Uncovering Effects of Schizophrenia upon a Maximally Significant, Minimally Complex Subset of Default Mode Network Connectivity Features.

A common analysis approach for resting state functional magnetic resonance imaging (rs-fMRI) dynamic functional network connectivity (dFNC) data involves clustering windowed correlation time-series and assigning time windows to clusters (i.e., states) that can be quantified to summarize aspects of the dFNC dynamics.

Altered Functional Network Energy Across Multiscale Brain Networks in Preterm vs. Full-Term Subjects: Insights from the Adolescent Brain Cognitive Development (ABCD) Study.

Infants born prematurely, or preterm, can experience altered brain connectivity, due in part to incomplete brain development at the time of parturition. Research has also shown structural and functional differences in the brain that persist in these individuals as they enter adolescence when compared to peers who were fully mature at birth. In this study, we examined functional network energy across multiscale functional connectivity in approximately 4600 adolescents from the Adolescent Brain Cognitive Development (ABCD) study who were either preterm or full term at birth.

Deciphering Multiway Multiscale Brain Network Connectivity: Insights from Birth to 6 Months.

Converging evidence suggests that understanding the human brain requires more than just examining pairwise functional brain interactions. The human brain is a complex, nonlinear system, and focusing solely on linear pairwise functional connectivity often overlooks important nonlinear and higher-order relationships. Infancy is a critical period marked by significant brain development that could contribute to future learning, health, and life success.

Health Literacy, General Health, and Lifestyle among Infertile Men and Women in the Southeastern Iran.

Background: Infertility is a growing social problem, and health literacy is one of the factors that affects infertility, thereby affecting life quality. On the other hand, lifestyle factors exert a considerable impact on reproductive capacity and general health. Against this backdrop, this study aims to determine health literacy, general health, and lifestyle in infertile people in Zahedan, Iran.

Effects of Educational Interventions on Dietary Adherence among Type 2 Diabetics in Zahedan: Using the Health Action Process Approach.

Background: Type 2 diabetes is the most common type of diabetes, and dietary adherence is a self-care practice. This research aims to improve dietary adherence among type 2 diabetics in Zahedan using the HAPA model.

Methods: In this cross-sectional study, a total of 210 type 2 diabetics admitted to hospital clinics in Zahedan during summer 2022 were selected.

Uncovering Effects of Schizophrenia upon a Maximally Significant, Minimally Complex Subset of Default Mode Network Connectivity Features.

A common analysis approach for resting state functional magnetic resonance imaging (rs-fMRI) dynamic functional network connectivity (dFNC) data involves clustering windowed correlation time-series and assigning time windows to clusters (i.e., states) that can be quantified to summarize aspects of the dFNC dynamics.

Analyzing User Behaviour Patterns in a Cross-Virtuality Immersive Analytics System.

Recent work in immersive analytics suggests benefits for systems that support work across both 2D and 3D data visualizations, i.e., cross-virtuality analytics systems.

COMPLEXITY MEASURES OF PSYCHOTIC BRAIN ACTIVITY IN THE FMRI SIGNAL.

When viewing the brain as a sophisticated, nonlinear dynamic system, employing complexity measures offers a valuable way to measure the intricate and dynamic aspects of spontaneous psychotic brain activity. These measures can help us identify irregularities and patterns in complex systems. In our study, we utilized fuzzy recurrence plots and sample entropy to evaluate the dynamic characteristics of psychiatric disorders.

Machine learning approaches and non-linear processing of extracted components in frontal region to predict rTMS treatment response in major depressive disorder.

Predicting the therapeutic result of repetitive transcranial magnetic stimulation (rTMS) treatment could save time and costs as ineffective treatment can be avoided. To this end, we presented a machine-learning-based strategy for classifying patients with major depression disorder (MDD) into responders (R) and nonresponders (NR) to rTMS treatment. Resting state EEG data were recorded using 32 electrodes from 88 MDD patients before treatment.

Psychopathology in virtual education for primary school students in the COVID-19 pandemic: A qualitative analysis.

Background: COVID-19 virus continues to be an international concern, challenging psychological resilience in all areas, especially virtual education, making the psychopathology and problems more evident.

Materials And Methods: The present study is a qualitative study of conventional content analysis, in which 24 participants (14 parents, 5 teachers, and 5 principals) were selected by purposive sampling from primary schools in Zahedan. Data collection tools included semi-structured interviews with open-ended questions.

Simultaneous electroencephalography-functional magnetic resonance imaging for assessment of human brain function.

Electroencephalography (EEG) and functional Magnetic Resonance Imaging (MRI) have long been used as tools to examine brain activity. Since both methods are very sensitive to changes of synaptic activity, simultaneous recording of EEG and fMRI can provide both high temporal and spatial resolution. Therefore, the two modalities are now integrated into a hybrid tool, EEG-fMRI, which encapsulates the useful properties of the two.

Localizing Epileptic Foci Using Simultaneous EEG-fMRI Recording: Template Component Cross-Correlation.

Conventional EEG-fMRI methods have been proven to be of limited use in the sense that they cannot reveal the information existing in between the spikes. To resolve this issue, the current study obtains the epileptic components time series detected on EEG and uses them to fit the Generalized Linear Model (GLM), as a substitution for classical regressors. This approach allows for a more precise localization, and equally importantly, the prediction of the future behavior of the epileptic generators.

Multiple sclerosis reduces synchrony of the magnocellular pathway.

