Items where Division is "University of La Romana > Research > Scientific Production" and Year is [pin missing: value2]

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
University of La Romana > Research > Scientific Production Abierto Inglés The provision of Wireless Fidelity (Wi-Fi) service in an indoor environment is a crucial task and the decay in signal strength issues arises especially in indoor environments. The Line-of-Sight (LOS) is a path for signal propagation that commonly impedes innumerable indoor objects damage signals and also causes signal fading. In addition, the Signal decay (signal penetration), signal reflection, and long transmission distance between transceivers are the key concerns. The signals lose their power due to the existence of obstacles (path of signals) and hence destroy received signal strength (RSS) between different communicating nodes and ultimately cause loss of the packet. Thus, to solve this issue, herein we propose an advanced model to maximize the LOS in communicating nodes using a modern indoor environment. Our proposal comprised various components for instance signal enhancers, repeaters, reflectors,. these components are connected. The signal attenuation and calculation model comprises of power algorithm and hence it can quickly and efficiently find the walls and corridors as obstacles in an indoor environment. We compared our proposed model with state of the art model using Received Signal Strength (RSS) and Packet Delivery Ratio (PDR) (different scenario) and found that our proposed model is efficient. Our proposed model achieved high network throughput as compared to the state-of-the-art models. metadata Khan, Muhammad Nasir and Waqas, Muhammad and Abbas, Qamar and Qureshi, Ahsan and Amin, Farhan and de la Torre Díez, Isabel and Uc Ríos, Carlos Eduardo and Fabian Gongora, Henry mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, carlos.uc@unini.edu.mx, henry.gongora@uneatlantico.es (2024) Advanced Line-of-Sight (LOS) model for communicating devices in modern indoor environment. PLOS ONE, 19 (7). e0305039. ISSN 1932-6203

Article Subjects > Nutrition Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
University of La Romana > Research > Scientific Production Abierto Inglés Background: Cardiovascular diseases (CVDs) encompass a variety of conditions that affect the heart and blood vessels. Carotenoids, a group of fat-soluble organic pigments synthesized by plants, fungi, algae, and some bacteria, may have a beneficial effect in reducing cardiovascular disease (CVD) risk. This study aims to examine and synthesize current research on the relationship between carotenoids and CVDs. Methods: A systematic review was conducted using MEDLINE and the Cochrane Library to identify relevant studies on the efficacy of carotenoid supplementation for CVD prevention. Interventional analytical studies (randomized and non-randomized clinical trials) published in English from January 2011 to February 2024 were included. Results: A total of 38 studies were included in the qualitative analysis. Of these, 17 epidemiological studies assessed the relationship between carotenoids and CVDs, 9 examined the effect of carotenoid supplementation, and 12 evaluated dietary interventions. Conclusions: Elevated serum carotenoid levels are associated with reduced CVD risk factors and inflammatory markers. Increasing the consumption of carotenoid-rich foods appears to be more effective than supplementation, though the specific effects of individual carotenoids on CVD risk remain uncertain. metadata Sumalla Cano, Sandra and Eguren García, Imanol and Lasarte García, Álvaro and Prola, Thomas and Martínez Díaz, Raquel and Elío Pascual, Iñaki mail sandra.sumalla@uneatlantico.es, imanol.eguren@uneatlantico.es, UNSPECIFIED, thomas.prola@uneatlantico.es, raquel.martinez@uneatlantico.es, inaki.elio@uneatlantico.es (2024) Carotenoids Intake and Cardiovascular Prevention: A Systematic Review. Nutrients, 16 (22). p. 3859. ISSN 2072-6643

Article Subjects > Biomedicine 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 > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
University of La Romana > Research > Scientific Production Abierto Inglés Emergency medical services (EMSs) face critical situations that require patient risk classification based on analytical and vital signs. We aimed to establish clustering-derived phenotypes based on prehospital analytical and vital signs that allow risk stratification. This was a prospective, multicenter, EMS-delivered, ambulance-based cohort study considering six advanced life support units, 38 basic life support units, and four tertiary hospitals in Spain. Adults with unselected acute diseases managed by the EMS and evacuated with discharge priority to emergency departments were considered between January 1, 2020, and June 30, 2023. Prehospital point-of-care testing and on-scene vital signs were used for the unsupervised machine learning method (clustering) to determine the phenotypes. Then phenotypes were compared with the primary outcome (cumulative mortality (all-cause) at 2, 7, and 30 days). A total of 7909 patients were included. The median (IQR) age was 64 (51–80) years, 41% were women, and 26% were living in rural areas. Three clusters were identified: alpha 16.2% (1281 patients), beta 28.8% (2279), and gamma 55% (4349). The mortality rates for alpha, beta and gamma at 2 days were 18.6%, 4.1%, and 0.8%, respectively; at 7 days, were 24.7%, 6.2%, and 1.7%; and at 30 days, were 33%, 10.2%, and 3.2%, respectively. Based on standard vital signs and blood test biomarkers in the prehospital scenario, three clusters were identified: alpha (high-risk), beta and gamma (medium- and low-risk, respectively). This permits the EMS system to quickly identify patients who are potentially compromised and to proactively implement the necessary interventions. metadata López-Izquierdo, Raúl and del Pozo Vegas, Carlos and Sanz-García, Ancor and Mayo Íscar, Agustín and Castro Villamor, Miguel A. and Silva Alvarado, Eduardo René and Gracia Villar, Santos and Dzul López, Luis Alonso and Aparicio Obregón, Silvia and Calderón Iglesias, Rubén and Soriano, Joan B. and Martín-Rodríguez, Francisco mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, eduardo.silva@funiber.org, santos.gracia@uneatlantico.es, luis.dzul@uneatlantico.es, silvia.aparicio@uneatlantico.es, ruben.calderon@uneatlantico.es, UNSPECIFIED, UNSPECIFIED (2024) Clinical phenotypes and short-term outcomes based on prehospital point-of-care testing and on-scene vital signs. npj Digital Medicine, 7 (1). ISSN 2398-6352

