According to the World Health Organization (WHO), cardiovascular illnesses, including arrhythmia, are the primary cause of mortality globally, responsible for over 31% of all fatalities each year. To reduce mortality, early and precise diagnosis is essential. Although the analysis of electrocardiograms (ECGs) is the primary means of detecting arrhythmias, it depends significantly on the expertise and subjectivity of the health professional reading and interpreting the ECG, and errors may occur in detection. Artificial intelligence provides tools, techniques, and models that can support health professionals in detecting arrhythmias. However, these tools are based only on ECG data, of which the process of obtaining is an invasive, high-cost method requiring specialized equipment and personnel. Smartwatches feature sensors that can record real-time signals indicating the heart’s behavior, such as ECG signals and heart rate. Using this approach, we propose a machine learning- and deep learning-based approach for detecting arrhythmias using heart rate data obtained with smartwatches. Heart rate data were collected from 252 patients with and without arrhythmias who attended a clinic in Arequipa, Peru. Heart rates were also collected from 25 patients who wore smartwatches. Ten machine learning algorithms were implemented to generate the most effective arrhythmia recognition model, with the decision tree algorithm being the most suitable. The results were analyzed using accuracy, sensitivity, and specificity metrics. Using Holter data yielded values of 93.2%, 91.89%, and 94.59%, respectively. Using smartwatch data yielded values of 70.83%, 91.67%, and 50%, respectively. These results indicate that our model can effectively recognize arrhythmias from heart rate data. The high sensitivity score suggests that our model adequately recognizes true positives; that is, patients with arrhythmia. Likewise, its specificity suggests an adequate recognition of false positives.

Background: Birth defects (BD) occur in 3-6% of live births (LB) and constitute significant morbimortality and disability. Some BD of the gastrointestinal (GI) tract requires surgical interventions during the first days of life. Knowing the prevalence in different city areas could guide decision-makers in developing public policies that guarantee timely attention. This study aims to determine the prevalence of 7 GI defects in LB in Bogotá, Colombia, and to explore factors related to their occurrence. Methods: We conducted an analytical cross-sectional observational study based on data from the BD database of the Public Health Surveillance System (PHSS) of Bogotá between 2015 and 2021, using all available records where BD of the GI tract was reported. We performed descriptive analyses in Jamovi and correlation analyses in SPSS. Results: We included 869 cases in the study. GI defects occurred more frequently in males (50.5%). Esophageal atresia was the BD most frequently reported (25.9%), followed by gastroschisis and anorectal malformation (19%). GI defects are more frequent in the city's center-east area of Bogota. The prevalence of GI defects in Bogota during the period studied was 14.2 per 10,000 LB. Of the LB with GI defects, 93.8% had low socioeconomic status. Conclusions: The results of this study point to the importance of centralizing neonatal surgical care services for these pathologies in the sub-network that serves this population. Prenatal care, housing conditions, and substance use in the home were related to a higher prevalence of GI defects.

Introducción: Las lesiones causadas por los accidentes de tránsito son consideradas en la actualidad una epidemia debido a la importante morbimortalidad que se reporta a nivel mundial por esta causa, por lo cual es necesario predecir su comportamiento. Considerando lo anterior, se busca confirmar la capacidad predictiva de una metodología que predice la cifra de fatalidades por lesiones causadas por los accidentes de tránsito aplicada en el contexto del estado de Texas, E.E.U.U, para el año 2015 mediante la caminata al azar probabilista. Métodos: se analizaron las cifras de los reportes anuales entre 1994 a 2014 emitidos por la National Highway Traffic Safety Administration (NHTSA) sobre las fatalidades por lesiones causadas por los accidentes de tránsito en Texas enanalogía con la caminata al azar probabilista para obtener una predicción para el 2015. Resultados: se observó que el comportamiento de esta variable es compatible al analizado con la caminata al azar, lo cual permitió aplicar esta metodología y obtener una predicción para el 2015 con un acierto del 96,3 % con respecto al valor oficial reportado. Conclusiones: la caminata al azar probabilista predice el comportamiento de variables aparentemente aleatorias en el tiempo con precisiones elevadas, lo cual permite su aplicación como herramienta de vigilancia en salud pública al evaluarcomplementariamente la efectividad de las intervenciones para reducir la mortalidad por las lesiones causadas por los accidentes de tránsito.

The normal and abnormal behavior of an adult heart dynamics and its state of evolution towards one of these two states has been characterized successfully in the context of the theory of dynamic systems and probability. The diagnostic methodology of clinical application designed under these two theories has managed to evaluate in an objective and reproducible way the cardiac dynamics from the values of the frequency of the Holter registers. The automation of this methodology through the design of a software that can be docked in any operating system for PC, and contributes as a diagnostic aid tool to generate more timely responses to the patient's clinical condition. Additionally, the values of the probability of these spaces occupied by the attractor, calculated by the Software, allow using an interface that can be consulted by the specialist to evaluate how far a cardiac dynamic is from normality, analyzing in this way the effectiveness of the treatment.

La enfermedad por el virus de la inmunodeficiencia humana (VIH) es un problema de magnitud mundial, que ha impuesto retos para la biología molecular, epidemiología y otras especialidades médicas, por la ausencia de tratamientos que curen la enfermedad y de vacunas que prevengan su desarrollo. Estas dificultades han requerido del abordaje multidisciplinar de las ciencias para entender aspectos claves tanto de la evolución de la enfermedad como de las potenciales consideraciones diagnósticas, terapéuticas y pronósticas. Las investigaciones básicas y clínicas han sugerido un rol importante de la vigilancia de marcadores como los linfocitos CD4+, no solamente porque son diana natural del virus e indican la evolución de la infección, sino porque se ha planteado que este marcador tiene implicaciones pronósticas para las personas infectadas. El propósito principal de este libro consiste en describir la aplicación de las teorías físicas y matemáticas que ha desarrollado el grupo Insight, en particular la teoría de la probabilidad y la teoría de conjuntos, para predecir el comportamiento de los linfocitos CD4+ en personas que viven con el VIH, considerando que es uno de los biomarcadores medidos con los cuales se evalúa la progresión de la enfermedad, entre otras. Para lograr lo anterior, se reseñarán generalidades de la enfermedad y el desarrollo de la inmunología así como los fundamentos matemáticos y físicos que sustentan el desarrollo y aplicabilidad de estas metodologías.