8,165 research outputs found
Sociodemographic, nutritional and health status factors associated with adherence to Mediterranean diet in an agricultural Moroccan adult's population
Background. Numerous studies have demonstrated beneficial effects of adherence to the Mediterranean diet (MD) on many chronic diseases, including chronic kidney disease (CKD).
Objective. The aim of this study was to assess the adherence of a rural population to the Mediterranean diet, to identify the sociodemographic and lifestyle determinants and to analyze the association between adherence to MD and CKD.
Material and Methods. In a cross-sectional study, data on sociodemographic, lifestyle factors, clinical, biochemical parameters and diet were collected on a sample of 154 subjects. Adherence to MD was assessed according to a simplified MD score based on the daily frequency of intake of eight food groups (vegetables, legumes, fruits, cereal or potatoes, fish, red meat, dairy products and MUFA/SFA), using the sex specific sample medians as cut-offs. A value of 0 or 1 was assigned to consumption of each component according to its presumed detrimental or beneficial effect on health.
Results. According to the simplified MD score, the study data show that high adherence (44.2%) to MD was characterized by intakes high in vegetables, fruits, fish, cereals, olive oil, and low in meat and moderate in dairy. Furthermore, several factors such as age, marital status, education level, and hypertension status were associated with the adherence to MD in the study population. The majority of subjects with CKD have poor adherence to the MD compared to non-CKD with a statistically insignificant difference.
Conclusions. In Morocco, maintaining the traditional MD pattern play crucial role for public health. More research is needed in this area to precisely measure this association
Beam scanning by liquid-crystal biasing in a modified SIW structure
A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium
Evaluation of different segmentation-based approaches for skin disorders from dermoscopic images
Treballs Finals de Grau d'Enginyeria Biomèdica. Facultat de Medicina i Ciències de la Salut. Universitat de Barcelona. Curs: 2022-2023. Tutor/Director: Sala Llonch, Roser, Mata Miquel, Christian, Munuera, JosepSkin disorders are the most common type of cancer in the world and the incident has been lately increasing over the past decades. Even with the most complex and advanced technologies, current image acquisition systems do not permit a reliable identification of the skin lesion by visual examination due to the challenging structure of the malignancy. This promotes the need for the implementation of automatic skin lesion segmentation methods in order to assist in physicians’ diagnostic when determining the lesion's region and to serve as a preliminary step for the classification of the skin lesion. Accurate and precise segmentation is crucial for a rigorous screening and monitoring of the disease's progression.
For the purpose of the commented concern, the present project aims to accomplish a state-of-the-art review about the most predominant conventional segmentation models for skin lesion segmentation, alongside with a market analysis examination. With the rise of automatic segmentation tools, a wide number of algorithms are currently being used, but many are the drawbacks when employing them for dermatological disorders due to the high-level presence of artefacts in the image acquired.
In light of the above, three segmentation techniques have been selected for the completion of the work: level set method, an algorithm combining GrabCut and k-means methods and an intensity automatic algorithm developed by Hospital Sant Joan de Déu de Barcelona research group. In addition, a validation of their performance is conducted for a further implementation of them in clinical training. The proposals, together with the got outcomes, have been accomplished by means of a publicly available skin lesion image database
Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives
Deep learning has demonstrated remarkable performance across various tasks in
medical imaging. However, these approaches primarily focus on supervised
learning, assuming that the training and testing data are drawn from the same
distribution. Unfortunately, this assumption may not always hold true in
practice. To address these issues, unsupervised domain adaptation (UDA)
techniques have been developed to transfer knowledge from a labeled domain to a
related but unlabeled domain. In recent years, significant advancements have
been made in UDA, resulting in a wide range of methodologies, including feature
alignment, image translation, self-supervision, and disentangled representation
methods, among others. In this paper, we provide a comprehensive literature
review of recent deep UDA approaches in medical imaging from a technical
perspective. Specifically, we categorize current UDA research in medical
imaging into six groups and further divide them into finer subcategories based
on the different tasks they perform. We also discuss the respective datasets
used in the studies to assess the divergence between the different domains.
Finally, we discuss emerging areas and provide insights and discussions on
future research directions to conclude this survey.Comment: Under Revie
Novel 129Xe Magnetic Resonance Imaging and Spectroscopy Measurements of Pulmonary Gas-Exchange
Gas-exchange is the primary function of the lungs and involves removing carbon dioxide from the body and exchanging it within the alveoli for inhaled oxygen. Several different pulmonary, cardiac and cardiovascular abnormalities have negative effects on pulmonary gas-exchange. Unfortunately, clinical tests do not always pinpoint the problem; sensitive and specific measurements are needed to probe the individual components participating in gas-exchange for a better understanding of pathophysiology, disease progression and response to therapy.
