Experiences of operating room nurses in disaster preparedness of a great disaster in Iran: a qualitative study.

BACKGROUND: In recent years, Iran has encountered a growing frequency of earthquake disasters. Given that nurses constitute the largest group of healthcare providers, it is imperative that they possess adequate disaster preparedness skills, irrespective of the location or time. Despite the operating room nurses' roles in disasters, their experiences and challenges in disaster preparedness have been overlooked. Consequently, this study aimed to investigate the experiences, challenges, perspectives, and factors influencing the disaster preparedness of operating room nurses during the 2017 earthquake in Kermanshah, Iran. METHODS: The present qualitative research was carried out in Iran In 2022 utilizing conventional content analysis. The study involved conducting semi-structured interviews with 16 operating room nurses who had participated in disaster preparedness during the Kermanshah earthquake. The participants were selected using a purposive sampling approach that aimed to achieve maximum diversity. The interviews were continued until the point of data saturation was reached, and the verbatim transcripts were analyzed using conventional content analysis in MAXQDA software. To ensure the rigor of the research, Guba and Lincoln's criteria were employed. RESULTS: The study conducted data analysis to identify the main theme as "insufficient disaster preparedness due to a faded preparedness", along with six major categories and eighteen subcategories related to earthquake disaster preparedness. The major categories included: knowledge and perception of preparedness for disasters; educational and training programs for disaster preparedness; equipment preparedness for disasters; managerial-organizational preparedness for disasters; clinical skills for responding to disasters; and resilient ability in disaster response situations. CONCLUSION: The findings of the study provide valuable insights into the dimensions of disaster preparedness in earthquake disasters among operating room nurses. Nursing managers can utilize these findings to develop effective strategies and provide support in areas such as improving knowledge and educational level, equipment preparedness, strengthening plans and managerial structures, enhancing skills, and explaining resilience strategies to improve the disaster preparedness of operating room nurses and medical organizations' disaster response teams.

Plasma p-tau181 associated with structural changes in mild cognitive impairment.

Alzheimer's disease (AD) is a progressive neurodegenerative disease associated with dementia and is a serious concern for the health of individuals and government health care systems worldwide. Gray matter atrophy and white matter damage are major contributors to cognitive deficits in AD patients, as demonstrated by magnetic resonance imaging (MRI). Many of these brain changes associated with AD begin to occur about 15 years before the onset of initial clinical symptoms. Therefore, it is critical to find biomarkers reflective of these brain changes associated with AD to identify this disease and monitor its prognosis and development. The increased plasma level of hyperphosphorylated tau 181 (p-tau181) has been recently considered a novel biomarker for the diagnosis of AD, preclinical AD, and mild cognitive impairment (MCI). In the current study, we examined the association of cerebrospinal fluid (CSF) and plasma levels of p-tau181 with structural brain changes in cortical thickness, cortical volume, surface area, and subcortical volume in MCI patients. In this cross-sectional study, we included the information of 461 MCI patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. The results of voxel-wise partial correlation analyses showed a significant negative correlation between the increased levels of plasma p-tau181, CSF total tau, and CSF p-tau181 with structural changes in widespread brain regions. These results provide evidence for the use of plasma p-tau181 as a diagnostic marker for structural changes in the brain associated with the early stages of AD and neurodegeneration.

Plasma neurofilament light levels correlate with white matter damage prior to Alzheimer's disease: results from ADNI.

