Produção Científica e Didática
Produção científica recente
Consumption of ultra-processed foods is associated with cognitive status in elderly patients
Background: Emerging evidence suggests that there might be an association between excess consumption of ultra-processed foods (UPFs) on cognitive health. UPF intake could promote systemic inflammation, oxidative stress phenomena, and metabolic dysregulation, contributing to neurodegeneration onset and cognitive decline in elderly population.Aim: The aim of this cross-sectional study was to examine the relation between UPF dietary pattern on MCI status in elderly patients taking into account the contribution of inflammatory markers.Design: The dietary intake was assessed using a validated food frequency questionnaire in ninety-two participants. All reported food items were categorized according to the NOVA system, classifying foods on the basis of the extent and purpose of industrial processing. Plasmatic concentrations of TGF-β1 and TNF-ɑ were measured by ELISA assay at the time of baseline neuropsychological evaluation. The Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) were administered to evaluate the cognitive function in all participants. Non-parametric tests, correlation analysis, and logistic regression models were performed to assess the relations between variables of interest.Results: No significant associations were observed for unprocessed/minimally processed foods, culinary processed foods, or processed foods across the different regression models. In contrast, higher consumption of UPF was associated with increased odds of MCI (adjusted OR = 4.24, 95% CI: 1.05–17.13). However, after additional adjustment for inflammatory biomarkers (TGF-β and TNF-α), the association was attenuated and no longer statistically significant (OR = 4.79, 95% CI: 0.73–31.24), although the direction of the association remained positive.Conclusion: UPF consumption may be associated with increased likelihood of MCI, and inflammatory status may potentially play a role in this association.
The legal governance of ai upskilling and reskilling: towards a new employer duty
This article examines whether the rapid adoption of artificial intelligence (AI) justifies a broader interpretation of employer responsibilities regarding continuous upskilling and reskilling. An interdisciplinary qualitative methodology was employed, combining doctrinal-comparative legal analysis with a systematic literature review conducted according to the PRISMA 2020 guidelines. Scientific evidence was collected from Scopus, Web of Science, ScienceDirect and SpringerLink, complemented by reports from the International Labour Organization (ILO), the Organisation for Economic Co-operation and Development (OECD) and the World Economic Forum (WEF). The findings indicate that AI-driven workforce transformation extends beyond technological innovation and significantly affects employability, requiring organisations to anticipate skills disruption through strategic workforce development. The study identifies a conceptual gap between labour law, strategic human resource management and corporate governance concerning employer responsibility for workforce capability development. To address this gap, it proposes the AI Employability Governance Framework (AEGF), an original model integrating strategic workforce foresight, continuous upskilling, reskilling pathways, shared employer responsibility, labour rights protection and sustainable employability. The framework contributes to the emerging debate on responsible AI governance by providing a conceptual foundation for more inclusive, resilient and legally informed models of workforce transformation.
Infrared thermography to assess fatigue, injury risk factors and recovery in soccer: a systematic review of original studies
Background: Recovery after a training session or match is a key factor in injury prevention and sports performance. The purpose of this systematic review was to analyze and consolidate the available scientific evidence from the main databases on the use of infrared thermography in the assessment of fatigue, injury risk factors, and recovery in soccer players.Methods: The literature search was conducted following the PRISMA guidelines and the PICOS model until June 30, 2025, in the main scientific databases (ScienceDirect, EMBASE, Web of Science (WOS), Cochrane Library, SciELO, MEDLINE/PubMed, SPORTDiscus, and Scopus). The risk of bias and methodological quality were assessed using the Cochrane Handbook guidelines and the PEDro scale.”Results: The initial literature search yielded a total of 510 records. After applying the inclusion and exclusion criteria, the final sample consisted of 20 studies, which were of high methodological quality. The results showed the effects of infrared thermography in assessing fatigue, identifying injury risk factors, and monitoring recovery processes in soccer players. The studies also systematically reported the characterization of the population, the assessment methods used, the variables analyzed, the methodological design, the main results, and the effects of the intervention.Conclusions: Infrared thermography shows promise as a valid, reliable, and non-invasive tool for assessing skin temperature, reflecting temperature changes in response to physiological processes. It allows for the analysis of structural or metabolic fatigue and thermal asymmetries. Therefore, thermography could be used to design individualized recovery protocols.
Advanced Wafer Hotspot Detection through Image Segmentation and Stacked Model
The wafer map is a data visualization of a thin semiconductor fabric made of crystalline silicon, such as defects or test results. The wafer map is a base for creating electronic coordinate circuits and photovoltaic cells. During the wafer map production, any fault results in a product failure. The wafer map faults are undetectable to the naked eye, which is a big challenge. Hotspot detection in wafer maps is significantly important to evaluate the manufacturing process and. improve product yield. The hotspot detection in the wafer maps is the primary aim of this research. A novel wafer map hotspot detector (WHD) is proposed based on three stack fully connected conventional neural network layers and a dense layer. Data augmentation uses the segmented images of the wafers to build the proposed model. The proposed model is evaluated through several evalua-tion parameters and state-of-the-art studies comparative analysis. The proposed model achieved a 94% training and 90% testing performance accuracy for hotspot detection and shows better results than existing approaches. This study helps semiconductor engineers improve wafer manufacturing designs and efficiency in the semiconductor industry.
Liquorice alters adipocyte–breast cancer cell crosstalk by modulating oxidative stress and suppressing aromatase and renin–angiotensin signalling
Obesity is recognised to be a risk factor for breast cancer since adipose tissue influences the tumour microenvironment. This study aims to investigate the effect of the secretome of 3T3-L1 adipocytes untreated or treated with liquorice root extract (LRE), containing flavonoids, phenolic acids, and saponins on MCF-7 breast cancer cells. By treating adipocytes with LRE, the secretion of certain pro-tumorigenic factors like IGFBP-6, resistin, and VEGF was reduced. MCF-7 cells exposed to conditioned medium from LRE-treated adipocytes exhibited an increase in reactive oxygen species levels, downregulation of the Nrf2 antioxidant pathway, and increased autophagy. Those conditions reduced cell viability, migration, and colony formation. Additionally, there was downregulation of genes associated with oestrogen signalling and tumour-related processes, including CYP19A1 (aromatase), ERα, Her2, and components of the renin–angiotensin system (RAS). These findings suggest that LRE can modulate the adipocyte secretome to influence breast cancer cell behaviour under obesity-related in vitro conditions.