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Effectiveness of Teaching Mini Handball through Non-Linear Pedagogy in Different Socioeconomic Contexts: A Pilot Study
(2022) Espoz Lazo, Sebastián Ignacio; Farías Valenzuela, Claudio; Reyes Contreras, Víctor; Ferrero Hernández, Paloma; Giakoni Ramírez, Frano; Tapia Zavala, Mauricio; Duclos Bastías, Daniel; Valdivia Moral, Pedro
Mini handball is among the sports included as part of school physical education in Chileto improve children’s motor skills and to motivate their adherence to a healthy and active lifestylein response to concerns about this country’s high level of childhood obesity. To this end, non-linearpedagogy (NLP) has been used to develop motor skills through mini handball in the school context.However, socioeconomic differences that influence the development of children’s motor skills havenot been considered to determine whether the methodology applies to everyone. The aim of thepresent observational study is to describe and compare the effectiveness of the previously appliedNLP methodology in two contrasting socioeconomic contexts to determine whether it helps todevelop motor skills through mini handball in both school contexts. The Levine test was usedto determine the homogeneity of the variances (p < 0.05), as the distribution of the data was notnormal. The Kruskal–Wallis H statistical test was used to analyse within-group data. Additionally,the Mann–Whitney U test was applied for comparisons between groups. The results show significantimprovements in the acquisition of the expected motor skills specific to mini handball. Additionally,a shortening of the gap was evidenced between the groups during the training process, with nosignificant differences at the end of the progression. Therefore, the investigated NLP is equally aseffective for schoolchildren in two opposite socioeconomic contexts.
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Climate Change, Health, and Migration in LAC
(Springer Cham, 2025) Batista, Carolina; Borjas-Cavero, Diego B.; Farante, Sofia Virginia; Melo Contreras, Óscar; Lescano, Andrés G.
This chapter examines the interconnectedness of climate change, health, and migration in Latin America and the Caribbean (LAC). A comprehensive literature review identified severe and disproportionate consequences of climate change on the health of migrants, with major impacts on vulnerable mobile groups such as indigenous peoples, children, women, and the LGBTI+ community. We analyzed the consequences of infectious diseases, such as vector-borne and neglected diseases, as well as non-communicable diseases and mental health. The findings highlight the urgent need to generate evidence on climate-induced migration at sub-regional and national level and to address the vulnerability of marginalized groups that contribute the least to greenhouse gas emissions, a matter of climate justice. Additionally, there is a need for further research on the health impacts of climate change on migrants in LAC, including those that migrate for non-climate-related reasons. These knowledge and action gaps underscore the importance of designing tailored health policies that ensure to reduce the vulnerability of migrants to health threats.
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End-to-end deep learning-based motion correction and reconstruction for accelerated whole-heart joint T 1/T 2 mapping.
(2025) Felsner, Lina; Velasco, Carlos; Phair, Andrew; Fletcher, Thomas J.; Qi, Haikun; Botnar, René Michael; Prieto Vásquez, Claudia
PURPOSE: To accelerate 3D whole-heart joint T 1/T 2 mapping for myocardial tissue characterization using an end-to-end deep learning algorithm for joint motion estimation and model-based motion-corrected reconstruction of multi-contrast undersampled data.METHODS: A free-breathing high-resolution motion-compensated 3D joint T 1/T 2 water/fat sequence is employed. The sequence consists of the acquisition of four interleaved volumes with 2-echo encoding, resulting in eight volumes with different contrasts. An end-to-end non-rigid motion-corrected reconstruction network is used to estimate high quality motion-corrected reconstructions from the eight multi-contrast undersampled data for subsequent joint T 1/T 2 mapping. Reconstruction with the proposed approach was compared against state-of-the-art motion-corrected HD-PROST reconstruction.RESULTS: The proposed approach yields images with good visual agreement compared to the reference reconstructions. The comparison of the quantitative values in the T 1 and T 2 maps showed the absence of systematic errors, and a small bias of -6.35 ms and -1.8 ms, respectively. The proposed reconstruction time was 24 seconds in comparison to 2.5 hours with motion-corrected HD-PROST, resulting in a reconstruction speed-up of over 370 times.CONCLUSION: In conclusion, this study presents a promising method for efficient whole-heart myocardial tissue characterization. Specifically, the research highlights the potential of the multi-contrast end-to-end deep learning algorithm for joint motion estimation and model-based motion-corrected reconstruction of multi-contrast undersampled data. The findings underscore its ability to compute T 1 and T 2 values with good agreement when compared to the reference motion-corrected HD-PROST method, while substantially reducing reconstruction time.