BACKGROUND: The blood biomarker neurofilament light (NFL) is one of the most widely used for monitoring Alzheimer's disease (AD). According to recent research, a higher NFL plasma level has a substantial predictive value for cognitive deterioration in AD patients. Diffusion tensor imaging (DTI) is an MRI-based approach for detecting neurodegeneration, white matter (WM) disruption, and synaptic damage. There have been few studies on the relationship between plasma NFL and WM microstructure integrity. AIMS: The goal of the current study is to assess the associations between plasma levels of NFL, CSF total tau, phosphorylated tau181 (P-tau181), and amyloid-ß (Aß) with WM microstructural alterations. METHODS: We herein have investigated the cross-sectional association between plasma levels of NFL and WM microstructural alterations as evaluated by DTI in 92 patients with mild cognitive impairment (MCI) provided by Alzheimer's Disease Neuroimaging Initiative (ADNI) participants. We analyzed the potential association between plasma NFL levels and radial diffusivity (RD), axial diffusivity (AxD), mean diffusivity (MD), and fractional anisotropy (FA) in each region of the Montreal Neurological Institute and Hospital (MNI) atlas, using simple linear regression models stratified by age, sex, and APOE ε4 genotype. RESULTS: Our findings demonstrated a significant association between plasma NFL levels and disrupted WM microstructure across the brain. In distinct areas, plasma NFL has a negative association with FA in the fornix, fronto-occipital fasciculus, corpus callosum, uncinate fasciculus, internal capsule, and corona radiata and a positive association with RD, AxD, and MD values in sagittal stratum, corpus callosum, fronto-occipital fasciculus, corona radiata, internal capsule, thalamic radiation, hippocampal cingulum, fornix, and cingulum. Lower FA and higher RD, AxD, and MD values are related to demyelination and degeneration in WM. CONCLUSION: Our findings revealed that the level of NFL in the blood is linked to WM alterations in MCI patients. Plasma NFL has the potential to be a biomarker for microstructural alterations. However, further longitudinal studies are necessary to validate the predictive role of plasma NFL in cognitive decline.

Classifying ADHD subtypes/presentations considering the joint effect of three levels of investigation.

AIM: Discriminant validity of the Attention Deficits/Hyperactive Disorders (ADHD) subtypes/presentations is not yet clear. The purpose of this study was to investigate joint contribution of the strongest factors of the three dimensions, namely psychopathology, neuropsychology and electrophysiology for subtyping of presentations. METHOD: A sample of 104 boys aged 7-12 years was subdivided into three groups with ADHD combined (n = 22), inattentive (n = 25) and hyperactive/impulsive subtype (n = 14), and 43 typically developing controls (TDC). Children were investigated regarding the Child Behavior Checklist (CBCL), the Integrated Visual and Auditory Test (IVA), and EEG spectral power during eyes closed resting state. Subsequently, statistical analysis included discriminant functional analysis and principle component analysis. RESULTS: Neuropsychological parameters had the highest contribution in classifying of the groups. EEG parameters had no effect on differentiation of the groups, and among the psychopathological parameters, only the oppositional behavioral disorder score contributed to correctly classify 74.3% of the groups. Furthermore, we found four factors with eigenvalues higher than 1 in the ADHD and typical groups, with one factor characterized by four CBCL scales, another one by auditory and visual vigilance, speed and beta band power, the third by auditory and visual prudence, and forth by theta band power. CONCLUSIONS: Our results demonstrated that ADHD subtypes/presentations can be differentiated from each other at different levels of investigation despite some clinical symptoms overlap. The results suggested that not only psychopathology but also the impairment of sensory processing should be assessed in children with ADHD in order to use this additional information for a jointly multilevel clinical intervention, which may improve treatment success.

Attention and inhibitory control deficits in patients with genetic generalized epilepsy and psychogenic nonepileptic seizure.

This study aimed to evaluate the attention and inhibitory control functions in patients with genetic generalized epilepsy (GGE) and psychogenic nonepileptic seizure (PNES) and compare the results with the healthy control subjects. A total of 30 patients with GGE, 30 patients with PNES, and 32 healthy control subjects were included in the study. The severity of attention and inhibitory control deficit, general intelligence status, and psychopathology screening in all subjects were respectively investigated with the Integrated Visual and Auditory Continuous Performance Test (IVA-CPT), the Wechsler Adult Intelligence Scale (WAIS), and the Symptoms Checklist 90-revised (SCL-90-R). Patients with PNES had severe impairments in all performed tasks compared with the control group and the group with GGE (p < 0.01), whereas patients with GGE had significantly lower attention quotient versus healthy subjects (p < 0.01). The full-scale attention quotient (FSAQ) and full-scale response control quotient (FSRCQ) in patients with PNES were significantly lower in comparison with GGE (47.83 ±â€¯32.68, 60.18 ±â€¯35.35, p < 0.01), respectively. Multiple regression analysis did not demonstrate any significant effect of seizure frequency or epilepsy duration on attention and inhibitory control deficits, but patient's intelligence quotient (IQ) showed a significant effect on FSAQ and FSRCQ (ß: 0.997, p < 0.001; ß: 0.933, p < 0.001, respectively). Attention and inhibitory control are significantly impaired in patients with GGE and PNES. The cognitive deficits in patients with GGE and PNES have potentially important clinical implications in planning their neuropsychological rehabilitation.