In vivo Xenon-129 gas-exchange magnetic resonance imaging (129Xe gas-exchange MRI) has the potential to overcome these challenges. When participants inhale hyperpolarized 129Xe gas, it has different MR spectral properties as a gas, as it diffuses through the alveolar membrane and as it binds to red-blood-cells. 129Xe MR spectroscopy and imaging provides a way to tease out the different anatomic components of gas-exchange simultaneously and provides spatial information about where abnormalities may occur.
In this thesis, I developed and applied 129Xe MR spectroscopy and imaging to measure gas-exchange in the lungs alongside other clinical and imaging measurements. I measured 129Xe gas-exchange in asymptomatic congenital heart disease and in prospective, controlled studies of long-COVID. I also developed mathematical tools to model 129Xe MR signals during acquisition and reconstruction. The insights gained from my work underscore the potential for 129Xe gas-exchange MRI biomarkers towards a better understanding of cardiopulmonary disease. My work also provides a way to generate a deeper imaging and physiologic understanding of gas-exchange in vivo in healthy participants and patients with chronic lung and heart disease
Confidence Estimation Using Unlabeled Data
Overconfidence is a common issue for deep neural networks, limiting their
deployment in real-world applications. To better estimate confidence, existing
methods mostly focus on fully-supervised scenarios and rely on training labels.
In this paper, we propose the first confidence estimation method for a
semi-supervised setting, when most training labels are unavailable. We
stipulate that even with limited training labels, we can still reasonably
approximate the confidence of model on unlabeled samples by inspecting the
prediction consistency through the training process. We use training
consistency as a surrogate function and propose a consistency ranking loss for
confidence estimation. On both image classification and segmentation tasks, our
method achieves state-of-the-art performances in confidence estimation.
Furthermore, we show the benefit of the proposed method through a downstream
active learning task. The code is available at
https://github.com/TopoXLab/consistency-ranking-lossComment: Accepted by ICLR'2
Trust and accountability in times of pandemics
La pandemia de COVID-19 llegó en un contexto de creciente polarización política y desconfianza en las instituciones políticas en muchos países. ¿Pudieron las deficiencias en la gestión de la pandemia erosionar la confianza en las instituciones públicas? ¿Interfirió la ideología de los ciudadanos en la forma en que procesaban la información sobre el desempeño de los Gobiernos? Para investigar ambas cuestiones, en noviembre de 2020 llevamos a cabo en España un experimento online prerregistrado. A los encuestados del grupo de tratamiento les proporcionamos información sobre el número de rastreadores de contactos en su comunidad autónoma, una política clave bajo el control de los Gobiernos autonómicos. Encontramos que las personas sobrestiman en gran medida el número de rastreadores de su región. Cuando proporcionamos el número real de rastreadores, encontramos lo siguiente: una pérdida de la confianza en los Gobiernos; una reducción en la voluntad de financiar instituciones públicas, y una disminución de la aceptación de la vacuna contra el COVID-19. También encontramos que los individuos cambian endógenamente su atribución de responsabilidades al recibir el tratamiento. En las regiones donde los Gobiernos regionales y central están gobernados por diferentes partidos, los simpatizantes del Gobierno regional reaccionan a las malas noticias sobre la gestión del Gobierno atribuyendo una mayor responsabilidad al Gobierno central. A esto lo llamamos «efecto de blame-shifting». En estas regiones, la información negativa no se traduce en una menor intención de voto para el Gobierno regional. Estos resultados sugieren que la rendición de cuentas puede ser particularmente difícil en entornos con alta polarización política y donde las áreas de responsabilidad no están claramente delimitadas.The COVID-19 pandemic took place against the backdrop of growing political polarization and distrust in political institutions in many countries. Did deficiencies in government performance further erode trust in public institutions? Did citizens’ ideology interfere with the way they processed information on government performance? To investigate these two questions, we conducted a pre-registered online experiment in Spain in November 2020. Respondents in the treatment group were provided information on the number of contact tracers in their region, a key policy variable under the control of regional governments. We find that individuals greatly over-estimate the number of contact tracers in their region. When we provide the actual number of contact tracers, we find a decline in trust in governments, a reduction in willingness to fund public institutions and a decrease in COVID-19 vaccine acceptance. We also find that individuals endogenously change their attribution of responsibilities when receiving the treatment. In regions where the regional and central governments are controlled by different parties, sympathizers of the regional incumbent react to the negative news on performance by attributing greater responsibility for it to the central government. We call this the blame shifting effect. In those regions, the negative information does not translate into lower voting intentions for the regional incumbent government. These results suggest that the exercise of political accountability may be particularly difficult in settings with high political polarization and areas of responsibility that are not clearly delineated
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