COVID-19 mothers' mother-baby bonding, feeding practices, postnatal care experiences in Qatar: A mixed-methods approach.

Introduction: The study aim was to examine mother-infant bonding, feeding practices, and postnatal care experiences of mothers diagnosed with COVID-19 in hospital settings from 2020 to 2022.

Methods: A mixed-methods research design was conducted, involving 117 participants in a cross-sectional online survey and 11 phone interviews. The study was conducted among mothers diagnosed with COVID-19 by PCR test and admitted to four maternity facilities in Qatar from 1 May 2020 to 16 January 2022.

The Role of Aldosterone and the Mineralocorticoid Receptor in Metabolic Dysfunction-Associated Steatotic Liver Disease.

Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) is one of the fastest-growing hepatic disorders worldwide. During its natural course, MASLD tends to progress from isolated steatosis of the liver to Metabolic Dysfunction-Associated Alcoholic Steatohepatitis (MASH), advanced fibrosis, and finally cirrhosis, with the risk of developing hepatocellular carcinoma (HCC). Although frequently related to overweight or obesity and other components of the metabolic syndrome (MS), MASLD can also be present in individuals without such risk factors.

Cervical cancer perceived risk factors behavior using logistic regression technique.

Cervical cancer represents a considerable global health challenge, mainly because of ineffective screening programs in middle-income countries. The current study aimed to forecast cervical cancer incidence by analyzing behavioral risk factors through logistic regression, employing feature engineering techniques such as principal component analysis (PCA). PCA successfully condensed the dataset into ten principal components, capturing 89% of the variance, while stratified K-fold cross-validation ensured a balanced representation of classes.

Comprehensive Fault Diagnosis of Three-Phase Induction Motors Using Synchronized Multi-Sensor Data Collection.

Induction motors are critical to industrial operations but are prone to mechanical and electrical faults. This paper introduces a new dataset for comprehensive fault diagnosis of three-phase induction motors, featuring synchronized multi-sensor data collection. Real-time measurements of vibration, voltage, and current were captured from a 0.

Metabolomics-based prediction model for diabetes: A comprehensive analysis of biomarkers and machine learning approaches.

Aims: To develop a prediction model for diabetes using metabolomics data and to evaluate various machine learning approaches and identify the most effective framework for disease prediction.

Methods: A comprehensive analysis was conducted on the Qatar Biobank dataset comprising metabolomics profiles, instrument measurements, and clinical diagnoses from 450 Qatari nationals. Targeted metabolites were selected based on correlation strength with diabetes status.

The burden of congenital heart disease in Malaysia: a comprehensive review and meta-analytic synthesis, 1970-2024.

Background: Congenital heart disease (CHD) remains a leading contributor to congenital anomalies globally. In Malaysia, available data on CHD are limited, with birth prevalence estimates varying widely across studies, suggesting potential underestimation and gaps in understanding CHD prevalence. This systematic review and meta-analysis aimed to assess the reported prevalence and subtypes of CHD in Malaysia.

A fine tuned EfficientNet-B0 convolutional neural network for accurate and efficient classification of apple leaf diseases.

Precise classification and detection of apple diseases are essential for efficient crop management and maximizing yield. This paper presents a fine-tuned EfficientNet-B0 convolutional neural network (CNN) for the automated classification of apple leaf diseases. The model builds upon a pre-trained EfficientNet-B0 base, enhanced through architectural modifications such as the integration of a global max pooling (GMP) layer, dropout, regularization, and full-model fine-tuning.

Enhanced detection of Argulus and epizootic ulcerative syndrome in fish aquaculture through an improved deep learning model.

Objective: Fish disease in aquaculture is a major risk to food safety. The identification of infected fish and disease categories present in fish farms remains difficult to determine at an early stage. Detecting infected fish in time is an essential step in preventing the spread of disease.

Spiritual care competency and spiritual sensitivity among nursing students: a cross-sectional study.

Background And Objectives: A holistic approach to nursing integrates biological, psychological, social, and spiritual aspects of health. Despite its importance, spiritual care often receives less attention than physical and psychological care. Studies show that nursing students lack the necessary competence in this area, preventing them to meet patients' spiritual needs.

A compact dual-band reconfigurable antenna with metamaterial for IoT applications.

This study presents a compact antenna designed for Internet of Things (IoT) applications, utilizing advanced wireless communication technologies. The antenna is designed to operate at dual frequencies (2.4 GHz and 6.

Deep learning model for hair artifact removal and Mpox skin lesion analysis and detection.

Accurate identification of Mpox is essential for timely diagnosis and treatment. However, traditional image-based diagnostic methods often struggle with challenges such as body hair obscuring skin lesions and complicating accurate assessment. To address this, the study introduces a novel deep learning-based approach to enhance Mpox detection by integrating a hair removal process with an upgraded U-Net model.

UrduSER: A comprehensive dataset for speech emotion recognition in Urdu language.

Speech Emotion Recognition (SER) is a rapidly evolving field of research that aims to identify and categorize emotional states through speech signal analysis. As SER holds considerable socio¬cultural and business significance, researchers are increasingly exploring machine learning and deep learning techniques to advance this technology. A well-suited dataset is a crucial resource for SER studies in a specific language.

Structural Variations Associated with Adaptation and Coat Color in Qinghai-Tibetan Plateau Cattle.

Structural variations (SVs) play crucial roles in the evolutionary adaptation of domesticated animals to natural and human-controlled environments, but SVs have not been explored in Tibetan cattle, which recently migrated and rapidly adapted to the high altitudes of the Qinghai-Tibetan Plateau (QTP). In this study, a de novo chromosome-level genome assembly for Tibetan cattle is constructed. It is found that using a lineage-specific reference genome significantly increased variant detection accuracy and completeness.

