Islamic Azad University of Tabriz

Liposomes, sphere-shaped vesicles consisting of one or more phospholipid bilayers, were first described in the mid-60s. Today, they are a very useful reproduction, reagent, and tool in various scientific disciplines, including mathematics and theoretical physics, biophysics, chemistry, colloid science, biochemistry, and biology. Since then, liposomes have made their way to the market. Among several talented new drug delivery systems, liposomes characterize an advanced technology to deliver active molecules to the site of action, and at present, several formulations are in clinical use. Research on liposome technology has progressed from conventional vesicles to 'second-generation liposomes', in which long-circulating liposomes are obtained by modulating the lipid composition, size, and charge of the vesicle. Liposomes with modified surfaces have also been developed using several molecules, such as glycolipids or sialic acid. This paper summarizes exclusively scalable techniques and focuses on strengths, respectively, limitations in respect to industrial applicability and regulatory requirements concerning liposomal drug formulations based on FDA and EMEA documents.

Current discoveries of different forms of carbon nanostructures have motivated research on their applications in various fields. They hold promise for applications in medicine, gene, and drug delivery areas. Many different production methods for carbon nanotubes (CNTs) have been introduced; functionalization, filling, doping, and chemical modification have been achieved, and characterization, separation, and manipulation of individual CNTs are now possible. Parameters such as structure, surface area, surface charge, size distribution, surface chemistry, and agglomeration state as well as purity of the samples have considerable impact on the reactivity of carbon nanotubes. Otherwise, the strength and flexibility of carbon nanotubes make them of potential use in controlling other nanoscale structures, which suggests they will have a significant role in nanotechnology engineering.

Liposomes provide an established basis for the sustainable development of different commercial products for treatment of medical diseases by the smart delivery of drugs. The industrial applications include the use of liposomes as drug delivery vehicles in medicine, adjuvants in vaccination, signal enhancers/carriers in medical diagnostics and analytical biochemistry, solubilizers for various ingredients as well as support matrices for various ingredients and penetration enhancers in cosmetics. In this review, we summarize the main applications and liposome-based commercial products that are currently used in the medical field.

This review introduces quantum dots (QDs) and explores their properties, synthesis, applications, delivery systems in biology, and their toxicity. QDs are one of the first nanotechnologies to be integrated with the biological sciences and are widely anticipated to eventually find application in a number of commercial consumer and clinical products. They exhibit unique luminescence characteristics and electronic properties such as wide and continuous absorption spectra, narrow emission spectra, and high light stability. The application of QDs, as a new technology for biosystems, has been typically studied on mammalian cells. Due to the small structures of QDs, some physical properties such as optical and electron transport characteristics are quite different from those of the bulk materials.

Nanoparticles are the simplest form of structures with sizes in the nanometer (nm) range. In principle any collection of atoms bonded together with a structural radius of < 100 nm can be considered nano particles. Nanotechnology offers unique approaches to probe and control a variety of biological and medical processes that occur at nanometer scales, and is expected to have a revolutionary impact on biology and medicine. Among the approaches for exploiting nanotechnology in medicine, nanoparticles offer some unique advantages as sensing, image enhancement, and delivery agents. Several varieties of nanoparticles with biomedical relevance are available including, polymeric nanoparticles, metal nanoparticles, liposomes, micelles, quantum dots, dendrimers, and nanoassemblies. To further the application of nanoparticles in disease diagnosis and therapy, it is important that the systems are biocompatible and capable of being functionalized for recognition of specific target sites in the body after systemic administration. In this review, we have explained some important applications of gold nanoparticles.

Silver nanoparticles size makes wide range of new applications in various fields of industry. Synthesis of noble metal nanoparticles for applications such as catalysis, electronics, optics, environmental and biotechnology is an area of constant interest. Two main methods for Silver nanoparticles are the physical and chemical methods. The problem with these methods is absorption of toxic substances onto them. Green synthesis approaches overcome this limitation. Silver nanoparticles size makes wide range of new applications in various fields of industry. This article summarizes exclusively scalable techniques and focuses on strengths, respectively, limitations with respect to the biomedical applicability and regulatory requirements concerning silver nanoparticles.

Abstract Background Nursing is considered a hard job and their work stresses can have negative effects on health and quality of life. The aim of this study was to investigate the correlation between job stress with quality of life and care behaviors in nurses. Methods This cross-sectional survey design study was performed with the participation of 115 nurses working in two hospitals. The nurses were selected via the availability sampling method and data were collected by demographic characteristics, nurses ‘job stress, quality of life (SF12), and Caring Dimension Inventory questionnaires. Results The mean (SD) total scores of job stress, quality of life and caring behavior were 2.77 (0.54), 56.64 (18.05) and 38.23 (9.39), respectively. There was a statistically significant and negative relationship between total job stress scores with quality of life ( r = -0.44, P &lt; 0.001, Medium effect) and caring behaviors ( r =-0.26, P &lt; 0.001, Small effect). Univariate linear regression showed that job stress alone could predict 27.9% of the changes in the total quality of life score (β =-0.534, SE = 0.051, R 2adj = 0.279, P &lt; 0.001) and 4.9% of the changes in the total score of caring behaviors (β =-0.098, SE = 0.037, R 2adj = 0.049 P &lt; 0.001). Conclusions Job stress has a negative effect on the quality of life related to nurses’ health. It can also overshadow the performance of care and reduce such behaviors in nurses, which may be one of the factors affecting the outcome of patients.