Article Subjects > Biomedicine 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 > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
University of La Romana > Research > Scientific Production Abierto Inglés Emergency medical services (EMSs) face critical situations that require patient risk classification based on analytical and vital signs. We aimed to establish clustering-derived phenotypes based on prehospital analytical and vital signs that allow risk stratification. This was a prospective, multicenter, EMS-delivered, ambulance-based cohort study considering six advanced life support units, 38 basic life support units, and four tertiary hospitals in Spain. Adults with unselected acute diseases managed by the EMS and evacuated with discharge priority to emergency departments were considered between January 1, 2020, and June 30, 2023. Prehospital point-of-care testing and on-scene vital signs were used for the unsupervised machine learning method (clustering) to determine the phenotypes. Then phenotypes were compared with the primary outcome (cumulative mortality (all-cause) at 2, 7, and 30 days). A total of 7909 patients were included. The median (IQR) age was 64 (51–80) years, 41% were women, and 26% were living in rural areas. Three clusters were identified: alpha 16.2% (1281 patients), beta 28.8% (2279), and gamma 55% (4349). The mortality rates for alpha, beta and gamma at 2 days were 18.6%, 4.1%, and 0.8%, respectively; at 7 days, were 24.7%, 6.2%, and 1.7%; and at 30 days, were 33%, 10.2%, and 3.2%, respectively. Based on standard vital signs and blood test biomarkers in the prehospital scenario, three clusters were identified: alpha (high-risk), beta and gamma (medium- and low-risk, respectively). This permits the EMS system to quickly identify patients who are potentially compromised and to proactively implement the necessary interventions. metadata López-Izquierdo, Raúl and del Pozo Vegas, Carlos and Sanz-García, Ancor and Mayo Íscar, Agustín and Castro Villamor, Miguel A. and Silva Alvarado, Eduardo René and Gracia Villar, Santos and Dzul López, Luis Alonso and Aparicio Obregón, Silvia and Calderón Iglesias, Rubén and Soriano, Joan B. and Martín-Rodríguez, Francisco mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, eduardo.silva@funiber.org, santos.gracia@uneatlantico.es, luis.dzul@uneatlantico.es, silvia.aparicio@uneatlantico.es, ruben.calderon@uneatlantico.es, UNSPECIFIED, UNSPECIFIED (2024) Clinical phenotypes and short-term outcomes based on prehospital point-of-care testing and on-scene vital signs. npj Digital Medicine, 7 (1). ISSN 2398-6352

Article Subjects > Biomedicine
Subjects > Social Sciences
Subjects > Engineering 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 > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
University of La Romana > Research > Scientific Production Abierto Inglés Aim: The development of predictive models for patients treated by emergency medical services (EMS) is on the rise in the emergency field. However, how these models evolve over time has not been studied. The objective of the present work is to compare the characteristics of patients who present mortality in the short, medium and long term, and to derive and validate a predictive model for each mortality time. Methods: A prospective multicenter study was conducted, which included adult patients with unselected acute illness who were treated by EMS. The primary outcome was noncumulative mortality from all causes by time windows including 30-day mortality, 31- to 180-day mortality, and 181- to 365-day mortality. Prehospital predictors included demographic variables, standard vital signs, prehospital laboratory tests, and comorbidities. Results: A total of 4830 patients were enrolled. The noncumulative mortalities at 30, 180, and 365 days were 10.8%, 6.6%, and 3.5%, respectively. The best predictive value was shown for 30-day mortality (AUC = 0.930; 95% CI: 0.919–0.940), followed by 180-day (AUC = 0.852; 95% CI: 0.832–0.871) and 365-day (AUC = 0.806; 95% CI: 0.778–0.833) mortality. Discussion: Rapid characterization of patients at risk of short-, medium-, or long-term mortality could help EMS to improve the treatment of patients suffering from acute illnesses. metadata Enriquez de Salamanca Gambara, Rodrigo and Sanz-García, Ancor and del Pozo Vegas, Carlos and López-Izquierdo, Raúl and Sánchez Soberón, Irene and Delgado Benito, Juan F. and Martínez Díaz, Raquel and Mazas Pérez-Oleaga, Cristina and Martínez López, Nohora Milena and Dominguez Azpíroz, Irma and Martín-Rodríguez, Francisco mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, raquel.martinez@uneatlantico.es, cristina.mazas@uneatlantico.es, nohora.martinez@uneatlantico.es, irma.dominguez@unini.edu.mx, UNSPECIFIED (2024) A Comparison of the Clinical Characteristics of Short-, Mid-, and Long-Term Mortality in Patients Attended by the Emergency Medical Services: An Observational Study. Diagnostics, 14 (12). p. 1292. ISSN 2075-4418

Article Subjects > Biomedicine
Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
University of La Romana > Research > Scientific Production Abierto Inglés UNSPECIFIED metadata Khawaja, Seher Ansar and Farooq, Muhammad Shoaib and Ishaq, Kashif and Alsubaie, Najah and Karamti, Hanen and Caro Montero, Elizabeth and Silva Alvarado, Eduardo René and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, elizabeth.caro@uneatlantico.es, eduardo.silva@funiber.org, UNSPECIFIED (2024) Correction: Prediction of leukemia peptides using convolutional neural network and protein compositions. BMC Cancer, 24 (1). ISSN 1471-2407

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
University of La Romana > Research > Scientific Production Abierto Inglés The perception and recognition of objects around us empower environmental interaction. Harnessing the brain’s signals to achieve this objective has consistently posed difficulties. Researchers are exploring whether the poor accuracy in this field is a result of the design of the temporal stimulation (block versus rapid event) or the inherent complexity of electroencephalogram (EEG) signals. Decoding perceptive signal responses in subjects has become increasingly complex due to high noise levels and the complex nature of brain activities. EEG signals have high temporal resolution and are non-stationary signals, i.e., their mean and variance vary overtime. This study aims to develop a deep learning model for the decoding of subjects’ responses to rapid-event visual stimuli and highlights the major factors that contribute to low accuracy in the EEG visual classification task.The proposed multi-class, multi-channel model integrates feature fusion to handle complex, non-stationary signals. This model is applied to the largest publicly available EEG dataset for visual classification consisting of 40 object classes, with 1000 images in each class. Contemporary state-of-the-art studies in this area investigating a large number of object classes have achieved a maximum accuracy of 17.6%. In contrast, our approach, which integrates Multi-Class, Multi-Channel Feature Fusion (MCCFF), achieves a classification accuracy of 33.17% for 40 classes. These results demonstrate the potential of EEG signals in advancing EEG visual classification and offering potential for future applications in visual machine models. metadata Rehman, Madiha and Anwer, Humaira and Garay, Helena and Alemany Iturriaga, Josep and Díez, Isabel De la Torre and Siddiqui, Hafeez ur Rehman and Ullah, Saleem mail UNSPECIFIED, UNSPECIFIED, helena.garay@uneatlantico.es, josep.alemany@uneatlantico.es, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED (2024) Decoding Brain Signals from Rapid-Event EEG for Visual Analysis Using Deep Learning. Sensors, 24 (21). p. 6965. ISSN 1424-8220

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
University of La Romana > Research > Scientific Production Abierto Inglés Generative intelligence relies heavily on the integration of vision and language. Much of the research has focused on image captioning, which involves describing images with meaningful sentences. Typically, when generating sentences that describe the visual content, a language model and a vision encoder are commonly employed. Because of the incorporation of object areas, properties, multi-modal connections, attentive techniques, and early fusion approaches like bidirectional encoder representations from transformers (BERT), these components have experienced substantial advancements over the years. This research offers a reference to the body of literature, identifies emerging trends in an area that blends computer vision as well as natural language processing in order to maximize their complementary effects, and identifies the most significant technological improvements in architectures employed for image captioning. It also discusses various problem variants and open challenges. This comparison allows for an objective assessment of different techniques, architectures, and training strategies by identifying the most significant technical innovations, and offers valuable insights into the current landscape of image captioning research. metadata Jamil, Azhar and Rehman, Saif Ur and Mahmood, Khalid and Gracia Villar, Mónica and Prola, Thomas and Diez, Isabel De La Torre and Samad, Md Abdus and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, monica.gracia@uneatlantico.es, thomas.prola@uneatlantico.es, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED (2024) Deep Learning Approaches for Image Captioning: Opportunities, Challenges and Future Potential. IEEE Access. p. 1. ISSN 2169-3536

