2024 - FAROOQ, SAJID; ZEZELL, DENISE M.

Tropical diseases present significant challenges to global health, particularly in resource-limited regions. Early and accurate detection of these diseases is vital for effective management and control. In recent years, metallic-based LSPR sensors have emerged as promising diagnostic tools for sensitive and rapid detection of tropical diseases. This comprehensive review aims to provide an in-depth analysis of the current state of research on metallic-based LSPR sensors for the detection of various tropical diseases. In this study, we focused on the connection between neglected tropical diseases (NTDs) and its risk using metallic-based LSPR sensors to identify potential inflammatory biomarkers. We conducted a literature search using PubMed, Web of Science, and Google Scholar. Only published materials written in English were considered, resulting in the identification of 220 articles. After a comprehensive evaluation, we selected 35 relevant ones. Our analysis revealed 35 links to neglected tropical diseases, providing valuable insights into their relationship using metallic-based LSPR sensors. Moreover, we explore the potential of metallic-based LSPR sensors in point-of-care testing and their integration with emerging technologies such as microfluidics and smartphone-based diagnostics. This review underscores the need for continued research efforts to develop affordable, sensitive, and user-friendly metallic-based LSPR sensors for early detection and surveillance of tropical diseases.

2024 - GODELI, J.; CUNHA, D.M.; POTIENS, M.P.A.; POLETTI, M.E.

The mean glandular dose (MGD) is the most suitable dosimetric quantity used in mammography to describe the absorbed dose by the breast, although it cannot be directly acquired. Studies have provided conversion factors widely implemented in international dosimetry protocols to estimate MGD, such as normalized glandular dose (DgN). Over time, the DgN estimation was refined by considering geometric models that approach a real clinical environment, such as new anode/filter combinations, compression plate and breast models. However, there is no detailed study of how the bucky (support plate, antiscatter grid and detector) can affect the DgN estimation. A modified PENELOPE Monte Carlo code was used for DgN estimation. The irradiation geometric model was built as a complete digital mammography system, considering a homogeneous breast and different typical bucky models in commercial mammography units. Simulations were carried out for mono and polyenergetic beams considering different imaging geometries. Studies with monoenergetic beams showed that the bucky presence affected DgN mainly for higher beam energies and thinner breasts. The breast support plate was the bucky component that most affected the DgN, followed by the antis-scatter grid and finally, the image detector. Studies with polyenergetic conventional (low-energy) spectra showed that the bucky exerted a minimal influence on DgN values (less than 1.0%). For high-energy spectra, mainly employed in modalities such as contrast-enhanced digital mammography, the DgN values were more affected by the bucky, increasing by 4.8% the DgN values for a 2 cm thick breast and a W/Cu 50 kV spectrum. Bucky inclusion in computer simulations is highly recommended mainly for thinner breasts and high-energy spectra. To simplify the simulations, we confirm that a homogeneous carbon fibre block support, with thickness between 3.9 and 4.1 mm, can be used as a good substitute for a complete bucky model.

2024 - DRIEMEIER, LARISSA; CABRAL, EDUARDO L.L.; RODRIGUES, GABRIEL L.; TSUZUKI, MARCOS; ALVES, MARCILIO; COSTA, LUCAS P. da; MOURA, RAFAEL T.

In scenarios where complex analyses are routinely conducted on similar structures, such as in a redesign process to meet performance requirements or when input parameters require frequent adjustments within a specified domain, a practical approach involves the use of metamodels calibrated using machine learning methodologies. In our investigation, we introduce a metamodel that utilizes an artificial neural network to analyze 3D nonlinear structures undergoing plastic deformations and large strains. Snap-through and snapback behaviors are addressed through network training, which is based on 10,000 Force vs Displacement curves (target outputs) obtained from nonlinear finite element analyses. This interplay between finite element analysis and machine learning, as demonstrated here, exhibits promising potential as an effective technique. The results indicate that the proposed deep neural network can learn from the simulations of finite elements. The discussion explores scenarios where the utilization of AI in the analysis of nonlinear structures is justified.

2024 - IZIDORO, JULIANA de C.; BOTELHO JUNIOR, AMILTON B.; MURACAMI, HENRY T.; BERTOLINI, THARCILA C.R.; RODRIGUES, REBECA P.C.; SILVA, KATIA; ORTEGA, MIGUEL G.C.; FUNGARO, DENISE A.; ESPINOSA, DENISE C.R.; TENORIO, JORGE A.S.

The search for a sustainable society makes necessary the reuse of residues contributing to the producing high-value product. Producing new materials with high-added value increases technological development and builds up new applications. The present study aimed to use residue from the alumina industry and coal ash generated in thermal plants in energy production to synthesize zeolite for wastewater treatment to remove Cs and Pb. Two types of nanomaterials were synthesized: zeolite 4A (ZEA) and zeolite sodalite (ZSD). The Cs adsorption efficiency achieved 73% and 9.4% for ZEA and ZSD, respectively, fitting better for Lineweaver-Burk isotherm and both zeolites removed 100% of Pb from synthetic solutions. Results here reported may be used to design novel wastewater treatment systems from nuclear plants and other industrial processes. The present study can contribute to achieving Sustainable Development Goals 9, 11, and 12.