Repositorio Universidad Europea del Atlántico
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Human‐based new approach methodologies to accelerate advances in nutrition research
No full textMuch of nutrition research has been conventionally based on the use of simplistic in vitro systems or animal models, which have been extensively employed in an effort to better understand the relationships between diet and complex diseases as well as to evaluate food safety. Although these models have undeniably contributed to increase our mechanistic understanding of basic biological processes, they do not adequately model complex human physiopathological phenomena, creating concerns about the translatability to humans. During the last decade, extraordinary advancement in stem cell culturing, three-dimensional cell cultures, sequencing technologies, and computer science has occurred, which has originated a wealth of novel human-based and more physiologically relevant tools. These tools, also known as “new approach methodologies,” which comprise patient-derived organoids, organs-on-chip, multi-omics approach, along with computational models and analysis, represent innovative and exciting tools to forward nutrition research from a human-biology-oriented perspective. After considering some shortcomings of conventional in vitro and vivo approaches, here we describe the main novel available and emerging tools that are appropriate for designing a more human-relevant nutrition research. Our aim is to encourage discussion on the opportunity to explore innovative paths in nutrition research and to promote a paradigm-change toward a more human biology-focused approach to better understand human nutritional pathophysiology, to evaluate novel food products, and to develop more effective targeted preventive or therapeutic strategies while helping in reducing the number and replacing animals employed in nutrition research
A deep learning approach for Named Entity Recognition in Urdu language
No full textNamed Entity Recognition (NER) is a natural language processing task that has been widely explored for different languages in the recent decade but is still an under-researched area for the Urdu language due to its rich morphology and language complexities. Existing state-of-the-art studies on Urdu NER use various deep-learning approaches through automatic feature selection using word embeddings. This paper presents a deep learning approach for Urdu NER that harnesses FastText and Floret word embeddings to capture the contextual information of words by considering the surrounding context of words for improved feature extraction. The pre-trained FastText and Floret word embeddings are publicly available for Urdu language which are utilized to generate feature vectors of four benchmark Urdu language datasets. These features are then used as input to train various combinations of Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), CRF, and deep learning models. The results show that our proposed approach significantly outperforms existing state-of-the-art studies on Urdu NER, achieving an F-score of up to 0.98 when using BiLSTM+GRU with Floret embeddings. Error analysis shows a low classification error rate ranging from 1.24% to 3.63% across various datasets showing the robustness of the proposed approach. The performance comparison shows that the proposed approach significantly outperforms similar existing studies
Do ICT firms manage R&D differently? Firm-level and macroeconomic effects on corporate R&D investment: Empirical evidence from a multi-countries context
No full textTechnological firms invest in R&D looking for innovative solutions but assuming high costs and great (technological) uncertainty regarding final results and returns. Additionally, they face other problems related to R&D management. This empirical study tries to determine which of the factors favour or constrain the decision of these firms to engage in R&D. The analysis uses financial data of 14,619 ICT listed companies of 22 countries from 2003 to 2018. Additionally, macroeconomic data specific for the countries and the sector were used. For the analysis of dynamic panel data, a System-GMM method is used. Among the findings, we highlight that cash flow, contrary to the known theoretical models and empirical evidences, negatively impacts on R&D investment. Debt is neither the right source for R&D funding, as the effect is also negative. This suggests that ICT companies are forced to manage their R&D activities differently, relying more on other funding sources, taking advantage of growth opportunities and benefiting from a favourable macroeconomic environment in terms of growth and increased business sector spending on R&D. These results are similar in both sub-sectors and in all countries, both bank- and market based. The exception is firms with few growth opportunities and little debt
Analyzing patients satisfaction level for medical services using twitter data
Get PDFPublic concern regarding health systems has experienced a rapid surge during the last two years due to the COVID-19 outbreak. Accordingly, medical professionals and health-related institutions reach out to patients and seek feedback to analyze, monitor, and uplift medical services. Such views and perceptions are often shared on social media platforms like Facebook, Instagram, Twitter, etc. Twitter is the most popular and commonly used by the researcher as an online platform for instant access to real-time news, opinions, and discussion. Its trending hashtags (#) and viral content make it an ideal hub for monitoring public opinion on a variety of topics. The tweets are extracted using three hashtags #healthcare, #healthcare services, and #medical facilities. Also, location and tweet sentiment analysis are considered in this study. Several recent studies deployed Twitter datasets using ML and DL models, but the results show lower accuracy. In addition, the studies did not perform extensive comparative analysis and lack validation. This study addresses two research questions: first, what are the sentiments of people toward medical services worldwide? and second, how effective are the machine learning and deep learning approaches for the classification of sentiment on healthcare tweets? Experiments are performed using several well-known machine learning models including support vector machine, logistic regression, Gaussian naive Bayes, extra tree classifier, k nearest neighbor, random forest, decision tree, and AdaBoost. In addition, this study proposes a transfer learning-based LSTM-ETC model that effectively predicts the customer’s satisfaction level from the healthcare dataset. Results indicate that despite the best performance by the ETC model with an 0.88 accuracy score, the proposed model outperforms with a 0.95 accuracy score. Predominantly, the people are happy about the provided medical services as the ratio of the positive sentiments is substantially higher than the negative sentiments. The sentiments, either positive or negative, play a crucial role in making important decisions through customer feedback and enhancing quality
Isoflavones Effects on Vascular and Endothelial Outcomes: How Is the Gut Microbiota Involved?
