ESS - TBIO - Centro de Investigação em Saúde Translacional e Biotecnologia Médica
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- Acceptance of industrial collaborative robots: Preliminar results of appliction of portuguese version of the Frankenstein Syndrome Questionnaire (FSQ)Publication . Pinto, Ana; Ferraz, Mariana; Nomura, Tatsuya; Santos, JoanaCobots are highly flexible and able to operate in the same workspace and at the same time with the worker. The use of these technologies allows for increased production performance while ensuring comfort and confidence for the worker. Robot acceptance is still a controversial topic with various approaches and methods to measure acceptance of humanoid robots. This study aimed to evaluate cobots acceptance after a motor assembly task in a collaborative workstation. 30 university students were divided into two groups, with group 1 having read the assembly instructions before the usage of the assembly workstation and group 2 without having any previous knowledge about the car engine. All participants completed the Portuguese version of the Frankenstein Syndrome Questionnaire (FSQ). Data analyses were carried out using descriptive and inferential statistics using IBM SPSS Statistics software, version 28.0. One correlations was found between the scales of the FSQ (p < 0.05). It was possible to conclude that the acceptance of robots by the participants in group 1 and group 2 was the same. This study can contribute to understanding which factors explain the acceptance of collaborative robots, to improve human-robot intercation.
- Advancing the understanding of pupil size variation in occupational safety and health: A systematic review and evaluation of open-source methodologiesPublication . Ferreira, Filipa; Ferreira, Simão; Mateus, Catarina; Rocha, Nuno; Coelho, Luís; Rodrigues, MatildePupil size can be used as an important biomarker for occupational risks. In recent years, there has been an increase in the development of open-source tools dedicated to obtaining and measuring pupil diameter. However, it remains undetermined determined whether these tools are suitable for use in occupational settings. This study explores the significance of pupil size variation as a biomarker for occupational risks and evaluates existing open-source methods for potential use in both research and occupational settings, with the goal of to prevent occupational accidents and improve the health and performance of workers. To this end, a two-phase systematic literature review was conducted in the Web of Science™, ScienceDirect®, and Scopus® databases. For the relevance of monitoring pupil size variation in occupational settings, 15 articles were included. The articles were divided into three groups: mental workload, occupational stress, and mental fatigue. In most cases, pupil dilation increased with workload enhancement and with higher levels of stress. Regarding fatigue, it was noted that an increase in this condition corresponded with a decrease in pupil size. With respect to the open-source methodologies, 16 articles were identified, which were categorized into two groups: algorithms and software. Convolutional neural networks (CNN)1 have exhibited superior performance among the various algorithmic approaches studied. Building on this insight, and considering the evaluations of software options, MEYE emerges as the premier open-source system for deployment in occupational settings due to its compatibility with a standard computer webcam. This feature positions MEYE as a particularly practical tool for workers in stable environments, like those of developers and administrators.
- Affinity coefficient for clustering autoregressive moving average modelsPublication . Nascimento, Ana Paula; Oliveira, Alexandra; Faria, Brígida Mónica; Pimenta, Rui; Vieira, Mónica; Prudêncio, Cristina; Nicolau, Helena BacelarIn various fields, such as economics, finance, bioinformatics, geology, and medicine, namely, in the cases of electroencephalogram, electrocardiogram, and biotechnology, cluster analysis of time series is necessary. The first step in cluster applications is to establish a similarity/dissimilarity coefficient between time series. This article introduces an extension of the affinity coefficient for the autoregressive expansions of the invertible autoregressive moving average models to measure their similarity between them. An application of the affinity coefficient between time series was developed and implemented in R. Cluster analysis is performed with the corresponding distance for the estimated simulated autoregressive moving average of order one. The primary findings indicate that processes with similar forecast functions are grouped (in the same cluster) as expected concerning the affinity coefficient. It was also possible to conclude that this affinity coefficient is very sensitive to the behavior changes of the forecast functions: processes with small different forecast functions appear to be well separated in different clusters. Moreover, if the two processes have at least an infinite number of π- weights with a symmetric signal, the affinity value is also symmetric.
