Browse by Acceso

Up a level
Export as [feed] Atom [feed] RSS 1.0 [feed] RSS 2.0
Group by: Date | Title | Creators | Item Type | No Grouping
Jump to: A | B | C | F | G | P
Number of items: 9.

A

Article Subjects > Biomedicine
Subjects > Nutrition
Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Universidad Internacional do Cuanza > Research > Scientific Production
Cerrado Inglés Cactus has been used in traditional folk medicine because of its role in treating a number of diseases and conditions. Prickly pear fruit is an excellent source of secondary metabolites (i.e., betalains, flavonoids, and ascorbic acid) with health-promoting properties against many common human diseases, including diabetes, hypertension, hypercholesterolemia, rheumatic pain, gastric mucosa diseases and asthma. In addition, prickly pears are potential candidates for the development of low-cost functional foods because they grow with low water requirements in arid regions of the world. This review describes the main bioactive compounds found in this fruit and shows the in vitro and some clinical studies about the fruit of most important cactus (Opuntia ficus-indica) and its relationship with some chronic diseases. Even though a lot of effort have been done to study the relationship between this fruit and the human health, more studies on Opuntia ficus-indica could help better understand its pharmacological mechanism of action to provide clear scientific evidence to explain its traditional uses, and to identify its therapeutic potential in other diseases. metadata Armas Diaz, Yasmany and Machì, Michele and Salinari, Alessia and Mazas Pérez-Oleaga, Cristina and Martínez López, Nohora Milena and Briones Urbano, Mercedes and Cianciosi, Danila mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, cristina.mazas@uneatlantico.es, nohora.martinez@uneatlantico.es, mercedes.briones@uneatlantico.es, UNSPECIFIED (2022) Prickly pear fruits from "Opuntia ficus-indica" varieties as a source of potential bioactive compounds in the Mediterranean diet. Mediterranean Journal of Nutrition and Metabolism, 15 (4). pp. 581-592. ISSN 1973798X

B

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Universidad Internacional do Cuanza > Research > Scientific Production
Cerrado Inglés Network slicing is expected to be critical in the deployment of 5G mobile networks and systems. On top of a single physical infrastructure, the technology enables operators to operate several virtual networks. As the 5G commercialization was recently deployed, network function virtualization (NFV) and software-defined networking (SDN) will drive network slicing. In this article, we present an overview of SDN in 5G, and the motivation, role, and market growth of network slicing. We then discuss usage scenarios of SDN in network slicing for 5G. The proposed architecture comprises the three usage scenarios: enhanced mobile broadband (eMBB) provides the support to varying types of services used; ultra-reliable low-latency communications (URLLC) provides a certain class of applications such as higher bandwidth, high definition video streaming, mobile TV, and so on; massive machine type communications (mMTC) throws light on the types of services used to connect huge numbers of devices. Finally, challenges and solutions based on network slicing in 5G are presented. metadata Babbar, Himanshi and Rani, Shalli and AlZubi, Ahmad Ali and Singh, Aman and Nasser, Nidal and Ali, Asmaa mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, aman.singh@uneatlantico.es, UNSPECIFIED, UNSPECIFIED (2022) Role of Network Slicing in Software Defined Networking for 5G: Use Cases and Future Directions. IEEE Wireless Communications, 29 (1). pp. 112-118. ISSN 1536-1284