2D logistic map with unit transfer function and modulus operation based pseudorandom number generation for image encryption.

This study presents a novel approach to generating high-quality random numbers using a two-dimensional logistic map with a unit transfer function (2DLMUTF). The method is built upon the chaotic dynamics of the logistic map, where the parameter [Formula: see text] governs the system's behavior, exhibiting chaotic nature in the range of 3.57 to 4.

Artificial intelligence models for predicting the mode of delivery in maternal care.

Background: Accurate prediction of the mode of delivery is critical in maternal care to improve prenatal counseling, optimize clinical decision-making, and reduce maternal and neonatal complications.

Objectives: This study aims to evaluate and compare the predictive accuracy of AI algorithms in predicting the mode of delivery (vaginal or cesarean) using routinely collected antepartum data from electronic health records (EHRs).

Methods: A retrospective dataset of 16,651 pregnancies monitored at St.

Improving Timely Medication Access for Endocrinology and Diabetes Patients: A Quality Improvement Initiative.

Introduction: Managing chronic conditions such as endocrinology and diabetes requires consistent access to medications. Traditional methods of medication refill often involve in-person visits to healthcare providers or pharmacies, posing challenges for patients. Online medication refill services offer a promising solution to improve accessibility and convenience.

Nano-Hydroxyapatite/Poly(methyl methacrylate) Composite Bone Scaffold: Surfactant Surface Effects.

In this study, poly(methyl methacrylate) (PMMA) nanofiber scaffolds reinforced with synthesized nano-hydroxyapatite (n-HA) were fabricated through electrospinning to enhance their potential for applications in bone tissue engineering. Sodium tripolyphosphate (STTP) was utilized as a surfactant to achieve a uniform distribution of particles and improve the structural integrity of the scaffolds. PMMA solutions were prepared at concentrations of the addition of STTP effectively stabilized n-HA dispersion, leading to enhanced fiber morphology, as confirmed by scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and transmission electron microscopy (TEM).

Development and Testing of Robust 3D Printed ZnO/Clay Photocatalysts for Sustainable Wastewater Treatment.

Contamination of water resources with organic pollutants is a serious environmental problem. Degradation of organic pollutants using photocatalytic materials is a promising method of wastewater treatment. In this work, we report on the fabrication of novel 3D printed (3DP) ZnO/Clay materials to be used as efficient photocatalysts for the degradation of methylene blue (MB).

Exploring Glucagon-Like Peptide-1 Receptor Agonists Usage Among Non-Diabetic Healthcare Providers: A Cross-Sectional Multi-Country Study.

Background And Aim: Glucagon-like peptide-1 receptor agonists (GLP-1RAs) were initially developed for type 2 diabetes but have gained widespread use for weight management, including among non-diabetic individuals. This study aimed to estimate the prevalence of GLP-1RA use, describe usage patterns, and explore healthcare providers' (HCPs) perceptions of their efficacy and safety.

Methods: A cross-sectional study was conducted among 657 HCPs from 10 countries using a structured online survey between September and December 2023.

Impact evaluation of the modified adverse inpatient medication event (AIME-Frail) model in hospitalised adults.

Aims: To assess whether the AIME-Frail tool assists in medication management prioritisation and reduces inpatient medication harm events, evaluate tool implementation challenges and enablers, and identify predictive risk factors for medication harm.

Methods: General and geriatric medicine patients at a tertiary hospital in Queensland, Australia were enrolled in a controlled study. Medication harm was identified through electronic medical record (EMR) reviews, a trigger tool, and discussions with treating teams.

Strategies for preventing ventilator-associated pneumonia in adults in the Middle East and North Africa Region: A systematic review and meta-analysis.

Background: Ventilator-associated pneumonia (VAP) is a common complication in intensive care units (ICUs), particularly in patients undergoing prolonged mechanical ventilation. VAP rates vary significantly across regions, with the Middle East and North Africa (MENA) region experiencing relatively high incidences. This study systematically reviews and analyses the efficacy of various VAP prevention strategies in the adult population of the MENA region.

MODAMS: design of a multimodal object-detection based augmentation model for satellite image sets.

Efficient image augmentation for hyperspectral satellite images requires design of multiband processing models that can assist in improving classification performance for different application scenarios. Existing models either work on dynamic band fusions, or use deep learning techniques for identification of application-specific augmentation operations. Moreover, these models use static augmentations, and do not take into consideration image-specific parameters which limits their efficiency levels.

Artificial intelligence approach for estimating energy density of liquid metal batteries.

Achieving a high energy density in liquid metal batteries (LMBs) still remains a big challenge. Due to the multitude of affecting parameters within the system, traditional ways may not fully capture the complexity of LMBs. The artificial intelligence approach can be effectively applied to deal with low energy density issues.

Applying Acoustic Signals to Monitor Hybrid Electrical Discharge-Turning with Artificial Neural Networks.

Artificial intelligence (AI) models have demonstrated their capabilities across various fields by performing tasks that are currently handled by humans. However, the training of these models faces several limitations, such as the need for sufficient data. This study proposes the use of acoustic signals as training data as this method offers a simpler way to obtain a large dataset compared to traditional approaches.

A 5G network based conceptual framework for real-time malaria parasite detection from thick and thin blood smear slides using modified YOLOv5 model.

Objective: This paper aims to address the need for real-time malaria disease detection that integrates a faster prediction model with a robust underlying network. The study first proposes a 5G network-based healthcare system and then develops an automated malaria detection model capable of providing an accurate diagnosis, particularly in areas with limited diagnostic resources.

Methods: The proposed system leverages a deep learning-based YOLOv5x algorithm to detect malaria parasites in thick and thin blood smear samples.