In an increasingly complex and competitive global environment, developing scientific thinking and research skills in university students has become a crucial component of higher education. This article highlights the importance of fostering these skills to train future innovators and problem solvers who can address scientific and societal challenges. We present a case study describing the implementation of a research stay experience designed for university students from various disciplines, offering them hands-on research experience in real-world scientific projects. At the initiative's core is an educational platform de-signed to cultivate complex thinking through the lens of scientific entrepreneurship. This platform integrates SDG-based learning modules and project-based activities to encourage students to de-velop original ideas, test hypotheses, and apply scientific methods in a dynamic learning environment. By linking theoretical knowl-edge with practical research experience, students are encouraged to conduct critical inquiry, collaborate with peers and mentors, and hone their problem-solving skills. In addition, the platform emphasizes scientific entrepreneurship, allowing students to apply their research to real-world challenges and explore the potential commercialization of their ideas. Preliminary data from the program's evaluations indicate that students who participate in this experience show a marked improvement in their ability to think critically, approach problems from multiple perspectives, and design innovative solutions. In addition, many students report being more confident about pursuing graduate studies or careers in research-intensive fields. This article contributes to the growing literature that underscores the need for early and ongoing exposure to scientific research in higher education. It offers valuable insights to educators, academic institutions, and policymakers who wish to implement or improve research-based learning programs in their curricula.

Alzheimer’s disease (AD) is one of the most common neurodegenerative disorders, affecting more than 50 million people worldwide. Current detection methods are often inefficient and inaccessible or invasive and inadequate for the early stages, as pathological biomarkers have not been developed. This study introduces a novel method to characterize hippocampal local field potentials (LFPs) to aid in the early detection of AD. We used fractal dimension (FD) analysis to process LFP recordings of the hippocampus region of animal models, recorded in both basal and kainic acid-induced active states. The LFP signals were classified using time-series clustering to identify the existence of more than one trend. The FD values of different states of an LFP in an AD model were analyzed. The results show that the transition phase triggers fluctuations in the FD value despite the basal- and active-state values remaining consistent. The findings of this analysis provide a promising basis for the development of a digital biomarker for conditions that disrupt normal brain behavior, such as AD. This approach could potentially improve our understanding of these diseases and contribute to more effective diagnostic tools.

The global coronavirus disease 2019 pandemic (COVID-19) has profoundly affected the world, impacting not only public health and sanitation, but also government policies. Countries have had to implement new rules and regulations to manage the disease, leading to debates about the effectiveness of these measures in curbing the spread of the virus or the potential to inadvertently exacerbating a global crisis. The objective is to analyze the effectiveness of Latin American countries’ public health strategies in mitigating the impact of the pandemic. To this end, data on the measures implemented by these countries and their impact on infection rates are examined. The analysis considers the suitability of these strategies to the specific sociocultural and economic realities of each region, with a focus on techniques such as time series analysis and clustering. The databases used in this study are from Our World in Data, an open-access public data repository. The results suggest a correlation between the reduction in preventive measures against the spread of COVID-19 and the subsequent increase in cases, possibly due to the ease of restrictions that resulted in an increase in infections or other variables not included in the analysis.

As diabetes management becomes more complicated, there is an increasing interest in understanding how to manage diabetes with physical activity. Our study aimed to investigate the role of wearable, non-invasive technologies in collecting data related to physical activity to model them via artificial intelligence methods for efficient diabetes management. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (also known as PRISMA) protocol and searched three databases, namely PubMed, Scopus, and Web of Science. Out of 960 titles, we included 32 in the full-text analysis. Results showed two main methods were used for the analysis, i.e., statistical and classification modeling. Results indicate among the employed regression methods, linear regression was used more than other methods, and the most common classification-based method for analyzing data was the Artificial Neural Network method. Assessing the quality of papers that used the classification method was done through Prediction model Risk Of Bias Assessment Tool (also known as PROBAST) and Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (also known as TRIPOD) tools. Based on PROBAST outcomes, although the risk of bias was low in most of the works, explaining the analyzing method specifically, the method of handling missing data needs more attention. Upon evaluating papers using the TRIPOD, it realized that there is a need to place emphasis on improving the quality of the presentation and explanation of the result. According to our review, the conjunction of non-invasive technologies and artificial intelligence is promising in managing diabetic risk factors for real-time monitoring of physical activities, enabling regular clinical intervention and optimized medical treatment.