The Impact of a School-Based Intervention Using the PBSEIM Model on Health Promoting Behaviors and Self-Care in Adolescent Females

AbstractIntroduction: Developing effective health habits during adolescence dramatically effects behavior formation during adulthood. Therefore, the current study was conducted with an aim to investigate the impact of school-based intervention using «Integrated Model of Planned Behavior and Self-Efficacy» (PBSEIM) on self-care and health promoting behaviors of female high school students of Abyek city, Qazvin Province (Iran), during year 2016.Methods: This experimental study was conducted on 100 female public high school students aged 15 to 19 years old in Abyek city, Qazvin Province. Two schools were randomly selected between 6 high schools. One of the high schools was randomly selected as the intervention group and the other one as the control group. Three classrooms in each school were randomly selected and the necessary samples were collected from each class. Overall, 100 samples had the inclusion criteria; 50 were included in the intervention and 50 in the control group. Demographic, “Health Promoting Lifestyle Profile” (HPLP II), and “Adolescent Girl’s Self-Care Questionnaire” was completed by both groups before and after the interventions. Face validity and content validity of the self-care questionnaire were assessed. Also, Cronbach’s alpha coefficient for this questionnaire was obtained as 0.73.The students in the intervention group were trained using the PBSEIM model and the control group received routine training. Data was collected and analyzed using the SPSS software (version 22) and independent and paired t tests. Values lower than 0.05 were considered significant.Results: There was a significant difference before and after the intervention in the average scores of health-promoting behaviors and self-care of adolescents in the intervention group in comparison to the control group (P < 0.05). Inter-group comparison demonstrated a significantly higher increase of health-promoting behaviors and self-care of adolescents in the intervention group before and after the intervention (P-value < 0.05).Conclusions: School-based educational intervention using psychosocial models is effective in changing health-promoting behaviors and self-care

Auditory Recognition of Words-in-Noise in Normal Hearing and Mild-to-Severe Sensorineural Hearing Loss with Different Configurations

Background and Aim: Sensorineural Hearing Loss (SNHL) reduces audibility and causes distortion, which result in difficulty with speech processing, especially in noisy environments. One of the new speech-in-noise tests is the Words-in-Noise (WIN) test. This study aimed to further investigate the Signal-to-Noise Ratio 50% (SNR-50) in subjects with mild to severe SNHL and different configurations using the Persian version of the WIN test compared to normal-hearing people. Methods: This cross-sectional study was conducted on 54 patients with SNHL aged 17–75 years and 49 normal-hearing people aged 20–48 years. The auditory recognition in the presence of multi-talker babble noise was evaluated by the Persian version of the WIN test (named ARWIN). Results: The mean SNR-50 in the normal-hearing group was 2.56±1.2 dB, which increased significantly in subgroups with mild (10.13±4.8 dB), moderate (14.51±4.7 dB) and moderate-to-severe (16.61±4.3 dB) SNHL (p<0.001). Conclusion: People with SNHL need more SNR by nearly 4–6 times than the normalhearing group for recognition of monosyllabic Persian words in the presence of multi-talker babble noise

Evaluation of the antibacterial effect of nickel oxide nanoparticles against bacteria involved in dental caries

Tooth decay is one of the most common diseases in the oral cavity and is one of the most widespread diseases in the human population. This study aimed to determine the antibacterial effect of nickel oxide nanoparticles against bacteria involved in tooth decay. In this study, the disk diffusion method was used to determine the antibiotic susceptibility and the microdilution broth method was used to determine the minimum inhibitory concentration (MIC). Nanoparticles were also synthesized in two molecular size (A: 8.1 and B: 12 nm) by the sol-gel method. The MIC of the first nanoparticle for Streptococcus sanguinis and Streptococcus mutans was 31.25 and 125 μg/ml, respectively. The MIC of the second nanoparticle for S. sanguinis was 125 μg/ml. In the case of S. mutans up to a concentration of 500 μg/ml, no growth inhibition was observed. The results showed that nickel oxide nanoparticles have acceptable antibacterial properties against S. mutans and S. sanguinis, which can be used in dental materials to prevent dental caries. However, this requires the determination of cellular toxicity and its side effects in future studies.

