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Microfluidics has emerged as a versatile technology that is applied to enhance the performance of analytical techniques, among others. Pursuing this, we present a capillary-driven microfluidic device that improves the sensitivity of lateral flow immunoassay rapid tests thanks to offering an automated washing step. A novel multilevel microfluidic chip was 3D-printed with a photocurable black resin, sealed by an optically clear pressure-sensitive adhesive, and linked to the lateral flow strip. To depict the efficacy of microfluidics and the washing step, cortisol was measured quantitatively within the proposed device. Measuring cortisol levels is a way to capture physiological stress responses. Among biofluids, saliva is less infectious and easier to sample than others. However, higher sensitivity is demanded because the salivary cortisol concentrations are much lower than in blood. We carried out a competitive lateral flow immunoassay protocol with the difference that the microfluidic device applies an automated washing step after the sample is drained downstream. It washes the trapped quantum-dot-labeled antibodies out from nitrocellulose, diminishing background noise as these are bonded to cortisols and not to the immobilized receptors. Fluorescence spectroscopy, as a high-precision analysis, was successfully applied to determine clinically relevant salivary cortisol concentrations within a buffer quantitatively. The microfluidic design relied on a 3D valve that avoids reagent cross-contamination. This cross-contamination could make the washing buffer impure and undesirably dilute the sample. The proposed device is cost-effective, self-powered, robust, and ideal for non-expert users.

Artificial intelligence has impacted education in recent years. Datafication of education has allowed developing automated methods to detect patterns in extensive collections of educational data to estimate unknown information and behavior about the students. This research has focused on finding accurate predictive models to identify at-risk students. This challenge may reduce the students’ risk of failure or disengage by decreasing the time lag between identification and the real at-risk state. The contribution of this paper is threefold. First, an in-depth analysis of a predictive model to detect at-risk students is performed. This model has been tested using data available in an institutional data mart where curated data from six semesters are available, and a method to obtain the best classifier and training set is proposed. Second, a method to determine a threshold for evaluating the quality of the predictive model is established. Third, an early warning system has been developed and tested in a real educational setting being accurate and useful for its purpose to detect at-risk students in online higher education. The stakeholders (i.e., students and teachers) can analyze the information through different dashboards, and teachers can also send early feedback as an intervention mechanism to mitigate at-risk situations. The system has been evaluated on two undergraduate courses where results shown a high accuracy to correctly detect at-risk students.

The north region of Brazil is characterized by significant vulnerabilities, notably surpassing national poverty indicators. These disparities exacerbated the impact of respiratory illnesses on the healthcare system during the COVID-19 pandemic, particularly in areas with limited healthcare resources, inadequate infrastructure, and barriers to healthcare access. The crisis was further influenced by multiple lineages that emerged as significant virus variants associated with increased transmissibility. Within this context, our ecological study focused on analyzing the epidemiological evolution of COVID-19 in the state of Acre. We constructed time-series trends in incidence, lethality, and mortality from March 2020 to December 2022 using the Prais–Winsten regression model. Our findings revealed that in 2020, there was an increasing trend in incidence, while mortality and lethality continued to decrease (p < 0.05). In the following year, both incidence and mortality decreased, while lethality increased at a rate of 1.02% per day. By the end of 2022, trends remained stationary across all rates. These results underscore the importance of ongoing surveillance and adaptive public health measures to bolster the resilience of healthcare systems in remote and vulnerable regions. Indeed, continuous monitoring of the most predominant SARS-CoV-2 lineages and their dynamics is imperative. Such proactive actions are essential for addressing emerging challenges and ensuring effective responses to adverse situations.

Northeast Brazil is a region with great international tourist potential. Among the states that make up this region, Paraíba stands out due to the presence of vulnerable groups and factors that contribute to adverse outcomes of COVID-19. Therefore, the aim of this study was to analyze the epidemiological data on the incidence, mortality, and case fatality of COVID-19 in Paraíba. An ecological, population-based study was performed, with data extracted from the Brazilian Ministry of Health database. All cases and deaths from COVID-19 from March 2020 to December 2022 were included. The time series was built by applying the Prais–Winsten regression model, and the daily percent change was calculated to analyze the trends. The highest case fatality of the entire period was in April 2020 (7.8%), but in March 2021, the state broke the dismal record of 1248 deaths and the highest mortality rate (30.5 deaths per 100,000 inhabitants). Stationary mortality and case fatality were better in 2022; however, in February 2022, the mortality rate was at levels similar to the same month of the previous year. These results illustrate that COVID-19 is evolving and needs to be constantly monitored.

Learning-by-doing is a pedagogical approach that helps in learning skills through practice. An online learning-by-doing tool, CodeLab, has been introduced to students undertaking the digital design and creation bachelor’s degree program at the Universitat Oberta de Catalunya. The tool has been used to facilitate and engage students, not well-acquainted with problem-solving techniques, in an introductory programming course. The aim of this study was to examine the factors that play vital roles in students’ acceptance of learning-by-doing tools that facilitate the development of problem-solving skills. The Unified Theory of Acceptance and the Use of Technology (UTAUT) model was used for this purpose and extended by adding the factor of motivation, which is essential for educational contexts. The results highlight that there is a strong relationship between acceptance and motivation, implying that students would use online learning-by-doing tools, such as CodeLab, depending on the amount of motivation and engagement while practicing the learning activities. A positive relationship between motivation and acceptance clearly supports the primary aim of using learning-by-doing tools in problem-solving courses.

Learning analytics is quickly evolving. Old fashioned dashboards with descriptive information and trends about what happened in the past are slightly substituted by new dashboards with forecasting information and predicting relevant outcomes about learning. Artificial intelligence is aiding this revolution. The accessibility to computational resources has increased, and specific tools and packages for integrating artificial intelligence techniques leverage such new analytical tools. However, it is crucial to develop trustworthy systems, especially in education where skepticism about their application is due to the risk of teachers’ replacement. However, artificial intelligence systems should be seen as companions to empower teachers during the teaching and learning process. During the past years, the Universitat Oberta de Catalunya has advanced developing a data mart where all data about learners and campus utilization are stored for research purposes. The extensive collection of these educational data has been used to build a trustworthy early warning system whose infrastructure is introduced in this paper. The infrastructure supports such a trustworthy system built with artificial intelligence procedures to detect at-risk learners early on in order to help them to pass the course. To assess the system’s trustworthiness, we carried out an evaluation on the basis of the seven requirements of the European Assessment List for trustworthy artificial intelligence (ALTAI) guidelines that recognize an artificial intelligence system as a trustworthy one. Results show that it is feasible to build a trustworthy system wherein all seven ALTAI requirements are considered at once from the very beginning during the design phase.