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Background and purpose: Health/Medical Humanities is considered a relatively new field of study, which seeks to answer the question of how to make medical education, processes, and practice more humane, ensuring hat the human aspects are more fully respected. In this research, we aim to identify and explain both the necessity of and methods for applying the essentials of medical humanities in the MD curriculum of Mazandaran University of Medical Sciences. Materials and methods: The current research employed a qualitative design, specifically of the content analysis type. The participants in this research were selected purposefully and included innovative university faculty members, general module lecturers, and experienced academics in medical education. Data collection was conducted through semi-structured group interviews utilizing a focus group discussion method. The data were analyzed using a conventional content analysis method. Results: Through content analysis, 155 codes were identified. These codes were then grouped into 14 categories, which were ultimately organized into two broad themes: “necessity” and “how.” Under the "necessity" theme, six categories and 60 codes were identified, including medical paradigms, Iranian medical traditions, challenges in current medical practice, medical technology, the benefits of health/medical humanities education, and issues within the curriculum. In the "how" theme, eight categories and 95 codes were identified, including defining practical modules, modifying existing courses, developing new curriculum, setting goals to enhance skills, integrating arts and humanities, education in clinical settings, policy-making, and the challenges involved. Conclusion: The results of this research demonstrated that it is necessary to include the content of medical/health humanities in the overall medical curriculum. A needs assessment should be conducted in the definition of new modules and topics, as well as the integration of existing courses into the curriculum, to improve the quality of medical education and to address challenges throughout the process. [ABSTRACT FROM AUTHOR]

Nowadays, medical devices are widely used around the world. In every country, the safety of these devices is inextricably related to public health and security. Despite all these sensitivities and legal oversight, issues such as lack of transparency in the medical device supply chain, lack of real feedback from incidents and clinical reports related to these devices, and consequently the failure to update risk management systems and improve the quality of these devices, resulting in increased safety risks for users/patients. Therefore, the development and implementation of an effective post-market surveillance and management system for medical devices is essential. In this article, in addition to a review of some applications of blockchain technology in the health sector, the requirements and structural framework for an information and management system for post-market surveillance of medical devices based on this technology were also presented. This system was developed with the approach of tracking medical devices to enhance transparency, provide rapid information dissemination, and reduce the rate of adverse incidents, and clinical reports related to these devices throughout their supply chain. A blockchain-based post-market surveillance system for medical devices could provide all stakeholders with valuable and real recommendations and information in the form of smart contracts by ensuring complete informational transparency throughout the supply chain. Blockchain technology could be used by regulatory and oversight organizations to effectively monitor and manage the performance of medical devices, ensuring greater transparency, and thereby guaranteeing enhanced safety and quality for stakeholders. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to map and analyze the knowledge structure of science, technology, and innovation policymaking studies using a cooccurrence network. Understanding the trends in the literature related to science, technology, and innovation policymaking enables policymakers to gain a broader perspective on significant global issues. Additionally, researchers can gain insights into the most prevalent topics in this field and navigate the boundaries of knowledge in science, technology, and innovation policymaking. Methodology: The current research is a descriptive study that employs scientometric indicators along with documentary and social network analysis methods. The research population consists of 3,868 articles and reviews published between 1969 and 2023 in the field of science, technology, and innovation policymaking. Data for the study was collected on June 3, 2023, by accessing the Web of Science database (a product of Clarivate Analytics). Microsoft Excel was utilized to analyze the research data and create a geographical distribution map. Additionally, Vosviewer software was employed to visualize the co-occurrence network of papers in the field of science, technology, and innovation policymaking, while Gephi was used to clean and remove unrelated keywords. Findings: The number of published articles on the topic of science, technology, and innovation policymaking has been steadily increasing. Among the countries, the United States, England, China, the Netherlands, and Germany have produced the highest number of publications in this field. An analysis of journal publications revealed that the Sustainability Journal, with 89 articles; the Environmental Science and Policy Journal, with 74 articles; and the Journal of Cleaner Production, with 69 articles, have the most publications related to science, technology, and innovation policymaking. The cooccurrence network in this field consists of 221 nodes and 8035 unique links, and the most frequently occurring keywords among these nodes include science, policy, policy making, innovation, government, and technology. Furthermore, over the past five years, topics such as COVID-19, big data, sustainable development, data science, citizen science, circular economy, urbanization, energy transition, and CO2 emissions have been extensively discussed. The co-occurrence network in the field of science, technology, and innovation policymaking consists of five clusters: sustainable development, public health, climate change, government support, and public participation. These clusters represent different aspects of science, technology, and innovation policymaking. The findings indicate that the articles published by Iranian researchers on science, technology, and innovation policymaking have received 924 citations. In other words, each of them received an average of 7.7 citations, and 77.59 percent of them were cited at least once. In addition, these articles have performed less than the global expectation by obtaining a categorynormalized citation impact equal to 0.92. Also, 79.38 percent of these scientific productions result from international collaborations and the highest amount of collaboration has been done with researchers from the USA, England, Australia, and Canada. Conclusion: The policymaking process for science and technology is highly complex, and influenced by various factors, including global conditions, challenges, developments, trends, country of origin, and territorial considerations. To promote sustainable development goals, science and technology policies should prioritize innovation and entrepreneurship. Additionally, governments should focus on supporting activities that contribute to economic prosperity in this area. Health is a vital economic sector that not only directly impacts economic growth but also influences the development of other sectors. Policymakers in science and technology across various countries emphasize the significance of health research and the rapid deployment of new technologies, such as pharmaceuticals, diagnostic tools, remote treatments, and medical equipment. Science, technology, and innovation are crucial for sustainable development, as they aid in the formulation and implementation of policies and regulations concerning climate change. These policies may involve establishing standards, implementing energy regulations and taxes, utilizing tools to control pollution and greenhouse gas emissions, and protecting water resources and the environment. The government plays a crucial role in the formulation of science and technology policies, especially in developing countries, where it aligns with national priorities through various policy instruments. These instruments, often referred to as governance tools, are measures utilized by the government to accomplish specific policy objectives. Public participation in the science and technology policy-making process allows society to directly influence policies and decisions in this domain. This promotes transparency, trust, and the empowerment of society in matters related to science and technology, ultimately resulting in the adoption of improved policies, informed decisions, and positive outcomes for the community. [ABSTRACT FROM AUTHOR]

Background Frequent nose defects due to inborn causes or accidents have increased the demand for rhinoplasty. In addition to a functional defect, nose defects are also known as aesthetic defects because the shape of the nose is one of the most prominent features that define the human face. Hence, common reconstruction methods could not meet the requirements caused by limitations. Objective This study investigates the ability of different three-dimensional bioprinting methods to repair defects in the nose area. Methods In this review study, articles available in ScienceDirect, Springer, Wiley, Cambridge, De Gruyter, and Google Scholar databases were used. The search was done using the following keywords: Tissue engineering, nasal cartilage, 3D bioprinting, and bioink materials. The search was done with a time limit of the last 4 years. Out of 300 eligible articles, 159 articles were finally reviewed. Results Bioprinting has special potential for repairing nasal cartilaginous tissue. Hydrogels have become an excellent option for mimicking the microenvironment of the host tissue due to their porosity and ability to load different materials, as well as their ability to absorb water and encapsulate cells. On the other hand, the two main cell sources for tissue engineering of nasal cartilage are autologous chondrocytes and mesenchymal stem cells. Conclusion Bioprinting technology can bio-mimic the cartilage host tissue to an acceptable degree morphologically, biochemically, and mechanically to be implanted surgically. Although the use of bioprinting at the clinical level still faces limitations, the prospect of this technology is promising since it can fix nasal defects with low cost, unique accuracy, and personalization. [ABSTRACT FROM AUTHOR]

