Unlocking the Potential of EEG in Alzheimer's Disease Research: Current Status and Pathways to Precision Detection.

Alzheimer's disease (AD) affects millions of individuals worldwide and is considered a serious global health issue due to its gradual neuro-degenerative effects on cognitive abilities such as memory, thinking, and behavior. There is no cure for this disease but early detection along with a supportive care plan may aid in improving the quality of life for patients. Automated detection of AD is challenging because its symptoms vary in patients due to genetic, environmental, or other co-existing health conditions.

University Students' Understanding and Utilization of Food Labels: A Cross-Sectional Study.

This study is aimed at investigating Zayed University students' knowledge, attitudes, and practices (KAPs) regarding food labels and at identifying key predictors of food label use through logistic regression analysis. This quantitative cross-sectional study used a validated questionnaire to elicit data on KAPs regarding the use of nutritional information and food labels. This study was conducted from January to May 2023 in the UAE among students from Zayed University.

Exploring Gendered Perspectives on Personality Traits and Entrepreneurial Performance in Lebanon during the COVID-19 Crisis.

We investigate the impact of gendered personality traits on the entrepreneurial performance of male and female entrepreneurs in Lebanon during the COVID-19 crisis. Using the Big Five personality model and survey data from 500 entrepreneurs, the research examines how traits such as agreeableness, conscientiousness, neuroticism, extraversion, and openness to experience influence business outcomes in high-pressure environments. The findings reveal that agreeableness and neuroticism benefit female entrepreneurs more than males during crises, while conscientiousness significantly deteriorates the financial performance of female entrepreneurs.

Are Neurocognitive Deficits Associated With Gastrointestinal Symptoms in Children due to COVID-19?

Background And Objectives: Children with gastrointestinal symptoms due to COVID-19 may experience neurocognitive deficits. Symptoms include difficulty in concentration, memory and other cognitive functions. The deficits could be caused by the virus itself or due to the physical and psychological stress of the pandemic.

A Bayesian Network model to integrate blue-green and gray infrastructure systems for different urban conditions.

In facing growing challenges in cities and the environment, cities need to make informed decisions on where and how to allocate resources for infrastructure investments. Nature-based Solutions present a promising approach to urban environmental and socioeconomic challenges, but their successful integration into urban planning requires a nuanced understanding of both their benefits and limitations. This paper presents a preliminary Bayesian Network model designed to model the optimal integration of specific blue-green and gray Infrastructure solutions in hybrid systems for specific local contexts.

Dysphagia in Rare Diseases and Syndromes: Current Approaches to Management and Therapeutic Innovations-A Systematic Review.

This study presents a comprehensive investigation into the correlation between Rare Diseases and Syndromes (RDS) and the dysphagic disorders manifested during childhood and adulthood in affected patients. Dysphagia is characterized by difficulty or an inability to swallow food of any consistency, as well as saliva or medications, from the oral cavity to the stomach. RDS often present with complex and heterogeneous clinical manifestations, making it challenging to develop standardized diagnostic and therapeutic approaches.

Enhancing Communication and Swallowing Skills in Children with Cri Du Chat Syndrome: A Comprehensive Speech Therapy Guide.

A specific deletion on the short arm of chromosome 5 (5p) is the hallmark of the rare genetic syndrome called Cri du Chat Syndrome (CdCS). It causes severe difficulty with swallowing, speech, motor skills, and cognitive deficiencies. These arise from characteristic laryngeal abnormalities and oral-motor dysfunctions.

Development and evaluation of a deep learning segmentation model for assessing non-surgical endodontic treatment outcomes on periapical radiographs: A retrospective study.

This study aimed to evaluate the performance of a deep learning-based segmentation model for predicting outcomes of non-surgical endodontic treatment. Preoperative and 3-year postoperative periapical radiographic images of each tooth from routine root canal treatments performed by endodontists from 2015 to 2021 were obtained retrospectively from Thammasat University hospital. Preoperative radiographic images of 1200 teeth with 3-year follow-up results (440 healed, 400 healing, and 360 disease) were collected.

A hybrid approach of vision transformers and CNNs for detection of ulcerative colitis.

Ulcerative Colitis is an Inflammatory Bowel disease caused by a variety of factors that lead to a serious impact on the quality of life of the patients if left untreated. Due to complexities in the identification procedures of this disease, the treatment timeline and quality can be severely affected, leading to further consequences for the sufferer. The difficulties in identification are due to high patients to healthcare professionals ratio.

Deep convolutional neural network for automatic segmentation and classification of jaw tumors in contrast-enhanced computed tomography images.

The purpose of this study was to evaluate the performance of convolutional neural network (CNN)-based image segmentation models for segmentation and classification of benign and malignant jaw tumors in contrast-enhanced computed tomography (CT) images. A dataset comprising 3416 CT images (1163 showing benign jaw tumors, 1253 showing malignant jaw tumors, and 1000 without pathological lesions) was obtained retrospectively from a cancer hospital and two regional hospitals in Thailand; the images were from 150 patients presenting with jaw tumors between 2016 and 2020. U-Net and Mask R-CNN image segmentation models were adopted.

Membrane filter removal in FTIR spectra through dictionary learning for exploring explainable environmental microplastic analysis.