- An integration of intelligent approaches and economic criteria for predictive analytics of occupational accidentsPublication . Gholamizadeh, Kamran; Zarei, Esmaeil; Yazdi, Mohammad; Rodrigues, Matilde A.; Shirmohammadi-Khorram, Nasrin; Mohammadfam, IrajOccupational accidents are a significant concern, resulting in human suffering, economic crises, and social issues. Despite ongoing efforts to comprehend their causes and predict their occurrences, the use of machine learning models in this domain remains limited. This study aims to address this gap by investigating intelligent approaches that incorporate economic criteria to predict occupational accidents. Four machine learning algorithms, Random Forest (RF), Support Vector Machine (SVM), Multivariate Adaptive Regression Spline (MARS), and M5 Tree Model (M5), were employed to predict occupational accidents, considering three economic criteria: basic income (BI), inflation index (II), and price index (PI). The study focuses on identifying the most suitable model for predicting the frequency of occupational accidents (FOA) and determining the economic criteria with the greatest influence. The results reveal that the RF model accurately predicts accidents across all income levels. Additionally, among the economic criteria, II had the most significant impact on accidents. The findings suggest that a reduction in FOA is unlikely in the coming years due to the increasing growth of II and PI, coupled with a slight annual increase in BI. Implementing appropriate countermeasures to enhance workers’ economic welfare, particularly for low-income employees, is crucial for reducing occupational accidents. This research underscores the potential of machine learning models in predicting and preventing occupational accidents while highlighting the critical role of economic factors. It contributes valuable insights for scholars, practitioners, and policymakers to develop effective strategies and interventions to improve workplace safety and workers’ economic well-being
- An unobtrusive multimodal stress detection model & recommender systemPublication . Ferreira, Simão; Correia, Hugo; Rodrigues, Fátima; Rodrigues, Matilde; Rocha, NunoStudies estimate that about 50% of all lost workdays are related to occupational stress. In recent years, several solutions for mental health management, including biofeedback applications, have emerged as a way to enhance employee mental health. Solutions to mitigate risk factors related to the working settings present an enormous potential and a clear contribution. However, most of the work that has been developed is limited to laboratory environments and does not suit real-life needs. Our study proposes an unobtrusive multimodal approach for detecting work-related stress combining videoplethysmography and self-reported measures for stablishing the ground truth in real-life settings. The study involved 28 volunteers over a two-month period. Various physiological signals were collected through a videopletismography solution, while users were performing daily working, for approximately eight hours a day. In parallel, selfreported measures were collected via a pop-up application (developed by the research team) that periodically retrieved the user's perceived stress (amongst other variables) in order to label the physiological data. In order to develop the stress detection model, we pre-processed the data and performed Heart Rate Variability (HRV) feature extraction. Then, we experimented with several machine learning models, utilizing both individual and combined physiological signals to explore all available alternatives. After rigorous evaluation, the best-trained model achieved an accuracy of over 80% and an F1 Score of over 85%. With the stress detection model in place, we are developing a structured intervention model to help reduce stress. This intervention model integrates two interconnected dimensions through digital coaching, which prioritizes personalized recommendations based on user preferences. Our top priority is to ensure user engagement, and we believe that adherence to and adoption of recommended interventions are more likely when users receive recommendations that align with their preferences. Thus, we prioritize personalized recommendations that are tailored to each individual's unique model. After detecting immediate stress peaks and providing real-time feedback on stress levels, our alarm system goes a step further by offering customized recommendations for brief stress relief. The digital coach (intervention model) offers various recommendations and active lifestyle changes such as exercise, task management, weight management, better sleep habits, structured pauses, and other critical interventions. These critical interventions are also based on user preferences, allowing our system to prevent future stress-related incidents and, most importantly, mitigate long-term stress. This project and its methodology demonstrate that truly unobtrusive stress detection is possible and can be performed within the scope of ethical demands. In future work, we will evaluate the responses and beneficial outcomes of implementing a recommender system.
- Antimicrobial activity of food-isolated fungi extractsPublication . Ferreira, Diogo; Areal-Hermida, Lara; Baylina, Pilar; Fernandes, Rúben; Sieiro, CarmenOne major source for drug discovery are microbial metabolites. Fungi, renowned for their ability to produce an array of broad and diverse secondary metabolites, due to their extensive dispersion and diversity, offer a rich resource for drug discovery. Antibiotic resistance is a major concern. Rapid increase of resistant bacteria worldwide, dampens antibiotic efficiency, burdens healthcare services and increase morbidity and mortality. Antibiotic misuse and lack of new drug development are the main responsible for this health crisis. So, the creation of fungal libraries to find and study new compounds is essential to tackle the rising of antimicrobial resistance and continue with industrial efforts of drug discovery and production. Isolation from chestnuts, chestnut flour and sunflower seeds allowed us to obtain a collection of 165 fungal isolates. Bioactivity of fungal extracts were screened against different antibiotic resistant bacteria. Bacteria grown overnight, adjusted to 1.5 x 108 CFU/mL was exposed to fungal extracts, at a concentration of 100 μg/mL for 24 hours and inhibition rates were calculated. Several extracts showed activity against antimicrobial resistant bacteria and further studies should be made in order to find if new molecules could be responsible for our fungi antimicrobial activity.