Neurochemicals of limbic system and thalamofrontal cortical network: Are they different between patients with idiopathic generalized epilepsy and psychogenic nonepileptic seizure?

OBJECTIVE: Thalamofrontal cortical network and limbic system are proposed to be involved in psychogenic nonepileptic seizure (PNES) and idiopathic generalized epilepsy (IGE). This study aimed to investigate neurochemical changes in prefrontal cortex, thalamus, and limbic circuits in patients with PNES and IGE. We also analyzed the interaction between cognitive functions and neurochemical changes in both groups. METHODS: Hydrogen proton magnetic resonance spectroscopy (1H-MRS) was used to measure N-acetyl aspartate (NAA), choline (Cho), creatine (Cr), glutamate-glutamine (Glx), and myo-inositol (MI). The voxels were placed on the bilateral dorsolateral prefrontal cortex (DLPFC), dorsomedial prefrontal cortex (DMPFC), anterior cingulate cortex (ACC), and thalamus. Attention and inhibitory control, as well as general intelligence status, were investigated using the Integrated Visual and Auditory Continuous Performance Test (IVA-CPT) and the Wechsler Adult Intelligence Scale (WAIS), respectively, in patients with PNES and IGE, as well as healthy volunteers. RESULTS: The 1H-MRS showed a decreased ratio of NAA/Cr in the right and left thalamus, right DMPFC, and right ACC in patients with IGE and PNES. Furthermore, a decrease of the NAA/Cr ratio in the left DMPFC and an increase of NAA/Cr ratio in the right DLPFC were observed in patients with PNES compared with the controls. The patient groups had a decreased ratio of Cho/Cr in right ACC compared with the healthy subjects. Moreover, the NAA/Cr ratio in the left thalamus and left DMPFC was correlated with seizure frequency in patient groups. Reduced NAA/Cr ratio in the right ACC and left DLPFC were also correlated with poor IVA-CPT scores. CONCLUSION: This study highlighted the dysfunction in prefrontal-thalamic-limbic circuits and impairment in neurocognition in patients with PNES and IGE.

Properties of artificial networks evolved to contend with natural spectra.

Understanding why spectra that are physically the same appear different in different contexts (color contrast), whereas spectra that are physically different appear similar (color constancy) presents a major challenge in vision research. Here, we show that the responses of biologically inspired neural networks evolved on the basis of accumulated experience with spectral stimuli automatically generate contrast and constancy. The results imply that these phenomena are signatures of a strategy that biological vision uses to circumvent the inverse optics problem as it pertains to light spectra, and that double-opponent neurons in early-level vision evolve to serve this purpose. This strategy provides a way of understanding the peculiar relationship between the objective world and subjective color experience, as well as rationalizing the relevant visual circuitry without invoking feature detection or image representation.

Improved region proposal network for enhanced few-shot object detection.

Despite significant success of deep learning in object detection tasks, the standard training of deep neural networks requires access to a substantial quantity of annotated images across all classes. Data annotation is an arduous and time-consuming endeavor, particularly when dealing with infrequent objects. Few-shot object detection (FSOD) methods have emerged as a solution to the limitations of classic object detection approaches based on deep learning. FSOD methods demonstrate remarkable performance by achieving robust object detection using a significantly smaller amount of training data. A challenge for FSOD is that instances from novel classes that do not belong to the fixed set of training classes appear in the background and the base model may pick them up as potential objects. These objects behave similarly to label noise because they are classified as one of the training dataset classes, leading to FSOD performance degradation. We develop a semi-supervised algorithm to detect and then utilize these unlabeled novel objects as positive samples during the FSOD training stage to improve FSOD performance. Specifically, we develop a hierarchical ternary classification region proposal network (HTRPN) to localize the potential unlabeled novel objects and assign them new objectness labels to distinguish these objects from the base training dataset classes. Our improved hierarchical sampling strategy for the region proposal network (RPN) also boosts the perception ability of the object detection model for large objects. We test our approach and COCO and PASCAL VOC baselines that are commonly used in FSOD literature. Our experimental results indicate that our method is effective and outperforms the existing state-of-the-art (SOTA) FSOD methods. Our implementation is provided as a supplement to support reproducibility of the results https://github.com/zshanggu/HTRPN.1.

