Revista Tecnología y Ciencias del Agua (TyCA) del Instituto Mexicano de Tecnología del Agua (IMTA)

Laryngo-pharyngeal mechano-sensitivity (LPMS) is involved in dysphagia, sleep apnea, stroke, irritable larynx syndrome and cough hypersensitivity syndrome among other disorders. These conditions are associated with a wide range of airway reflex abnormalities. However, the current device for exploring LPMS is limited because it assesses only the laryngeal adductor reflex during fiber-optic endoscopic evaluations of swallowing and requires a high degree of expertise to obtain reliable results, introducing intrinsic expert variability and subjectivity. We designed, developed and validated a new air-pulse laryngo-pharyngeal endoscopic esthesiometer with a built-in laser range-finder (LPEER) based on the evaluation and control of air-pulse variability determinants and on intrinsic observer variability and subjectivity determinants of the distance, angle and site of stimulus impact. The LPEER was designed to be capable of delivering precise and accurate stimuli with a wide range of intensities that can explore most laryngo-pharyngeal reflexes. We initially explored the potential factors affecting the reliability of LPMS tests and included these factors in a multiple linear regression model. The following factors significantly affected the precision and accuracy of the test (P < 0.001): the tube conducting the air-pulses, the supply pressure of the system, the duration of the air-pulses, and the distance and angle between the end of the tube conducting the air-pulses and the site of impact. To control all of these factors, an LPEER consisting of an air-pulse generator and an endoscopic laser range-finder was designed and manufactured. We assessed the precision and accuracy of the LPEER’s stimulus and range-finder according to the coefficient of variation (CV) and by looking at the differences between the measured properties and the desired values, and we performed a pilot validation on ten human subjects. The air-pulses and range-finder exhibited good precision and accuracy (CV < 0.06), with differences between the desired and measured properties at <3 % and a range-finder measurement error of <1 mm. The tests in patients demonstrated obtainable and reproducible thresholds for the laryngeal adductor, cough and gag reflexes. The new LPEER was capable of delivering precise and accurate stimuli for exploring laryngo-pharyngeal reflexes.

Obstructive sleep apnea (OSA) is a common sleep disorder characterized by repetitive upper airway obstruction, intermittent hypoxemia, and recurrent awakenings during sleep. The most used treatment for this syndrome is a device that generates a positive airway pressure—Continuous Positive Airway Pressure (CPAP), but it works continuously, whether or not there is apnea. An alternative consists on systems that detect apnea episodes and produce a stimulus that eliminates them. Article focuses on the development of a simple and autonomous processing system for the detection of obstructive sleep apneas, using polysomnography (PSG) signals: electroencephalography (EEG), electromyography (EMG), respiratory effort (RE), respiratory flow (RF), and oxygen saturation (SO₂). The system is evaluated using, as a gold standard, 20 PSG tests labeled by sleep experts and it performs two analyses. A first analysis detects awake/sleep stages and is based on the accumulated amplitude in a channel-dependent frequency range, according to the criteria of the American Academy of Sleep Medicine (AASM). The second analysis detects hypopneas and apneas, based on analysis of the breathing cycle and oxygen saturation. The results show a good estimation of sleep events, where for 75% of the cases of patients analyzed it is possible to determine the awake/asleep states with an effectiveness of >92% and apneas and hypopneas with an effectiveness of >55%, through a simple processing system that could be implemented in an electronic device to be used in possible OSA treatments.

Objective: To review the technologies, biomarkers and processing algorithms used in diagnosing depression, anxiety, and stress, informing future research endeavors to enhance healthcare and the well-being of individuals affected by these mental disorders. Methods: A systematic review was conducted through searches in electronic databases such as PubMed, Google Scholar, IEEE, Nature, ProQuest, and Science Direct. Search queries combined terms related to physiological signals, behavioral variables, electronic devices, mobile applications, and the disorders of depression, anxiety, and stress. After screening 292 initial records, 77 studies met specific criteria, which included discussion of quantitative results and advanced processing algorithms. Results: The review of 77 articles revealed an increasing use of electronic devices and applications for measuring physiological and behavioral variables in the diagnosis of mental disorders. The wrist was the most common device location, accounting for 53.2%, primarily utilizing smartwatches to monitor heart rate, electrodermal activity, and sleep patterns. The analyzed technologies included wearable sensors, facial recognition cameras, electroencephalographs, and virtual reality devices. The classification algorithms used—such as decision trees, neural networks, and support vector machines—achieved accuracy rates ranging from 75% to 90%, highlighting the effectiveness of these tools. However, limitations were identified regarding the generalizability of results and the need for personalized diagnostic models. Conclusion: Electronic devices and mobile applications represent a significant advancement in the detection and monitoring of depression, anxiety, and stress by providing objective data continuously and in real time. However, their clinical application still faces challenges related to accuracy, personalization, and user acceptance. For these technologies to be effectively integrated into clinical practice, it is recommended to conduct studies in real-world settings and foster collaboration with mental health professionals. Such efforts would enable the adaptation of diagnostic models to individual needs and enhance the accuracy of early interventions.

STEM (Science, Technology, Engineering, and Mathematics) is essential for the development of 21st-century skills, particularly in a world driven by technological innovation. However, in vulnerable school contexts, access to meaningful STEM experiences remains limited. This study addresses this issue through the design and implementation of a didactic strategy in a public high school in Bogotá, Colombia, based on two educational resources: the BioSen electronic board, which is compatible with Arduino technology and designed to acquire physiological signals such as electrocardiography (ECG), electromyography (EMG), electrooculography (EOG), and body temperature; and the Space Exploration instructional guide, which is organized around contextualized learning missions. This study employed a quasi-experimental mixed-methods design that combined pre–post perception questionnaires, unstructured classroom observations, and a contextualized knowledge test administered to three student groups. Findings demonstrate that after eight weeks of implementation with upper secondary students, the strategy had a positive impact on the development of 21st-century skills, such as creativity, computational thinking, and critical thinking. These skills were assessed through a mixed quasi-experimental design combining perception questionnaires, qualitative observations, and knowledge evaluations. Unlike the control groups, students who participated in the intervention adjusted their self-perceptions when facing real-world challenges and showed progress in the application of key competencies. Overall, the results support the effectiveness of integrating low-cost biomedical tools with gamified STEM instruction to enhance higher-order thinking skills and student engagement in vulnerable educational contexts.