C

Article Subjects > Nutrition Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Universidad Internacional do Cuanza > Research > Scientific Production
Cerrado Inglés Inflammatory bowel disease (IBD) patients are at substantially higher risk of colorectal cancer (CRC) and IBD-associated CRC accounts for roughly 10-15% of the annual mortality in IBD patients. IBD-related CRC also affects younger patients if compared with sporadic CRC, with a 5-year survival rate of 50%. Regardless of medical therapies, the persistent inflammation state characterizing IBD raises the risk for precancerous changes and CRC, with additional input from several elements including genetic and environmental risk factors, IBD-associated comorbidities, intestinal barrier disfunction, and gut microbiota modifications. It is well known that nutritional habits and dietary bioactive compounds can influence IBD-associated inflammation, microbiome abundance and composition, oxidative stress balance, and gut permeability. In addition, in the last years, results from broad epidemiological and experimental studies have associated certain foods or nutritional patterns with the risk of colorectal neoplasia. Here we review the possible role of nutrition in the prevention of IBD-related CRC, focusing specifically on human studies. In conclusion it emerges that nutritional interventions based on healthy, nutrient-dense dietary patterns characterized by a high intake of fiber, vegetables, fruit, Omega-3 PUFAs, and low amount of animal proteins, processed foods and alcohol, combined with probiotic supplementation have the potential of reducing IBD-activity and preventing the risk of IBD-related CRC through different mechanisms, suggesting that targeted nutritional interventions may represent a novel promising approach for the prevention and management of IBD-associated CRC. metadata Cassotta, Manuela and Cianciosi, Danila and De Giuseppe, Rachele and Navarro-Hortal, Maria Dolores and Diaz, Yasmany Armas and Forbes-Hernández, Tamara Yuliett and Tutusaus, Kilian and Pascual Barrera, Alina Eugenia and Grosso, Giuseppe and Xiao, Jianbo and Battino, Maurizio and Giampieri, Francesca mail manucassotta@gmail.com, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, kilian.tutusaus@uneatlantico.es, alina.pascual@unini.edu.mx, UNSPECIFIED, UNSPECIFIED, maurizio.battino@uneatlantico.es, francesca.giampieri@uneatlantico.es (2023) Possible role of nutrition in the prevention of Inflammatory Bowel Disease-related colorectal cancer: a focus on human studies. Nutrition. p. 111980. ISSN 08999007

F

Article Subjects > Biomedicine
Subjects > Engineering
Subjects > Nutrition
Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Universidad Internacional do Cuanza > Research > Scientific Production
Cerrado Inglés Obesity and overweight has increased in the last year and has become a pandemic disease, the result of sedentary lifestyles and unhealthy diets rich in sugars, refined starches, fats and calories. Machine learning (ML) has proven to be very useful in the scientific community, especially in the health sector. With the aim of providing useful tools to help nutritionists and dieticians, research focused on the development of ML and Deep Learning (DL) algorithms and models is searched in the literature. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol has been used, a very common technique applied to carry out revisions. In our proposal, 17 articles have been filtered in which ML and DL are applied in the prediction of diseases, in the delineation of treatment strategies, in the improvement of personalized nutrition and more. Despite expecting better results with the use of DL, according to the selected investigations, the traditional methods are still the most used and the yields in both cases fluctuate around positive values, conditioned by the databases (transformed in each case) to a greater extent than by the artificial intelligence paradigm used. Conclusions: An important compilation is provided for the literature in this area. ML models are time-consuming to clean data, but (like DL) they allow automatic modeling of large volumes of data which makes them superior to traditional statistics. metadata Ferreras, Antonio and Sumalla Cano, Sandra and Martínez-Licort, Rosmeri and Elío Pascual, Iñaki and Tutusaus, Kilian and Prola, Thomas and Vidal Mazón, Juan Luis and Sahelices, Benjamín and de la Torre Díez, Isabel mail UNSPECIFIED, sandra.sumalla@uneatlantico.es, UNSPECIFIED, inaki.elio@uneatlantico.es, kilian.tutusaus@uneatlantico.es, thomas.prola@uneatlantico.es, juanluis.vidal@uneatlantico.es, UNSPECIFIED, UNSPECIFIED (2023) Systematic Review of Machine Learning applied to the Prediction of Obesity and Overweight. Journal of Medical Systems, 47 (1). ISSN 1573-689X