Article Subjects > Biomedicine
Subjects > Engineering 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 > Articles and books
University of La Romana > Research > Scientific Production Abierto Inglés Non-Insulin-Dependent Diabetes Mellitus (NIDDM) is a chronic health condition caused by high blood sugar levels, and if not treated early, it can lead to serious complications i.e. blindness. Human Activity Recognition (HAR) offers potential for early NIDDM diagnosis, emerging as a key application for HAR technology. This research introduces DiabSense, a state-of-the-art smartphone-dependent system for early staging of NIDDM. DiabSense incorporates HAR and Diabetic Retinopathy (DR) upon leveraging the power of two different Graph Neural Networks (GNN). HAR uses a comprehensive array of 23 human activities resembling Diabetes symptoms, and DR is a prevalent complication of NIDDM. Graph Attention Network (GAT) in HAR achieved 98.32% accuracy on sensor data, while Graph Convolutional Network (GCN) in the Aptos 2019 dataset scored 84.48%, surpassing other state-of-the-art models. The trained GCN analyzed retinal images of four experimental human subjects for DR report generation, and GAT generated their average duration of daily activities over 30 days. The daily activities in non-diabetic periods of diabetic patients were measured and compared with the daily activities of the experimental subjects, which helped generate risk factors. Fusing risk factors with DR conditions enabled early diagnosis recommendations for the experimental subjects despite the absence of any apparent symptoms. The comparison of DiabSense system outcome with clinical diagnosis reports in the experimental subjects was conducted using the A1C test. The test results confirmed the accurate assessment of early diagnosis requirements for experimental subjects by the system. Overall, DiabSense exhibits significant potential for ensuring early NIDDM treatment, improving millions of lives worldwide. metadata Alam, Md Nuho Ul and Hasnine, Ibrahim and Bahadur, Erfanul Hoque and Masum, Abdul Kadar Muhammad and Briones Urbano, Mercedes and Masías Vergara, Manuel and Uddin, Jia and Ashraf, Imran and Samad, Md. Abdus mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, mercedes.briones@uneatlantico.es, manuel.masias@uneatlantico.es, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED (2024) DiabSense: early diagnosis of non-insulin-dependent diabetes mellitus using smartphone-based human activity recognition and diabetic retinopathy analysis with Graph Neural Network. Journal of Big Data, 11 (1). ISSN 2196-1115

Article Subjects > Biomedicine Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Articles and books
University of La Romana > Research > Scientific Production Abierto Inglés Objective: This study aims to determine the efficacy of the Acacia arabica (Lam.) Willd. and Cinnamomum camphora (L.) J. Presl. vaginal suppository in addressing heavy menstrual bleeding (HMB) and their impact on participants' health-related quality of life (HRQoL) analyzed using machine learning algorithms. Method: A total of 62 participants were enrolled in a double-dummy, single-center study. They were randomly assigned to either the suppository group (SG), receiving a formulation prepared with Acacia arabica gum (Gond Babul) and camphor from Cinnamomum camphora (Kafoor) through two vaginal suppositories (each weighing 3,500 mg) for 7 days at bedtime along with oral placebo capsules, or the tranexamic group (TG), receiving oral tranexamic acid (500 mg) twice a day for 5 days and two placebo vaginal suppositories during menstruation at bedtime for three consecutive menstrual cycles. The primary outcome was the pictorial blood loss assessment chart (PBLAC) for HMB, and secondary outcomes included hemoglobin level and SF-36 HRQoL questionnaire scores. Additionally, machine learning algorithms such as k-nearest neighbor (KNN), AdaBoost (AB), naive Bayes (NB), and random forest (RF) classifiers were employed for analysis. Results: In the SG and TG, the mean PBLAC score decreased from 635.322 ± 504.23 to 67.70 ± 22.37 and 512.93 ± 283.57 to 97.96 ± 39.25, respectively, at post-intervention (TF3), demonstrating a statistically significant difference (p < 0.001). A higher percentage of participants in the SG achieved normal menstrual blood loss compared to the TG (93.5% vs 74.2%). The SG showed a considerable improvement in total SF-36 scores (73.56%) compared to the TG (65.65%), with a statistically significant difference (p < 0.001). Additionally, no serious adverse events were reported in either group. Notably, machine learning algorithms, particularly AB and KNN, demonstrated the highest accuracy within cross-validation models for both primary and secondary outcomes. Conclusion: The A. arabica and C. camphora vaginal suppository is effective, cost-effective, and safe in controlling HMB. This botanical vaginal suppository provides a novel and innovative alternative to traditional interventions, demonstrating promise as an effective management approach for HMB. metadata Fazmiya, Mohamed Joonus Aynul and Sultana, Arshiya and Heyat, Md Belal Bin and Parveen, Saba and Rahman, Khaleequr and Akhtar, Faijan and Khan, Azmat Ali and Alanazi, Amer M. and Ahmed, Zaheer and Díez, Isabel de la Torre and Brito Ballester, Julién and Saripalli, Tirumala Santhosh Kumar mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, julien.brito@uneatlantico.es, UNSPECIFIED (2024) Efficacy of a vaginal suppository formulation prepared with Acacia arabica (Lam.) Willd. gum and Cinnamomum camphora (L.) J. Presl. in heavy menstrual bleeding analyzed using a machine learning technique. Frontiers in Pharmacology, 15. ISSN 1663-9812

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
University of La Romana > Research > Scientific Production Abierto Inglés Malaria is an extremely malignant disease and is caused by the bites of infected female mosquitoes. This disease is not only infectious among humans, but among animals as well. Malaria causes mild symptoms like fever, headache, sweating and vomiting, and muscle discomfort; severe symptoms include coma, seizures, and kidney failure. The timely identification of malaria parasites is a challenging and chaotic endeavor for health staff. An expert technician examines the schematic blood smears of infected red blood cells through a microscope. The conventional methods for identifying malaria are not efficient. Machine learning approaches are effective for simple classification challenges but not for complex tasks. Furthermore, machine learning involves rigorous feature engineering to train the model and detect patterns in the features. On the other hand, deep learning works well with complex tasks and automatically extracts low and high-level features from the images to detect disease. In this paper, EfficientNet, a deep learning-based approach for detecting Malaria, is proposed that uses red blood cell images. Experiments are carried out and performance comparison is made with pre-trained deep learning models. In addition, k-fold cross-validation is also used to substantiate the results of the proposed approach. Experiments show that the proposed approach is 97.57% accurate in detecting Malaria from red blood cell images and can be beneficial practically for medical healthcare staff. metadata Mujahid, Muhammad and Rustam, Furqan and Shafique, Rahman and Caro Montero, Elizabeth and Silva Alvarado, Eduardo René and de la Torre Diez, Isabel and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, elizabeth.caro@uneatlantico.es, eduardo.silva@funiber.org, UNSPECIFIED, UNSPECIFIED (2024) Efficient deep learning-based approach for malaria detection using red blood cell smears. Scientific Reports, 14 (1). ISSN 2045-2322

