https://repositorio.ufu.br/handle/123456789/20866 2026-09-18T03:09:36Z 2026-09-18T03:09:36Z 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 https://repositorio.ufu.br/handle/123456789/48089 2026-03-03T19:38:32Z 2025-12-18T00:00:00Z

Title: Aplicação de algoritmos de machine learning para análise e predição de desfechos clínicos em pacientes com covid-19 Abstract: The COVID-19 pandemic imposed substantial challenges on healthcare systems worldwide, particularly due to the rapid clinical deterioration observed in many patients and the urgent need to allocate ventilatory support and intensive care resources efficiently, highlighting a gap in objective tools capable of assisting early risk stratification and outcome prediction. In this context, Machine Learning (ML) models, such as decision trees, offer the potential to support clinical decision-making by analyzing multiple variables simultaneously and generating easily interpretable predictive structures. This study aimed to apply ML algorithms to clinical data from hospitalized patients with COVID-19 to predict relevant clinical outcomes and identify factors associated with disease severity. This retrospective, quantitative study used secondary data from the public Severe Acute Respiratory Infection (SARI) database of Londrina, Paraná, Brazil, which initially contained 15,655 records of hospitalized patients between January 2021 and February 2022. After data cleaning and preprocessing, 5,704 records were used for the outcome prediction model and 7,182 for complementary analyses. Data were analyzed using the WEKA software (v.3.8.6) with the J48 decision tree algorithm and cross-validation, following attribute selection and removal of redundant or incomplete variables. The generated models achieved approximately 80% accuracy. Type of ventilatory support emerged as the most relevant predictor across analyses, followed by ICU admission, preexisting heart disease, age, and hospital type. Results showed that invasive mechanical ventilation and ICU admission were strongly associated with mortality, whereas non-invasive ventilation or absence of ventilatory support were associated with recovery. Advanced age substantially increased mortality risk among patients receiving invasive ventilation, and hospital type influenced outcomes among those not admitted to the ICU, with higher mortality in public hospitals. The study concludes that decision tree models are effective for identifying predictors of severity in hospitalized COVID-19 patients, providing clinically interpretable structures with potential application for risk stratification, resource allocation, and management of critical care pathways, especially in contexts of healthcare overload such as that experienced during the pandemic.

2025-12-18T00:00:00Z