- Application of CytoPath®easy vials in Cervical Cancer screening: Self‑sampling approachPublication . Fernandes, Sílvia P. M.; Vilarinho, Ana Sofia; Frutuoso, Amaro; Teixeira, Cidália; Silva, Regina Augusta A. P."CytoPath®Easy kit (DiaPath S.p.A.) offers a major advantage compared to other commercially available kits available for the screening of cervical cancer, as it does not require additional equipment for sample processing. Using this methodology, collected epithelial cells are immersed in a preservative liquid before setting as a thin layer on a slide via gravity sedimentation. Aims: To evaluate the suitability of the CytoPath®Easy kit for the processing of cervicalsamples, detection of pre‑neoplastic lesions, and nucleic preservation and extraction for HR‑HPV diagnosis. A total of 242 self‑sampled cervicalspecimens were utilized, with 192 collected in CytoPath®Easy vials and 50 collected and processed using the ThinPrepTM for comparative analysis. The samples underwent processing, Papanicolaou staining, and microscopic evaluation for morphological parameters. The extracted nucleic acids were assessed for purity and integrity, and the detection of high‑risk human papillomavirus (HR‑HPV) was carried out using the Alinitym HR HPV system kit (Abbott Laboratórios Lda). Both methods demonstrated effective performance, enabling the morphological assessment of the cervical epithelium. Statistical analysis indicated that ThinPrepTM yielded significantly better results in terms of cellularity. Conversely, CytoPath®Easy exhibited superior performance in terms of the quantity of extracted DNA and its degree of purification. Concerning the time consumed during processing, both methods were comparable, with the CytoPath®Easy methodology standing out for its cost‑effectiveness, as it does not necessitate additional instruments and consumables. The novel CytoPath®Easy methodology proves effective in preserving both nucleic acids and cell morphology characteristics, two crucial features for cervical cancer screening."
- Avaliação do potencial antimicrobiano de fungos filamentosos: Um estudo promissor na abordagem do pé diabético e da resistência bacterianaPublication . Ferreira, D.; BAYLINA MACHADO, PILAR; Sá, S.; Areal Hermida, L.; Rocha, A. C.; Baylina, Pilar; Fernandes, R.; Sieiro, C.Prevê-se que até 2045, cerca de 700 milhões de pessoas possam ser afetadas pela Diabetes Mellitus (DM). Uma complicação frequente em indivíduos com DM é o pé diabético, que se manifesta por feridas nos pés causadas por danos nos nervos e vasos sanguíneos, resultando frequentemente na amputação dos membros inferiores. Esta situação é agravada por infeções bacterianas, causadas por estirpes resistentes, como Staphylococcus aureus resistente à meticilina (MRSA), Pseudomonas aeruginosa e Klebsiella spp. produtoras de beta-lactamases de espectro estendido (ESBL), dificultando o tratamento destas lesões. A resistência antibiótica, impulsionada pelo uso excessivo e indiscriminado de antibióticos, destaca a necessidade urgente de novos fármacos e terapias mais eficazes. Neste contexto, os fungos apresentam-se como uma fonte promissora de novos agentes antimicrobianos, devido à vasta gama e diversidade de compostos bioativos que conseguem sintetizar.
- Avaliação do potenicial antimicrobiano do extrato de Gnomoniopsis sp. contra agentes infeciosos do pé diabéticoPublication . Rocha, A. C.; Areal Hermida, L.; Baylina, Pilar; Fernandes, R.; Sieiro, C.; BAYLINA MACHADO, PILAREstima-se que até ao ano de 2045, aproximadamente 700 milhões de pessoas sofram de Diabetes mellitus (DM). O pé diabético é uma complicação comum em pacientes com DM e caracteriza-se por lesões nos pés devido a danos nos nervos e vasos sanguíneos, levando muitas vezes à amputação dos membros inferiores. A infeção causada por bactérias resistentes, como Staphyloccus aureus resistente a meticilina (MRSA), Pseudomonas aeruginosa e espécies de Klebsiella beta-lactamases de espetro estendido (ESBL), acentuam a gravidade destas lesões, tornando o seu tratamento mais complexo. A resistência a antibióticos resulta do uso exagerado e indiscriminado de antibióticos e o desenvolvimento de medicamentos inovadores e de terapias mais eficazes é urgente. Assim, os fungos, nomeadamente fungos filamentosos, surgem como um potencial reservatório para novos compostos antimicrobianos, devido à grande quantidade e diversidade de compostos bioativos produzidos por estes organismos.
- Betulinic acid for glioblastoma treatment: Reality, challenges and perspectivesPublication . Fernandes, Sílvia; Vieira, Mariana; Prudêncio, Cristina; Ferraz, RicardoBetulinic acid is a naturally occurring compound that can be obtained through methanolic or ethanolic extraction from plant sources, as well as through chemical synthesis or microbial biotransformation. Betulinic acid has been investigated for its potential therapeutic properties, and exhibits anti-inflammatory, antiviral, antimalarial, and antioxidant activities. Notably, its ability to cross the blood–brain barrier addresses a significant challenge in treating neurological pathologies. This review aims to compile information about the impact of betulinic acid as an antitumor agent, particularly in the context of glioblastoma. Importantly, betulinic acid demonstrates selective antitumor activity against glioblastoma cells by inhibiting proliferation and inducing apoptosis, consistent with observations in other cancer types. Compelling evidence published highlights the acid’s therapeutic action in suppressing the Akt/NFκB-p65 signaling cascade and enhancing the cytotoxic effects of the chemotherapeutic agent temozolomide. Interesting findings with betulinic acid also suggest a focus on researching the reduction of glioblastoma’s invasiveness and aggressiveness profile. This involves modulation of extracellular matrix components, remodeling of the cytoskeleton, and secretion of proteolytic proteins. Drawing from a comprehensive review, we conclude that betulinic acid formulations as nanoparticles and/or ionic liquids are promising drug delivery approaches with the potential for translation into clinical applications for the treatment and management of glioblastoma.