In recent years, the use of information technology in the health industry has expanded, and in order to improve the quality and speed of providing services to patients, telemedicine has been proposed as a new solution. Therefore, for the successful implementation and optimal use of telemedicine, in addition to the necessary infrastructure and success factors of this technology, such as highspeed Internet, up-to-date hardware and software, remote video communication systems, and distance education equipment, Specialist human resources should also be used. Sistan and Baluchistan province is a vast province, and due to the uneven distribution of specialized healthcare facilities and specialists, as well as the lack of adequate health care centers in its cities and villages, people do not have adequate access to medical services. Therefore, the current research seeks to provide a model for establishing telemedicine in selected hospitals of Sistan and Baluchistan province. Introduction Telemedicine was introduced in the 1970s by Thomas Bird. Scott Cruz et al. (2018) defined telemedicine as use of medical and communication technologies to exchange any information, including data, voice or video communications between doctors and patients or doctors and healthcare professionals in separate geographical locations, in order to creating the possibility of exchange for medical, healthcare, research and educational purposes. The benefits of telemedicine can be seen as socio-economic benefits, reducing costs, reducing waiting lists, reducing travel, improving consultations, and reducing emotional stress (Jang et al., 2020). Telemedicine enables health and treatment experts to conduct quick consultations with relevant specialists in far-flung locations around the world, thus eliminate the costs and risks needed to transport a sick or injured person to distant places for further examinations (Brulé et al., 2020). In addition, the presence of telemedicine in developing countries, where specialized and subspecialty services are not readily available and are usually costly, is economical and effective (McDonald et al., 2014). Based on this, the goal of implementing telemedicine can be to improve the processes of providing health services and products, improve patient care and reduce the per capita costs of medical care, provide medical services without time and place restrictions (on a wide geographical and population level), improve access rural and disadvantaged areas and medical care in them, reducing the space of faceto-face care in hospitals and medical centers and introducing the use of artificial intelligence in diagnostic and treatment processes (babaMahmoodi et al., 2021). Case study The statistical population was the conducted and existing researches (between 2010 and 2022) in domestic and foreign scientific databases, which were searched and reviewed based on specified keywords, and among them, the most relevant documents with Purposive sampling method were selected. Materials and Methods The research was applied and exploratory research. In order to collect data, meta-analysis method was used. The analysis of the texts was done based on the content analysis, and finally, based on the extracted codes, the dimensions and components of telemedicine deployment were determined and the proposed research pattern was developed. The extracted codes controlled by Kappa index and determining the weight of indices by Shannon entropy. Discussion and Results Based on the meta-analysis method and focusing on selected keywords, the articles in the field of telemedicine (in the years 2010 to 2022 and in domestic and foreign databases and publications) were reviewed and finally, 184 articles were found. Then, according to parameters such as title, abstract, content, access, quality and content of the research method, the articles were reviewed and evaluated, and finally, after removing 137 articles, 47 articles were analyzed. Based on the content analysis of the final articles, a total of 63 indicators, 6 sub-components and 5 components were discovered and labeled. To control the extracted codes, the Kappa index was used, and the value obtained for it (0.915) showed the level of agreement between the experts. Also, the amount of Shannon's entropy for all components showed that past researches support the findings of the current research. Conclusion The pattern of telemedicine deployment was formed in three layers of feasibility, implementation structure and deployment. In the feasibility layer of telemedicine, cultural, human and institutional empowerment, knowledge management, strategies and infrastructure (with components of technical infrastructure, security infrastructure, software and hardware, network, legal infrastructure and organizational infrastructure) dimensions were included. In the second layer, which deals with the implementation of telemedicine, taking into account the current situation of telemedicine, strategies are adopted to reduce or even eliminate the gap, and strategies for the establishing telemedicine are designed and operational plans are compiled. In the establishment phase, cultural foundation for more acceptance of telemedicine by the public, legal foundation and the formation of efficient executive committees, and trainings in the form of workshops are given to the public and experts in order to become more familiar with this concept and its benefits and effects in various fields. By implementing telemedicine processes and testing them, possible problems and obstacles during implementation, which is one of the best control methods, are identified and appropriate plans are made to solve them. [ABSTRACT FROM AUTHOR]

Introduction: According to the World Health Organization, lung diseases are the third cause of death in the world. These diseases are chronic, so early diagnosis of these diseases is very important. Pulmonary function tests are important tools in examining and monitoring patients with respiratory injuries. This research aimed to optimize the K-Nearest Neighbor algorithm, which facilitates and accelerates self-assessment and interpretation of spirometry test results with higher accuracy. Method: In this study, a method is proposed that improves the limitations of the basic algorithm by optimizing, valuing features, and weighted voting. Using this method, obstructive pulmonary diseases are detected based on the data set of spirometry tests, and general parameters are classified into three categories, namely, asthma, chronic bronchitis, and emphysema. Results: In determining the appropriate method for calculating the data distance, the Minkowski method was chosen, and by applying the coefficients of the feature values, the accuracy of the classification increased. Weighted voting was done in the final part of the algorithm based on the Gaussian kernel, based on which a constant performance was obtained for changing the parameter of the number of neighbors. The results of the evaluations were carried out in the form of mutual validation. 95.4% accuracy and 93.2% precision were obtained in 3.12 seconds. Conclusion: The use of machine learning algorithms can be effective in the analysis of medical data. Therefore, in this study, these approaches were used to provide a new method of classification, so that the proposed algorithm could improve the basic method, and also, had better accuracy and performance than other previous methods.

Introduction: The present study was conducted to evaluate the feasibility of telemedicine implementation in two cities in Kerman province with tourist attractions. Method: This descriptive cross-sectional study was conducted in 2021. Research data were collected using the standard questionnaire "Telemedicine Assessment Tool," designed by the Institute for Medicare (United States). Using the G-power software, the sample size of 68 people was determined. The researchers collected the required data in person at the hospitals under study. The collected data were analyzed using descriptive and analytical statistical methods through SPSS version 26. Results: According to demographic characteristics, the majority of people were women (78.7%), aged less than 30 years old (37.3%), with less than five years of work experience (40.0%), bachelor’s degree (88.0%), and clinical (57.3%). Regarding P-value, there is a significant difference between telemedicine services in terms of importance. Therefore, telemedicine services, consultation with specialists outside the hospital, patient referrals, and remote drug prescribing had respectively the highest priority. In contrast, changing the processes and structure of the hospital had the lowest priority. The lack of technical staff and the problems of initial costs were the main obstacles to the use of telemedicine, and the attitude of employees and competition were the least essential obstacles. Conclusion: Due to the tourism situation of the studied cities, hospitals must try to establish telemedicine. In this regard, investment to solve the problems of initial costs and technical staff has priority.

Introduction: Breast cancer is the most common cancer in women that causes more deaths than other cancers. Thermography is one of the methods of breast cancer diagnosis. The most important challenge in early detection of these images can be human error or lack of access to a skilled person. The use of artificial intelligence methods in image processing can be effective in early detection and reduction of human error. The main aim of this research was to introduce hybrid networks for intelligent diagnosis of breast cancer from thermographic images. Method: The thermographic images used in this study were collected from the DMR-IR database. First, the main features of the images were extracted by deep convolutional network (CNN). Then, FCNNs and SVM algorithms were used to classify breast cancer from thermographic images. Results: The accuracy rate for CNN_FC and CNN-SVM algorithms was 94.2% and 0.95%, respectively. In addition, the reliability parameters for these classifiers were calculated as 92.1%, and 97.5%, and the sensitivity for each of these classifiers as 95.5%, and 94.1%, respectively. Conclusion: The proposed model based on the deep hybrid network has good accuracy compared to similar algorithms; therefore, it can help doctors in the early diagnosis of breast cancer through thermographic images and minimize human error.

Introduction: In recent years, the collection of data from the monitoring of clinical trials in the form of electronic case report forms (eCRF) has been increasingly used, because avoiding mistakes, reducing the duration of clinical studies, and preventing increasing data collection costs can affect the success of the study. Accordingly, this research aimed to present the conceptual model and requirements of the proposed eCRF, which is based on NoSQL database to achieve complications caused by the COVID-19 vaccine. Method: In this study, using the descriptive-applicative method, the conceptual model of the eCRF system was first designed to collect the minimum set of data on the complications of the COVID-19 vaccine. Then, the functional and non-functional requirements of the proposed system were identified using the methods of observation, thinking and reflection, and questionnaire. Results: This research has two main parts: the proposed conceptual model and the functional and non-functional requirements of the eCRF system. A conceptual model was proposed based on the components, modules, and objectives of the eCRF core, which can be used by researchers and clinical trial specialists. Requirements including the ability to register, insert, delete, and edit the case report form, were also calculated for four groups of patients, health care workers, disease experts, and the executive manager. Conclusion: The proposed system provides researchers and experts of clinical trials with classified and valid data regarding the registration of complications caused by the COVID-19 vaccine in an integrated manner. Therefore, some eCRF stakeholders such as patients, healthcare workers, disease experts, and executive managers and researchers can achieve a vaccine with acceptable safety and effectiveness by analyzing, interpreting, and sharing the recorded information.