Microplastic analysis is a crucial step for locating the environmental contamination sources and controlling plastic contamination. A popular tool like Fourier transform infrared (FTIR) spectroscopy is capable of identifying plastic types and can be carried out through a variety of containers. Unfortunately, sample collection from water sources like rivers usually involves filtration so the measurements inevitably include the membrane filter that also has its own FTIR characteristic bands.

The effects of a facilitator-enabled online multicomponent iSupport for dementia programme: A multicentre randomised controlled trial.

Background: Multicomponent interventions with carers of people with dementia demonstrate positive effects on the health and quality of life for carers and care recipients. The World Health Organization's iSupport for Dementia is an evidence-based online psychoeducation programme for carers. However, the programme was mainly implemented as a self-learning tool which might have limited its positive effects on carers and care recipients.

Community (dis)connectedness and identity among LGBTQIA+ people during the COVID-19 pandemic: A qualitative cross-sectional and longitudinal trajectory study.

The coronavirus disease 2019 (COVID-19) pandemic and associated shelter-in-place ordinances passed in the first year of the pandemic rapidly limited access to in-person social interactions, raising concerns of diminishing social support and community cohesion while psychological stressors increased. For LGBTQIA+ people, connectedness to the LGBTQIA+ community is known to buffer against the harmful effects of stressors and decrease risks for poor psychological and behavioral health outcomes. The current study uses qualitative cross-sectional and trajectory analysis methods to characterize how LGBTQIA+ people's perceptions of community connectedness shifted during the first year of the pandemic.

Comparing and assessing four AI chatbots' competence in economics.

Artificial Intelligence (AI) chatbots have emerged as powerful tools in modern academic endeavors, presenting both opportunities and challenges in the learning landscape. They can provide content information and analysis across most academic disciplines, but significant differences exist in terms of response accuracy for conclusions and explanations, as well as word counts. This study explores four distinct AI chatbots, GPT-3.

Eosinophilia Is a Favorable Marker for Pneumonia in Chronic Obstructive Pulmonary Disease.

Background: Patients with chronic obstructive pulmonary disease (COPD) expressing eosinophilia experience slightly fewer episodes of community-acquired pneumonia (CAP), than those without eosinophilia. However, the severity and burden of hospitalized pneumonia patients with COPD involving eosinophilia have not been assessed.

Methods: We evaluated the differences in clinical characteristics between patients with CAP and COPD with or without eosinophilia by a post hoc analysis of a prospective, multi-center, cohort study data.

Nanozyme-based sensing of dopamine using cobalt-doped hydroxyapatite nanocomposite from waste bones.

Dopamine is one of the most important neurotransmitters and plays a crucial role in various neurological, renal, and cardiovascular systems. However, the abnormal levels of dopamine mainly point to Parkinson's, Alzheimer's, cardiovascular diseases, etc. Hydroxyapatite (HAp), owing to its catalytic nature, nanoporous structure, easy synthesis, and biocompatibility, is a promising matrix material.

Deep Learning for Midfacial Fracture Detection in CT Images.

This study deploys the deep learning-based object detection algorithms to detect midfacial fractures in computed tomography (CT) images. The object detection models were created using faster R-CNN and RetinaNet from 2,000 CT images. The best detection model, faster R-CNN, yielded an average precision of 0.

The Metaverse, Religious Practice and Wellbeing: A Narrative Review.

The metaverse is touted as the next phase in the evolution of the Internet. This emerging digital ecosystem is widely conceptualized as a persistent matrix of interconnected multiuser, massively scaled online environments optimally experienced through immersive digital technologies such as virtual reality (VR). Much of the prognostication about the social implications of the metaverse center on secular activities.

Illuminating practitioner challenges in energy transitions.

Sustainable development (SD) is a concept that can be used to address complex challenges, including energy transitions. SD offers diverse strategies that provide useful direction in navigating tensions, trade-offs and synergies in energy transitions. The purpose of this research was to identify the challenges that energy practitioners are faced with in energy transitions and explore potential solutions.

Efficacy of Radiomics in Predicting Oncologic Outcome of Liver-Directed Combined Radiotherapy in Locally Advanced Hepatocellular Carcinoma.

Purpose: We investigated whether radiomic features extracted from three-phase dynamic contrast-enhanced computed tomography (CECT) can be used to predict clinical outcomes, including objective treatment response (OR) and in-field failure-free survival rate (IFFR), in patients with hepatocellular carcinoma (HCC) who received liver-directed combined radiotherapy (LD-CRT).

Methods: We included 409 patients, and they were randomly divided into training (n = 307) and validation (n = 102) cohorts. For radiomics models, we extracted 116 radiomic features from the region of interest on the CECT images.

DOTA functionalized adsorbent DOTA@Sludge@Chitosan derived from recycled shrimp shells and sludge and its application for lead and chromium removal from water.

DOTA@Sludge@Chitosan was synthesized by a facile treatment using DOTA (1,4,7,10-tetraazacyclododecane-N,N',N,N'-tetraacetic acid) to modify dry sludge and chitosan in an acidic solution. The performance of developed DOTA@Sludge@Chitosan was investigated for the adsorptive removal of Cr and Pb from water. Characterization studies showed that the materials possess a large surface area (52.