G

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Universidad Internacional do Cuanza > Research > Scientific Production
Cerrado Inglés Agriculture is an important sector that plays an essential role in the economic development of a country. Each year farmers face numerous challenges in producing good quality crops. One of the major reasons behind the failure of the harvest is the use of unscientific agricultural practices. Moreover, every year enormous crop loss is encountered either by pests, specific diseases, or natural disasters. It raises a strong concern to employ sustainable advanced technologies to address agriculture-related issues. In this paper, a sustainable real-time crop disease detection and prevention system, called CROPCARE is proposed. The system integrates mobile vision, Internet of Things (IoT), and Google Cloud services for sustainable growth of crops. The primary function of the proposed intelligent system is to detect crop diseases through the CROPCARE -mobile application. It uses Super-Resolution Convolution Network (SRCNN) and the pretrained model MobileNet-V2 to generate a decision model trained over various diseases. To maintain sustainability, the mobile app is integrated with IoT sensors and Google Cloud services. The proposed system also provides recommendations that help farmers know about current soil conditions, weather conditions, disease prevention methods, etc. It supports both Hindi and English dictionaries for the convenience of the farmers. The proposed approach is validated by using the PlantVillage dataset. The obtained results confirm the performance strength of the proposed system. metadata Garg, Garima and Gupta, Shivam and Mishra, Preeti and Vidyarthi, Ankit and Singh, Aman and Ali, Asmaa mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, aman.singh@uneatlantico.es, UNSPECIFIED (2023) CROPCARE: An Intelligent Real-Time Sustainable IoT System for Crop Disease Detection Using Mobile Vision. IEEE Internet of Things Journal. p. 1. ISSN 2372-2541

Article Subjects > Psychology Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Universidad Internacional do Cuanza > Research > Scientific Production
Cerrado Inglés Many earlier studies conducted on sports betting and addiction have examined sports betting in the context of gambling and have not taken into account the specific motivations of sports betting. Therefore, the effects of motivational elements of sports betting on sports betting addiction risk are unknown. The aim of the present study was to examine the effects of motivation factors specific to sports betting on sports betting addiction. Accordingly, three linked studies were conducted. Firstly, to determine sports betting motivations “Sports Betting Motivation Scale (SBMS)” developed and validated. Secondly, to determine the risks of sports betting addiction “Problem Sports Betting Severity Index (PSBSI)” was adapted from Problem Gambling Severity Index (PGSI). Finally, the third study examined effects of the sports betting motivations on sports betting addiction risk. Study one (n=281), study two comprised (n=230), and the final study comprised (n=643) sports fans who bet on sports regularly for 12 months with different motivations. The findings demonstrate that the SBMS appears to be a reliable and valid instrument for assessing sports betting motivations. Also, the findings provided PSBSI validity for the use of the Turkish and sports betting adapted version of PGSI. As a result of the main research, “make money,” “socialization,” and “being in the game” motivations were found to be positive predictors of sports betting addiction risk, while “fun” motivation was a negative predictor. The motivations “recreation/escape,” “knowledge of the game,” and “interest in sport” were found not to be significant predictors of the risk of sports betting addiction. metadata Gökce Yüce, Sevda and Yüce, Arif and Katırcı, Hakan and Nogueira-López, Abel and González-Hernández, Juan mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, abel.nogueira@uneatlantico.es, UNSPECIFIED (2021) Effects of Sports Betting Motivations on Sports Betting Addiction in a Turkish Sample. International Journal of Mental Health and Addiction. ISSN 1557-1874

Article Subjects > Psychology Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Universidad Internacional do Cuanza > Research > Scientific Production
Cerrado Inglés In recent decades, perfectionism has generated growing interest from the scientific community in understanding exercise addiction, due to the explicative contributions offered its characteristics that can make individuals more susceptible to unhealthy and compulsive exercise. There have been limited studies of such constructions in sports contexts. With the purpose of identifying the most relevant evidence on the constructs in sports contexts, the main links between perfectionism and exercise addiction in athletes were described. Taking into account the principles established by the PRISMA and AMSTAR statements for the qualitative and quantitative description of findings in systematic reviews, a compendium of original articles in English, French and Spanish published on the Web of Science electronic platforms and databases is presented, Scopus, ProQuest, MEDLINE and EBSCO-HOST, and included major resources such as PSY Articles, PsycINFO, LWW, ERIC, SportDISCUS, PubMed, ERIC, Dialnet, PubMed, ISOC, the Cochrane Library and Google Scholar. Of the 754 articles identified, only 22 met the established inclusion criteria. Finally, the relationship between exercise addiction and perfectionism, and the risk function of certain personality traits, such as narcissism, in this association is confirmed. metadata González-Hernández, J. and Nogueira-López, Abel and Zangeneh, M. and López-Mora, C. mail UNSPECIFIED, abel.nogueira@uneatlantico.es, UNSPECIFIED, UNSPECIFIED (2021) Exercise Addiction and Perfectionism, Joint in the Same Path? A Systematic Review. International Journal of Mental Health and Addiction. ISSN 1557-1874