Unsupervised model adaptation for source-free segmentation of medical images.

The recent prevalence of deep neural networks has led semantic segmentation networks to achieve human-level performance in the medical field, provided they are given sufficient training data. However, these networks often fail to generalize when tasked with creating semantic maps for out-of-distribution images, necessitating re-training on new distributions. This labor-intensive process requires expert knowledge for generating training labels. In the medical field, distribution shifts can naturally occur due to the choice of imaging devices, such as MRI or CT scanners. To mitigate the need for labeling images in a target domain after successful model training in a fully annotated source domain with a different data distribution, unsupervised domain adaptation (UDA) can be employed. Most UDA approaches ensure target generalization by generating a shared source/target latent feature space, allowing a source-trained classifier to maintain performance in the target domain. However, such approaches necessitate joint source and target data access, potentially leading to privacy leaks with respect to patient information. We propose a UDA algorithm for medical image segmentation that does not require access to source data during adaptation, thereby preserving patient data privacy. Our method relies on approximating the source latent features at the time of adaptation and creates a joint source/target embedding space by minimizing a distributional distance metric based on optimal transport. We demonstrate that our approach is competitive with recent UDA medical segmentation works, even with the added requirement of privacy. 1.

Source-free domain adaptation for semantic image segmentation using internal representations.

Semantic segmentation models trained on annotated data fail to generalize well when the input data distribution changes over extended time period, leading to requiring re-training to maintain performance. Classic unsupervised domain adaptation (UDA) attempts to address a similar problem when there is target domain with no annotated data points through transferring knowledge from a source domain with annotated data. We develop an online UDA algorithm for semantic segmentation of images that improves model generalization on unannotated domains in scenarios where source data access is restricted during adaptation. We perform model adaptation by minimizing the distributional distance between the source latent features and the target features in a shared embedding space. Our solution promotes a shared domain-agnostic latent feature space between the two domains, which allows for classifier generalization on the target dataset. To alleviate the need of access to source samples during adaptation, we approximate the source latent feature distribution via an appropriate surrogate distribution, in this case a Gaussian mixture model (GMM).

Recent advances in chitosan-based nanocomposites for adsorption and removal of heavy metal ions.

Due to the high concentration of various toxic and dangerous pollutants, industrial effluents have imposed increasing threats. Among the various processes for wastewater treatment, adsorption is widely used due to its simplicity, good treatment efficiency, availability of a wide range of adsorbents, and cost-effectiveness. Chitosan (CS) has received great attention as a pollutant adsorbent due to its low cost and many -OH and -NH2 functional groups that can bind heavy metal ions. However, weaknesses such as sensitivity to pH, low thermal stability and low mechanical strength, limit the application of CS in wastewater treatment. The modification of these functional groups can improve its performance via cross-linking and grafting agents. The porosity and specific surface area of CS in powder form are not ideal, so physical modification of CS via integration with other materials (e.g., metal oxide, zeolite, clay, etc.) leads to the creation of composite materials with improved absorption performance. This review provides reports on the application of CS and its nanocomposites (NCs) for the removal of various heavy metal ions. Synthesis strategy, adsorption mechanism and influencing factors on sorbents for heavy metals are discussed in detail.

Preparation and characterization of CS/PAT/ MWCNT@MgAl-LDHs nanocomposite for Cd2+ removal and 4-nitrophenol reduction.