Introduction: In recent years, COVID-19 has been recognized as a health threat. Despite vaccination, people still get the disease because the new variants have mutations in their genomes that allow them to bind to host receptors and evade the immune system’s responses. Therefore, the main aim of this study was to use bioinformatics tools to introduce a rapid and practical vaccine to fight against these diverse mutations in different SARS-CoV-2 strains. Method: To epitopes mapping, 32 different spike protein variants were retrieved. We then used the Immune Epitope Database (IEDB), NetCTL, and NetMHCIIpan to predict T and B cell epitopes. The vaccine based on protected epitopes was evaluated in terms of antigenicity, allergenicity, toxicity, solubility, physicochemical properties, population coverage, and secondary structure with relevant servers. Modeling using Robetta and docking with Toll-like receptor (TLR3) were performed using Cluspro, PatchDock, and FireDock, respectively. Results: After detailed evaluations, all the results confirmed the optimal quality of the vaccine. According to further investigations, this structure is similar to native proteins and there is a stable and strong interaction between the vaccine and the receptor. Based on molecular dynamics simulation, structural compactness and stability in binding were also observed. In addition, the immune simulation showed that the vaccine can stimulate immune responses similar to real conditions. Finally, codon optimization and in silico cloning confirmed efficient expression in Escherichia coli. Conclusion: Based on the obtained results, the designed multi-epitope vaccine can serve as a prophylactic candidate against SARS-CoV-2.

Introduction: Heart diseases are one of the most common types of diseases, which cause the death of many people. Arrhythmias are an irregular heartbeat that causes the heart to beat abnormally fast (tachycardia) or slow (bradycardia). Therefore, the identification and classification of cardiac arrhythmias using ECG signals is of great importance. This research aimed to provide a data mining-based model to improve the diagnosis of previous arrhythmia. Method: In this descriptive-analytical study, the UCI reference dataset, which consists of 452 samples with 279 features, was used. The samples were categorized into five classes for the detection and identification of different types of cardiac arrhythmias. The algorithm employed in this research is a combination of hierarchical neural networks (expert system combination). Results: In all networks, 70% of the samples were used for training, while the remaining 30% were used for testing. After modeling and comparing the generated models and recording the results, the prediction accuracy for cardiac arrhythmia in the absence of combination learning reached 89.5%, and it increased to 93.5% after employing the hierarchical expert combination approach. Conclusion: The results of this research show that the proposed method based on the combination of neural networks in a hierarchical form, which leads to the specialization of the task of each class, can have better performance compared to similar models in diagnosing cardiac arrhythmia.

Introduction: People with weakened immune system are more prone to opportunistic infections (OIs). Finding the association of these viruses and the transmission pathways associated with these viruses, such as EBV and CMV, play a significant role in investigating their association with helper T lymphocytes. In this study, bioinformatics analysis was used to examine and candidate genes in pathways associated with opportunistic viruses with helper T lymphocytes. Method: In this study, by referring to the GEO database, a suitable database was selected for analysis. This dataset included gene expression profiles in EBV and CMV virus infections. Gene clusters were classified into high and low expression. Rich databases such as Enrichr, STRING, and Networkanalyst were used to evaluate the data more accurately. Finally, the candidate genes were isolated and their protein binding was measured. Results: 964 high-expression genes and 837 low-expression genes are involved in the progression of opportunistic viral infections with lymphocytes. Cell cycle pathways, oxidative stress, RNA synthesis and TGFB were significantly observed. Conclusion: The present study showed that important proteins and genes played a major role in enhancing the inflammation of opportunistic viruses such as Epstein-Barr and Cytomegalovirus, among which CDK2, CCNB1, GSK3B, SRC, and SMAD3 showed a more prominent role in this pathway.

Introduction: Mobile health (mHealth) is an emerging technology with great potential for improving people's health. Due to its availability, this technology can create more motivation to improve women's health by providing safe, coordinated, and effective health care. Considering the importance of women's health, the present study was conducted to systematically investigate the impact of mHealth interventions on women's health outcomes. Method: The present study was conducted as a systematic review of clinical trial studies in English. PubMed, Scopus, Science Direct, the Cochrane Library, and ClinicalTrials.gov databases were reviewed according to the inclusion criteria from 2011 to 2021. Results: A total of 28 articles were included in the study. mHealth tools, such as applications, text messages, voice or video calls, and wearable devices, were found to have multiple functions in this study, including providing information, reminders, education, and practical recommendations to improve the lifestyles of pregnant women in various areas. The study results indicated that in 82% of the studies, mHealth tools were effective in improving health outcomes for pregnant women, while in 18% of the studies, the intervention results did not differ from the standard method. Conclusion: mHealth interventions can have a positive effect on improving the health of pregnant women, including their lifestyle, knowledge, and attitude regarding their own and their baby's health. However, in all these studies, high heterogeneity limits the certainty of the evidence for these findings. Therefore, future studies should identify the context in which mHealth can be more effective for women's health.

Introduction: Diabetes is a chronic disease worldwide, with an increasing annual death rate. Many health professionals seek innovative ways to detect and treat it early. Rapid advances in machine learning have improved disease diagnosis. However, because of the small amount of labeled data, the frequency of null and missing values, and the imbalance of databases, creating an optimal predictor for disease diagnosis has become a great challenge. This study aimed to present a pipeline-based classification framework for predicting diabetes on two datasets of Indian diabetic patients with two classes (patient and healthy groups) and Iraqi with three classes (patient, healthy, and prediabetes groups). Method: An important part of this framework is preprocessing. Different ML models based on the One-Vs-One approach for the three-class mode are implemented in the framework. Because of the imbalance of the data set, besides the accuracy evaluation criterion, the area under the receiver operating characteristic (ROC) curve is also used. To increase the level of these two criteria, the Hyper-parameters of each model are optimized using optimization methods to build a powerful model with less training and testing time through various feature selection methods. Results: The proposed framework was assessed for diabetes prediction on two datasets of Indian and Iraqi diabetic patients. It was revealed that using AdaBoost for the Indian dataset (ACC=89.98, AUC=94.11) and random forest for the Iraqi dataset (ACC=98.66, AUC= 98.62), good accuracy and performance were obtained. Conclusion: Regarding ACC parameters, precision, accuracy, recall, and F1-Score, the pipeline-based framework has an optimal performance in predicting diabetes, therefore, it can be used in clinical decision support systems.

Introduction: Cardiovascular disease is one of the main causes of death worldwide. One of the important factors is readmission due to the lack of training. Considering the widespread use of mobile phones, this study aimed to design a recommender system for patients with cardiac arrhythmia. Method: This is an applied-developmental study that was conducted in a quantitative (descriptive) method and in 2 stages. First, the recommended educational parameters and application requirements of the software were determined based on literature review. Then, the obtained contents were provided to 16 cardiologists working in Shahid Rajaei and Hazrat Rasool Akram Hospitals in Tehran. To evaluate the usability, the application was given to 50 patients with cardiac arrhythmia and was checked with the quiz questionnaire version 5.5. Results: In the recommendations section, experts deemed all items except "Omega-3 consumption" necessary. In the usability evaluation section, the items "Identifying the nearest emergency center, sending the patient’s geographic location to the doctor, finding the nearest medical center, and the possibility of changing color, and calculating BMI" were not confirmed. The usability evaluation of the application with an average score of 7.98±0.62 indicates a good level. Conclusion: The mobile-based recommender application can cause the patients to play a greater role in their treatment and acquire skills in the field of training after hospital discharge and lead to a reduction in re-hospitalization and cost.

Introduction: COVID-19 is a new virus that causes infection in the upper respiratory tract and lungs, and the number of deaths due to the disease has increased daily on the scale of a global epidemic. Chest X-ray images have been useful for monitoring various lung diseases and have recently been used to monitor COVID-19 disease. Method: In this research, a multi-stage process was used to recognize COVID-19 from X-ray images. In the first stage, pre-processing was done to normalize the data. In the second step, which is the most important step of the proposed method, feature extraction was done. The feature extraction operation was based on deep learning networks. After feature extraction, machine learning algorithms were used to classify images. The algorithms used in this section are support vector machine, nearest neighbor, and decision tree algorithms. The results of these categories are combined in the fourth step based on the majority vote. Results: The parameters used in this research are among the classification parameters, including precision, accuracy, recall, and F-criterion, which were obtained as 96.5, 92.25, 94, and 93, respectively. Conclusion: The results of the experiments show the acceptable efficiency of the proposed method because, in addition to reducing the calculations by the separable layer, the combination of categories and their weighting has been used to obtain the final result.

Introduction: Accurate diagnosis of Alzheimer’s disease in the early stages plays an important role in patient care, and preventive measures should be taken before irreversible brain damage occurs. With increasing age, there are changes in memory, which is normal, but the symptoms of Alzheimer’s disease are more than temporary forgetfulness. Early and intelligent diagnosis of Alzheimer’s disease in different situations can greatly help patients and physicians. Method: In the proposed method, a convolutional neural network will be used to improve the recognition of people with Alzheimer’s disease from healthy people in emotional states. First, the required pre-processing is done on the electroencephalography signal, and then, it will be applied as an input to the network. Next, the genetic algorithm is used to optimize the weights of the convolutional neural network. Results: The research shows that the frontal lobe of the brain is related to emotions and the use of F3 and F4 channels reflects more information compared to other channels, so with this information, the process of recognizing Alzheimer’s patients in emotional states is better. Conclusion: The proposed method was evaluated with other categories in valence and arousal states. It was observed that this method has a better efficiency compared to other methods with an accuracy of 92.3% in valence and 94.3% in arousal in recognizing people with Alzheimer’s disease.