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Universidad Internacional do Cuanza > Research > Scientific Production
Cerrado Inglés The Information Centric Networking (ICN) is a future internet architecture to support efficient content distribution in a vehicular environment. In-network caching in ICN provides a realistic solution for vehicular communication due to storage of content replicas inside network vehicles. However, the challenge still exists while caching content replicas in resource constraint vehicles ( such as limited power and cache capacity) to minimize the communication latency. To address the above mentioned challenge, this paper proposes EPC - an ICN based Energy efficient Placement of Content chunk that fits well in a vehicular environment. The proposed resource management strategy mainly aims to reduce the content fetching delay by caching content replicas towards the network edge router. The EPC strategy decides on placement of content chunks on each vehicle by jointly considering residual power of current vehicle, local popularity of content, and caching gain. The EPC supports efficient utilization of network available resources by allowing only vehicles with their residual power greater than threshold to perform chunk caching and hence, further offers reduced content duplication in the whole network. The effectiveness of the proposed scheme is evaluated in Icarus- an ICN simulator for analyzing the performance of ICN caching and routing strategies. The EPC outperforms various state of the art caching strategies approximately by 30% when gets evaluated in terms of offered cache hit ratio, content retrieval delay, and the average number of hops utilized for fetching the requested content. metadata Gupta, Divya and Rani, Shalli and Singh, Aman and Rodrigues, Joel J. P. C. mail UNSPECIFIED, UNSPECIFIED, aman.singh@uneatlantico.es, UNSPECIFIED (2022) ICN Based Efficient Content Caching Scheme for Vehicular Networks. IEEE Transactions on Intelligent Transportation Systems. pp. 1-9. ISSN 1524-9050

P

Article Subjects > Biomedicine Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Universidad Internacional do Cuanza > Research > Scientific Production
Cerrado Inglés Fasting, caloric restriction and foods or compounds mimicking the biological effects of caloric restriction, known as caloric restriction mimetics, have been associated with a lower risk of age-related diseases, including cardiovascular diseases, cancer and cognitive decline, and a longer lifespan. Reduced calorie intake has been shown to stimulate cancer immunosurveillance, reducing the migration of immunosuppressive regulatory T cells towards the tumor bulk. Autophagy stimulation via reduction of lysine acetylation, increased sensitivity to chemo- and immunotherapy, along with a reduction of insulin-like growth factor 1 and reactive oxygen species have been described as some of the major effects triggered by caloric restriction. Fasting and caloric restriction have also been shown to beneficially influence gut microbiota composition, modify host metabolism, reduce total cholesterol and triglyceride levels, lower diastolic blood pressure and elevate morning cortisol level, with beneficial modulatory effects on cardiopulmonary fitness, body fat and weight, fatigue and weakness, and general quality of life. Moreover, caloric restriction may reduce the carcinogenic and metastatic potential of cancer stem cells, which are generally considered responsible of tumor formation and relapse. Here, we reviewed in vitro and in vivo studies describing the effects of fasting, caloric restriction and some caloric restriction mimetics on immunosurveillance, gut microbiota, metabolism, and cancer stem cell growth, highlighting the molecular and cellular mechanisms underlying these effects. Additionally, studies on caloric restriction interventions in cancer patients or cancer risk subjects are discussed. Considering the promising effects associated with caloric restriction and caloric restriction mimetics, we think that controlled-randomized large clinical trials are warranted to evaluate the inclusion of these non-pharmacological approaches in clinical practice. metadata Pistollato, Francesca and Forbes-Hernández, Tamara Y. and Calderón Iglesias, Rubén and Ruiz Salces, Roberto and Elexpuru Zabaleta, Maria and Dominguez Azpíroz, Irma and Cianciosi, Danila and Quiles, José L. and Giampieri, Francesca and Battino, Maurizio mail francesca.pistollato@uneatlantico.es, UNSPECIFIED, ruben.calderon@uneatlantico.es, roberto.ruiz@uneatlantico.es, maria.elexpuru@uneatlantico.es, irma.dominguez@unini.edu.mx, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED (2021) Effects of caloric restriction on immunosurveillance, microbiota and cancer cell phenotype: Possible implications for cancer treatment. Seminars in Cancer Biology. pp. 45-57. ISSN 1044-579X

This list was generated on Sat Apr 13 23:45:48 2024 UTC.