No full textIsoflavones are a group of (poly)phenols, also defined as phytoestrogens, with chemical structures comparable with estrogen, that exert weak estrogenic effects. These phytochemical compounds have been targeted for their proven antioxidant and protective effects. Recognizing the increasing prevalence of cardiovascular diseases (CVD), there is a growing interest in understanding the potential cardiovascular benefits associated with these phytochemical compounds. Gut microbiota may play a key role in mediating the effects of isoflavones on vascular and endothelial functions, as it is directly implicated in isoflavones metabolism. The findings from randomized clinical trials indicate that isoflavone supplementation may exert putative effects on vascular biomarkers among healthy individuals, but not among patients affected by cardiometabolic disorders. These results might be explained by the enzymatic transformation to which isoflavones are subjected by the gut microbiota, suggesting that a diverse composition of the microbiota may determine the diverse bioavailability of these compounds. Specifically, the conversion of isoflavones in equol—a microbiota-derived metabolite—seems to differ between individuals. Further studies are needed to clarify the intricate molecular mechanisms behind these contrasting results
Influence of the Encapsulating Agent on the Bioaccessibility of Phenolic Compounds from Microencapsulated Propolis Extract during "In Vitro" Gastrointestinal Digestion
No full textThe aim of this work is to develop different encapsulated propolis ingredients by spray-drying and to evaluate their bioaccessibility using simulated in vitro digestion. To achieve these goals, first, microparticles of a propolis extract with inulin as the coating polymer were prepared under the optimal conditions previously determined. Then, a fraction of inulin was replaced with other encapsulating agents, namely sodium alginate, pectin, and chitosan, to obtain different ingredients with controlled release properties in the gastrointestinal tract. The analysis of the phenolic profile in the propolis extract and microparticles showed 58 compounds tentatively identified, belonging mainly to phenolic acid derivatives and flavonoids. Then, the behavior of the free extract and the formulated microparticles under gastrointestinal conditions was studied through an in vitro gastrointestinal digestion process using the INFOGEST protocol. Digestion of the free extract resulted in the degradation of most compounds, which was minimized in the encapsulated formulations. Thus, all developed microparticles could be promising strategies for improving the stability of this bioactive extract under gastrointestinal conditions, thereby enhancing its beneficial effect
Differences in carotid to femoral pulse wave velocity and carotid intima media thickness between vegetarian and omnivorous diets in healthy subjects: a systematic review and meta-analysis
No full textBackground: Arterial stiffness and atherosclerosis are known risk factors for cardiovascular morbidity and mortality. Vegetarian diets have been associated with cardiovascular benefits, including improvements in metabolic parameters. However, the impact of a vegetarian diet on cardiovascular parameters, specifically arterial stiffness and atherosclerosis, in healthy individuals remains unclear. Thus, this study aims to analyze differences in arterial stiffness and atherosclerosis between vegetarian and omnivorous diets in healthy subjects. Methods: A systematic review and meta-analysis were conducted following established guidelines. PubMed, Scopus, Web of Science, and Cochrane Library databases were searched for studies examining the association between vegetarian and omnivorous diets with arterial stiffness and atherosclerosis. Cross-sectional studies reporting carotid to femoral pulse wave velocity (cf-PWv) as a measure of arterial stiffness and carotid intima media thickness (c-IMT) as a measure of atherosclerosis were included. Data were synthesized using random effects models, and sensitivity analyses, meta-regressions, and assessment of publication bias were performed. Results: Ten studies were included in the systematic review, and seven studies were included in the meta-analysis. The pooled analysis demonstrated that individuals following a vegetarian diet had differences in the levels of arterial stiffness (cf-PWv) compared to those following an omnivorous diet (MD: −0.43 m s−1; 95% CI: −0.63, −0.23). Similarly, atherosclerosis (c-IMT) was found to be different in individuals adhering to a vegetarian dietary pattern (MD = −29.86 mm; 95% CI: −58.41, −1.32). Conclusions: Our findings suggest that a vegetarian diet is associated with improved arterial stiffness and reduced atherosclerosis in healthy individuals. These results support the inclusion of a well-balanced vegetarian dietary pattern in the prevention and management of cardiovascular diseases. However, further research is needed to explore the effects of a vegetarian diet on arterial health in diverse populations and to assess long-term cardiovascular outcomes
Design and development of patient health tracking, monitoring and big data storage using Internet of Things and real time cloud computing