BACKGROUND: The spread of COVID-19 creates disruption, uncertainty, complexity, and ambiguity in all organizations. People are the primary asset of any organization and help achieve their goals. Accordingly, to manage human resources sustainably, the organizational strategy review is an appropriate retort. OBJECTIVE: The purpose of this comprehensive review study is to identify unknown challenges, strategies, and unusual decisions related to human resource management other than clinical organizations during the COVID-19 pandemic. METHODS: The study applied a narrative review approach dissection based on organizations' human resource management strategies to combat the COVID-19 impacts. The review study conducted published literature research through the electronic databases at Web of Science, PubMed, Scopus, PsycINFO, and LISTA. The study extracted 1281 articles from the mentioned databases from November 2021 to the first quarter of 2021. This study reviewed selected papers, included 15 relevant articles, and removed duplicates according to inclusion and exclusion criteria. Finally, the study developed a conceptual framework of human resource management strategies based on the literature findings to fight against the COVID-19 pandemic. RESULTS: The COVID-19 pandemic posed numerous adverse consequences, such as economic shock, global health crisis, change in social behaviors, and challenges at the organization level to continue business operations. Besides, the strategies included flexibility, strengthening internal efficiency, talent acquisition, and making innovative changes based on organizational assessment and needs for smooth business activities. CONCLUSION: The appropriate human resource management strategies implementations would increase employees' mental well-being, satisfaction, productivity, motivation, and health safety at the workplace.

Non-alcoholic fatty liver disease (NAFLD) is a common chronic liver disorder which is associated with accumulation of fats in the liver. It causes a wide variety of pathological effects such as non-alcoholic steatohepatitis (NASH) and cirrhosis, insulin resistance, obesity, hypertension, dyslipidaemia, diabetes and cardiovascular disease. The molecular mechanisms that cause the initiation and progression of NAFLD are not fully understood. Oxidative stress (OS) induced by reactive oxygen species (ROS) and inflammation are likely a significant mechanism which can lead to hepatic cell death and tissue injury. Mitochondrial abnormalities, down-regulation of several antioxidant enzymes, glutathione (GSH) depletion and decreased activity of GSH-dependent antioxidants, accumulation of leukocytes and hepatic inflammation are the major sources of ROS overproduction in NAFLD. Excessive production of ROS suppresses the capacity of other antioxidant defence systems in NAFLD and causes further oxidative damage. Regular exercise can be considered as an effective strategy for treatment of NAFLD. It improves NAFLD by reducing intrahepatic fat content, increasing β-oxidation of fatty acids, inducing hepato-protective autophagy, overexpressing peroxisome proliferator-activated receptor- γ (PPAR-γ), as well as attenuating hepatocyte apoptosis and increasing insulin sensitivity. Exercise training also suppresses ROS overproduction and OS in NAFLD via up-regulation of several antioxidant enzymes and anti-inflammatory mediators. Therefore, an understanding of these molecules and signalling pathways gives us valuable information about NAFLD progression and a method for developing a suitable clinical treatment. This review aimed to evaluate sources of ROS and OS in NAFLD and the molecular mechanisms involved in the beneficial effects of exercises on NAFLD.

Mitogen-activated protein kinase (MAPK) signaling pathways organize a great constitution network that regulates several physiological processes, like cell growth, differentiation, and apoptotic cell death. Due to the crucial importance of this signaling pathway, dysregulation of the MAPK signaling cascades is involved in the pathogenesis of various human cancer types. Oxidative stress and DNA damage are two important factors which in common lead to carcinogenesis through dysregulation of this signaling pathway. Reactive oxygen species (ROS) are a common subproduct of oxidative energy metabolism and are considered to be a significant physiological modulator of several intracellular signaling pathways including the MAPK pathway. Studies demonstrated that the MAP kinases extracellular signal-regulated kinase (ERK) 1/2 and p38 were activated in response to oxidative stress. In addition, DNA damage is a partly common circumstance in cell life and may result in mutation, cancer, and even cell death. Recently, accumulating evidence illustrated that the MEK/ERK pathway is associated with the suitable performance of cellular DNA damage response (DDR), the main pathway of tumor suppression. During DDR, the MEK/ERK pathway is regularly activated, which contributes to the appropriate activation of DDR checkpoints to inhibit cell division. Therefore, the aim of this review is to comprehensively discuss the critical function of MAPK signaling in oxidative stress, DNA damage, and cancer progression.

