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Complementary feeding (CF) is defined as the feeding of infants that complements breastfeeding, or alternatively, feeding with a breast milk substitute, and is a process that is more than simply a guide as to what and how to introduce foods. The information provided by healthcare professionals must be up-to-date and evidence-based. Most of the recommendations that appear in the different international guidelines and position papers are widely applicable, but some must be regionalized or adapted to fit the conditions and reality of each geographic zone. The Nutrition Working Group of the Latin American Society for Pediatric Gastroenterology, Hepatology and Nutrition (LASPGHAN) summoned a group of experts from each of the society’s member countries, to develop a consensus on CF, incorporating, whenever possible, local information adapted to the reality of the region. The aim of the present document is to show the results of that endeavor. Utilizing the Delphi method, a total of 34 statements on relevant aspects of CF were evaluated, discussed, and voted upon. Resumen: La alimentación complementaria (AC) se define como la alimentación de los lactantes que complementa a la lactancia humana o en su defecto, a la lactancia con un sucedáneo de la leche humana, y es un proceso que va más allá de simplemente una guía sobre qué y cómo introducir los alimentos. La información brindada por parte de los profesionales de la salud debe ser actualizada y basada en evidencia. Existen diferentes guías o documentos de posición a nivel internacional, que, aunque la mayoría de las recomendaciones pueden ser aplicables, hay algunas otras que requieren una regionalización o adecuación a las condiciones y realidad de cada zona. El grupo de trabajo de Nutrición de la Sociedad Latinoamericana de Gastroenterología, Hepatología y Nutrición Pediátrica convocó a un grupo de expertos, representantes de cada uno de los países que conforman la sociedad, con el objetivo de desarrollar un consenso sobre AC, que incorporó cuando así fue posible, información local que se adapte a la realidad de la región. El objetivo de este documento es mostrar los resultados de dicho trabajo. A través de metodología Delphi, se evaluaron, discutieron y votaron un total de 34 declaraciones o enunciados con respecto a aspectos relevantes de la AC.

Resumen: La alimentación complementaria (AC) se define como la alimentación de los lactantes que complementa a la lactancia materna o en su defecto, a la lactancia con un sucedáneo de la leche materna, y es un proceso que va más allá de simplemente una guía sobre qué y cómo introducir los alimentos. La información brindada por parte de los profesionales de la salud debe ser actualizada y basada en evidencia. Existen diferentes guías o documentos de posición a nivel internacional, que, aunque la mayoría de las recomendaciones pueden ser aplicables, hay algunas otras que requieren una regionalización o adecuación a las condiciones y realidad de cada zona. El grupo de trabajo de Nutrición de la Sociedad Latinoamericana de Gastroenterología, Hepatología y Nutrición Pediátrica convocó a un grupo de expertos, representantes de cada uno de los países que conforman la sociedad, con el objetivo de desarrollar un consenso sobre la AC, que incorporó cuando así fue posible, información local que se adapte a la realidad de la región. El objetivo de este documento es mostrar los resultados de dicho trabajo. A través de metodología Delphi, se evaluaron, discutieron y votaron un total de 34 declaraciones o enunciados con respecto a aspectos relevantes de la AC. Abstract: Complementary feeding (CF) is defined as the feeding of infants that complements breastfeeding, or alternatively, feeding with a breast milk substitute, and is a process that is more than simply a guide as to what and how to introduce foods. The information provided by healthcare professionals must be up-to-date and evidence-based. Most of the recommendations that appear in the different international guidelines and position papers are widely applicable, but some must be regionalized or adapted to fit the conditions and reality of each geographic zone. The Nutrition Working Group of the Latin American Society for Pediatric Gastroenterology, Hepatology and Nutrition (LASPGHAN) summoned a group of experts from each of the society's member countries, to develop a consensus on CF, incorporating, whenever possible, local information adapted to the reality of the region. The aim of the present document is to show the results of that endeavor. Utilizing the Delphi method, a total of 34 statements on relevant aspects of CF were evaluated, discussed, and voted upon.

Objective High-dimensional databases make it difficult to apply traditional learning algorithms to biomedical applications. Recent developments in computer technology have introduced deep learning (DL) as a potential solution to these difficulties. This study presents a novel intelligent decision support system based on a novel interpretation of data formalisation from tabular data in DL techniques. Once defined, it is used to diagnose the severity of obstructive sleep apnoea, distinguishing between moderate to severe and mild/no cases. Methods The study uses a complete database extract from electronic health records of 2472 patients, including anthropometric data, habits, medications, comorbidities, and patient-reported symptoms. The novelty of this methodology lies in the initial processing of the patients’ data, which is formalised into images. These images are then used as input to train a convolutional neural network (CNN), which acts as the inference engine of the system. Results The initial tests of the system were performed on a set of 247 samples from the Pulmonary Department of the Álvaro Cunqueiro Hospital in Vigo (Galicia, Spain), with an AUC value of ≈ 0.8. Conclusions This study demonstrates the benefits of an intelligent decision support system based on a novel data formalisation approach that allows the use of advanced DL techniques starting from tabular data. In this way, the ability of CNNs to recognise complex patterns using visual elements such as gradients and contrasts can be exploited. This approach effectively addresses the challenges of analysing large amounts of tabular data and reduces common problems such as bias and variance, resulting in improved diagnostic accuracy.