A new link prediction in multiplex networks using topologically biased random walks

Link prediction is a technique to forecast future new or missing relationships between nodes based on the current network information. However, the link prediction in monoplex networks seems to have a long background, the attempts to accomplish the same task on multiplex networks are not abundant, and it was often a challenge to apply conventional similarity methods to multiplex networks. The issue of link prediction in multiplex networks is the way of predicting the links in one layer, taking structural information of other layers into account. One of the most important methods of link prediction in a monoplex network is a local random walk (LRW) that captures the network structure using pure random walking to measure nodes similarity of the graph and find unknown connections. The goal of this paper is to propose an extended version of local random walk based on pure random walking for solving link prediction in the multiplex network, referred to as the Multiplex Local Random Walk (MLRW). We explore approaches for leveraging information mined from inter-layer and intra-layer in a multiplex network to define a biased random walk for finding the probability of the appearance of a new link in one target layer. Experimental studies on seven multiplex networks in the real world demonstrate that a multiplex biased local random walk performs better than the state-of-the-art methods of link prediction and corresponding unbiased case and improves prediction accuracy

Robust graph regularization nonnegative matrix factorization for link prediction in attributed networks

Link prediction is one of the most widely studied problems in the area of complex network analysis, in which machine learning techniques can be applied to deal with it. The biggest drawback of the existing methods, however, is that in most cases they only consider the topological structure of the network, and therefore completely miss out on the great potential that stems from the nodal attributes. Both topological structure and nodes’ attributes are essential in predicting the evolution of attributed networks and can act as complements to each other. To bring out their full potential in solving the link prediction problem, a novel Robust Graph Regularization Nonnegative Matrix Factorization for Attributed Networks (RGNMF-AN) was proposed, which models not only the topology structure of networks but also their node attributes for direct link prediction. This model, in particular, combines two types of information, namely network topology, and nodal attributes information, and calculates high-order proximities between nodes using the Structure-Attribute Random Walk Similarity (SARWS) method. The SARWS score matrix is an indicator structural and attributed matrix that collects more useful attributed information in high-order proximities, whereas graph regularization technology combines the SARWS score matrix with topological and attribute information to collect more valuable attributed information in high-order proximities. Furthermore, the RGNMF-AN employs the ℓ2,1-norm to constrain the loss function and regularization terms, effectively removing random noise and spurious links. According to empirical findings on nine real-world complex network datasets, the use of a combination of attributed and topological information in tandem enhances the prediction performance significantly compared to the baseline and other NMF-based algorithms.</p

Impact of Centrality Measures on the Common Neighbors in Link Prediction for Multiplex Networks

Complex networks are representations of real-world systems that can be better modeled as multiplex networks, where the same nodes develop multi-type connections. One of the important concerns about these networks is link prediction, which has many applications in social networks and recommender systems. In this article, similarity-based methods such as common neighbors (CNs) are the mainstream. However, in the CN method, the contribution of each CN in the likelihood of new connections is equally taken into account. In this work, we propose a new link prediction method namely Weighted Common Neighbors (WCN), which is based on CNs and various types of Centrality measures (including degree, k-core, closeness, betweenness, Eigenvector, and PageRank) to predict the formation of new links in multiplex networks. So, in this model, each CN has a different impact on the node connection likelihood. Moreover, we investigate the impact of interlayer information on improving the performance of link prediction in the target layer. Using Area under the ROC Curve and precision as evaluation metrics, we perform a comprehensive experimental evaluation of our proposed method on seven real multiplex networks. The results validate the improved performance of our proposed method compared with existing methods, and we show that the performance of proposed methods is significantly improved while using interlayer information in multiplex networks. </p

