A neural network model of mathematics anxiety: The role of attention.

Anxiety about performing numerical calculations is becoming an increasingly important issue. Termed mathematics anxiety, this condition negatively impacts performance in numerical tasks which can affect education outcomes and future employment. The disruption account proposes poor performance is due to anxiety disrupting limited attentional and inhibitory resources leaving fewer cognitive resources for the current task.

An Alzheimer's disease category progression sub-grouping analysis using manifold learning on ADNI.

Many current statistical and machine learning methods have been used to explore Alzheimer's disease (AD) and its associated patterns that contribute to the disease. However, there has been limited success in understanding the relationship between cognitive tests, biomarker data, and patient AD category progressions. In this work, we perform exploratory data analysis of AD health record data by analyzing various learned lower dimensional manifolds to separate early-stage AD categories further.

Prediction of Site Directed miRNAs as Key Players of Transcriptional Regulators Against Influenza C Virus Infection Through Computational Approaches.

MicroRNAs (miRNAs) are small non-coding RNAs that play critical roles in gene expression, cell differentiation, and immunity against viral infections. In this study, we have used the computational tools, RNA22, RNAhybrid, and miRanda, to predict the microRNA-mRNA binding sites to find the putative microRNAs playing role in the host response to influenza C virus infection. This computational research screened the following four miRNAs: hsa-mir-3155a, hsa-mir-6796-5p, hsa-mir-3194-3p and hsa-mir-4673, which were further investigated for binding site prediction to the influenza C genome.

A Synaptic Pruning-Based Spiking Neural Network for Hand-Written Digits Classification.

A spiking neural network model inspired by synaptic pruning is developed and trained to extract features of hand-written digits. The network is composed of three spiking neural layers and one output neuron whose firing rate is used for classification. The model detects and collects the geometric features of the images from the Modified National Institute of Standards and Technology database (MNIST).

Neural Substrates of the Drift-Diffusion Model in Brain Disorders.

Many studies on the drift-diffusion model (DDM) explain decision-making based on a unified analysis of both accuracy and response times. This review provides an in-depth account of the recent advances in DDM research which ground different DDM parameters on several brain areas, including the cortex and basal ganglia. Furthermore, we discuss the changes in DDM parameters due to structural and functional impairments in several clinical disorders, including Parkinson's disease, Attention Deficit Hyperactivity Disorder (ADHD), Autism Spectrum Disorders, Obsessive-Compulsive Disorder (OCD), and schizophrenia.

Vaccine versus Variants (3Vs): Are the COVID-19 Vaccines Effective against the Variants? A Systematic Review.

Background: With the emergence and spread of new SARS-CoV-2 variants, concerns are raised about the effectiveness of the existing vaccines to protect against these new variants. Although many vaccines were found to be highly effective against the reference COVID-19 strain, the same level of protection may not be found against mutation strains. The objective of this study is to systematically review relevant studies in the literature and compare the efficacy of COVID-19 vaccines against new variants.

Harnessing forgetfulness: can episodic-memory tests predict early Alzheimer's disease?

A rapid increase in the number of patients with Alzheimer's disease (AD) is expected over the next decades. Accordingly, there is a critical need for early-stage AD detection methods that can enable effective treatment strategies. In this study, we consider the ability of episodic-memory measures to predict mild cognitive impairment (MCI) to AD conversion and thus, detect early-stage AD.

Mechanistic insights into TNFR1/MADD death domains in Alzheimer's disease through conformational molecular dynamic analysis.

Proteins are tiny players involved in the activation and deactivation of multiple signaling cascades through interactions in cells. The TNFR1 and MADD interact with each other and mediate downstream protein signaling pathways which cause neuronal cell death and Alzheimer's disease. In the current study, a molecular docking approach was employed to explore the interactive behavior of TNFR1 and MADD proteins and their role in the activation of downstream signaling pathways.

A longitudinal study using latent curve models of groups with mild cognitive impairment and Alzheimer's disease.

Background: This study explores how mild cognitive impairment (MCI) and Alzheimer's disease (AD) develop over time. NEW METHOD: this study involves a new application of latent curve models (LCM) to examine the development trajectory of a healthy, MCI, and AD groups on a series of clinical and neural measures. Multiple-group latent curve models were used to compare the parameters of the trajectories across groups.