Article Subjects > Biomedicine
Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
University of La Romana > Research > Scientific Production Abierto Inglés Diabetes is a persistent health condition led by insufficient use or inappropriate use of insulin in the body. If left undetected, it can lead to further complications involving organ damage such as heart, lungs, and eyes. Timely detection of diabetes helps obtain the right medication, diet, and exercise plan to lead a healthy life. ML approach has been utilized to obtain rapid and reliable diabetes detection, however, existing approaches suffer from the use of limited datasets, lack of generalizability, and lower accuracy. This study proposes a novel feature extraction approach to overcome these limitations by using an ensemble of convolutional neural network (CNN) and long short-term memory (LSTM) models. Multiple datasets are combined to make a larger dataset for experiments and multiple features are utilized for investigating the efficacy of the proposed approach. Features from the extra tree classifier, CNN, and LSTM are also considered for comparison. Experimental results reveal the superb performance of CNN-LSTM-based features with random forest model obtaining a 0.99 accuracy score. This performance is further validated by comparison with existing approaches and k-fold cross-validation which shows the proposed approach provides robust results. metadata Rustam, Furqan and Al-Shamayleh, Ahmad Sami and Shafique, Rahman and Aparicio Obregón, Silvia and Calderón Iglesias, Rubén and Gonzalez, J. Pablo Miramontes and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, silvia.aparicio@uneatlantico.es, ruben.calderon@uneatlantico.es, UNSPECIFIED, UNSPECIFIED (2024) Enhanced detection of diabetes mellitus using novel ensemble feature engineering approach and machine learning model. Scientific Reports, 14 (1). ISSN 2045-2322

Article Subjects > Biomedicine 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 > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
University of La Romana > Research > Scientific Production Abierto Inglés Background: The 2023 dengue outbreak has proven that dengue is not only an endemic disease but also an emerging health threat in Bangladesh. Integrated studies on the epidemiology, clinical characteristics, seasonality, and genotype of dengue are limited. This study was conducted to determine recent trends in the molecular epidemiology, clinical features, and seasonality of dengue outbreaks. Methods: We analyzed data from 41 original studies, extracting epidemiological information from all 41 articles, clinical symptoms from 30 articles, and genotypic diversity from 11 articles. The study adhered to the standards of the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) Statement and Cochrane Collaboration guidelines. Conclusion: This study provides integrated insights into the molecular epidemiology, clinical features, seasonality, and transmission of dengue in Bangladesh and highlights research gaps for future studies. metadata Sharif, Nadim and Opu, Rubayet Rayhan and Saha, Tama and Masud, Abdullah Ibna and Naim, Jannatin and Alsharif, Khalaf F. and Alzahrani, Khalid J. and Silva Alvarado, Eduardo René and Delgado Noya, Irene and De la Torre Díez, Isabel and Dey, Shuvra Kanti mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, eduardo.silva@funiber.org, irene.delgado@uneatlantico.es, UNSPECIFIED, UNSPECIFIED (2024) Evolving epidemiology, clinical features, and genotyping of dengue outbreaks in Bangladesh, 2000–2024: a systematic review. Frontiers in Microbiology, 15. ISSN 1664-302X

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
University of La Romana > Research > Scientific Production Abierto Inglés In contemporary society, depression has emerged as a prominent mental disorder that exhibits exponential growth and exerts a substantial influence on premature mortality. Although numerous research applied machine learning methods to forecast signs of depression. Nevertheless, only a limited number of research have taken into account the severity level as a multiclass variable. Besides, maintaining the equality of data distribution among all the classes rarely happens in practical communities. So, the inevitable class imbalance for multiple variables is considered a substantial challenge in this domain. Furthermore, this research emphasizes the significance of addressing class imbalance issues in the context of multiple classes. We introduced a new approach Feature group partitioning (FGP) in the data preprocessing phase which effectively reduces the dimensionality of features to a minimum. This study utilized synthetic oversampling techniques, specifically Synthetic Minority Over-sampling Technique (SMOTE) and Adaptive Synthetic (ADASYN), for class balancing. The dataset used in this research was collected from university students by administering the Burn Depression Checklist (BDC). For methodological modifications, we implemented heterogeneous ensemble learning stacking, homogeneous ensemble bagging, and five distinct supervised machine learning algorithms. The issue of overfitting was mitigated by evaluating the accuracy of the training, validation, and testing datasets. To justify the effectiveness of the prediction models, balanced accuracy, sensitivity, specificity, precision, and f1-score indices are used. Overall, comprehensive analysis demonstrates the discrimination between the Conventional Depression Screening (CDS) and FGP approach. In summary, the results show that the stacking classifier for FGP with SMOTE approach yields the highest balanced accuracy, with a rate of 92.81%. The empirical evidence has demonstrated that the FGP approach, when combined with the SMOTE, able to produce better performance in predicting the severity of depression. Most importantly the optimization of the training time of the FGP approach for all of the classifiers is a significant achievement of this research. metadata Shaha, Tumpa Rani and Begum, Momotaz and Uddin, Jia and Yélamos Torres, Vanessa and Alemany Iturriaga, Josep and Ashraf, Imran and Samad, Md. Abdus mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, vanessa.yelamos@funiber.org, josep.alemany@uneatlantico.es, UNSPECIFIED, UNSPECIFIED (2024) Feature group partitioning: an approach for depression severity prediction with class balancing using machine learning algorithms. BMC Medical Research Methodology, 24 (1). ISSN 1471-2288

