Preclinical evaluations of glycaemic control algorithms often rely on simulators that underestimate real-world metabolic variability, leading to overly optimistic performance assessments. A novel type 1 diabetes (T1D) simulation framework, DT1-UAN v2, was developed to generate physiologically realistic and adaptive scenarios for an improved assessment of insulin therapies. DT1-UAN v2 integrates two disturbances of intrapatient variability: eight stochastically parameterised insulin sensitivity (SI) patterns and sixty data-driven profiles for the rate of blood glucose appearance (Ra). Incorporating these features exposed vulnerabilities in insulin therapies that previously relied on static physiological parameters. In silico trials on virtual T1D subjects, under both openand closed-loop therapies, revealed contrasts between outcomes obtained with DT1-UAN v2 and the earlier DT1UAN v1. One particular test showed that a well-known closed-loop control therapy produced inferior performances (-17.57% in TIR or + 121.78% in TAR) under a set of realistic and altered conditions defined in v2 compared to v1. Control strategies that appeared effective in non-realistic scenarios often produced different results in the enhanced framework, uncovering underestimated risks of post-prandial hypoglycaemia or sustained hyperglycaemia. These findings demonstrate that incorporating realistic SI and Ra variability is essential for rigorous preclinical testing, as simplified models can conceal clinically relevant risks. The proposed framework provides a valuable tool for identifying hidden design flaws and enabling the development of more adaptive, and safer control strategies for people with T1D. This enhanced realism provides a critical foundation for testing and optimising personalised insulin treatments.
This study evaluated the psychometric properties of the RS-14 Resilience Scale in Colombian victims of armed conflict and forced displacement. A confirmatory factor analysis (CFA) using diagonally weighted least squares estimation was conducted on a sample of 613 victims of violence aged 18 to 65 years. The results indicated high levels of resilience (M = 78.40) and showed that, after removing items 5, 7, and 10, the unidimensional model (Model 5), controlling for age and sex, demonstrated a significantly better fit compared to the other models (CFI = .89, IFI = .89, GFI = .98, AGFI = .98, RNI = .89, RMSEA = .08, SRMR = .04). The refined model also showed high internal consistency (α = .87, ω = .87). These findings suggest that the RS-14, under an adjusted unidimensional structure, is a valid and reliable instrument for assessing resilience in populations exposed to prolonged violence and extreme trauma.
Introduction: Lung cancer is one of the main causes of morbidity and mortality worldwide and high-intensity interval training (HIIT) and moderate-intensity continuous training (MICT) have emerged as exercise modalities with the potential to improve lung cancer. cardiopulmonary capacity. Materials and methods: Randomized clinical trial with 79 participants with lung cancer who were randomized into 3 groups (MICT-HIIT-Control group) and subsequently underwent cardiovascular, pulmonary, metabolic tests and questionnaires, among others. The interventions lasted 36 weeks and after that, Kolmogorov-Smirnov, Tukey tests and analysis of variance (two-way ANOVA) were performed, having a significance level of 5% (p < 0.05). Results: Estimated VO2 levels (GE1:13±1.3 vs 16.4±1.2; GE2:13.3±0.5 vs 17.7±1.3; GC:13.5±3.2 vs 13.6±2.2; p= <0.05), meters traveled (GE1: 215±35 vs 260±36; GE2: 230±36 vs 329±21; GC: 218±43 vs 215±21; p= <0.05) left ventricular ejection fraction (GE1:39±3.7 vs 42±2.5; p= 0.023) and maximum heart rate (GE1:155±4 vs 163±5; p= 0.001) of experimental group 1 and experimental group 2 (FE: 40±2.8 vs 45±3.1; p= 0.023; FCM: 156±12 vs 168 ± 2; p= 0.001) showed a significant increase; but not in the control group (p= >0.05). Conclusions: The MICT and HIIT group improved all the variables evaluated. And HIIT training was superior compared to MICT group and control group. The latter being where the participants did not present significant changes or improvements in the evaluated variables.
El estrés académico y la fatiga son fenómenos capaces de impactar el rendimiento, la salud y el bienestar de los estudiantes adscritos a programas de formación militar. El abordaje de ambas variables es limitado en contextos de formación militar. El objetivo de este trabajo es realizar un análisis alrededor del estado del conocimiento sobre el estrés académico y la fatiga en militares en formación, buscando identificar asociaciones relevantes, vacíos de investigación y oportunidades para el desarrollo de estrategias de intervención. Se realizó una revisión bibliográfica en la base de datos Scopus, considerando artículos publicados entre los años 2019 y 2024, en inglés y español, con criterios específicos para cada concepto. Dentro de los resultados se identificaron relaciones entre el estrés académico y el cansancio emocional, la inteligencia emocional, los estilos de aprendizaje, el rendimiento académico, los hábitos de estudio, el estado nutricional y la calidad del sueño; los estudios en contextos de formación militar son escasos, predominando investigaciones en escenarios universitarios de carácter civil. Alrededor de la fatiga en militares, la literatura se concentra en escenarios operativos y de entrenamiento de personal activo, siendo particularmente limitados los estudios realizados en entornos académicos y de formación militar. Los hallazgos evidencian la necesidad de ampliar la investigación sobre el estrés académico y la fatiga en contextos de formación militar, integrando enfoques que aborden sus dimensiones físicas, académicas y psicosociales, lo cual podrá dotar de herramientas necesarias para la construcción de programas de seguimiento y afrontamiento orientadas al bienestar de los futuros militares.
This paper presents the development and evaluation of an educational software simulator designed to enhance student understanding of key concepts in Signals and Systems. The simulator incorporates modules for generating and analyzing basic signals, Fourier series, Fourier transforms, convolution, and voice signal analysis. Developed using Python, the open-source nature of the software ensures accessibility and compatibility across multiple platforms, allowing students to use the tool outside of traditional classroom environments without incurring additional costs. The interactive nature of the software allows real-time visualization of complex mathematical transformations, aiding in the comprehension of abstract concepts. Feedback from students highlights the effectiveness of the tool in improving their understanding of convolution and Fourier series, as well as its accessibility compared to more expensive alternatives. However, areas for future development include expanding content coverage to advanced topics like the Laplace transform and improving the feedback provided during exercises. Overall, the simulator serves as a valuable resource for both students and instructors, offering a flexible, user-friendly solution for learning and teaching signals and systems.