Clustering is a powerful technique in data-mining, which involves identifing homogeneous groups of objects based on the values of attributes. Meta-heuristic algorithms such as particle swarm optimization, artificial bee colony, genetic algorithm and differential evolution are now becoming powerful methods for clustering. In this paper, we propose a new meta-heuristic clustering method, the Whale Clustering Optimization Algorithm, based on the swarm foraging behavior of humpback whales. After a detailed formulation and explanation of its implementation, we will then compare the proposed algorithm with other existing well-known algorithms in clustering, including PSO, ABC, GA, DE and k-means. Proposed algorithm was tested using one artificial and seven real benchmark data sets from the UCI machine learning repository. Simulations show that the proposed algorithm can successfully be used for data clustering.

OBJECTIVES: Mesenchymal stem cells (MSCs) are adult stem cells which identified by adherence to plastic, expression of cell surface markers including CD44, CD90, CD105, CD106, CD166, and Stro-1, lack of the expression of hematopoietic markers, no immunogenic effect and replacement of damaged tissues. These properties led to development of progressive methods to isolation and characterization of MSCs from various sources for therapeutic applications in regenerative medicine. METHODS: We isolated MSC-like cells from testis biopsies, ovary, hair follicle and umbilical cord Wharton's jelly and investigated the expression of specific cell surface antigens using flow cytometry in order to verify stemness properties of these cells. RESULTS: All four cell types adhered to plastic culture flask a few days after primary culture. All our cells positively expressed common MSC- specific cell surface markers. Moreover, our results revealed the expression of CD19and CD45 antigens in these cells. CONCLUSION: According to our results, high expression of CD44 in spermatogonial stem cells (SSCs), hair follicle stem cells (HFSCs),granulosa cells (GCs)and Wharton's jelly- MSCs (WJ-MSCs)may help them to maintain stemness properties. Furthermore, we suggest that CD105+SSCs, HFSCs and WJ-MSCs revealed the osteogenic potential of these cells. Moreover, high expression of CD90 in SSCs and HFSCs may associate to higher growth and differentiation potential of these cells. Further, the presence of CD19 on SSCs and GCs may help them to efficiency in response to trans-membrane signals. Thus, these four types of MSCs may be useful in clinical applications and cell therapy.

Channel shear connectors are known as an appropriate alternative for common shear connectors due to having a lower manufacturing cost and an easier installation process. The behavior of channel connectors is generally determined through conducting experiments. However, these experiments are not only costly but also time-consuming. Moreover, the impact of other parameters cannot be easily seen in the behavior of the connectors. This paper aims to investigate the application of a hybrid artificial neural network–particle swarm optimization (ANN-PSO) model in the behavior prediction of channel connectors embedded in normal and high-strength concrete (HSC). To generate the required data, an experimental project was conducted. Dimensions of the channel connectors and the compressive strength of concrete were adopted as the inputs of the model, and load and slip were predicted as the outputs. To evaluate the ANN-PSO model, an ANN model was also developed and tuned by a backpropagation (BP) learning algorithm. The results of the paper revealed that an ANN model could properly predict the behavior of channel connectors and eliminate the need for conducting costly experiments to some extent. In addition, in this case, the ANN-PSO model showed better performance than the ANN-BP model by resulting in superior performance indices.

In this paper, Energy Valley Optimizer (EVO) is proposed as a novel metaheuristic algorithm inspired by advanced physics principles regarding stability and different modes of particle decay. Twenty unconstrained mathematical test functions are utilized in different dimensions to evaluate the proposed algorithm's performance. For statistical purposes, 100 independent optimization runs are conducted to determine the statistical measurements, including the mean, standard deviation, and the required number of objective function evaluations, by considering a predefined stopping criterion. Some well-known statistical analyses are also used for comparative purposes, including the Kolmogorov-Smirnov, Wilcoxon, and Kruskal-Wallis analysis. Besides, the latest Competitions on Evolutionary Computation (CEC), regarding real-world optimization, are also considered for comparing the results of the EVO to the most successful state-of-the-art algorithms. The results demonstrate that the proposed algorithm can provide competitive and outstanding results in dealing with complex benchmarks and real-world problems.