Article Subjects > Biomedicine
Subjects > Nutrition Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
University of La Romana > Research > Scientific Production Abierto Inglés Cardiovascular diseases (CVDs) are one of the main causes of mortality and morbidity worldwide. A healthy diet rich in plant-derived compounds such as (poly)phenols appears to have a key role in improving cardiovascular health. Flavan-3-ols represent a subclass of (poly)phenols of great interest for their possible health benefits. In this review, we summarized the results of clinical studies on vascular outcomes of flavan-3-ol supplementation and we focused on the role of the microbiota in CVD. Clinical trials included in this review showed that supplementation with flavan-3-ols mostly derived from cocoa products significantly reduces blood pressure and improves endothelial function. Studies on catechins from green tea demonstrated better results when involving healthy individuals. From a mechanistic point of view, emerging evidence suggests that microbial metabolites may play a role in the observed effects. Their function extends beyond the previous belief of ROS scavenging activity and encompasses a direct impact on gene expression and protein function. Although flavan-3-ols appear to have effects on cardiovascular health, further studies are needed to clarify and confirm these potential benefits and the rising evidence of the potential involvement of the microbiota. metadata Godos, Justyna and Romano, Giovanni Luca and Laudani, Samuele and Gozzo, Lucia and Guerrera, Ida and Dominguez Azpíroz, Irma and Martínez Díaz, Raquel and Quiles, José L. and Battino, Maurizio and Drago, Filippo and Giampieri, Francesca and Galvano, Fabio and Grosso, Giuseppe mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, irma.dominguez@unini.edu.mx, raquel.martinez@uneatlantico.es, jose.quiles@uneatlantico.es, maurizio.battino@uneatlantico.es, UNSPECIFIED, francesca.giampieri@uneatlantico.es, UNSPECIFIED, UNSPECIFIED (2024) Flavan-3-ols and Vascular Health: Clinical Evidence and Mechanisms of Action. Nutrients, 16 (15). p. 2471. ISSN 2072-6643

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
University of La Romana > Research > Scientific Production Abierto Inglés Wafer mappings (WM) help diagnose low-yield issues in semiconductor production by offering vital information about process anomalies. As integrated circuits continue to grow in complexity, doing efficient yield analyses is becoming more essential but also more difficult. Semiconductor manufacturers require constant attention to reliability and efficiency. Using the capabilities of convolutional neural network (CNN) models improved by hierarchical attention module (HAM), wafer hotspot detection is achieved throughout the fabrication process. In an effort to achieve accurate hotspot detection, this study examines a variety of model combinations, including CNN, CNN+long short-term memory (LSTM) LSTM, CNN+Autoencoder, CNN+artificial neural network (ANN), LSTM+HAM, Autoencoder+HAM, ANN+HAM, and CNN+HAM. Data augmentation strategies are utilized to enhance the model’s resilience by optimizing its performance on a variety of datasets. Experimental results indicate a superior performance of 94.58% accuracy using the CNN+HAM model. K-fold cross-validation results using 3, 5, 7, and 10 folds indicate mean accuracy of 94.66%, 94.67%, 94.66%, and 94.66%, for the proposed approach, respectively. The proposed model performs better than recent existing works on wafer hotspot detection. Performance comparison with existing models further validates its robustness and performance. metadata Shahroz, Mobeen and Ali, Mudasir and Tahir, Alishba and Fabian Gongora, Henry and Uc Ríos, Carlos Eduardo and Abdus Samad, Md and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, henry.gongora@uneatlantico.es, carlos.uc@unini.edu.mx, UNSPECIFIED, UNSPECIFIED (2024) Hierarchical Attention Module-Based Hotspot Detection in Wafer Fabrication Using Convolutional Neural Network Model. IEEE Access, 12. pp. 92840-92855. ISSN 2169-3536

Article Subjects > Biomedicine
Subjects > Nutrition Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
University of La Romana > Research > Scientific Production Abierto Inglés Isoflavones 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. metadata Laudani, Samuele and Godos, Justyna and Romano, Giovanni Luca and Gozzo, Lucia and Di Domenico, Federica Martina and Dominguez Azpíroz, Irma and Martínez Díaz, Raquel and Giampieri, Francesca and Quiles, José L. and Battino, Maurizio and Drago, Filippo and Galvano, Fabio and Grosso, Giuseppe mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, irma.dominguez@unini.edu.mx, raquel.martinez@uneatlantico.es, francesca.giampieri@uneatlantico.es, jose.quiles@uneatlantico.es, maurizio.battino@uneatlantico.es, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED (2024) Isoflavones Effects on Vascular and Endothelial Outcomes: How Is the Gut Microbiota Involved? Pharmaceuticals, 17 (2). p. 236. ISSN 1424-8247

Article Subjects > Nutrition Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
University of La Romana > Research > Scientific Production Abierto Inglés The prevalence of sleep disorders, characterized by issues with quality, timing, and sleep duration is increasing globally. Among modifiable risk factors, diet quality has been suggested to influence sleep features. The Mediterranean diet is considered a landmark dietary pattern in terms of quality and effects on human health. However, dietary habits characterized by this cultural heritage should also be considered in the context of overall lifestyle behaviors, including sleep habits. This study aimed to systematically revise the literature relating to adherence to the Mediterranean diet and sleep features in observational studies. The systematic review comprised 23 reports describing the relation between adherence to the Mediterranean diet and different sleep features, including sleep quality, sleep duration, daytime sleepiness, and insomnia symptoms. The majority of the included studies were conducted in the Mediterranean basin and reported a significant association between a higher adherence to the Mediterranean diet and a lower likelihood of having poor sleep quality, inadequate sleep duration, excessive daytime sleepiness or symptoms of insomnia. Interestingly, additional studies conducted outside the Mediterranean basin showed a relationship between the adoption of a Mediterranean-type diet and sleep quality, suggesting that biological mechanisms sustaining such an association may exist. In conclusion, current evidence suggests a relationship between adhering to the Mediterranean diet and overall sleep quality and different sleep parameters. The plausible bidirectional association should be further investigated to understand whether the promotion of a healthy diet could be used as a tool to improve sleep quality. metadata Godos, Justyna and Ferri, Raffaele and Lanza, Giuseppe and Caraci, Filippo and Rojas Vistorte, Angel Olider and Yélamos Torres, Vanessa and Grosso, Giuseppe and Castellano, Sabrina mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, angel.rojas@uneatlantico.es, vanessa.yelamos@funiber.org, UNSPECIFIED, UNSPECIFIED (2024) Mediterranean Diet and Sleep Features: A Systematic Review of Current Evidence. Nutrients, 16 (2). p. 282. ISSN 2072-6643

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
University of La Romana > Research > Scientific Production Abierto Inglés New approaches to software testing are required due to the rising complexity of today’s software applications and the rapid growth of software engineering practices. Among these methods, one that has shown promise is the introduction of Natural Language Processing (NLP) tools to software testing practices. NLP has witnessed a rise in popularity within all IT fields, especially in software engineering, where its use has improved the way we extract information from textual data. The goal of this systematic literature review (SLR) is to provide an in-depth analysis of the present body of the literature on the expanding subject of NLP-based software testing. Through a repeatable process, that takes into account the quality of the research, we examined 24 papers extracted from Web of Science and Scopus databases to extract insights about the usage of NLP techniques in the field of software testing. Requirements analysis and test case generation popped up as the most hot topics in the field. We also explored NLP techniques, software testing types, machine/deep learning algorithms, and NLP tools and frameworks used in the studied body of literature. This study also stressed some recurrent open challenges that need further work in future research such as the generalization of the NLP algorithm across domains and languages and the ambiguity in the natural language requirements. Software testing professionals and researchers can get important insights from the findings of this SLR, which will help them comprehend the advantages and challenges of using NLP in software testing. metadata Boukhlif, Mohamed and Hanine, Mohamed and Kharmoum, Nassim and Ruigómez Noriega, Atenea and García Obeso, David and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, atenea.ruigomez@uneatlantico.es, david.garcia@uneatlantico.es, UNSPECIFIED (2024) Natural Language Processing-Based Software Testing: A Systematic Literature Review. IEEE Access, 12. pp. 79383-79400. ISSN 2169-3536