<a href="/11592/1/fchem-12-1361980.pdf" class="ep_document_link"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>

en

open

Efficacy and classification of Sesamum indicum linn seeds with Rosa damascena mill oil in uncomplicated pelvic inflammatory disease using machine learning

Background and objectives: As microbes are developing resistance to antibiotics, natural, botanical drugs or traditional herbal medicine are presently being studied with an eye of great curiosity and hope. Hence, complementary and alternative treatments for uncomplicated pelvic inflammatory disease (uPID) are explored for their efficacy. Therefore, this study determined the therapeutic efficacy and safety of Sesamum indicum Linn seeds with Rosa damascena Mill Oil in uPID with standard control. Additionally, we analyzed the data with machine learning. Materials and methods: We included 60 participants in a double-blind, double-dummy, randomized standard-controlled study. Participants in the Sesame and Rose oil group (SR group) (n = 30) received 14 days course of black sesame powder (5 gm) mixed with rose oil (10 mL) per vaginum at bedtime once daily plus placebo capsules orally. The standard group (SC), received doxycycline 100 mg twice and metronidazole 400 mg thrice orally plus placebo per vaginum for the same duration. The primary outcome was a clinical cure at post-intervention for visual analogue scale (VAS) for lower abdominal pain (LAP), and McCormack pain scale (McPS) for abdominal-pelvic tenderness. The secondary outcome included white blood cells (WBC) cells in the vaginal wet mount test, safety profile, and health-related quality of life assessed by SF-12. In addition, we used AdaBoost (AB), Naïve Bayes (NB), and Decision Tree (DT) classifiers in this study to analyze the experimental data. Results: The clinical cure for LAP and McPS in the SR vs SC group was 82.85% vs 81.48% and 83.85% vs 81.60% on Day 15 respectively. On Day 15, pus cells less than 10 in the SR vs SC group were 86.6% vs 76.6% respectively. No adverse effects were reported in both groups. The improvement in total SF-12 score on Day 30 for the SR vs SC group was 82.79% vs 80.04% respectively. In addition, our Naive Bayes classifier based on the leave-one-out model achieved the maximum accuracy (68.30%) for the classification of both groups of uPID. Conclusion: We concluded that the SR group is cost-effective, safer, and efficacious for curing uPID. Proposed alternative treatment (test drug) could be a substitute of standard drug used for Female genital tract infections.

Producción Científica

X. Sumbul mail , Arshiya Sultana mail , Md Belal Bin Heyat mail , Khaleequr Rahman mail , Faijan Akhtar mail , Saba Parveen mail , Mercedes Briones Urbano mail mercedes.briones@uneatlantico.es, Vivian Lipari mail vivian.lipari@uneatlantico.es, Isabel De la Torre Díez mail , Azmat Ali Khan mail , Abdul Malik mail ,

Sumbul

<a href="/11642/1/s41598-024-57547-4.pdf" class="ep_document_link"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>

en

open

Adaptive neighborhood rough set model for hybrid data processing: a case study on Parkinson’s disease behavioral analysis

Extracting knowledge from hybrid data, comprising both categorical and numerical data, poses significant challenges due to the inherent difficulty in preserving information and practical meanings during the conversion process. To address this challenge, hybrid data processing methods, combining complementary rough sets, have emerged as a promising approach for handling uncertainty. However, selecting an appropriate model and effectively utilizing it in data mining requires a thorough qualitative and quantitative comparison of existing hybrid data processing models. This research aims to contribute to the analysis of hybrid data processing models based on neighborhood rough sets by investigating the inherent relationships among these models. We propose a generic neighborhood rough set-based hybrid model specifically designed for processing hybrid data, thereby enhancing the efficacy of the data mining process without resorting to discretization and avoiding information loss or practical meaning degradation in datasets. The proposed scheme dynamically adapts the threshold value for the neighborhood approximation space according to the characteristics of the given datasets, ensuring optimal performance without sacrificing accuracy. To evaluate the effectiveness of the proposed scheme, we develop a testbed tailored for Parkinson’s patients, a domain where hybrid data processing is particularly relevant. The experimental results demonstrate that the proposed scheme consistently outperforms existing schemes in adaptively handling both numerical and categorical data, achieving an impressive accuracy of 95% on the Parkinson’s dataset. Overall, this research contributes to advancing hybrid data processing techniques by providing a robust and adaptive solution that addresses the challenges associated with handling hybrid data, particularly in the context of Parkinson’s disease analysis.

