca sativa): novas descobertas sobre a regulação gênica pós-transcricional Abstract: MicroRNAs (miRNAs) are small non-coding RNAs involved in the post-transcriptional regulation of gene expression in plants, participating in processes related to development, metabolism, and responses to biotic and abiotic stresses. Despite the biological relevance of these regulators, there is still limited information regarding the components of the miRNA processing pathway in Lactuca sativa L., one of the most economically important leafy vegetables worldwide. Therefore, this study aimed to identify and characterize proteins involved in miRNA biogenesis, as well as to predict and analyze conserved miRNA families in L. sativa cv. Salinas and L. sativa var. angustana. The analyses were performed through genome mining, multiple sequence alignments, conserved domain prediction, thermodynamic analyses, and phylogenetic inference using orthologous sequences from plant species. Additionally, 193 mature miRNA sequences (3p and 5p) and 137 precursor miRNAs were identified, distributed among 36 distinct miRNA families. The precursors exhibited stable secondary structures and thermodynamic parameters compatible with true miRNAs. Overall, the results obtained expand the current knowledge regarding the miRNA processing pathway in lettuce and provide an important basis for future functional studies related to gene regulation, plant development, and biotechnological applications aimed at crop improvement.

2026-06-02T00:00:00Z https://repositorio.ufu.br/handle/123456789/50279 2026-09-16T06:21:40Z 2026-08-26T00:00:00Z

Title: Biossíntese e caracterização de nanopartículas de ouro a partir de extratos de flores e folhas de Calliandra dysantha: potencial antioxidante e antibiofilme Abstract: Nanotechnology is a research field that explores materials with nanometric dimensions, integrating knowledge from chemistry, physics, biology, and engineering to develop, manipulate, and apply these materials with unique properties. Among nanomaterials, gold nanoparticles are notable due to their physicochemical, optical, and biological properties. Green synthesis mediated by plant extracts represents a sustainable alternative to traditional physical and chemical methods, as it employs bioactive compounds capable of acting as reducing, stabilizing, and antimicrobial agents. Furthermore, antimicrobial resistance and bacterial biofilm formation represent significant public health challenges, as they hinder the efficacy of conventional treatments and promote infection persistence. Thus, this study aimed to perform, for the first time, the biosynthesis of gold nanoparticles using methanolic extracts from the flowers and leaves of Calliandra dysantha, a species native to the Brazilian Cerrado, as well as to characterize the resulting nanoparticles and evaluate their antioxidant and anti-biofilm potential against Staphylococcus aureus. Therefore, the present study aimed, for the first time, to biosynthesize gold nanoparticles using methanolic extracts of flowers and leaves of Calliandra dysantha, a species native to the Brazilian Cerrado, as well as to characterize these nanoparticles and evaluate their antioxidant and antibiofilm potential against Staphylococcus aureus. Gold nanoparticles were synthesized in aqueous medium using tetrachloroauric acid as the metal precursor under heating and stirring. Nanoparticle formation was confirmed by the presence of absorption bands at approximately 528 nm. Transmission electron microscopy analyses revealed the formation of spherical particles, with an average diameter of 13.8 ± 3.4 nm for gold nanoparticles synthesized with flower extract (F-AuNPs) and 20.8 ± 12.7 nm for those synthesized with leaf extract (L-AuNPs). The mean hydrodynamic diameters were 64.47 ± 10.62 nm for F-AuNPs and 80.55 ± 0.04 nm for L-AuNPs, with low polydispersity indices. Zeta potential values indicated moderate colloidal stability with low aggregation propensity of the dispersions. Assays performed using the DPPH and ABTS methods at concentrations ranging from 125 µg/mL to 15.625 µg/mL demonstrated high antioxidant activity in the plant extracts, suggesting the presence of antioxidant compounds that may contribute to the biosynthesis and stabilization of the nanoparticles. Antioxidant assays using the DPPH and ABTS methods at concentrations ranging from 125 µg/mL to 15.625 µg/mL demonstrated the high antioxidant activity of the plant extracts, suggesting the presence of antioxidant compounds that may contribute to the biosynthesis and stabilization of the nanoparticles. Conversely, the nanoparticles themselves exhibited low antioxidant activity, possibly due to decreased availability of bioactive compounds after synthesis. In the antibiofilm assay, all treatments showed higher activity at a concentration of 125 µg/mL. At this concentration, no significant differences were observed between treatments, indicating that both the extracts and the gold nanoparticles were able to significantly reduce the metabolic viability of Staphylococcus aureus biofilms. Thus, the results demonstrate that flower and leaf extracts of Calliandra dysantha have potential for the biosynthesis of gold nanoparticles with relevant physicochemical characteristics and antioxidant and antibiofilm activities, highlighting their potential for future biotechnological and biomedical applications.

2026-08-26T00:00:00Z https://repositorio.ufu.br/handle/123456789/48372 2026-02-24T06:20:07Z 2025-12-15T00:00:00Z

Title: Inteligência artificial para predição de diabetes mellitus tipo 2 com base em dados sociodemográficos e estilo de vida Abstract: Non-communicable chronic diseases, especially type 2 Diabetes Mellitus (T2DM), represent one of the greatest global public health challenges due to their high prevalence and their economic and social impact. In this context, this study applied Artificial Intelligence (AI) techniques, with an emphasis on Machine Learning (ML), to predict T2DM risk based on sociodemographic, clinical, and lifestyle data. Two public datasets from the Behavioral Risk Factor Surveillance System (BRFSS) were used, comprising 250,360 records and 21 variables. The J48 (C4.5) algorithm was implemented using Weka 3.8.6 software with 10-fold cross-validation. The model achieved an average accuracy of 83.85% for binary classification and 88.84% for multiclass classification. Feature selection identified six most relevant variables: hypertension, high cholesterol, heart disease, excessive alcohol consumption, self-rated health, and difficulty walking. The results demonstrate the potential of AI techniques for the early identification and prevention of T2DM, reinforcing the importance of integrating clinical, nutritional, and behavioral data in the development of predictive models. It is concluded that the application of AI in precision nutrition can optimize monitoring and preventive diagnosis, reducing costs and promoting quality of life.

2025-12-15T00:00:00Z