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Myokine Secretion Dynamics and Their Role in Critically Ill Patients: A Scoping Review
(2025) Jalil Contreras, Yorschua Frederick; Damiani Rebolledo, L. Felipe; García Valdés, Patricio; Basoalto Escobar, Roque Ignacio; Gallastegui Guajardo, Julen Manuel; Gutiérrez Arias, Ruvistay
Background/Objectives: Myokines can modulate organ function and metabolism, offering a protective profile against ICU complications beyond preventing local muscle wasting. This scoping review aims to explore and summarize the evidence regarding the secretion of myokines and their potential local or systemic effects in critically ill patients. Methods: A scoping review following Joana Briggs Institute recommendations was conducted. A systematic search of MEDLINE (Ovid), Embase (Ovid), CENTRAL, CINAHL (EBSCOhost), WoS, and Scopus was conducted from inception to February 2023. We included primary studies evaluating myokine secretion/concentration in critically ill adults undergoing physical rehabilitation interventions. Two independent reviewers performed study selection and data extraction. Results: Seventeen studies published between 2012 and 2023 were included. Most were randomized clinical trials (47%). Physical rehabilitation interventions included electrical muscle stimulation, as well as passive and active mobilization, delivered alone or combined, in single or daily sessions lasting 20–60 min. Twelve studies (70%) evaluated interleukin-6, while interleukin-10, tumour necrosis factor-α, Interleukin-8, and myostatin were also commonly studied. Thirteen studies (76%) reported changes in myokine secretion or gene expression, although no clear concentration change pattern emerged. Myokines involved in muscle protein synthesis and breakdown may protect against muscle waste and weakness. Conclusions: The study of myokine dynamics in critically ill patients highlights the systemic impact of physical rehabilitation. This emerging field has grown in interest over the past decade, offering significant research potential. However, challenges such as study design, small sample sizes, and variability in physical therapy protocols hinder a comprehensive understanding of myokine responses.
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AI and Data Analytics in the Dairy Farms: A Scoping Review
(MDPI, 2025) Palma, Osvaldo; Plà-Aragonés, Lluis M.; Mac Cawley Vergara, Alejandro Francisco; Albornoz, Víctor M.
The strong growth of the world population will cause a major increase in demand for bovine milk, making it necessary to use various technologies to increase milk production efficiently. Some technologies that can contribute to solving part of this problem are those related to data analytics tools, big data, and sensor development. It is timely to review modern technologies and data analytics methods for milk predictions in view of supporting decision-making in dairy farms. To this end, a scoping review was carried out, which resulted in 151 articles of interest. Among the most important results, we found that (i) the identified studies are relatively recent with an average publication time of 5.95 years; (ii) the scope of the selected studies is mostly concentrated on milk and prediction (29%), early detection of lameness (26%), and timely detection of mastitis (13%); (iii) the type of analysis is mostly predictive (87%), and prescriptive is barely present (3%); (iv) the types of input data used in the studies are preferably historical (70%), and real-time data (25%) are used less frequently; (v) we found that the method of artificial neural networks (47%) and the convolutional neural networks (24%) are the most used for the studies regarding bovine milk output predictions. In the selected studies, the artificial neural network methods have considerable accuracy, recall, precision, and F1 Scores on average but with high ranges and standard deviations. (vi) Simulation tools are scarcely used, being present in 4% of cases. In the treatment of variability, the models reviewed are mostly deterministic (77%), and the stochastic models (5%) are considered in a small number of cases. Based on our analysis, we conclude that future research on decision-making tools will benefit from the advantages of artificial neural networks in data analytics combined with optimization–simulation methods.