Introduction: Simultaneously with the COVID-19 epidemic, many efforts were made to manage and treat the disease. One of the main activities was the development of health information systems to help monitor this disease, but due to the speed of the disease’s spread, there was not enough time to evaluate the usability of these systems. Therefore, the present research was conducted to evaluate the usability of the Medical Care Monitoring Center (MCMC) system. Method: In this cross-sectional descriptive study (2022), the usability of the MCMS system was evaluated using a heuristic evaluation method. The system’s usability was investigated based on the Nielsen’s usability principles. All the problems identified by evaluators were collected in a single list and the mean severity of each problem was calculated. Results: There were 41 usability problems. The lowest mismatch with usability principles was related to “Help and documentation” (n=1) and the highest one was related to "user control and freedom” (n=8) and “Match with real world” (n=7). In addition, most of the identified problems had a relatively high degree of severity and were placed in the category of major problems. Conclusion: Using the usability evaluation, a significant number of usability problems of the system were identified which, if not resolved, can cause increased errors, dissatisfaction, and decreased quality of information; therefore, it is necessary to solve these problems by the designers and developers of this system.

Introduction: Alzheimer’s disease is one of the most common neurodegenerative diseases in adults. The progressive nature of Alzheimer’s disease causes widespread damage to the brain, and early diagnosis can manage the disease and slow down its progression effectively. Method: In this study, a dataset related to the early prediction of Alzheimer’s was used. Spark framework was used for data management and three machine learning algorithms including Naïve Bayes, Decision Tree, and Artificial Neural Networks with the best hyperparameters were implemented and compared. To prevent overfitting and measure the efficiency of the models, a 5-fold cross-validation method was utilized. Furthermore, a method was used for interpreting machine learning black box models. Results: The decision tree and artificial neural network models obtained 98.61% accuracy and 98.60% F1-Score in the Spark framework including one or three computers. Important features in the decision-making process of the artificial neural network were identified using the interpretability technique. In addition, the computational time required for training the proposed models was calculated through different approaches, and the use of multiple computers was 35.95% faster than a single computer. Conclusion: With increasing the number of Alzheimer’s disease patients around the world, the need for a decision support system using machine learning algorithms, which can predict the disease early in a huge amount of data, is felt more. Therefore, the machine learning models proposed in this study for early prediction of Alzheimer’s disease as an interpretable auxiliary tool in the decision-making process can help clinicians.

Introduction: Integrated Health Information Systems (HIS) are necessary to achieve national and international goals in health and development, which are facing cooperation problems due to diversity and heterogeneity. Interoperability as a key characteristic of data sharing and the main prerequisite of integration enables healthcare providers to make timely decisions for diagnosis, treatment, action, and patient care. Therefore, this research aimed to provide a category of interoperability concepts in electronic health (E-Health). Method: This research is a narrative review to categorize the interoperability concepts in E-Health. For this purpose, Scopus, Google Scholar, PubMed, Science Direct, and IEEE databases were searched using the keywords "Interoperability framework" and "Integration" with the combination of AND along with "E-Health". Therefore, all the articles referring to the concepts of interoperability were selected. Results: The result of the search in the aforementioned databases was 304 articles. By removing duplicate, out-of-reach, and unrelated articles, only 40 articles were examined, and topics related to the concepts of interoperability were classified based on six axes (dimensions, levels, obstacles, integration, architecture, and measurement). Finally, its applications and challenges were described in a narrative review. Conclusion: Due to the nature of the health system, integrity and intelligence are very important in this area. Therefore, to facilitate the exchange of information and the use of health information systems from each other’s services, paying attention to the presented classification has an undeniable effect, and also, an important role in the integrity and intelligence of the environment, and increases the efficiency and effectiveness in the quality of providing health care services.

Introduction: COVID-19 has had a devastating impact on public health around the world. Since early diagnosis and timely treatment have an impact on reducing mortality due to infection with COVID-19 and existing diagnostic methods such as RT-PCR test are prone to error, the alternative solution is to use artificial intelligence and image processing techniques. The overall goal is to introduce an intelligent model based on deep learning and convolutional neural network to identify cases of COVID-19 and pneumonia for the purpose of subsequent treatment measures with the help of lung medical images. Method: The proposed model includes two datasets of radiography and CT-scan. These datasets are pre -processed and the data enhancement process is applied to the images. In the next step, three architectures EfficientNetB4, InceptionV3, and InceptionResNetV2 are used using transfer learning method. Results: The best result obtained for CT-scan images belongs to the InceptionResNetV2 architecture with an accuracy of 99.366% and for radiology images related to the InceptionV3 architecture with an accuracy of 96.943%. In addition, the results indicate that CT-scan images have more features than radiographic images, and disease diagnosis is performed more accurately on this type of data. Conclusion: The proposed model based on a convolutional neural network has higher accuracy than other similar models. Also, this method by generating instant results can help in the initial evaluation of patients in medical centers, especially during the peak of epidemics, when medical centers face various challenges, such as lacking specialists and medical staffs.

Introduction: Pupil and iris disorders form an important category of eye diseases. Accurate segmentation of the pupil is the first and most important step in the automatic diagnosis of diseases related to the pupil and iris. Most of the existing methods do not have enough accuracy and are sensitive to the effects of noise and specular spot reflection. In addition, the images used in these methods usually have limitations, such as the viewing angle. Method: In the proposed algorithm, a stable method is offered to remove the effects of specular spot reflection in the pupil, and necessary preprocessing is done to detect the exact location of the pupil. An optimized color mapping algorithm is proposed and the mapping is calculated with the help of the LM algorithm to accurately determine the pupil boundary. This method does not impose any restrictions on the eye image and shape, and the angle of the pupil in the image can be in any shape and direction. Results: The proposed method does not assume any specific model as the final pupil boundary (circle or oval) and is robust to noise and specular reflection factors as well. This method has been able to accurately detect the pupil boundary with the accuracy of 98.8% using UBIRIS dataset and 98% using the collected data by authors. Conclusion: The method presented in this paper can be used to increase the accuracy in determining the internal and external border of the iris to diagnose diseases related to the pupil and iris, as well as identity identification based on iris tissue.

Introduction: This study aimed to investigate the role of clinical decision support systems in reducing medical errors from the perspective of physicians and nurses in the teaching and therapeutic hospitals. Method: This descriptive cross-sectional study was conducted in 2021-2022 in two teaching and therapeutic hospitals in Ferdows City, Iran. Physicians and nurses have participated in the research. In this study, the census method was used, and the research community was considered the research sample. The questionnaire of Ariyai et al. was used as a data collection tool. After collecting the questionnaires, the data were analyzed using descriptive statistics methods. Results: 42 medical staff were included in the study. From a physician’s point of view, decision support systems can be helpful by reducing the risk of severe allergic reactions and drug interactions (60%), quick access to patient records (50%), and computerized order registries (30%). From nurses' point of view, eliminating problems related to doctors' handwriting (25%), avoiding disremember of repeating tests or imaging (18.8%), quick access to updated information during work (12.5%), and reducing the risk of embolism (9.4%) are the advantages of clinical decision support systems. Conclusion: due to the importance of developing a decision support system in hospitals and measuring the readiness of medical staff to adopt it, it is suggested that the necessary trainings be provided.

Introduction: One of the important challenges for physicians is the choice of the right antibiotics for various diseases. In this regard, a mobile application could be helpful. The main purpose of this study was to compare the quality of mobile applications designed for an antibiotic prescription to help physicians in choosing the right antibiotic. Method: In this study, English non-commercial apps from the Apple App Store and the Google Play Store were searched using relevant keywords. The apps were selected and independently scored by an infectious disease specialist and a pharmacist using the Mobile Application Rating Scale (MARS). Kendall’s coefficient of concordance was used to assess inter-rater agreement. The Kolmogorov-Smirnov test was used to verify the normal distribution of the quantitative variables. Spearman's rank-order correlation was applied to determine the relationship between MARS scores and quantitative background variables and Mann-Whitney U tests for dichotomous variables. - Results: In the initial search until August 2022, 13 apps were eligible for evaluation. The MARS score obtained by applications without in-app advertisements (median: 3.9, IQR: 3.4-6.1) was significantly higher than applications containing advertisements (median: 2.9, IQR: 2.3-8.1) (P=0.029). In the objective domain of MARS, The highest mean domain score belonged to the engagement section (3.9±0.4) and the lowest mean domain score belonged to the functionality section (3.5±0.5). Conclusion: This study indicated that apps designed to help physicians prescribe antibiotics meet acceptable criteria. Considering objective scores of MARS, lower scores in the “engagement” section demonstrated that designers have paid less attention to this section in comparison to the “information quality” section.