The present study evaluated the performance of multiwalled carbon nanotube (MWCNT)@MgAl-layered double hydroxide (LDH) nanoparticles loaded on poly-2 aminothiazole (PAT)/chitosan (CS) matrix (CPML) to remove Cd2+ ions from aqueous solution. The removal efficiency of modified CS/PAT with MWCNT@MgAl-LDHs was increased significantly compared to pure CS/PAT. The influence of heavy metal ion concentration, pH, temperature, adsorbent dosage, and contact time on the adsorption was examined. The optimum conditions for the adsorption of Cd2+ ions were 25 0C with the adsorbent dosage of 0.06 g and initial concentration for adsorption of the Cd2+ 100 mg/L at pH = 8. The maximum adsorption capacity was measured to be 1106.19 mg/g. The values of thermodynamic parameters namely Gibbs free energy (ΔG°), entropy change (ΔS°), and enthalpy change (ΔH°) indicated the feasibility, spontaneity and the endothermic nature of the adsorption process, respectively. The pseudo-second-order kinetics and the Langmuir model were selected as the best models for the adsorption process. Also, CPML nanocomposite (NC) was successfully tested for p-nitrophenol (p-NP) reduction in the presence of NaBH4. The reaction was nearly completed in 6 min. The fabricated CPML-NC could be reused for three consecutive cycles.

Surface modified of chitosan by TiO2@MWCNT nanohybrid for the efficient removal of organic dyes and antibiotics.

Considering the increase in the discharge of industrial effluents containing dyes and antibiotic resistance as a consequence of increasing the prescription and easy distribution of antibiotic drugs at the global level, designing efficient, biodegradable and non-toxic absorbents is necessary to reduce environmental harm effects. Herein, we present a series of novel eco-friendly ternary hybrid nanocomposite hydrogels CS/TiO2@MWCNT (CTM) composed of chitosan (CS), TiO2, and multiwalled carbon nanotube (MWCNT) for removal of methylene blue (MB) and methyl orange (MO) and common antibiotic ciprofloxacin (CIP) in aqueous medium. The combination of MWCNT and TiO2 improves the physicochemical properties of CS hydrogel and increases the adsorption capacity toward pollutants in the presence of different loadings. CTM hydrogel showed a specific surface area of 236.45 m2 g-1 with a pore diameter of 7.89 nm. Adsorption mechanisms were investigated in detail using kinetic, isotherm, and thermodynamic studies of adsorption as well as various spectroscopic techniques. Adsorption of these pollutants by CTM nanocomposite hydrogel occurred using various interactions at different pHs, which showed the obvious dependence of CTM adsorption capacity on pH. Electrostatic attractions, complex formation, π-π stacking and hydrogen bonds played a key role in the adsorption process. The adsorption of MB, MO, and CIP was fitted with the Langmuir isotherm with maximum adsorption capacities of 531.91, 1763.6, and 1510.5 mg g-1, respectively. CTM had a minor decrease in adsorption strength and showed good structural stability even after 8 adsorptions-desorption cycles. The total cost of producing a 1 kg adsorbent was calculated to be $ 450, which helped us determine the economic feasibility of the adsorbent in large-scale applications.

Multi-Source Data Integration for Segmentation of Unannotated MRI Images.

Automatic semantic segmentation of magnetic resonance imaging (MRI) images using deep neural networks greatly assists in evaluating and planning treatments for various clinical applications. However, training these models is conditioned on the availability of abundant annotated data. Even if we annotate enough data, MRI images display considerable variability due to factors such as differences among patients, MRI scanners, and imaging protocols. This variability necessitates retraining neural networks for each specific application domain, which, in turn requires manual annotation by expert radiologists for all new domains. To relax the need for persistent data annotation, we develop a method for unsupervised federated domain adaptation using multiple annotated source domains. Our approach enables the transfer of knowledge from several annotated source domains for use in an unannotated target domain. Initially, we ensure that the target domain data shares similar representations with each source domain in a latent embedding space by minimizing the pair-wise distances between the distributions for the target and the source domains. We then employ an ensemble approach to leverage the knowledge obtained from all domains to build an integrated outcome. We perform experiments on two datasets to demonstrate our method is effective. Our implementation code is publicly available: https://github.com/navapatn/Unsupervised -Federated-Domain-Adaptation-for-Image-Segmentation new.

Artificial Intelligence to Differentiate Pediatric Pseudopapilledema and True Papilledema on Fundus Photographs.