Reproduction of different tissues using scaffolds and materials is a major element in regenerative medicine. The regeneration of whole organs with decellularized extracellular matrix (dECM) has remained a goal despite the use of these materials for different purposes. Recently, decellularization techniques have been widely used in producing scaffolds that are appropriate for regenerating damaged organs and may be able to overcome the shortage of donor organs. Decellularized ECM offers several advantages over synthetic compounds, including the preserved natural microenvironment features. Different decellularization methods have been developed, each of which is appropriate for removing cells from specific tissues under certain conditions. A variety of methods have been advanced for evaluating the decellularization process in terms of cell removal efficiency, tissue ultrastructure preservation, toxicity, biocompatibility, biodegradability, and mechanical resistance in order to enhance the efficacy of decellularization methods. Modification techniques improve the characteristics of decellularized scaffolds, making them available for the regeneration of damaged tissues. Moreover, modification of scaffolds makes them appropriate options for drug delivery, disease modeling, and improving stem cells growth and proliferation. However, considering different challenges in the way of decellularization methods and application of decellularized scaffolds, this field is constantly developing and progressively moving forward. This review has outlined recent decellularization and sterilization strategies, evaluation tests for efficient decellularization, materials processing, application, and challenges and future outlooks of decellularization in regenerative medicine and tissue engineering.

Biofilm-related infections have been a major clinical problem and include chronic infections, device-related infections and malfunction of medical devices. Since biofilms are not fully available for the human immune system and antibiotics, they are difficult to eradicate and control; therefore, imposing a global threat to human health. There have been avenues to tackle biofilms largely based on the disruption of their adhesion and maturation. Nowadays, the use of probiotics and their derivatives has gained a growing interest in battling against pathogenic biofilms. In the present review, we have a close look at probiotics with the ultimate objective of inhibiting biofilm formation and maturation. Overall, insights into the mechanisms by which probiotics and their derivatives can be used in the management of biofilm infections would be warranted.

The Internet of Drones (IoD) is built on the Internet of Things (IoT) by replacing “Things” with “Drones” while retaining incomparable features. Because of its vital applications, IoD technologies have attracted much attention in recent years. Nevertheless, gaining the necessary degree of public acceptability of IoD without demonstrating safety and security for human life is exceedingly difficult. In addition, intrusion detection systems (IDSs) in IoD confront several obstacles because of the dynamic network architecture, particularly in balancing detection accuracy and efficiency. To increase the performance of the IoD network, we proposed a blockchain-based radial basis function neural networks (RBFNNs) model in this article. The proposed method can improve data integrity and storage for smart decision-making across different IoDs. We discussed the usage of blockchain to create decentralized predictive analytics and a model for effectively applying and sharing deep learning (DL) methods in a decentralized fashion. We also assessed the model using a variety of data sets to demonstrate the viability and efficacy of implementing the blockchain-based DL technique in IoD contexts. The findings showed that the suggested model is an excellent option for developing classifiers while adhering to the constraints placed by network intrusion detection. Furthermore, the proposed model can outperform the cutting-edge methods in terms of specificity, F1, recall, precision, and accuracy.

The present study uses a bibliometric and systematic literature review (SLR) to examine the use of Building Information Modeling (BIM), the Internet of Things (IoT), and Digital Twins (DT) in the construction industry. The network visualization and other approaches based on the Web of Science (WOS) database and the patterns of research interactions were explored in 1879 academic publications using co-occurrence and co-citation investigations. Significant publications, conferences, influential authors, countries, organizations, and funding agencies have been recognized. Our study demonstrates that BIM, IoT, and DT in construction, Heritage BIM (HBIM), Smart Contracts, BIM, and Ontology, and VR and AR in BIM and DT are the main study themes. Finally, several prospective areas for future study are identified, including BIM and Metaverse technology, BIM and Artificial Intelligence (AI), Metaheuristic algorithms for optimization purposes in BIM, and the Circular Economy with BIM and IoT.

Today, the concepts of human capital and strategic management of human resources are very common in the organizations in terms of philosophy and technique. The term of “human capital” is considered as a key element in improving the assets of an organization, since it is a sustainable competitive advantage and increases the employees’ efficiency. Some organizational theorists apply the rules of human capital theory to prove the ability to create useful competitions between companies by means of developing individual human resources. Therefore, in the present research, after studying more than 100 papers, the role of human capital in the organization and the characteristics of human capital have been studied. The results indicate that the common index, which is important at all levels of management in the organization, is human skill. Those who work in the central core of the organization must develop higher skills. These people must have enough knowledge, information, innovation, and creativity to increase the customer's satisfaction and create competitive advantage for the organization.

This paper proposes a new intrusion detection system (IDS) based on a combination of a multilayer perceptron (MLP) network, and artificial bee colony (ABC) and fuzzy clustering algorithms. Normal and abnormal network traffic packets are identified by the MLP, while the MLP training is done by the ABC algorithm through optimizing the values of linkage weights and biases. The CloudSim simulator and NSL-KDD dataset are used to verify the proposed method. Mean absolute error (MAE), root mean square error (RMSE), and the kappa statistic are considered as evaluation criteria. The obtained results have indicated the superiority of the proposed method in comparison with state-of-the-art methods.