Association of hypertension, diabetes, stroke, cancer, kidney disease, and high-cholesterol with COVID-19 disease severity and fatality: A systematic review.

Background And Aims: To undertake a review and critical appraisal of published/preprint reports that offer methods of determining the effects of hypertension, diabetes, stroke, cancer, kidney issues, and high-cholesterol on COVID-19 disease severity.

Methods: A search was conducted by two authors independently on the freely available COVID-19 Open Research Dataset (CORD-19). We developed an automated search engine to screen a total of 59,000 articles in a few seconds.

The Application of Unsupervised Clustering Methods to Alzheimer's Disease.

Clustering is a powerful machine learning tool for detecting structures in datasets. In the medical field, clustering has been proven to be a powerful tool for discovering patterns and structure in labeled and unlabeled datasets. Unlike supervised methods, clustering is an unsupervised method that works on datasets in which there is no outcome (target) variable nor is anything known about the relationship between the observations, that is, unlabeled data.

Improving the Detection of Protein Complexes by Predicting Novel Missing Interactome Links in the Protein-Protein Interaction Network.

Identifying protein complexes within a protein-protein interaction (PPI) networks is a crucial task in computational biology that helps to facilitate a better understanding of the cellular mechanisms it is possible to observe in various organisms. Datasets of predicted PPIs have been determined using high-throughput experimental technology. However, the datasets typically contain many spurious interactions.

Computational investigation of mechanistic insights of Aβ42 interactions against extracellular domain of nAChRα7 in Alzheimer's disease.

Aim: Amyloid beta (Aβ) 1-42, which is a basic constituent of amyloid plaques, binds with extracellular transmembrane receptor nicotine acetylcholine receptor α7 (nAChRα7) in Alzheimer's disease.

Materials And Methods: In the current study, a computational approach was employed to explore the active binding sites of nAChRα7 through Aβ 1-42 interactions and their involvement in the activation of downstream signalling pathways. Sequential and structural analyses were performed on the extracellular part of nAChRα7 to identify its core active binding site.

Molecular Docking and Dynamic Simulation of AZD3293 and Solanezumab Effects Against BACE1 to Treat Alzheimer's Disease.

The design of novel inhibitors to target BACE1 with reduced cytotoxicity effects is a promising approach to treat Alzheimer's disease (AD). Multiple clinical drugs and antibodies such as AZD3293 and Solanezumab are being tested to investigate their therapeutical potential against AD. The current study explores the binding pattern of AZD3293 and Solanezumab against their target proteins such as β-secretase (BACE1) and mid-region amyloid-beta (Aβ) (PDBIDs: 2ZHV & 4XXD), respectively using molecular docking and dynamic simulation (MD) approaches.

Exploring the mechanistic insights of Cas scaffolding protein family member 4 with protein tyrosine kinase 2 in Alzheimer's disease by evaluating protein interactions through molecular docking and dynamic simulations.

Cas scaffolding protein family member 4 and protein tyrosine kinase 2 are signaling proteins, which are involved in neuritic plaques burden, neurofibrillary tangles, and disruption of synaptic connections in Alzheimer's disease. In the current study, a computational approach was employed to explore the active binding sites of Cas scaffolding protein family member 4 and protein tyrosine kinase 2 proteins and their significant role in the activation of downstream signaling pathways. Sequential and structural analyses were performed on Cas scaffolding protein family member 4 and protein tyrosine kinase 2 to identify their core active binding sites.

Integration of genome-wide expression and methylation data: relevance to aging and Alzheimer's disease.

The progressive and latent nature of neurodegenerative diseases, such as Alzheimer's disease (AD) indicates the role of epigenetic modification in disease susceptibility. Previous studies from our lab show that developmental exposure to lead (Pb) perturbs the expression of AD-associated proteins. In order to better understand the role of DNA methylation as an epigenetic modifications mechanism in gene expression regulation, an integrative study of global gene expression and methylation profiles is essential.

Genome-wide expression and methylation profiling in the aged rodent brain due to early-life Pb exposure and its relevance to aging.

In this study, we assessed global gene expression patterns in adolescent mice exposed to lead (Pb) as infants and their aged siblings to identify reprogrammed genes. Global expression on postnatal day 20 and 700 was analyzed and genes that were down- and up-regulated (≥2 fold) were identified, clustered and analyzed for their relationship to DNA methylation. About 150 genes were differentially expressed in old age.