Stereological Evidence of Non-Selective Hippocampal Neurodegeneration, IGF-1 Depletion, and Behavioral Deficit following Short Term Bilateral Adrenalectomy in Wistar Rats.

The development of animal models to study cell death in the brain is a delicate task. One of the models, that was discovered in the late eighties, is the induction of neurodegeneration through glucocorticoid withdrawal by adrenalectomy in albino rats. Such a model is one of the few noninvasive models for studying neurodegeneration.

A deep learning based dual encoder-decoder framework for anatomical structure segmentation in chest X-ray images.

Automated multi-organ segmentation plays an essential part in the computer-aided diagnostic (CAD) of chest X-ray fluoroscopy. However, developing a CAD system for the anatomical structure segmentation remains challenging due to several indistinct structures, variations in the anatomical structure shape among different individuals, the presence of medical tools, such as pacemakers and catheters, and various artifacts in the chest radiographic images. In this paper, we propose a robust deep learning segmentation framework for the anatomical structure in chest radiographs that utilizes a dual encoder-decoder convolutional neural network (CNN).

The impact of the COVID-19 outbreak on the connectedness of the BRICS's term structure.

This study aims to examine the impact of the different waves of the COVID-19 pandemic on the connectedness of the BRICS (Brazil, Russia, India, China, and South Africa) term structure of interest rates and its components (level, slope and curvature). For that purpose, this research applies the time-varying parameter vector autoregression (TVP-VAR) approach in order to assess the direction of spillovers among countries and factors and measure their contribution to the connectedness system. Our results show that the total connectedness measure changes over time, and the level and curvature components show connectedness that persists longer than the slope component, both in the first wave of the COVID-19 pandemic.

Quality of Life of Emirati Women with Breast Cancer.

To examine the quality of life (QoL) of Emirati women with breast cancer (BC) and determine its relationships with their sociodemographic characteristics and clinical factors. The study will play a leading role in providing information about the QoL of Emirati women with BC and will help in recognizing the aspects of QoL in BC survivorship that requires special attention. A population-based cross-sectional study was conducted on 250 Emirati women using a multistage stratified clustered random sampling.

Random forest models for motorcycle accident prediction using naturalistic driving based big data.

Motorcycle accident studies usually rely upon data collected from road accidents collected through questionnaire surveys/police reports including characteristics of motorcycle riders and contextual data such as road environment. The present study utilizes big data, in the form of vehicle trajectory patterns collected through GPS, coupled with self-reported road accident information along with motorcycle rider characteristics to predict the likelihood of involvement of a motorcyclist in an accident. Random Forest-based machine learning algorithm is employed by taking inputs based on a variety of features derived from trajectory data.

A deep learning-based model for plant lesion segmentation, subtype identification, and survival probability estimation.

Plants are the primary source of food for world's population. Diseases in plants can cause yield loss, which can be mitigated by continual monitoring. Monitoring plant diseases manually is difficult and prone to errors.

Cross-cultural comparison of mental illness stigma and help-seeking attitudes: a multinational population-based study from 16 Arab countries and 10,036 individuals.

Background: There is evidence that culture deeply affects beliefs about mental illnesses' causes, treatment, and help-seeking. We aimed to explore and compare knowledge, attitudes toward mental illness and help-seeking, causal attributions, and help-seeking recommendations for mental illnesses across various Arab countries and investigate factors related to attitudes toward help-seeking.

Methods: We carried out a multinational cross-sectional study using online self-administered surveys in the Arabic language from June to November 2021 across 16 Arab countries among participants from the general public.

Short term energy consumption forecasting using neural basis expansion analysis for interpretable time series.

Smart grids and smart homes are getting people's attention in the modern era of smart cities. The advancements of smart technologies and smart grids have created challenges related to energy efficiency and production according to the future demand of clients. Machine learning, specifically neural network-based methods, remained successful in energy consumption prediction, but still, there are gaps due to uncertainty in the data and limitations of the algorithms.

Elucidating the effect of iron acquisition systems in Klebsiella pneumoniae on susceptibility to the novel siderophore-cephalosporin cefiderocol.

Background: Cefiderocol (CFDC) is a novel siderophore-cephalosporin, effective against multidrug-resistant Gram-negative bacteria. As it has a siderophore side chain, it can utilize iron acquisition systems for penetration of the bacterial outer membrane. We aimed to elucidate the role of siderophores and iron uptake receptors in defining Klebsiella pneumoniae susceptibility to CFDC.

Native Americans' Responses to Obesity Attributions and Message Sources in an Obesity Prevention Campaign.

This study investigated the effectiveness of Native American (NA) targeted obesity prevention messages. The researchers manipulated obesity attributions (internal vs. external) and message sources (NAs vs.

The Intriguing Carbapenemases of : Current Status, Genetic Profile, and Global Epidemiology.

Worldwide, remains a leading nosocomial pathogen that is difficult to treat and constitutes a challenging menace to healthcare systems. shows increased and alarming resistance to carbapenems, long acknowledged as last-resort antibiotics for treatment of resistant infections. Varied and recalcitrant pathways of resistance to carbapenems can simultaneously occur in , including the production of carbapenemases, broadest spectrum types of β-lactamases that hydrolyze virtually almost all β-lactams, including carbapenems.

