Nowadays, different serious game projects help the teaching-learning process of Object Oriented Programming (OOP) or Software Engineering (SE) concepts, but none of these projects are doing an integration of both fields for knowledge to children. The research presented in this paper proposes a serious game to help comprehension of basic concepts and attributes of OOP and SE to promote teaching-learning process of these concepts for children older than eight years old through Android devices.

Mental disorders like depression represent 28% of global disability. It affects around 7.5% percent of global disability. Depression is a common disorder that affects the state of mind, normal activities, emotions, and produces sleep disorders. It is estimated that approximately 50% of depressive patients suffering from sleep disturbances. In this paper, a data mining process to classify depressive and not depressive episodes during nighttime is carried out based on a formal method of data mining called Knowledge Discovery in Databases (KDD). KDD guides the process of data mining with stages well established: Pre-KDD, Selection, Pre-processing, Transformation, Data Mining, Evaluation, and Post-KDD. The dataset used for the classification is the DEPRESJON dataset, which contains the motor activity of 23 unipolar and bipolar depressed patients and 32 healthy controls. The classification is carried out with two different approaches; a multivariate and univariate analysis to classify depressive and non-depressive episodes. For the multivariate analysis, the Random Forest algorithm is implemented with a model construct of 8 features and, results of the classification are; specificity equal to 0.9927 and sensitivity equal to 0.9991. The univariate analysis shows that the maximum in time of the activity is the most descriptive characteristic of the model with 0.908 in accuracy for the classification of depressive episodes.

Heavy metal contamination of foods remains a persistent global challenge for food safety and public health, driven by industrialization, mining activities, intensive agriculture, and ongoing environmental degradation. This scoping review synthesizes peer-reviewed literature on the occurrence of priority toxic metals—arsenic, cadmium, lead, mercury, and nickel—in food matrices, with emphasis on contamination pathways, analytical detection strategies, and documented human health effects. The reviewed studies reveal widespread accumulation of heavy metals in staple foods, including cereals, vegetables, seafood, and processed products, with concentrations frequently approaching or exceeding international regulatory limits, particularly in regions exposed to strong anthropogenic pressure. Conventional laboratory-based techniques, such as atomic absorption spectrometry and inductively coupled plasma methods, remain the reference standards for quantitative determination and regulatory compliance; however, their application to large-scale or continuous monitoring is often constrained by cost, infrastructure, and operational complexity. Consequently, increasing attention has been directed toward emerging detection approaches, including portable X-Ray fluorescence, Raman/SERS spectroscopy, electrochemical biosensors, electronic tongues, and in situ magnetic measurements, as complementary tools for rapid screening and field-based surveillance. Among these, environmental magnetism and in situ magnetic techniques stand out as non-destructive, low-cost proxies capable of identifying metal-associated particulate contamination linked to food production systems. Chronic dietary exposure to heavy metals is consistently associated with neurotoxicity, nephrotoxicity, carcinogenicity, and oxidative stress, underscoring the need for integrated, multi-tiered monitoring frameworks to support early detection, risk assessment, and prevention.

The impact of the COVID-19 health crisis on the mental health of the population requires the implementation of new primary screening strategies of mental health disorders to intervene in a timelier manner, and technology may provide solutions. We aimed to evaluate the usefulness of the mobile app Mentali (version 1.1.2; creators: Jorge Alfonso Solís Galván Sodel Vázquez Reyes, Margarita de la Luz Martínez Fierro, Perla Velasco Elizondo, Idalia Garza Veloz, Alejandro Mauricio González and Claudia Caldera Villalobos, Zacatecas, México) as a primary screening tool for anxiety and depression disorders in medical students and to assess the triggering risk factors. This was a descriptive and longitudinal study and included 155 Mexican medical students. Participants interacted with Mentali for 6 months. The mobile app integrated the Beck anxiety and depression inventories together with a mood module. At the end of the interaction, the students received psychological and psychiatric interventions to confirm their primary diagnoses. Symptoms of moderate/severe anxiety and depression were present in 62.6% and 54.6% of the studied population. When corroborating the diagnoses, Mentali obtained a sensitivity of 100%, 95%, and 43% to classify a mental health disorder, anxiety, and depression, respectively. The most important triggers found were as follows: belonging to a dysfunctional family, being introverted, and having suffered from bullying. The proportion of users with excellent/good mood decreased from 78.7% to 34.4% at the end of the semester, and the proportion of users who claimed to have bad/very bad mood increased from 7.4% to 34.4% at the end of the semester (p < 0.05). Mentali was useful for identifying users with anxiety and/or depression, and as an auxiliary tool to coordinate the provision of specialized interventions, allowing us to increase the proportion of patients who needed psychological care and received it by 30%. The efficacy of Mentali in identifying activities through time with an impact on the mood and mental health of the users was confirmed. Our results support the use of Mentali for the primary screening of mental health disorders in young adults, including medical students.