Article Subjects > Biomedicine 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 > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
University of La Romana > Research > Scientific Production Abierto Inglés The evolution of the COVID-19 pandemic has been associated with variations in clinical presentation and severity. Similarly, prediction scores may suffer changes in their diagnostic accuracy. The aim of this study was to test the 30-day mortality predictive validity of the 4C and SEIMC scores during the sixth wave of the pandemic and to compare them with those of validation studies. This was a longitudinal retrospective observational study. COVID-19 patients who were admitted to the Emergency Department of a Spanish hospital from December 15, 2021, to January 31, 2022, were selected. A side-by-side comparison with the pivotal validation studies was subsequently performed. The main measures were 30-day mortality and the 4C and SEIMC scores. A total of 27,614 patients were considered in the study, including 22,361 from the 4C, 4,627 from the SEIMC and 626 from our hospital. The 30-day mortality rate was significantly lower than that reported in the validation studies. The AUCs were 0.931 (95% CI: 0.90–0.95) for 4C and 0.903 (95% CI: 086–0.93) for SEIMC, which were significantly greater than those obtained in the first wave. Despite the changes that have occurred during the coronavirus disease 2019 (COVID-19) pandemic, with a reduction in lethality, scorecard systems are currently still useful tools for detecting patients with poor disease risk, with better prognostic capacity. metadata de Santos Castro, Pedro Ángel and del Pozo Vegas, Carlos and Pinilla Arribas, Leyre Teresa and Zalama Sánchez, Daniel and Sanz-García, Ancor and Vásquez del Águila, Tony Giancarlo and González Izquierdo, Pablo and de Santos Sánchez, Sara and Mazas Pérez-Oleaga, Cristina and Dominguez Azpíroz, Irma and Elío Pascual, Iñaki and Martín-Rodríguez, Francisco mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, cristina.mazas@uneatlantico.es, irma.dominguez@unini.edu.mx, inaki.elio@uneatlantico.es, UNSPECIFIED (2024) Performance of the 4C and SEIMC scoring systems in predicting mortality from onset to current COVID-19 pandemic in emergency departments. Scientific Reports, 14 (1). ISSN 2045-2322

Article Subjects > Biomedicine
Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
University of La Romana > Research > Scientific Production Cerrado Inglés Leukemia is a type of blood cell cancer that is in the bone marrow’s blood-forming cells. Two types of Leukemia are acute and chronic; acute enhances fast and chronic growth gradually which are further classified into lymphocytic and myeloid leukemias. This work evaluates a unique deep convolutional neural network (CNN) classifier that improves identification precision by carefully examining concatenated peptide patterns. The study uses leukemia protein expression for experiments supporting two different techniques including independence and applied cross-validation. In addition to CNN, multilayer perceptron (MLP), gated recurrent unit (GRU), and recurrent neural network (RNN) are applied. The experimental results show that the CNN model surpasses competitors with its outstanding predictability in independent and cross-validation testing applied on different features extracted from protein expressions such as amino acid composition (AAC) with a group of AAC (GAAC), tripeptide composition (TPC) with a group of TPC (GTPC), and dipeptide composition (DPC) for calculating its accuracies with their receiver operating characteristic (ROC) curve. In independence testing, a feature expression of AAC and a group of GAAC are applied using MLP and CNN modules, and ROC curves are achieved with overall 100% accuracy for the detection of protein patterns. In cross-validation testing, a feature expression on a group of AAC and GAAC patterns achieved 98.33% accuracy which is the highest for the CNN module. Furthermore, ROC curves show a 0.965% extraordinary result for the GRU module. The findings show that the CNN model is excellent at figuring out leukemia illnesses from protein expressions with higher accuracy. metadata Khawaja, Seher Ansar and Farooq, Muhammad Shoaib and Ishaq, Kashif and Alsubaie, Najah and Karamti, Hanen and Caro Montero, Elizabeth and Silva Alvarado, Eduardo René and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, elizabeth.caro@uneatlantico.es, eduardo.silva@funiber.org, UNSPECIFIED (2024) Prediction of leukemia peptides using convolutional neural network and protein compositions. BMC Cancer, 24 (1). ISSN 1471-2407

Article Subjects > Biomedicine 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 > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
University of La Romana > Research > Scientific Production Abierto Inglés Objective The aim was to explore the association of demographic and prehospital parameters with short-term and long-term mortality in acute life-threatening cardiovascular disease by using a hazard model, focusing on elderly individuals, by comparing patients under 75 years versus patients over 75 years of age. Design Prospective, multicentre, observational study. Setting Emergency medical services (EMS) delivery study gathering data from two back-to-back studies between 1 October 2019 and 30 November 2021. Six advanced life support (ALS), 43 basic life support and five hospitals in Spain were considered. Participants Adult patients suffering from acute life-threatening cardiovascular disease attended by the EMS. Primary and secondary outcome measures The primary outcome was in-hospital mortality from any cause within the first to the 365 days following EMS attendance. The main measures included prehospital demographics, biochemical variables, prehospital ALS techniques used and syndromic suspected conditions. Results A total of 1744 patients fulfilled the inclusion criteria. The 365-day cumulative mortality in the elderly amounted to 26.1% (229 cases) versus 11.6% (11.6%) in patients under 75 years old. Elderly patients (≥75 years) presented a twofold risk of mortality compared with patients ≤74 years. Life-threatening interventions (mechanical ventilation, cardioversion and defibrillation) were also related to a twofold increased risk of mortality. Importantly, patients suffering from acute heart failure presented a more than twofold increased risk of mortality. Conclusions This study revealed the prehospital variables associated with the long-term mortality of patients suffering from acute cardiovascular disease. Our results provide important insights for the development of specific codes or scores for cardiovascular diseases to facilitate the risk of mortality characterisation. metadata del Pozo Vegas, Carlos and Zalama-Sánchez, Daniel and Sanz-Garcia, Ancor and López-Izquierdo, Raúl and Sáez-Belloso, Silvia and Mazas Pérez-Oleaga, Cristina and Dominguez Azpíroz, Irma and Elío Pascual, Iñaki and Martín-Rodríguez, Francisco mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, cristina.mazas@uneatlantico.es, irma.dominguez@unini.edu.mx, inaki.elio@uneatlantico.es, UNSPECIFIED (2023) Prehospital acute life-threatening cardiovascular disease in elderly: an observational, prospective, multicentre, ambulance-based cohort study. BMJ Open, 13 (11). e078815. ISSN 2044-6055