Introduction: Stomach cancer is the fourth most common cancer and the second leading cause of cancer death in the world. In addition to significant costs, it imposes many problems on families that is a proper justification for identifying related biomarkers. Method: The miRBASE website was used to find bioinformatics information. In addition, mirWALK and miRNASN databases were searched to find miRNAs information. To find cancer pathways, the DAVID and KEGG databases were adopted. Results: The studies conducted on the first pathway reported that microRNA and has-miR-10a-3p, through binding to rs1049216 in the CASP3 gene, by exerting an inhibitory effect on this gene, prevents the development of cancer. In the second pathway, microRNA and has-miR-296-3p, by binding to rs1564483 in the BCL2 gene, by exerting an inhibitory effect on this gene, causes cancer. In the third pathway, microRNA and has-miR-194-3p, through binding to rs7177 in the CCND1 gene, by exerting an inhibitory effect on this gene, prevents the development of cancer. Conclusion: In this research, cancer pathways involved in cancer were predicted. By finding three SNPs on specific regions of the three above-said genes, we found that these SNPs can be the cause of stomach cancer and can be introduced as biological biomarkers.

Introduction: Tourism as one of the programs to earn money leads to economic growth; however, due to the non-providing of medical and health services by insurance organizations, the Iranian tourism industry has faced a recession. Therefore, this study aimed to provide a smart tourism health insurance architecture in the context of the Internet of Things (IoT) and cloud to serve tourists. Method: This practical study was conducted to achieve a smart tourism health insurance architecture. First, its requirements were determined by reviewing the related literature and open interview with four experts from Iran Insurance Corporation. Then, by examining different types of tourism health insurance architectural models, a proposed service-oriented layered architecture was presented on the platform of IoT and cloud. Finally, the proposed architecture was evaluated by the ATAM method. Results: The proposed architecture has two components (i.e., the requester and the service provider) with user, network, cloud, and database layers that are connected in a service-oriented manner. Also, the evaluation of the proposed architecture by the ATAM method showed that it can respond to the health needs of tourists. Conclusion: This architecture is presented on the platform of IoT and cloud that provides an intelligent background, and by recording people's data in personal risk profiles, it facilitates providing special tariffs and reducing tourism costs. Therefore, tourists will be able to travel with more confidence in controlling their health status.

Introduction: The internet of Things has become more popular as a new technology since the advent of wireless technology, and attracted attention from supply chain management promoters. This study aimed to identify and analyze IoT applications in the pharmaceutical supply chain agility in the post-COVID-19 era Method: We first review previous studies in applications of IoT in pharmaceutical supply chain agility and identified the findings through qualitative content analysis. Then, the identified factors were given to 20 management and information technology experts to confirm and validate. Results: The results showed that IoT applications on the pharmaceutical supply chain agility in 9 components and 50 indicators. First of all, the most important factor is faster communication with other stakeholders (suppliers, manufacturers, distributors, and customers) using the IoT in the supply chain. Secondly, the use of IoT for drug production; manufacturers invest in technologies that lead to lower operating costs in the long term, because network devices and sensors do not make mistakes, do not need rest and training, and do not take vacations. Therefore, they increase the agility of the production process and are a reliable and cost-effective alternative for productivity in production. Conclusion: The wide applications of the proposed model indicate the need to consider the use of the IoT in the pharmaceutical industry supply chain in order to improve overall supply chain performance and focus on supply chain agility.

Introduction: During the COVID-19 pandemic, the use of mobile applications increased among people coping with mental health problems. This study aimed to review mobile applications developed or used during the COVID-19 pandemic. Method: In order to retrieve related articles, the keywords "COVID-19", "Mental health", and "Mobile application" were searched in Web of Science, PubMed, and Scopus databases without a time limit until April 12, 2022. After selecting the articles according to the entry criteria, the features of the introduced applications were checked. Results: Finally, 25 articles were included in the study, of which 20 applications were developed during the pandemic and five practical programs were already designed. Four applications were designed for medical staff, and 16 were designed for the general public, for each of students, children aged 3-9, COVID-19 patients, cancer patients, and both medical staff and patients with stress disorders one app was designed. The reviewed applications presented educational tips for overcoming negative and destructive thoughts, and getting information about a healthy lifestyle. Also, eight applications provided contractions between users and psychologists. Conclusion: The results of this study can be used as a comprehensive source to examine the features and capabilities of mental health applications during the Coronavirus pandemic. It is suggested that by identifying the strengths and weaknesses of such applications, measures should be taken to develop more comprehensive applications that meet users' needs.

Introduction: One of the main challenges of the health system is improving the quality of healthcare services by adjusting the costs. The right choice of tools will bring economic savings besides excellent service quality. Since medical supplies are among the fundamental and costly elements in providing services, the expertise of purchasing has become an important and complex issue. This study aimed to prioritize criteria for purchasing medical supplies. Method: This research is an applied study conducted with a descriptive survey method. In this study, the criteria collected from related studies and completed with the opinion of experts were prioritized and weighted for four categories of medical supplies by the linear best-worst multi-criteria decision-making method using Lingo and Excel software. Results: By weighting sixteen sub-criteria of purchasing medical supplies in the form of five categories, the importance of each criterion was determined. According to the results, the two sub-criteria of "quality" and "price" were recognized as the most important and "seller's record" as the least important indicator in the purchase of medical supplies. Conclusion: Using the linear best-worst multi-criteria decision-making method with definite consistency of results and high reliability led to the prioritization and accurate weighting of criteria for purchasing supplies. Paying attention to the indicators in the purchase of medical supplies, in addition to guaranteeing the quality and continuity of services, will lead to a significant reduction in supply costs.

Introduction: The increasing growth of science and specialized articles in various fields, especially in health and treatment, has faced researchers with the challenge of keeping up with this progress. Review research provides researchers with the possibility to gain access to an acceptable and categorized amount of information by spending less time and money and helps them reach the original essence of the research. However, carrying out such research is very difficult because it requires deep knowledge of the specific field as well as reviewing several studies to find and gather appropriate information. By developing a system, we intended to help researchers in the process of categorizing, extracting, and aggregating data and findings. This study aimed to design and implement a database management system to review studies in the field of medicine and health. Method: This practical study was done in 2022, and associated with software development. The system was developed in three stages as follows: 1) analysis, 2) implementation, and 3) testing and troubleshooting. This system is implemented using the frameworks based on JavaScript, Angular on the front-end side and Node JS on the back-end side. In the final stage, the system was tested using actual research, and possible defects and problems were detected and solved; finally, the system was handed over to the research team to conduct new research. Results: In the process of testing the system, we found that by using this system, the accuracy of the information entry process can be greatly increased, and the analysis of the results can be done online with high speed and accuracy. Conclusion: The results of the present analysis can help policy makers for appropriate legislation and business managers to solve identified problems and increase user trust and satisfaction.

Introduction: Recent developments in the use of mobile technologies have given rise to opportunities for more effective and cost-efficient services for people with chronic diseases. Considering the relatively high prevalence of post-traumatic stress disorder (PTSD) in Iran and the lack of a native application for the self-management of many complications of this disorder, the present study aims to develop and evaluate a mobile application for PTSD self-management. Method: This developmental-applied study was conducted in two stages. In the first stage, interviews and a literature review were carried out to identify users’ requirements and expectations of the application. Then, the application was modeled, designed, developed, and tested based on the requirements. In the second stage, the usability of developed application was evaluated by experts and users. Results: The following services were implemented in the application: psychoeducation, coping skills, breathing exercise, relaxation, positive affect, social support, self-assessment, reminders, useful links, and self-monitoring. According to experts’ evaluation, the application was of acceptable quality (78.25%). The application was relatively acceptable from the users’ point of view (68.67%). In the evaluation process, experts were most satisfied with the functionality of the application (80 %), and users were most satisfied with the application’s ease of use (76%). Conclusion: Using the application can be useful in promoting access to healthcare services for patients with PTSD, and in addition to saving time and costs, it can be effective in the self-management of this disorder.

Introduction: Due to the increasing use of virtual reality (VR) technology in the healthcare field, the present study was conducted to systematically investigate the use of VR in hospitals. Method: To access related articles, the keywords "Virtual Reality" and "Hospital" and their synonyms were searched in databases of Web of Science, PubMed, Scopus, and Google Scholar without a time limit until August 15, 2022. The original articles that were published in English and dealt with "the application of virtual reality in the hospital" were included in the study, and their characteristics were analyzed. Results: Eleven articles were included in the present study, in which various applications for VR in the hospital environment have been reported. The most common uses of VR in hospitals are as follows: managing behavioral symptoms, creating relaxation and inducing positive emotions in patients, providing treatment and physical, mental, and emotional rehabilitation in patients, providing training for care providers in different hospital departments, virtual presence at home for hospitalized children and teenagers; VR can also work as a supplement to the procedure for the children's lives and an adaptive substitute for experiences in the real environment for the medical residents.. Conclusion: Considering the positive impact of using VR in hospitals, using this technology as an auxiliary tool to improve education, treatment, and prevention in the healthcare system is suggested.

