1 2423-3870 دانشگاه علوم پزشکی کرمان 874 Internet & Medical Sciences Usability Evaluation of the Ministry of Health Exam System: Viewpoint the Users Nikokaran Jalal b Fatemi Aghda Seyed Ali c Dehghan Hamidreza d Mirfakhraei Mojgan e Poornematy Mohammad Mehdi f Mirfakhraei Mahdi g b PhD in Computer Simulation, Department of Health Information Technology and Management, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran c PhD Student in Medical Informatics, Student Research and Technology Committee, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran d Research Center for Health Technology Assessment and Medical Informatics, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran e Master of Computer Engineering, Research Center for Health Technology Assessment and Medical Informatics, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran f Bachelor in Operating Room, Research Center for Health Technology Assessment and Medical Informatics, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran g Master of Computer Engineering, Research Center for Health Technology Assessment and Medical Informatics, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran 1 12 2024 11 3 176 185 18 06 2024 09 11 2024 Introduction: Electronic education has increased due to the growth of technology, the spread of the Coronavirus, and the need to maintain social distancing. Electronic test systems are used to evaluate students. This study aims to evaluate the usability of the test system from the users' perspective. Method: This research is a descriptive cross-sectional study. The statistical population was all the students and faculty members of Shahid Sadoughi University of Medical Sciences. A random sample of 318 students was selected, and faculty members were selected using a Census approach. The research data were obtained based on the standard questionnaire for evaluating the usability of Quiz version 5.5. Results: Students rated the usability of the test system as "average," whereas faculty members rated it as "good." Both groups identified "Learning by the user" as the section with the highest average score. However, their opinions diverged regarding the lowest-scoring section: students indicated "system capabilities," while faculty members pointed to "terminology and system information." Conclusion: The findings of this research suggest that more attention should be given to students' opinions regarding the electronic testing system, and its problems should be addressed. Furthermore, conducting additional studies with other universities could enhance the system and increase user satisfaction.
875 Bioinformatics Investigating the microRNAs Involved in the Acute Phase of Hepatocellular Carcinoma Caused by Hepatitis C Virus Mousavi Kardar Seyedeh Fatemeh h Mohammadi Mahnaz i Esfandiari Behnaz j h MSC in Biology, Department of Biology, Faculty of Basic Sciences, Islamic Azad University, Islamshahr Branch of Islamic Azad University, Islamshahr, Tehran, Iran i Assistant Professor, Department of Biology, IsI.C., Islamic Azad University, Islamshahr, Iran. j Assistant Professor, Department of Biology, IsI.C., Islamic Azad University, Islamshahr, Iran. 1 12 2024 11 3 186 202 30 06 2024 19 12 2024 Introduction: Hepatocellular carcinoma is one of the most common cancers in the world. In this study, we examined and nominated the genes present in the pathways of hepatocellular carcinoma associated with HCV using bioinformatics analysis. Method: The appropriate dataset for analysis was selected from the GEO database. This dataset included gene expression profiles in hepatocellular carcinoma associated with HCV. Gene clusters with high and low expression levels were categorized. Rich databases such as Enrichr, STRING, and GEPIA were also used. Finally, the candidate genes were isolated. Results: A total of 512 genes with high expression and 500 genes with low expression were involved in the progression pathways of hepatocellular carcinoma. The pathways associated with the cell cycle, cell adhesion, AMPK, PPAR, and MAPK were clearly observed. After evaluating the relationship between protein networks, ADH4, FBP1, and ACS1 showed increased expression, while CDK4, E2F1, and MAPK3 genes displayed decreased expression. All these genes were noted in the survival curve; over a period of about 15 months, the survival rate of patients was less than 20%. miR-21-5p, hsa-miR-24-3p, and hsa-miR-25-3p were significantly more effective in regulating these genes. Conclusion: Bioinformatics analyses of key and important genes were introduced through the examination of gene expression profile data. ADH4, FBP1, and ACS1 genes showed increased expression, whereas the CDK4, E2F1, and MAPK3 genes displayed decreased expression, which may play an important role in targeting the genes involved in hepatocellular carcinoma associated with HCV. 883 Artificial Intelligence in Healthcare Digital Twins in Healthcare: Necessities, Pros, Cons, and Future Directions Nabizadeh Amir Hossein k Ghaemi Mohammad Mehdi l k Assistant Professor of Artificial Intelligence, Medical Informatics Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran / INESC-ID, Lisbon, Portugal l Assistant Professor of Medical Informatics, Medical Informatics Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran 1 12 2024 11 3 203 213 12 08 2024 30 09 2024 Introduction: Digital Twin (DT) refers to a digital model of a physical product, system, or process used for simulation, monitoring, and optimization. In healthcare, this technology has transformed patient care by generating virtual models of patients, enabling the prediction and optimization of health outcomes. This study examines the benefits, challenges, implementation requirements, and future directions of this technology in healthcare, aiming to review and summarize prior studies while avoiding duplication of research. Method: This study employs a narrative approach in the context of digital twins in healthcare. It does not follow a systematic methodology but rather relies on existing models, hypotheses, and personal expertise to derive general conclusions. Results: This study describes how DT has facilitated personalized medicine, improved surgical outcomes, managed chronic diseases, streamlined clinical trials, and optimized hospital operations. Additionally, it highlights challenges associated with DT in healthcare, such as ethical implications, privacy concerns, and regulatory issues. It