Multiple Sclerosis (MS) is an autoimmune demyelinating disease that damages the insulation of nerve cell fibers in the brain and spinal cord. In the visual system, this demyelination results in a robust delay of visually evoked potentials (VEPs), even in the absence of overt clinical symptoms such as blurred vision. VEPs, therefore, offer an avenue for early diagnosis, monitoring disease progression, and, potentially, insight into the differential impairment of specific pathways.

The protection motivation theory for predict intention of COVID-19 vaccination in Iran: a structural equation modeling approach.

Background: Many efforts are being made around the world to discover the vaccine against COVID-19. After discovering the vaccine, its acceptance by individuals is a fundamental issue for disease control. This study aimed to examine COVID-19 vaccination intention determinants based on the protection motivation theory (PMT).

The use of mobile health interventions for gestational diabetes mellitus: a descriptive literature review.

This study attempted to review the evidence for or against the effectiveness of mobile health (m-health) interventions on health outcomes improvement and/or gestational diabetes mellitus (GDM) management. PubMed, Web of Science, Scopus, and Embase databases were searched from 2000 to 10 July 2018 to find studies investigating the effect of m-health on GDM management. After removing duplications, a total of 27 articles met our defined inclusion criteria.

Nurses' perception of ethical challenges in caring for patients with COVID-19: a qualitative analysis.

Nurses face several challenges in providing care for patients with coronavirus disease in 2019 (COVID-19). The study aimed to explain the nurses' perception of ethical challenges in this regard. The qualitative study was carried out using a content analysis method.

Localization of Epileptic Foci Based on Simultaneous EEG-fMRI Data.

Combining functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) enables a non-invasive investigation of the human brain function and evaluation of the correlation of these two important modalities of brain activity. This paper explores recent reports on using advanced simultaneous EEG-fMRI methods proposed to map the regions and networks involved in focal epileptic seizure generation. One of the applications of EEG and fMRI combination as a valuable clinical approach is the pre-surgical evaluation of patients with epilepsy to map and localize the precise brain regions associated with epileptiform activity.

Knowledge and Attitude of Iranian Youth Toward AIDS: A Systematic Review and Meta-Analysis in Iran.

Inadequate knowledge and negative attitudes are the major hindrances to prevent the spread of the human immunodeficiency virus. This study aims to assess the knowledge and attitude toward the human immunodeficiency virus and acquired immune deficiency syndrome among youths in Iran. We conducted a systematic review, searching online databases until July 2018, focusing on knowledge and attitudes about the human immunodeficiency virus and acquired immune deficiency syndrome in Iranian youths.

Phase-synchrony evaluation of EEG signals for Multiple Sclerosis diagnosis based on bivariate empirical mode decomposition during a visual task.

Despite the widespread prevalence of Multiple Sclerosis (MS), the study of brain interactions is still poorly understood. Moreover, there has always been a great need to automate the MS diagnosis procedure to eliminate the evaluation errors thereby improving its consistency and reliability. To address these issues, in this work, we proposed a robust pattern recognition algorithm as a computer-aided diagnosis system.

Syntheses of ZnO with Different Morphologies: Catalytic Activity toward Coumarin Synthesis via the Knoevenagel Condensation Reaction.

Heterogeneous catalysts are preferred in fine chemical industries due to their easy recovery and reusability. Here, we report an easily scalable method of ZnO catalysts for coumarin synthesis. Nanocrystalline ZnO particles with diverse morphologies and crystallite sizes were prepared using different solvents.

The effect of high temperature sol-gel polymerization parameters on the microstructure and properties of hydrophobic phenol-formaldehyde/silica hybrid aerogels.

Phenol-formaldehyde/silica hybrid aerogels with different degree of hydrophobicity were successfully synthesized via high temperature sol-gel polymerization. Tetraethoxysilane (TEOS) and methyltriethoxysilane (MTES) were used as precursor and co-precursor of the hydrophobic silica-based phase, respectively. The hydrolysis step of silica based sols were conducted by acid catalyzed reactions and HCl was used as hydrolysis catalyst.

Planning for subacute care: predicting demand using acute activity data.

Objective The aim of the present study was to develop a robust model that uses the concept of 'rehabilitation-sensitive' Diagnosis Related Groups (DRGs) in predicting demand for rehabilitation and geriatric evaluation and management (GEM) care following acute in-patient episodes provided in Australian hospitals. Methods The model was developed using statistical analyses of national datasets, informed by a panel of expert clinicians and jurisdictional advice. Logistic regression analysis was undertaken using acute in-patient data, published national hospital statistics and data from the Australasian Rehabilitation Outcomes Centre.

Antibody-like Biorecognition Sites for Proteins from Surface Imprinting on Nanoparticles.

Natural antibodies are used widely for important applications such as biomedical analysis, cancer therapy, and directed drug delivery, but they are expensive and may have limited stability. This study describes synthesis of antibody-like binding sites by molecular imprinting on silica nanoparticles (SiNP) using a combination of four organosilane monomers with amino acid-like side chains providing hydrophobic, hydrophilic, and H-bonding interactions with target proteins. This approach provided artificial antibody (AA) nanoparticles with good selectivity and specificity to binding domains on target proteins in a relatively low-cost synthesis.

Crystalline mesoporous K(2-x)Mn₈O₁₆ and ε-MnO₂ by mild transformations of amorphous mesoporous manganese oxides and their enhanced redox properties.

Synthesis of crystalline mesoporous K(2-x)Mn8O16 (Meso-OMS-2), and ε-MnO2 (Meso-ε-MnO2) is reported. The synthesis is based on the transformation of amorphous mesoporous manganese oxide (Meso-Mn-A) under mild conditions: aqueous acidic solutions (0.5 M H(+) and 0.