Introduction: Nowadays, the world of technology is facing a growing phenomenon called the Internet of Things (IoT). The purpose of this study is to provide the infrastructure requirements, service model, and conceptual model for a smart hospital based on IoT, which is a safe and reliable method due to the integration of exchanges between objects. Method: In this strategic applied study, the infrastructure requirements of a smart hospital based on IoT are explained explains. First, basic requirements were prepared through library studies and, by setting up a questionnaire for medical staff and specialists in medical informatics, information technology, and health information technology, they were evaluated and then finalized using the Delphi technique. Then, based on extracted requirements, the service model of a smart hospital was presented. Finally, the conceptual model of IoT-based smart hospital infrastructure was suggested. Results: Thirty functional and non-functional requirements for IoT-based smart hospital infrastructure were determined. After that, the proposed model of services that can be provided in the hospital, including infrastructure, core and value-added services, was presented. Then, a conceptual model that includes the health ecosystem of the country, consisting of the levels of communication and information infrastructure, digital service systems, and the health system was provided. Conclusion: Agreement on the requirements, service and conceptual model, will help to design and implement the IoT-based smart hospital and make it more accurate and simple, according to factors affecting such as precision, speed and reliability in healthcare.

Introduction: In recent years, especially with the spread of the COVID-19 disease, the use of electronic health services has increased greatly. Along with the growth and development of the use of electronic services among users, the concern about ethical and legal issues increases. The purpose of this article was to review the current state of electronic health provision by active platforms in this field and to identify the challenges and problems of users in using these services to achieve the desired state. Method: This qualitative study was conducted using the content analysis method. User comments were collected in Bazaar and Myket mobile applications. Then, using the purposeful sampling method, 300 selected comments containing users' difficulties were analyzed by qualitative content analysis method based on the six steps proposed by Graneheim & Lundman using MAXQDA software. Results: The most important problems reported by users of e-health services fall into two main categories: issues related to care and issues related to the service provider's platform; these difficulties are classified into six subcategories, including doctor-patient relationships, the standard of care, platform quality, user feedback, responsibility, and service tariff. Among the user comments, the most dissatisfaction was associated with the subcategory of the platform's responsibility and accountability (especially the issue of not receiving services despite paying, poor support, and no refunds), followed by issues related to the standard of care. Conclusion: The results of the present analysis can help policymakers with appropriate legislation and regulation and help business managers with solving identified problems. It also increases user satisfaction and trust.

Introduction: Computerized programs for brain empowerment with regular, intensive, and continuous cognitive exercises improve cognitive functions such as attention in attention deficit hyperactivity disorder. This review study aimed to identify effective computerized programs for improving attention in children and adolescents with attention deficit hyperactivity disorder (ADHD). Method: This descriptive narrative review was done in 2018 through library review and searching keywords including "attention deficit hyperactivity disorder", "computerized cognitive rehabilitation programs", and "play therapy" in databases such as PubMed, Scopus, and Google Scholar. The results of this review were investigated through the semi-structured interview and classified based on the attention areas of children and adolescents with ADHD. Results: Based on the review, Caption Log, Pay Attention, Cogmed Working Memory, Memory GYM, Attention GYM, Brainware Safari, 8 Sciences' ACTIVATET, Lumosity, My Happy Neuron, Smart Driver, and educational packages such as Maghzine were effective programs to improve different types of attention (focused, divided, intermittent, continuous, and selective) in the ADHD children aged four years old and older. Conclusion: Considering the significant effects of computerized cognitive rehabilitation programs in empowering cognitive skills such as attention, their use as a complementary treatment in improving the quality of life of children and adolescents with ADHD is promising. However, it is suggested to evaluate such programs with larger samples, transfer their educational effects to daily life, and improve their quality by providing more components of such programs.

Introduction: A quality criterion for evaluating information systems (ISs) is their usability, which can be determined by examining the user interface. User interface issues may generate user errors, dissatisfaction, and inefficiency, making IS adoption difficult. Therefore, the International Standard Organization recommends usability testing to evaluate IS acceptability and deployment. So, this study aimed to develop a native model for the usability testing of hospital information systems (HISs) via heuristic evaluation (HE). Method: This multi-staged qualitative study was conducted in 2020; ScienceDirect, PubMed, Web of Science, Scopus, Civilica, and Magiran databases were searched. After meeting the inclusion criteria, the articles were scored. Then, the initial model was created in the following stage by extracting data from library sources and validated using the Delphi technique, and consequently, the final model was presented. Results: : The native model was developed using thirteen principles: 1) visibility of system status, 2) the match between the system and the real world, 3) users’ control and freedom, 4) consistency and standards, 5) error prevention, 6) recognition rather than recall, 7) flexibility and efficiency of use, 8) aesthetic and minimalist design, 9) helping users recognize, diagnose, and recover errors, 10) documentation and help, 11) skill , 12) pleasurable, respectful interaction with users, and 13) privacy protection. Conclusion: This study demonstrated that localizing the usability evaluation of HISs based on trustworthy sources and HE is a wise strategy, because not only it avoids repetitive processes, but also it uses the best up-to-date resources to enable the presentation of the highest evaluation quality.

Introduction: Using artificial intelligence tools in pharmacogenomics is one of the latest bioinformatics research fields. One of the most important drugs that determining its initial therapeutic dose is difficult is the anticoagulant warfarin. Warfarin is an oral anticoagulant that, due to its narrow therapeutic window and complex interrelationships of individual factors, the selection of its optimal dose is challenging. Method: Among the relatively successful methods of kernel-based estimation, comparison and identification of suitable kernels have not been researched. In the present research, while carefully examining this approach, different features of selection algorithms were analyzed based on expert opinions, and an appropriate subset of efficient predictor variables was identified for dose estimation. Results: In the current study, a dataset collected by the International Warfarin Consortium was used. The results showed that the support vector machine with a suitable kernel and a subset of the proposed features can successfully predict the ideal dose of warfarin for a significant percentage of patients with an error of approximately 0.7 mg per week. Conclusion: The estimation was conducted using the least squares version of the support vector regression based on a suitable kernel and feature selection strategy. In this method, a better approach for predicting the optimal therapeutic dose of warfarin was presented, which can significantly reduce the wrong dose error and its consequences.

Introduction: The notion of electronic mental health care is suggested in today's world. With the aid of this care, people's talents are no longer limited by time or location, and those in need of mental health care can get these services by downloading and installing software on their mobile devices. The Cognogene application software has been created to provide electronic mental health services on the smartphone platform. This study aimed to measure the impact of the mobile app “Cognogene” on university students' communication skills. Method: The statistical population of this study included 104 students studying at Tehran University in the winter semester (2021-2022). They were randomly divided into two 54-member groups (experimental and control). The experimental group received electronic psychological education (consisting of short video clips, daily homework, motivational letters, etc.) in 21 sessions, while the control group did not. To collect data, Queen Dom's communication skills questionnaire was used in two stages: pre-test and post-test. The data were analyzed using the one-way analysis of variance. Results: Results showed that the intervention made a significant difference in the mean score of all components of the study of communication skills except the components of "receiving and sending messages" and "emotional control". Conclusion: This finding suggests that training through the Cognogene application can be used as a complement to other training methods to improve communication skills

Introduction: With the rapid growth of information technology (IT) and the expectations of health care environments, the presence of nurses with informatics skills in the health care environment to effectively use and manage health care information in health care delivery seems necessary; therefore, this study was conducted to evaluate the IT competency of special care nurses from their point of view. Method: This cross-sectional study aimed to investigate the IT competency of nurses in the special care departments of Afzalipur Hospital of Kerman University of Medical Sciences in 1400 SH. The study population included 60 nurses working in the intensive care units (ICU and CCU) who were included in the study using a census method. The Nursing Informatics Competency Assessment Tool (NICAT) was used in this research. Results: The findings of the study showed that the average score of IT qualification of nurses in the special care department (2.70 ± 0.85) is within the range of “competent”. The data showed that the highest score of IT competency of the studied nurses was attributed to the computer literacy section (2.82±0.93), and the lowest score was related to the information management skills section (2.58±0.85). Conclusion: It is suggested that clinical nursing managers improve the IT competency of nurses by implementing educational programs in the field of “IT literacy in the hospital”, especially in the division of information management skills which had the lowest average score, so that they can use the latest evidence to provide more effective care at the bedside.