Predicting Publication of Clinical Trials Using Structured and Unstructured Data: Model Development and Validation Study.

Background: Publication of registered clinical trials is a critical step in the timely dissemination of trial findings. However, a significant proportion of completed clinical trials are never published, motivating the need to analyze the factors behind success or failure to publish. This could inform study design, help regulatory decision-making, and improve resource allocation.

Towards a Machine Learning-Based Digital Twin for Non-Invasive Human Bio-Signal Fusion.

Human bio-signal fusion is considered a critical technological solution that needs to be advanced to enable modern and secure digital health and well-being applications in the metaverse. To support such efforts, we propose a new data-driven digital twin (DT) system to fuse three human physiological bio-signals: heart rate (HR), breathing rate (BR), and blood oxygen saturation level (SpO2). To accomplish this goal, we design a computer vision technology based on the non-invasive photoplethysmography (PPG) technique to extract raw time-series bio-signal data from facial video frames.

L. Extracts Inhibit Breast Cancer in Mice by Apoptosis Induction and Immune System Modulation.

Plants have been considered for many years as an important source of medicine to treat different diseases. L. (Asteraceae, Compositae) is known for its diuretic, anti-inflammatory, and sedative effects.

Associations between Dietary Intake of Vitamin D, Sun Exposure, and Generalized Anxiety among College Women.

Vitamin D insufficiency impacts about half of the population worldwide. Almost one billion individuals across all ages and ethnicities suffer from vitamin D deficiency. Hypovitaminosis D is mainly related to lifestyle choices and habits, such as outdoor activities and food intake.

The Role of Radiomics and AI Technologies in the Segmentation, Detection, and Management of Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is the most common primary hepatic neoplasm. Thanks to recent advances in computed tomography (CT) and magnetic resonance imaging (MRI), there is potential to improve detection, segmentation, discrimination from HCC mimics, and monitoring of therapeutic response. Radiomics, artificial intelligence (AI), and derived tools have already been applied in other areas of diagnostic imaging with promising results.

Self-omics: A Self-supervised Learning Framework for Multi-omics Cancer Data.

We have gained access to vast amounts of multi-omics data thanks to Next Generation Sequencing. However, it is challenging to analyse this data due to its high dimensionality and much of it not being annotated. Lack of annotated data is a significant problem in machine learning, and Self-Supervised Learning (SSL) methods are typically used to deal with limited labelled data.

Covid-19 pandemic and spillover effects in stock markets: A financial network approach.

This paper examines the impact of the COVID-19 pandemic on 51 major stock markets, both emerging and developed. We isolated the countries susceptible to shock transmissions, and evaluated countries with immunity, during the lockdown. Specifically, using dependence dynamics and network analysis on a bivariate basis, we identify volatility and contagion risk among stock markets during the COVID-19 pandemic.

Efficient framework for brain tumor detection using different deep learning techniques.

The brain tumor is an urgent malignancy caused by unregulated cell division. Tumors are classified using a biopsy, which is normally performed after the final brain surgery. Deep learning technology advancements have assisted the health professionals in medical imaging for the medical diagnosis of several symptoms.

An effective deep learning approach for the classification of Bacteriosis in peach leave.

Bacteriosis is one of the most prevalent and deadly infections that affect peach crops globally. Timely detection of Bacteriosis disease is essential for lowering pesticide use and preventing crop loss. It takes time and effort to distinguish and detect Bacteriosis or a short hole in a peach leaf.

Representations of depression and schizophrenia in the community: The role of illness and risk perceptions on help-seeking intentions.

Objective: Illness perceptions (IPs) are important in understanding human reactions to illnesses, including mental health disorders. They influence risk perceptions and several variables relevant to the adjustment to a disorder, treatment seeking, and health outcomes. This study sought to compare IP, risk perception, and help-seeking intention for depression and schizophrenia in a community sample and to assess the mediating role of risk perception in the relationship between IP and help-seeking intention.

The temporal dynamics between work stressors and health behaviors.

Applying dynamic equilibrium theory (DET), we examined the temporal dynamics between role overload and three health behaviors (sleep, diet, physical activity). Participants ( = 781) completed five surveys, with 1-month lag between assessments, and the data were analyzed using general cross-lagged panel modeling (GCLM). Results indicated that people had stable health behavior patterns (i.

COVID-19 and the quantile connectedness between energy and metal markets.

This study analyzes the relationship between clean and dirty energy sources and energy metals during the COVID-19 pandemic. We document a sharp increase in connectedness after the COVID-19 pandemic, that is asymmetric at the lower and upper quantiles, with stronger dependence among the variables at the upper quantiles. Among the energy metals, cobalt is the least connected to the energy markets.

Establishing a marine monitoring programme to assess antibiotic resistance: A case study from the Gulf Cooperation Council (GCC) region.

The World Health Organization considers antimicrobial resistance as one of the most pressing global issues which poses a fundamental threat to human health, development, and security. Due to demographic and environmental factors, the marine environment of the Gulf Cooperation Council (GCC) region may be particularly susceptible to the threat of antimicrobial resistance. However, there is currently little information on the presence of AMR in the GCC marine environment to inform the design of appropriate targeted surveillance activities.