Purpose: To develop and test an artificial intelligence (AI) model to aid in differentiating pediatric pseudopapilledema from true papilledema on fundus photographs. Design: Multicenter retrospective study. Subjects: A total of 851 fundus photographs from 235 children (age < 18 years) with pseudopapilledema and true papilledema. Methods: Four pediatric neuro-ophthalmologists at 4 different institutions contributed fundus photographs of children with confirmed diagnoses of papilledema or pseudopapilledema. An AI model to classify fundus photographs as papilledema or pseudopapilledema was developed using a DenseNet backbone and a tribranch convolutional neural network. We performed 10-fold cross-validation and separately analyzed an external test set. The AI model's performance was compared with 2 masked human expert pediatric neuro-ophthalmologists, who performed the same classification task. Main Outcome Measures: Accuracy, sensitivity, and specificity of the AI model compared with human experts. Results: The area under receiver operating curve of the AI model was 0.77 for the cross-validation set and 0.81 for the external test set. The accuracy of the AI model was 70.0% for the cross-validation set and 73.9% for the external test set. The sensitivity of the AI model was 73.4% for the cross-validation set and 90.4% for the external test set. The AI model's accuracy was significantly higher than human experts on the cross validation set (P < 0.002), and the model's sensitivity was significantly higher on the external test set (P = 0.0002). The specificity of the AI model and human experts was similar (56.4%-67.3%). Moreover, the AI model was significantly more sensitive at detecting mild papilledema than human experts, whereas AI and humans performed similarly on photographs of moderate-to-severe papilledema. On review of the external test set, only 1 child (with nearly resolved pseudotumor cerebri) had both eyes with papilledema incorrectly classified as pseudopapilledema. Conclusions: When classifying fundus photographs of pediatric papilledema and pseudopapilledema, our AI model achieved > 90% sensitivity at detecting papilledema, superior to human experts. Due to the high sensitivity and low false negative rate, AI may be useful to triage children with suspected papilledema requiring work-up to evaluate for serious underlying neurologic conditions. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Hematopoietic Stem Cell Transplantation in Sickle Cell Disease: A Multidimentional Review.

While exagamglogene autotemcel (Casgevy) and lovotibeglogene autotemcel (Lyfgenia) have been approved by the US Food and Drug Administration (FDA) as the first cell-based gene therapies for the treatment of patients 12 years of age and older with sickle cell disease (SCD), this treatment is not universally accessible. Allogeneic hematopoietic stem cell transplant (HSCT) has the potential to eradicate the symptoms of patients with SCD, but a significant obstacle in HSCT for SCD is the availability of suitable donors, particularly human leukocyte antigen (HLA)-matched related donors. Furthermore, individuals with SCD face an elevated risk of complications during stem cell transplantation due to SCD-related tissue damage, endothelial activation, and inflammation. Therefore, it is imperative to consider optimal conditioning regimens and investigate HSCT from alternative donors. This review encompasses information on the use of HSCT in patients with SCD, including the indications for HSCT, conditioning regimens, alternative donors, and posttransplant outcomes.

Graft failure after allogeneic hematopoietic stem cell transplantation in pediatric patients with acute leukemia: autologous reconstitution or second transplant?

BACKGROUND: Graft failure (GF) is a rare but serious complication after allogeneic hematopoietic stem cell transplantation (HSCT). Prevention of graft failure remains the most advisable approach as there is no clear recommendation for the best strategies for reversing this complication. Administration of growth factor, additional hematopoietic progenitor boost, or a salvage HSCT are current modalities recommended for the treatment of GF. Autologous recovery without evidence of disease relapse occurs rarely in patients with GF, and in the absence of autologous recovery, further salvage transplantation following a second conditioning regimen is a potential treatment option that offers the best chances of long-term disease-free survival. The preconditioning regimens of second HSCT have a significant impact on engraftment and outcome, however, currently there is no consensus on optimal conditioning regimen for second HSCT in patients who have developed GF. Furthermore, a second transplant from a different donor or the same donor is still a matter of debate. OBSERVATIONS: We present our experience in managing pediatric patients with acute leukemia who encountered graft failure following stem cell transplantation. CONCLUSIONS AND RELEVANCE: Although a second transplantation is almost the only salvage method, we illustrate that some pediatric patients with acute leukemia who experience graft failure after an allogeneic stem cell transplant using Myeloablative conditioning (MAC) regimen may achieve long-term disease-free survival through autologous hematopoiesis recovery.