Introduction: Healthcare as an industry has unique requirements such as patient security and privacy, interoperability, sharing, transmission, and access control of patient data. On the other hand, the advantages of blockchain technology and the compliance of these advantages with the requirements of the health industry have encouraged researchers to investigate the methods of applying blockchain in healthcare. The rapid increase in blockchain and health research has created many applications. Despite the high potential of this technology for health applications, there are still challenges to be reviewed. In this article, we provide a narrative overview of blockchain applications and challenges in the healthcare industry. Method: In this narrative review, published studies until October 2022 that were accessible in PubMed, IEEE Xplore, Web of Science, and Scopus databases were searched; from 254 related studies, 171 were identified by their titles, and finally, after applying inclusion and exclusion criteria, 30 articles were selected to be reviewed. Results: The results showed that 10 applications of blockchain technology in healthcare are as follows: safe sharing of health data, establishing electronic medical records, medical record tracking, opioid prescription tracking, deep learning, drug supply chain, clinical trials, COVID-19 pandemic management, and remote patient monitoring. The application of blockchain technology in healthcare also has challenges such as interoperability, security and privacy, immutability, scalability, patient engagement, transparency, and confidentiality. Conclusion: The compatibility between the requirements of the health industry and the characteristics of the blockchain has created a suitable platform for the use of this technology in the health industry. However, there are challenges in the path of these applications that must be solved.

Introduction: The prevalence of hypertension in children is increasing, and this complication is considered the most important risk factor for cardiovascular diseases in older age. Early detection and control of hypertension can prevent its progress and reduce its consequences. Machine learning methods can help predict this complication promptly and reduce cost and time. This study aimed to provide a model based on ensemble machine learning methods to more accurately predict the hypertension of primary school children. Method: This is an applied developmental study that was conducted using the information of 1287 primary school children aged 7-13 years in Kashmar city. After data preprocessing, to achieve a more accurate diagnosis of hypertension in children, the output results of five common machine learning methods in disease diagnosis including decision tree, naive Bayesian, nearest neighbors, artificial neural network, and support vector machine using weighted majority voting method were combined. Results: The results showed that the accuracy, sensitivity, and specificity of the proposed model were 90.31%, 80.65%, and 93.54%, respectively, and compared to similar studies it performed better. Conclusion: The proposed model can better predict and diagnose hypertension in children and improve accuracy and reduce the error rate. This model can be a useful and early tool in the diagnosis of hypertension in children, reducing the consequences and costs of this complication and being a big step in the fight against hypertension.

Introduction: Since the delay or mistake in the diagnosis of mood disorders due to the similarity of their symptoms hinders effective treatment, this study aimed to accurately diagnose mood disorders including psychosis, autism, personality disorder, bipolar, depression, and schizophrenia, through modeling and analyzing patients' data. Method: Data collected in this applied developmental research included 996 records with 130 features obtained by interviewing and completing questionnaires in a mental hospital in the city of Sari, Iran in 2021. After preprocessing, the number of features was reduced to 91, and then through Principal Component Analysis (PCA) reduced to 35 factors. Modeling was done in Python software with K-Nearest Neighbor (KNN), Naive Bayes (NB), Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), and Support Vector Machine (SVM) algorithms. The models were evaluated to select algorithms with higher accuracy. Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) were applied to determine the optimal parameters of the selected algorithms. Results: Among the machine learning algorithms, random forest with 91% accuracy and support vector machine with 90% accuracy showed better performance. The genetic algorithms did not make any notable increase in prediction accuracy. Whereas considering N=30, T=150, W=0.9, c1=2, and c2=2 in the particle swarm optimization algorithm increased the prediction accuracy up to 3.3 %. Conclusion: With less classification error compared to similar studies, the PSO-SVM model designed in this study can be used in patient data monitoring with acceptable accuracy and can be used in intelligent systems in psychiatric centers.

Introduction: Blockchain is a widely used technology in the health area; however, it also comes with challenges. By identifying these challenges, the road to blockchain maturity can be made smoother in this field. This study aimed to identify the challenges of the blockchain technology maturity model in health-oriented organizations. Method: In this phenomenological qualitative study, experts in the field of blockchain technology were the statistical population 12 of whom were selected to conduct an in-depth interview. The validity of the findings was guaranteed by the methods of matching by members and peer review. In this manner, the participants reviewed the process of primary data (interview results) analysis. And in the peer review procedure, supervisors, advisors, and two doctoral students commented on the findings. To analyze the data, open and axial coding was used. Results: The challenges of this model, in the technical and organizational sections, have been drawn and presented in the form of a structural model. In the resulting model, each of the categories is specified with its subset, and their frequency is also specified in the model to prioritize and determine their importance. Conclusion: In health-oriented organizations, the challenges of information security in the technology sector and transparency in the organizational structure are among the most important challenges. The security of data, due to the high importance of maintaining them, and creating transparency, due to the impossibility of manipulating data, are challenges that if ignored, the process of reaching technology maturity in health-oriented organizations will fail.

Introduction: With the change in the epidemiology of diseases, chronic kidney disease has shown a significant increase worldwide. Kidney transplantation is the optimal treatment option to improve patient quality of life. The main challenge in benefiting from a brain-dead patient's kidney is the selection of a right recipient in limited time. The purpose of this study was to review the requirements for designing and implementing clinical decision support systems for allocating brain-dead kidneys and the challenges facing them. Method: This scoping review was conducted by searching Scopus, PubMed, and Google scholar to find full-text English articles published until the end of 2021. The articles were analyzed based on the objectives of the study, the factors used in decision-making for allocation, the algorithms used in the decision-making process, and their outcome evaluations. Results: Two categories of medical (including the quality of the donated kidney) and equity/fairness factors were used in most allocation algorithms. However, the number of factors, the method, and the order of their application were different in different studies. Studies have reported different challenges but a positive impact using decision support systems. Conclusion: One of the main challenges in the proper management of kidney transplants from brain-dead donors is to have local, agreed-upon protocols and algorithms regarding the methods, order, and weight of each of the medical and equity factors in the proposed protocol. Algorithms based on scoring have mostly been favored. Therefore, the probability of using a decision support system on this base seems to be higher.

Background The rheologic, mechanical, and pharmacological characteristics of hyaluronic acid (HA) gels vary in terms of their specific crosslinking technologies. Objective To evaluate the various characteristics of selected HA gels available in Iran, using simple, inexpensive, and reproducible experimental tests are necessary to better understand their appropriateness for specific indications. Methods The HA gels produced by various crosslinking technologies were evaluated by three experiments, including cohesivity, resistance to stretch, and microscopic examination. The gels subjected to these experiments in this study included Neuramis, Desirial, Belotero, Restylane, and Juvéderm. Results We found that cohesivity was different among the gels so that Desirial was the least cohesive and Belotero was the most cohesive gel. Other HA gels were observed as partially cohesive. The resistance to stretch test confirmed the cohesivity results and Belotero had the most significant resistance. Microscopic examination of the HA gels showed HA particles of varying distribution and size. Belotero and Desirial were the gels with no particles visible under the microscope. Conclusion Simple and inexpensive laboratory tests help to understand the way that they can affect the specific gel performance, and may be beneficial for physicians to decide on the best product for a particular therapeutic indication. [ABSTRACT FROM AUTHOR]