Scale-free brain activity, linked with learning, the integration of different time scales, and the formation of mental models, is correlated with a metastable cognitive basis. The spectral slope, a key aspect of scale-free dynamics, was proposed as a potential indicator to distinguish between different sleep stages. Studies suggest that brain networks maintain a consistent scale-free structure across wakefulness, anesthesia, and recovery. Although differences in anesthetic sensitivity between the sexes are recognized, these variations are not evident in clinical electroencephalographic recordings of the cortex. Recently, changes in the slope of the power law exponent of neural activity were found to correlate with changes in Rényi entropy, an extended concept of Shannon's information entropy. These findings establish quantifiers as a promising tool for the study of scale-free dynamics in the brain. Our study presents a novel visual representation called the Rényi entropy-complexity causality space, which encapsulates complexity, permutation entropy, and the Rényi parameter q. The main goal of this study is to define this space for classical dynamical systems within theoretical bounds. In addition, the study aims to investigate how well different time series mimicking scale-free activity can be discriminated. Finally, this tool is used to detect dynamic features in intracranial electroencephalography (iEEG) signals. To achieve these goals, the study implementse the Bandt and Pompe method for ordinal patterns. In this process, each signal is associated with a probability distribution, and the causal measures of Rényi entropy and complexity are computed based on the parameter q. This method is a valuable tool for analyzing simulated time series. It effectively distinguishes elements of correlated noise and provides a straightforward means of examining differences in behaviors, characteristics, and classifications. For the iEEG experimental data, the REM state showed a greater number of significant sex-based differences, while the supramarginal gyrus region showed the most variation across different modes and analyzes. Exploring scale-free brain activity with this framework could provide valuable insights into cognition and neurological disorders. The results may have implications for understanding differences in brain function between the sexes and their possible relevance to neurological disorders.

The use of digital technologies in teaching determines the development of new intellectual skills in students, which facilitates the application of investigation strategies, improves their capabilities in an integral way, helps them face the difficulties and demands of the current world and allows them to acquire more significant learning to have a good performance in the socio-educational environment. With a view to fulfilling the above, an institutional repository of open educational resources was developed for the Virtual Health University, based on the use of free and open-source technology tools that allow the standardization and interoperability among each of the repositories established in the National Health System, which will guarantee logic of academic organization.

Long COVID is a condition that affects a significant proportion of patients who have had COVID-19. It is characterised by the persistence of associated symptoms after the acute phase of the illness has subsided. Although several studies have investigated the risk factors associated with long COVID, identifying which patients will experience long-term symptoms remains a complex task. Among the various symptoms, dyspnea is one of the most prominent due to its close association with the respiratory nature of COVID-19 and its disabling consequences. This work proposes a new intelligent clinical decision support system to predict dyspnea 12 months after a severe episode of COVID-19 based on the SeguiCovid database from the Álvaro Cunqueiro Hospital in Vigo (Galicia, Spain). The database is initially processed using a CART-type decision tree to identify the variables with the highest predictive power. Based on these variables, a cascade of expert systems has been defined with Mamdani-type fuzzy-inference engines. The rules for each system were generated using the Wang-Mendel automatic rule generation algorithm. At the output of the cascade, a risk indicator is obtained, which allows for the categorisation of patients into two groups: those with dyspnea and those without dyspnea at 12 months. This simplifies follow-up and the performance of studies aimed at those patients at risk. The system has produced satisfactory results in initial tests, supported by an AUC of 0.75, demonstrating the potential and usefulness of this tool in clinical practice.

This research focuses on the study of the virtual community of fans of the procedural video game No Man's Sky and how the contributions of the wiki fandom are projected in the official updates published until February 2024. Using a scientific methodology based on the content analysis of user interactions, we propose an exploratory study consisting of a virtual ethnography that identifies the audio content generated that has led to changes in the production of the updates of the video game analysed, thus establishing an effective correlation with the technological evolution of the title in question. [ABSTRACT FROM AUTHOR]