A preference random walk algorithm for link prediction through mutual influence nodes in complex networks

Predicting links in complex networks has been one of the essential topics within the realm of data mining and science discovery over the past few years. This problem remains an attempt to identify future, deleted, and redundant links using the existing links in a graph. Local random walk is considered to be one of the most well-known algorithms in the category of quasi-local methods. It traverses the network using the traditional random walk with a limited number of steps, randomly selecting one adjacent node in each step among the nodes which have equal importance. Then this method uses the transition probability between node pairs to calculate the similarity between them. However, in most datasets this method is not able to perform accurately in scoring remarkably similar nodes. In the present article, an efficient method is proposed for improving local random walk by encouraging random walk to move, in every step, towards the node which has a stronger influence. Therefore, the next node is selected according to the influence of the source node. To do so, using mutual information, the concept of the asymmetric mutual influence of nodes is presented. A comparison between the proposed method and other similarity-based methods (local, quasi-local, and global) has been performed, and results have been reported for 11 real-world networks. It had a higher prediction accuracy compared with other link prediction approaches.</p

Spectral clustering on protein-protein interaction networks via constructing affinity matrix using attributed graph embedding

The identification of protein complexes in protein-protein interaction networks is the most fundamental and essential problem for revealing the underlying mechanism of biological processes. However, most existing protein complexes identification methods only consider a network's topology structures, and in doing so, these methods miss the advantage of using nodes' feature information. In protein-protein interaction, both topological structure and node features are essential ingredients for protein complexes. The spectral clustering method utilizes the eigenvalues of the affinity matrix of the data to map to a low-dimensional space. It has attracted much attention in recent years as one of the most efficient algorithms in the subcategory of dimensionality reduction. In this paper, a new version of spectral clustering, named text-associated DeepWalk-Spectral Clustering (TADW-SC), is proposed for attributed networks in which the identified protein complexes have structural cohesiveness and attribute homogeneity. Since the performance of spectral clustering heavily depends on the effectiveness of the affinity matrix, our proposed method will use the text-associated DeepWalk (TADW) to calculate the embedding vectors of proteins. In the following, the affinity matrix will be computed by utilizing the cosine similarity between the two low dimensional vectors, which will be considerable to improve the accuracy of the affinity matrix. Experimental results show that our method performs unexpectedly well in comparison to existing state-of-the-art methods in both real protein network datasets and synthetic networks.</p

Comparative Study of Surgical Outcomes in Patients with and without COVID-19 Undergoing Emergency Surgery

Background and purpose: Many studies suggest that surgery in patients with COVID-19 increases the risk of complications and mortality after surgery. The purpose of this research was to compare the frequency of outcomes during and after surgery between patients with and without COVID-19 undergoing emergency surgery in Gonbad Kavus hospitals, 2020-2021. Materials and methods: In this cross-sectional study, two groups of adults (n= 114) with and without COVID-19 (group A and group B, respectively) who underwent emergency surgery were examined. Demographic data, laboratory findings, and during and post-operative outcomes were recorded. Data analysis was done in SPSS V26. Results: Average age and weight in group A (45.5 years, 78.3±16.6 Kg, respectively) were found to be higher than those in group B (39.3 years and 67.9±11.5 Kg, respectively). Significant difference was seen in the percentage of arterial blood oxygen saturation after operations between group A (94.7±1.38) and group B (91.7±2.83) (P<0.0001). Also, mean ICU and hospital length of stay were significantly longer in group A (8.5 and 9.8 days, respectively) compared with group B (5.4 and 6 days, respectively) (P<0.0001). Moreover, death was observed more in group A (76%) than group B (23%) (P=0.041). Conclusion: All members of the surgical team are required to pay special attention to the increase in the incidence of complications during and after surgery in patients with COVID-19 undergoing emergency surgery in order to take necessary preventive and therapeutic measures