Several challenges, including rapid drug release from the carrier, repeated drug administration, fluctuations in plasma drug concentration, and reduced patient compliance have been reported for conventional drug delivery systems. Liposomes were first developed by a scientist, A.D. Bangham, in 1961. The word liposome is a combination of the two Greek words "lipos" meaning fat and "soma" meaning body. A liposome is a system, which its membrane is made up of fatty compounds such as lipids. The lipid membrane and its entrapped space are suitable for the loading of lipophilic and hydrophilic drug molecules, respectively. Liposomes, as optimal drug delivery systems, could be an appropriate solution to many of the challenges of conventional drug delivery systems. Liposomes, which are made from phospholipid and cholesterol, have many benefits as optimal drug delivery systems. A wide range of therapeutics could be incorporated within liposomal carriers. They are biodegradable and biocompatible. They do not stimulate the immune system and are used as a synthetic membrane model. These systems increase the stability of drugs in the body by protecting the drug compounds within them and also reduce the toxic effects of drugs by minimizing drug exposure to sensitive tissues. Liposomes can also control drug release. The particle size of liposomes varies from nanometers to micrometers depending on the preparation method. Liposomes can be classified on the basis of their size and bilayers number, into: giant unilamellar vesicles (GUVs) 0.5 - 10 µm; multilamellar vesicles (MLV) 1 - 5 µm; large unilamellar vesicles (LUV) around 100 - 500 nm; small unilamellar vesicles (SUV) 20 - 100 nm; and multivesicular liposomes (MVL). MVL are characterized by their structure of multiple non-concentric aqueous chambers surrounded by a network of lipoidal membranes. The particle size of monolayer and multilayer liposomes is about a few hundred nanometers, while the average particle size of MVLs is 1 to 100 µm. Methods of liposomes preparation include thin layer hydration, reverse phase evaporation, solvent injection, dehydration-rehydration, and ultrasonic. The MVL system is a water in oil in water emulsion (W / O / W) that organic phase contains hydrophobic material and liquid phase contains hydrophilic material. The double emulsification method is the most common method of MVL preparation. Small drugs as well as large molecules such as peptides and proteins can be loaded in the unique structure of MVL. So far, several peptides or protein compounds have been successfully loaded in MVL and their therapeutic effects investigated in various diseases. In vitro and in vivo studies show that MVLs well control the drug release rate. There are some examples for MVL applications regarding these compounds.; MVL containing insulin or liraglutide in diabetes mellitus treatment, bevacizumab in laser-induced choroidal neovascularization, thymopentin in immune disorders, LXT-101 in the treatment of prostate cancer, angiotensin I converting enzyme (ACE) inhibitory peptide in the treatment of hypertension, alfa interferon in hepatitis C, and leridistim in chemotherapy-induced bone marrow suppression are some of the studies in this field. MVL consists of four compounds: phospholipids, cholesterol, neutral triglycerides, and negatively charged lipids. Phospholipid chains form the bilayer membrane of the liposome, and the cholesterol in the system is located between these chains. The cholesterol to phospholipids ratio, lipid to drug ratio, and lipidic composition affect the stability of the system and the rate of drug release. Neutral triglycerides such as triolein, tricaprylene, trilaureine and tributyrine allow the formation of this unique multivesicular structure, stabilize the membranes of internal vesicles at the point of contact with each other, preventing them from merging and controlling the drug release rate. Negatively charged lipids such as dicetyl phosphate (DCP) also control particle accumulation by inducing a negative charge on the surface of liposomes. Observation and determination of MVL structure are usually performed by optical and electron microscopes. The sysyems should also be characterized regarding drug encapsulation, drug release profile and kinetics, storage stability, and drug-vesicle interaction. Most studies on the MVL drug delivery system have been performed on drug-loaded MVLs. To date, application of MVLs containing tramadol in pain management, various compounds (include rose bengal, oxaliplatin, oleanolic acid, and cytarabine) in cancer, dexamethasone sodium phosphate in hearing impairment, acyclovir sodium in infectious diseases of herpes simplex virus (HSV), desferrioxamine mesylate in iron toxicity, celecoxib as an anti-inflammatory and analgesic agent, breviscapine in cardiovascular disease and cerebrovascular ischemia, flucinolone acetonide in ocular inflammatory diseases, naltrexone hydrochloride in opioid abuse and alcohol dependence and ropivacaine hydrochloride in local anesthesia has been investigated. In some treatment protocols, the simultaneous administration of two-drug combinations is required to achieve the desired result. The large aqueous space in the MVL and the division of this space into intravesicular and extra-vesicular parts, allows multiple drug combinations to be loaded in it and enter the body during an administration. The efficacy of co-delivery of mitoxantrone hydrochloride with horseradish peroxidase and insulin with metformin in a system was investigated in the treatment of cancer and diabetes mellitus, respectively, and showed satisfactory effects. Another class of drugs that have been successfully loaded in MVL is antibiotics. Antibiotics should have the least fluctuations in plasma concentrations in order to be more effective on microorganisms, so they should be prescribed according to a regular schedule. In MVL studies, researchers found satisfactory results from the study of the two antibiotics vancomycin and gentamicin. Many liposomal systems are used clinically to control drug release. These systems include daunorubicin, desferrioxamine mesylate, insulin-like growth factor 1 (IGF-1), doxorubicin, vincristine sulfate, and irinotecan respectively under the brand names DaunoXome®, DepoDFO®, DepoIGF-I®, Doxil®, Marqibo®, and Onivyde®. Depofoam is a type of MVL system with a particle size of 10 to 20 µm that could be a good depot in the body and releases the drug in a controlled manner. Depofoams are good choices for drug delivery to tumor areas and sensitive areas of the body such as the eyes, spinal cord, and epidural, and can reduce drug toxicity and side effects by minimizing drug exposure to healthy parts of the body. There are several Depofoam systems which three of these systems have been approved by the US Food and Drug Administration (FDA). DepoCyt® is a Depofoam system of the anticancer drug cytarabine by intrathecal injection. This system releases the drug in the body for two weeks and makes an optimum concentration of it in the cerebrospinal fluid (CSF). DepoDur® is the second product, which contains morphine. It relieves post-surgery pain after epidural administration and reduces the use of other injectable analgesics. Exparel® is a new formulation of bupivacaine with an analgesic effect that is injected near the surgical site. Numerous animal and human studies showed that after intrathecal, epidural, intraocular, intramuscular, intravenous, intra-arterial, and subcutaneous administration of Depofoam. This review article provides an overview of recent studies on the MVL system and its effect on optimizing rate of drug release. Successful loading of large molecules such as proteins and the possibility of loading multiple drug combinations into one system shows a hopeful view for the future of MVL in the pharmaceutical industry. It seems that this new technology, by overcoming the problems caused by frequent drug administration, has created an additional incentive for researchers to advance in this field. However, problems such as the high cost of these liposomes are some of the challenges that they face. [ABSTRACT FROM AUTHOR]

Introduction: Hospital information system (HIS) as an integrated information system is used for daily operations, planning, care, and treatment of patients. Due to the existence of various and heterogeneous healthcare information systems, HIS encounters interoperability problems. In this regard, this study aimed to propose an integrated architecture for HIS based on the interoperability model. Method: This research is a strategic-applied study for achieving an integrated HIS architecture, focusing on clinical information systems (CIS). First, the requirements of clinical information systems were carried out through library studies, the review of the statistics office of the Ministry of Health, Treatment and Medical Education, and it was also reviewed and confirmed by information technology experts. Then, different interoperability frameworks and HIS architecture models were studied. Finally, the integrated HIS architecture model was proposed based on the interoperability model. Results: The integrated HIS architecture based on the interoperability model includes three architecture models as follows: references, software, and information. Reference architecture is proposed generally for the HIS and customized for CIS. For implementing various HISs, software architecture is proposed. And information architecture is used for storing, processing, and transmitting the information. Conclusion: Considering the necessity of integration in the health system, this study creates the possibility of interoperability and integration between HIS systems by applying the guidelines of integration in the architecture, which leads to cost reduction, quality improvement, and timely decision-making.

Introduction: The increase in stress and anxiety among students has a negative effect on their physical and mental health and can prevent their efficient academic performance. Self-care applications can help control and reduce students' anxiety and depression. The aim of this study was to determine the necessary requirements related to the design and development of a mobile-based self-care application to control and reduce stress and anxiety in students. Method: This research is a cross-sectional study that was conducted in 2022. To conduct this study, 38 students of Varastegan Institute for Medical Sciences (Razavi Khorasan province, Mashhad city) were included in the study based on the inclusion criteria and answered the questions of the questionnaire. Data were analyzed using descriptive statistics and SPSS version 25. Results: Out of 91 informational-educational needs and capabilities, 67 items with a mean score of ≥3.75 were considered essential for application design. Information-educational needs and capabilities were categorized into eight axes: user profile, clinical records, lifestyle, disease management and control, sedation instructions, warnings and reminders, reporting and application capabilities. Conclusion: Identifying and determining the requirements related to the design of an application based on a mobile phone is one of the basic principles of developing an application that by involving users and obtaining their opinions can have an important role in their continue use of these applications. Therefore, application designers can design and develop an organized mobile-based self-care application to control and manage students' stress and anxiety by using the information-educational needs and capabilities presented in the present study.

Introduction: Since endometriosis is a chronic disease and requires long-term care, it can have a devastating effect on life quality. For this reason, it is necessary to increase awareness and educate women about endometriosis. In this regard, a mobile phone-based endometriosis training program was designed. Method: The present paper is a development-applied study and was conducted in two stages. In the first stage, to determine the educational content, after reviewing previous studies, the questions obtained were given to eleven gynecologists and reproductive health doctors to measure the necessity and effectiveness of each question. After being designed, the program was provided to 40 women of reproductive age, and the usability of the program was assessed. Data analysis was performed using SPSS 22 software. Results: The required educational content of the application was presented in five groups and confirmed by specialists. The mean age of women who participated in this study was 32.35 ± 11.70 years. The average score of usability of the program was 8.34 out of 9 points which indicates a "good" level of satisfaction. Conclusion: Educating women through new popular tools such as mobile phones can play an important role in raising awareness and influencing their treatment. Therefore, designing a mobile phone-based training program in the field of education and awareness for women at risk of endometriosis can be advantageous.