Producción Científica

Imran Raza mail , Muhammad Hasan Jamal mail , Rizwan Qureshi mail , Abdul Karim Shahid mail , Angel Olider Rojas Vistorte mail angel.rojas@uneatlantico.es, Md Abdus Samad mail , Imran Ashraf mail ,

Raza

<a href="/11265/1/Food%20Frontiers%20-%202024%20-%20Cassotta%20-%20Human%E2%80%90based%20new%20approach%20methodologies%20to%20accelerate%20advances%20in%20nutrition%20research.pdf" class="ep_document_link"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>

en

open

Human‐based new approach methodologies to accelerate advances in nutrition research

Much 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.

Producción Científica

Manuela Cassotta mail manucassotta@gmail.com, Danila Cianciosi mail , Maria Elexpuru Zabaleta mail maria.elexpuru@uneatlantico.es, Iñaki Elío Pascual mail inaki.elio@uneatlantico.es, Sandra Sumalla Cano mail sandra.sumalla@uneatlantico.es, Francesca Giampieri mail francesca.giampieri@uneatlantico.es, Maurizio Battino mail maurizio.battino@uneatlantico.es,

Cassotta

<a class="ep_document_link" href="/11322/1/journal.pone.0298582.pdf"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>

en

open

Design and development of patient health tracking, monitoring and big data storage using Internet of Things and real time cloud computing

With 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.

Producción Científica

Md. Milon Islam mail , Imran Shafi mail , Sadia Din mail , Siddique Farooq mail , Isabel de la Torre Díez mail , Jose Breñosa mail josemanuel.brenosa@uneatlantico.es, Julio César Martínez Espinosa mail ulio.martinez@unini.edu.mx, Imran Ashraf mail ,

Islam

<a class="ep_document_link" href="/11666/1/Pneumonia_Detection_Using_Chest_Radiographs_With_Novel_EfficientNetV2L_Model.pdf"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>

en

open

Pneumonia Detection Using Chest Radiographs With Novel EfficientNetV2L Model

Pneumonia is a potentially life-threatening infectious disease that is typically diagnosed through physical examinations and diagnostic imaging techniques such as chest X-rays, ultrasounds or lung biopsies. Accurate diagnosis is crucial as wrong diagnosis, inadequate treatment or lack of treatment can cause serious consequences for patients and may become fatal. The advancements in deep learning have significantly contributed to aiding medical experts in diagnosing pneumonia by assisting in their decision-making process. By leveraging deep learning models, healthcare professionals can enhance diagnostic accuracy and make informed treatment decisions for patients suspected of having pneumonia. In this study, six deep learning models including CNN, InceptionResNetV2, Xception, VGG16, ResNet50 and EfficientNetV2L are implemented and evaluated. The study also incorporates the Adam optimizer, which effectively adjusts the epoch for all the models. The models are trained on a dataset of 5856 chest X-ray images and show 87.78%, 88.94%, 90.7%, 91.66%, 87.98% and 94.02% accuracy for CNN, InceptionResNetV2, Xception, VGG16, ResNet50 and EfficientNetV2L, respectively. Notably, EfficientNetV2L demonstrates the highest accuracy and proves its robustness for pneumonia detection. These findings highlight the potential of deep learning models in accurately detecting and predicting pneumonia based on chest X-ray images, providing valuable support in clinical decision-making and improving patient treatment.

Producción Científica

Mudasir Ali mail , Mobeen Shahroz mail , Urooj Akram mail , Muhammad Faheem Mushtaq mail , Stefanía Carvajal-Altamiranda mail stefania.carvajal@uneatlantico.es, Silvia Aparicio Obregón mail silvia.aparicio@uneatlantico.es, Isabel De La Torre Díez mail , Imran Ashraf mail ,

Ali