Article Subjects > Biomedicine Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
University of La Romana > Research > Scientific Production Abierto Inglés Introduction: Rotavirus infection is a major cause of mortality among children under 5 years in Bangladesh. There is lack of integrated studies on rotavirus prevalence and genetic diversity during 1973 to 2023 in Bangladesh. Methods: This meta-analysis was conducted to determine the prevalence, genotypic diversity and seasonal distribution of rotavirus during pre-vaccination period in Bangladesh. This study included published articles on rotavirus A, rotavirus B and rotavirus C. We used Medline, Scopus and Google Scholar for published articles. Selected literatures were published between 1973 to 2023. Results: This study detected 12431 research articles published on rotavirus. Based on the inclusion criteria, 29 of 75 (30.2%) studies were selected. Molecular epidemiological data was taken from 29 articles, prevalence data from 29 articles, and clinical symptoms from 19 articles. The pooled prevalence of rotavirus was 30.1% (95% CI: 22%-45%, p = 0.005). Rotavirus G1 (27.1%, 2228 of 8219) was the most prevalent followed by G2 (21.09%, 1733 of 8219), G4 (11.58%, 952 of 8219), G9 (9.37%, 770 of 8219), G12 (8.48%, 697 of 8219), and G3 (2.79%, 229 of 8219), respectively. Genotype P[8] (40.6%, 2548 of 6274) was the most prevalent followed by P[4] (12.4%, 777 of 6274) and P[6] (6.4%, 400 of 6274), respectively. Rotavirus G1P[8] (19%) was the most frequent followed by G2P [4] (9.4%), G12P[8] (7.2%), and G9P[8], respectively. Rotavirus infection had higher odds of occurrence during December and February (aOR: 2.86, 95% CI: 2.43-3.6, p = 0.001). Discussion: This is the first meta-analysis including all the studies on prevalence, molecular epidemiology, and genetic diversity of rotavirus from 1973 to 2023, pre-vaccination period in Bangladesh. This study will provide overall scenario of rotavirus genetic diversity and seasonality during pre-vaccination period and aids in policy making for rotavirus vaccination program in Bangladesh. This work will add valuable knowledge for vaccination against rotavirus and compare the data after starting vaccination in Bangladesh. metadata Sharif, Nadim and Sharif, Nazmul and Khan, Afsana and Dominguez Azpíroz, Irma and Martínez Díaz, Raquel and Díez, Isabel De la Torre and Parvez, Anowar Khasru and Dey, Shuvra Kanti mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, irma.dominguez@unini.edu.mx, raquel.martinez@uneatlantico.es, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED (2023) Prevalence and genetic diversity of rotavirus in Bangladesh during pre-vaccination period, 1973-2023: a meta-analysis. Frontiers in Immunology, 14. ISSN 1664-3224

Article Subjects > Nutrition Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
University of La Romana > Research > Scientific Production Abierto Inglés The purpose of the study is to assess the risk of developing general eating disorders (ED), anorexia nervosa (AN), and bulimia nervosa (BN), as well as to examine the effects of gender, academic year, place of residence, faculty, and diet quality on that risk. Over two academic years, 129 first- and fourth-year Uneatlántico students were included in an observational descriptive study. The self-administered tests SCOFF, EAT-26, and BITE were used to determine the participants’ risk of developing ED. The degree of adherence to the Mediterranean diet (MD) was used to evaluate the quality of the diet. Data were collected at the beginning (T1) and at the end (T2) of the academic year. The main results were that at T1, 34.9% of participants were at risk of developing general ED, AN 3.9%, and BN 16.3%. At T2, these percentages were 37.2%, 14.7%, and 8.5%, respectively. At T2, the frequency of general ED in the female group was 2.5 times higher (OR: 2.55, 95% CI: 1.22–5.32, p = 0.012). The low-moderate adherence to the MD students’ group was 0.92 times less frequent than general ED at T2 (OR: 0.921, 95%CI: 0.385–2.20, p < 0.001). The most significant risk factor for developing ED is being a female in the first year of university. Moreover, it appears that the likelihood of developing ED generally increases during the academic year. metadata Eguren García, Imanol and Sumalla Cano, Sandra and Conde González, Sandra and Vila-Martí, Anna and Briones Urbano, Mercedes and Martínez Díaz, Raquel and Elío Pascual, Iñaki mail imanol.eguren@uneatlantico.es, sandra.sumalla@uneatlantico.es, UNSPECIFIED, UNSPECIFIED, mercedes.briones@uneatlantico.es, raquel.martinez@uneatlantico.es, inaki.elio@uneatlantico.es (2024) Risk Factors for Eating Disorders in University Students: The RUNEAT Study. Healthcare, 12 (9). p. 942. ISSN 2227-9032

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
University of La Romana > Research > Scientific Production Abierto Inglés With the rapid increase of users over social media, cyberbullying, and hate speech problems have arisen over the past years. Automatic hate speech detection (HSD) from text is an emerging research problem in natural language processing (NLP). Researchers developed various approaches to solve the automatic hate speech detection problem using different corpora in various languages, however, research on the Urdu language is rather scarce. This study aims to address the HSD task on Twitter using Roman Urdu text. The contribution of this research is the development of a hybrid model for Roman Urdu HSD, which has not been previously explored. The novel hybrid model integrates deep learning (DL) and transformer models for automatic feature extraction, combined with machine learning algorithms (MLAs) for classification. To further enhance model performance, we employ several hyperparameter optimization (HPO) techniques, including Grid Search (GS), Randomized Search (RS), and Bayesian Optimization with Gaussian Processes (BOGP). Evaluation is carried out on two publicly available benchmarks Roman Urdu corpora comprising HS-RU-20 corpus and RUHSOLD hate speech corpus. Results demonstrate that the Multilingual BERT (MBERT) feature learner, paired with a Support Vector Machine (SVM) classifier and optimized using RS, achieves state-of-the-art performance. On the HS-RU-20 corpus, this model attained an accuracy of 0.93 and an F1 score of 0.95 for the Neutral-Hostile classification task, and an accuracy of 0.89 with an F1 score of 0.88 for the Hate Speech-Offensive task. On the RUHSOLD corpus, the same model achieved an accuracy of 0.95 and an F1 score of 0.94 for the Coarse-grained task, alongside an accuracy of 0.87 and an F1 score of 0.84 for the Fine-grained task. These results demonstrate the effectiveness of our hybrid approach for Roman Urdu hate speech detection. metadata Ashiq, Waqar and Kanwal, Samra and Rafique, Adnan and Waqas, Muhammad and Khurshaid, Tahir and Caro Montero, Elizabeth and Bustamante Alonso, Alicia and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, elizabeth.caro@uneatlantico.es, alicia.bustamante@uneatlantico.es, UNSPECIFIED (2024) Roman urdu hate speech detection using hybrid machine learning models and hyperparameter optimization. Scientific Reports, 14 (1). ISSN 2045-2322