Scale-free brain activity, linked with learning, the integration of different time scales, and the formation of mental models, is correlated with a metastable cognitive basis. The spectral slope, a key aspect of scale-free dynamics, was proposed as a potential indicator to distinguish between different sleep stages. Studies suggest that brain networks maintain a consistent scale-free structure across wakefulness, anesthesia, and recovery. Although differences in anesthetic sensitivity between the sexes are recognized, these variations are not evident in clinical electroencephalographic recordings of the cortex. Recently, changes in the slope of the power law exponent of neural activity were found to correlate with changes in Rényi entropy, an extended concept of Shannon's information entropy. These findings establish quantifiers as a promising tool for the study of scale-free dynamics in the brain. Our study presents a novel visual representation called the Rényi entropy-complexity causality space, which encapsulates complexity, permutation entropy, and the Rényi parameter q. The main goal of this study is to define this space for classical dynamical systems within theoretical bounds. In addition, the study aims to investigate how well different time series mimicking scale-free activity can be discriminated. Finally, this tool is used to detect dynamic features in intracranial electroencephalography (iEEG) signals. To achieve these goals, the study implementse the Bandt and Pompe method for ordinal patterns. In this process, each signal is associated with a probability distribution, and the causal measures of Rényi entropy and complexity are computed based on the parameter q. This method is a valuable tool for analyzing simulated time series. It effectively distinguishes elements of correlated noise and provides a straightforwardmeans of examining differences in behaviors, characteristics, and classifications. For the iEEG experimental data, the REM state showed a greater number of significant sex-based differences, while the supramarginal gyrus region showed themost variation across differentmodes and analyzes. Exploring scale-free brain activity with this framework could provide valuable insights into cognition and neurological disorders. The results may have implications for understanding differences in brain function between the sexes and their possible relevance to neurological disorders. [ABSTRACT FROM AUTHOR]

Healthcare-Associated Infections (HAIs) are an important public health problem that impacts negatively on hospital costs and patient prognosis. Given the importance of the hospital environment in the development of HAIs, the objective was to evaluate the bacterial profile on surfaces and equipment of the Orthopedic Clinic of the Hospital Universitário do Vale do São Francisco. Methods: This is a cross-sectional, descriptive, quantitative study. Samples were collected in 13 wards, each ward with four beds and one was chosen at random, where surfaces and equipment were sampled using swabs soaked in saline and a 1cm2 filter paper mold to standardize the samples. After passing the swab, they were stored in a tube containing 5mL of BHI (Brain Heart Infusion) liquid medium. Then, samples were transported to the Clinical Analysis Laboratory/Microbiology Sector where the microbiological analyzes were performed. Results: In total, 257 bacteria were observed, of which 5.11% were possible causes of hospital infection and 79% coagulase-negative Staphylococcus. Antibiograms of these were performed and different resistance profiles were found. The bathroom doorknob, a high-touch surface, presented the greatest variety of species among the evaluated surfaces. Conclusion: Surfaces and equipment of the evaluated clinic present possible bacteria that cause hospital infection with different profiles of antimicrobial resistance, contributing to possible cross infections

The triage processes prior to the assignation of healthcare resources in hospitals are some of the decision-making processes that more severely affect patients. This effect gets even worse in health emergency situations and intensive care units (ICUs). Aiming to facilitate the decision-making process, in this work the use of vague fuzzy numbers is proposed, aiming to define a multi-attribute patient hierarchization method to be used in emergency situations at hospital ICUs. The incorporation of fuzzy models allows for modelling the vagueness and uncertainty associated with decision criteria evaluation, with which more efficient support is provided to the decision-making process. After defining the methodology, the effectiveness of this new system for patient hierarchization is shown in a case study. As a consequence of that, it is proved that the integration of decision-support systems into healthcare environments results to be efficient and productive, suggesting that if a part of the decision process is supported by these systems, then the errors associated with wrong interpretations and/or diagnoses might be reduced.

The workers operating and supervising machining tools are often in charge of monitoring a high number of parameters of the machining process, and they usually make use of, among others, cutting sound signals, for following-up and assessing that process. The interpretation of those signals is closely related to the operational conditions of the machine and to the work environment itself, because such signals are sensitive to changes in the process’ input parameters. Additionally, they could be considered as a valid indicator for detecting working conditions that either negatively affect the tools’ lifespan, or might even put the machine operators themselves at risk. In light of those circumstances, this work deals with the proposal and conceptual development of a new methodology for monitoring the work conditions of machining tools, based on expert systems that incorporate a reinforcement strategy into their knowledge base. By means of the combination of sound-processing techniques, together with the use of fuzzy-logic inference engines and hierarchization methods based on vague fuzzy numbers, it will be possible to determine existing undesirable behaviors in the machining tools, thus reducing errors, accidents and harmful failures, with consequent savings in time and costs. Aiming to show the potential for the use of this methodology, a concept test has been developed, implemented in the form of a short case study. The results obtained, even if they require more extensive validation, suggest that the methodology would allow for improving the performance and operation of machining tools, as well as the ergonomic conditions of the workplace.

The use of digital technologies in teaching determines the development of new intellectual skills in students, which facilitates the application of investigation strategies, improves their capabilities in an integral way, helps them face the difficulties and demands of the current world and allows them to acquire more significant learning to have a good performance in the socio-educational environment. With a view to fulfilling the above, an institutional repository of open educational resources was developed for the Virtual Health University, based on the use of free and open-source technology tools that allow the standardization and interoperability among each of the repositories established in the National Health System, which will guarantee logic of academic organization. [ABSTRACT FROM AUTHOR]

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