The Relationship of Mobile-Based Social Network Addiction and Family Communication Patterns, with Behavioral Problems in Secondary School Students: The Mediating Role of Emotional Self-Regulation.

Background: New technologies offer endless possibilities for students and schools, but as the use of smartphones increases, psychological and behavioral problems may also increase. Objective: To investigate the relationship of mobile-based social network addiction and family communication patterns on the one hand, and behavioral problems in students on the other, with a focus on the mediating role of emotional self-regulation. Design: This study used a quantitative approach and a cross-sectional design. The participants were 384 students (female/male: 226/168; mean age: 16 ± 1.98) in secondary high schools in Tehran in the academic year 2022-2023. The students were selected using convenience sampling. The data were collected online using the Revised Family Communication Pattern Scale (1994), Mobile-Based Social Network Addiction Questionnaire (2016), Child Behavior Checklist - Adolescent Version (2001), and the Affective Style Questionnaire (2010). The data were analyzed using structural equation modeling with SPSS-25 and AMOS-24 statistical software. Results: The study showed that emotional self-regulation plays a mediating role in the relationship between mobile-based social network addiction and internalized and externalized behavioral problems (P ≤ .05). The indirect effect of conversation orientation on internalized and externalized behavioral problems mediated by emotional self-regulation was not significant, but the indirect effect of conformity orientation on internalized and externalized behavioral problems with the mediation of emotional self-regulation was significant (P ≤ .05). Conclusion: Based on the findings, it is suggested that school officials and parents should develop emotional self-regulation and communication skills in students and parenting skills in their parents to prevent and reduce potential harm such as internet addiction and behavioral problems in students.

Chitosan-based composite films to remove cationic and anionic dyes simultaneously from aqueous solutions: Modeling and optimization using RSM.

Regarding the existence of cationic and anionic dyes in the water environment developing new and effective techniques to remove them simultaneously is essential. Herein, a chitosan/poly-2-aminothiazole composite film reinforced with multi-walled carbon nanotube-Mg Al-layered double hydroxide (CPML) was created, characterized, and used as an effective adsorbent for methylene blue (MB) and methyl orange (MO) dyes removal from the aquatic medium. The SEM, TGA, FTIR, XRD, and BET methods were used to characterize the synthesized CPML. Response surface methodology (RSM) was utilized to evaluate dye removal based on the initial concentration, dosage, and pH factors. The highest adsorption capacities were measured at 471.12 and 230.87 mg g-1 for MB and MO, respectively. The study of different isotherm and kinetic models revealed that the adsorption of the dyes onto CPML nanocomposite (NC) was correlated with the Langmuir and pseudo-second-order kinetic model, which indicated a monolayer adsorption manner on the homogeneous surface of NCs. The reusability experiment clarified that the CPML NC could be applied multiple times. Experimental results show that the CPML NC has sufficient potential for treating cationic and anionic dye-contaminated water.

Cognitive deficits in multiple sclerosis: Auditory and visual attention and inhibitory control.

BACKGROUND: A growing body of evidence has been paid to the cognitive impairment in patients with multiple sclerosis (MS). However, studies concerning cognitive functions in MS have also yielded conflicting results. This study investigates the attention and inhibitory control functions in patients with MS and their relationship with other clinical features, such as depression and fatigue in these patients. METHODS: Participants included 80 patients with MS and 60 healthy controls. The attention and inhibitory control, fatigue, and psychiatric screening in all subjects were studied, respectively with the Integrated Visual and Auditory Continuous Performance Test (IVA-CPT), Fatigue Severity Scale (FSS), and the Hospital Anxiety and Depression Scale (HADS). RESULTS: Patients with MS performed the IVA-CPT task more poorly than the healthy control group (p < 0.001). However, multiple regression analysis did not show any significant relationship between disease duration, FSS, and HADS on attention and inhibitory control. CONCLUSION: Inhibitory control and attention are significantly impaired in patients with MS. Finding the basics of cognitive deficits in MS have potentially important clinical implications for developing better cognitive rehabilitation strategies.