No full textWith the outbreak of the COVID-19 pandemic, social isolation and quarantine have become commonplace across the world. IoT health monitoring solutions eliminate the need for regular doctor visits and interactions among patients and medical personnel. Many patients in wards or intensive care units require continuous monitoring of their health. Continuous patient monitoring is a hectic practice in hospitals with limited staff; in a pandemic situation like COVID-19, it becomes much more difficult practice when hospitals are working at full capacity and there is still a risk of medical workers being infected. In this study, we propose an Internet of Things (IoT)-based patient health monitoring system that collects real-time data on important health indicators such as pulse rate, blood oxygen saturation, and body temperature but can be expanded to include more parameters. Our system is comprised of a hardware component that collects and transmits data from sensors to a cloud-based storage system, where it can be accessed and analyzed by healthcare specialists. The ESP-32 microcontroller interfaces with the multiple sensors and wirelessly transmits the collected data to the cloud storage system. A pulse oximeter is utilized in our system to measure blood oxygen saturation and body temperature, as well as a heart rate monitor to measure pulse rate. A web-based interface is also implemented, allowing healthcare practitioners to access and visualize the collected data in real-time, making remote patient monitoring easier. Overall, our IoT-based patient health monitoring system represents a significant advancement in remote patient monitoring, allowing healthcare practitioners to access real-time data on important health metrics and detect potential health issues before they escalate
Supervised, structured and individualized exercise in metastatic breast cancer: a randomized controlled trial
No full textPhysical exercise both during and after curative cancer treatment has been shown to reduce side effects. Evidence in the metastatic cancer setting is scarce, and interventions that improve health-related quality of life (HRQOL) are much needed for patients with metastatic breast cancer (MBC). The multinational randomized controlled PREFERABLE-EFFECT trial assessed the effects of exercise on fatigue and HRQOL in patients with MBC. In total, 357 patients with MBC and a life expectancy of ≥6 months but without unstable bone metastases were recruited at eight study centers across five European countries and Australia. Participants were randomly assigned (1:1) to usual care (control group, n = 179) or a 9-month supervised exercise program (exercise group, n = 178). Intervention effects on physical fatigue (European Organization for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire (QLQ)-FA12 scale) and HRQOL (EORTC QLQ-C30 summary score) were determined by comparing the change from baseline to 3, 6 (primary timepoint) and 9 months between groups using mixed models for repeated measures, adjusted for baseline values of the outcome, line of treatment (first or second versus third or higher) and study center. Exercise resulted in significant positive effects on both primary outcomes. Physical fatigue was significantly lower (−5.3 (95% confidence interval (CI), −10.0 to −0.6), Bonferroni–Holm-adjusted P = 0.027; Cohen's effect size, 0.22) and HRQOL significantly higher (4.8 (95% CI, 2.2–7.4), Bonferroni–Holm-adjusted P = 0.0003; effect size, 0.33) in the exercise group than in the control group at 6 months. Two serious adverse events occurred (that is, fractures), but both were not related to bone metastases. These results demonstrate that supervised exercise has positive effects on physical fatigue and HRQOL in patients with MBC and should be recommended as part of supportive care
Methodology for the Monitoring and Control of the Alterations Related to Biodeterioration and Physical-Chemical Processes Produced on the Paintings on the Ceiling of the Polychrome Hall at Altamira
No full textOn the surface of the Cave of Altamira’s prehistoric paintings, a series of active deterioration processes are evident, leading to significant alterations of this invaluable heritage. This study proposes a comprehensive methodology for the systematic recording and management of these alterations. To achieve this, advanced microphotogrammetric monitoring techniques are employed, allowing for the acquisition of very high-resolution images that provide objective and quantifiable data that let us determine the evolution of the alterations. By comparing these images with those from earlier campaigns, the study tracks changes. The data collected through this protocol has helped with the development of new research avenues to understand, among the many alteration processes that impact paintings, the dynamics of water and fluid mechanics affecting the conservation of Cave of Altamira. These investigations help clarify how, why, and at what rate degradation processes such as pigment migration, washing, and bacterial colonization occur. The insights gained from these techniques inform indirect conservation measures aimed at reducing the deterioration of the cave art, located both on the Polychrome ceiling and throughout the rest of the Cave of Altamira. The results underline the importance of regular monitoring and the application of precise, non-invasive techniques to protect rock art from continued degradation. This research provides a model for similar conservation initiatives at other vulnerable heritage sites