Article Subjects > Biomedicine
Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
University of La Romana > Research > Scientific Production Abierto Inglés Telephysiotherapy has emerged as a vital solution for delivering remote healthcare, particularly in response to global challenges such as the COVID-19 pandemic. This study seeks to enhance telephysiotherapy by developing a system capable of accurately classifying physiotherapeutic exercises using PoseNet, a state-of-the-art pose estimation model. A dataset was collected from 49 participants (35 males, 14 females) performing seven distinct exercises, with twelve anatomical landmarks then extracted using the Google MediaPipe library. Each landmark was represented by four features, which were used for classification. The core challenge addressed in this research involves ensuring accurate and real-time exercise classification across diverse body morphologies and exercise types. Several tree-based classifiers, including Random Forest, Extra Tree Classifier, XGBoost, LightGBM, and Hist Gradient Boosting, were employed. Furthermore, two novel ensemble models called RandomLightHist Fusion and StackedXLightRF are proposed to enhance classification accuracy. The RandomLightHist Fusion model achieved superior accuracy of 99.6%, demonstrating the system’s robustness and effectiveness. This innovation offers a practical solution for providing real-time feedback in telephysiotherapy, with potential to improve patient outcomes through accurate monitoring and assessment of exercise performance. metadata Hussain, Shahzad and Siddiqui, Hafeez Ur Rehman and Saleem, Adil Ali and Raza, Muhammad Amjad and Alemany Iturriaga, Josep and Velarde-Sotres, Álvaro and Díez, Isabel De la Torre and Dudley, Sandra mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, josep.alemany@uneatlantico.es, alvaro.velarde@uneatlantico.es, UNSPECIFIED, UNSPECIFIED (2024) Smart Physiotherapy: Advancing Arm-Based Exercise Classification with PoseNet and Ensemble Models. Sensors, 24 (19). p. 6325. ISSN 1424-8220

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
University of La Romana > Research > Scientific Production Abierto Inglés Software cost and effort estimation is one of the most significant tasks in the area of software engineering. Research conducted in this field has been evolving with new techniques that necessitate periodic comparative analyses. Software project success largely depends on accurate software cost estimation as it gives an idea of the challenges and risks involved in the development. The great diversity of ML and Non-ML techniques has generated a comparison and progressed into the integration of these techniques. Based on varying advantages it has become imperative to work out preferred estimation techniques to improve the project development process. This study aims to present a systematic literature review (SLR) to investigate the trends of the articles published in the recent one and a half decades and to propose a way forward. This systematic literature review has proposed a three-stage approach to plan (Tollgate approach), conduct (Likert type scale), and report the results from five renowned digital libraries. For the selected 52 articles, artificial neural network model (ANN) and constructive cost model (COCOMO) based approaches have been the favored techniques. The mean magnitude of relative error (MMRE) has been the preferred accuracy metric, software engineering, and project management are the most relevant fields, and the promise repository has been identified as the widely accessed database. This review is likely to be of value for the development, cost, and effort estimations. metadata Rashid, Chaudhary Hamza and Shafi, Imran and Ahmad, Jamil and Bautista Thompson, Ernesto and Masías Vergara, Manuel and Diez, Isabel De La Torre and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, ernesto.bautista@unini.edu.mx, manuel.masias@uneatlantico.es, UNSPECIFIED, UNSPECIFIED (2023) Software Cost and Effort Estimation: Current Approaches and Future Trends. IEEE Access. p. 1. ISSN 2169-3536

Article Subjects > Biomedicine Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
University of La Romana > Research > Scientific Production Abierto Inglés Interleukin-10, a highly effective cytokine recognized for its anti-inflammatory properties, plays a critical role in the immune system. In addition to its well-documented capacity to mitigate inflammation, IL-10 can unexpectedly demonstrate pro-inflammatory characteristics under specific circumstances. The presence of both aspects emphasizes the vital need to identify the IL-10-induced peptide. To mitigate the drawbacks of manual identification, which include its high cost, this study introduces StackIL10, an ensemble learning model based on stacking, to identify IL-10-inducing peptides in a precise and efficient manner. Ten Amino-acid-composition-based Feature Extraction approaches are considered. The StackIL10, stacking ensemble, the model with five optimized Machine Learning Algorithm (specifically LGBM, RF, SVM, Decision Tree, KNN) as the base learners and a Logistic Regression as the meta learner was constructed, and the identification rate reached 91.7%, MCC of 0.833 with 0.9078 Specificity. Experiments were conducted to examine the impact of various enhancement techniques on the correctness of IL-10 Prediction. These experiments included comparisons between single models and various combinations of stacking-based ensemble models. It was demonstrated that the model proposed in this study was more effective than singular models and produced satisfactory results, thereby improving the identification of peptides that induce IL-10. metadata Usmani, Salman Sadullah and Tuhin, Izaz Ahmmed and Mia, Md. Rajib and Islam, Md. Monirul and Mahmud, Imran and Uc Ríos, Carlos Eduardo and Fabian Gongora, Henry and Ashraf, Imran and Samad, Md. Abdus mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, carlos.uc@unini.edu.mx, henry.gongora@uneatlantico.es, UNSPECIFIED, UNSPECIFIED (2024) StackIL10: A stacking ensemble model for the improved prediction of IL-10 inducing peptides. PLOS ONE, 19 (11). e0313835. ISSN 1932-6203

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Articles and books
Universidad Internacional do Cuanza > Research > Scientific Production
University of La Romana > Research > Scientific Production Abierto Inglés Video content on the web platform has increased explosively during the past decade, thanks to the open access to Facebook, YouTube, etc. YouTube is the second-largest social media platform nowadays containing more than 37 million YouTube channels. YouTube revealed at a recent press event that 30,000 new content videos per hour and 720,000 per day are posted. There is a need for an advanced deep learning-based approach to categorize the huge database of YouTube videos. This study aims to develop an artificial intelligence-based approach to categorize YouTube videos. This study analyzes the textual information related to videos like titles, descriptions, user tags, etc. using YouTube exploratory data analysis (YEDA) and shows that such information can be potentially used to categorize videos. A deep convolutional neural network (DCNN) is designed to categorize YouTube videos with efficiency and high accuracy. In addition, recurrent neural network (RNN), and gated recurrent unit (GRU) are also employed for performance comparison. Moreover, logistic regression, support vector machines, decision trees, and random forest models are also used. A large dataset with 9 classes is used for experiments. Experimental findings indicate that the proposed DCNN achieves the highest receiver operating characteristics (ROC) area under the curve (AUC) score of 99% in the context of YouTube video categorization and 96% accuracy which is better than existing approaches. The proposed approach can be used to help YouTube users suggest relevant videos and sort them by video category. metadata Raza, Ali and Younas, Faizan and Siddiqui, Hafeez Ur Rehman and Rustam, Furqan and Gracia Villar, Mónica and Silva Alvarado, Eduardo René and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, monica.gracia@uneatlantico.es, eduardo.silva@funiber.org, UNSPECIFIED (2024) An improved deep convolutional neural network-based YouTube video classification using textual features. Heliyon, 10 (16). e35812. ISSN 24058440