emphasizes the need for strong data governance and interdisciplinary collaboration to fully leverage the potential of DT. Finally, this research explores key areas for the future development of this technology. Conclusion: The requirements, challenges, benefits, and future directions presented in this study can guide the design and implementation of accurate and intelligent DT in the healthcare domain 876 Bioinformatics Investigating the Interaction of Herbal Medicinal Compounds Against Mpro Protease in SARS-CoV-2 Virus Strains Fallah Pour Roya m Jamaldini Seyed Hamid n Allahyari Fard Najaf o m MSc of Science, Islamic Azad University, Medical Branch, Tehran, Iran n Assistant professor, Islamic Azad University, Medical Branch, Tehran, Iran o Assistant professor, National Institute of Genetic Engineering and Biotechnology, Tehran, Iran 1 12 2024 11 3 214 228 11 07 2024 19 12 2024 Introduction: COVID-19 is an infectious disease caused by the SARS-CoV-2 virus. The identification of beneficial plant compounds and their use for targeting important proteins involved in the infection and replication of the coronavirus is considered an effective option in the fight against this disease. The aim of this research was to investigate the interaction of native medicinal plant compounds against the Mpro protease of different SARS-CoV-2 strains and to determine and evaluate the final compound using the HPLC method. Methods: The three-dimensional structure of the Mpro protease was obtained from the PDB database. Various antiviral plant compounds were collected from databases and articles. Molecular docking was performed using AutoDock Vina. The features and properties of the selected compounds were examined using different servers. Mutagenesis for the methionine amino acid 49 was performed, and a new three-dimensional structure of the Mpro protein was modeled for different virus strains. Docking analysis for four selected compounds was conducted based on binding energy. Molecular dynamics simulation (MD) was carried out to examine the stability of the final structure, and HPLC was used to evaluate the presence of the effective compound in the desired plant. Results: Docking results showed that the compound Cyanidin-3, 5-di-o-glucoside is effective in inhibiting Mpro, as indicated by its favorable total binding energy. The pharmacokinetic properties of this compound were also determined. MD results indicated that the Mpro-Cyanidin-3, 5-di-o-glucoside complex is stable. HPLC confirmed the presence of Cyanidin-3, 5-di-o-glucoside in Iranian pomegranate extract. Conclusion: The compound Cyanidin-3, 5-di-o-glucoside found in Iranian pomegranate extract can bind to Mpro with high affinity and inhibit its activity, potentially serving as a drug that directly targets the coronavirus. 877 Artificial Intelligence in Healthcare Identification of Important Features to Predict 30-Day Mortality in Heart Failure Patients with Cardiogenic Shock Kanani Shiva p Mahdavi Iraj Ziaie Naghmeh Rahimpour Cami Bagher p PhD Student in Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran Professor, Department of Industrial Engineering, University of Science and Technology of Babol, Babol, Iran Assistant Professor, Department of Cardiology, Fellowship of Heart Failure and Transplantation, Babol University of Medical ‎Sciences, Rouhani Hospital Research Center, Babol, Iran Assistant Professor, Department of Computer Engineering, University of Science and Technology of Babol, Babol, Iran 1 12 2024 11 3 229 243 13 07 2024 09 11 2024 Introduction: Cardiovascular diseases remain a leading global cause of mortality, with ischemic heart disease projected to account for 23.3 million deaths by 2030. Heart failure and cardiogenic shock account for a significant proportion of these deaths and require timely treatment as medical emergencies. This study aims to predict mortality within one month in patients experiencing cardiogenic shock secondary to heart failure using a concise set of predictive features. Method: An analytical cross-sectional study was conducted at Babol Razi Hospital, involving 201 adult patients (≥18 years) treated for cardiogenic shock in 2020. Data from 34 clinical variables, including age, history of cardiac surgery, pH levels, lactate concentration, diabetes status, and blood pressure, were meticulously analyzed. Mortality outcomes within one month were assessed via structured telephone follow-up. Logistic regression and Gradient Boosting Machine (GBM) algorithms were used for predictive modeling. Results: The average age of patients was 69.44 ±15.71 years. Among them, 47.7% died. The study identified age, lactate levels, diabetes, and initial confusion as significant predictors of mortality risk. Each additional year of age was associated with a 7% higher probability of mortality. Diabetic patients faced more than double the mortality risk compared to non-diabetics. Confusion at presentation increased the mortality risk fourfold, while elevated lactate levels raised it by 1.5 times. Conclusion: Logistic regression and GBM algorithms demonstrated effectiveness in predicting one-month mortality among cardiogenic shock patients with heart failure based on selected features. This approach holds promise for improving referral processes and reducing costs in healthcare settings. 887 Artificial Intelligence in Healthcare Providing a Foresight Model for Selecting the Appropriate Breast Cancer Diagnosis Model Shakibaeinia Abdolhossein Chegin Mohsen Golabpour Amin Khosravi Ahmad Department of Computer Engineering, Dezful Branch, Islamic Azad University, Dezful, Iran Department of Computer Engineering, Dezful Branch, Islamic Azad University, Dezful, Iran Shahroud University of Medical Sciences, Shahroud, Iran Shahroud University of Medical Sciences, Shahroud, Iran 1 12 2024 11 3 244 256 16 09 2024 25 12 2024 Introduction: Selecting an appropriate model for breast cancer diagnosis is critical. Unsuitable models can compromise diagnostic accuracy, lead to incorrect outcomes, and impact clinical decision-making. In this context, foresight models are valuable tools for identifying and selecting the most effective diagnostic models. The objective of this study was to identify optimal models for breast cancer detection using foresight models.  Method: This study began by extracting articles related to artificial intelligence-based breast cancer diagnosis. The number of articles associated with each algorithm was determined, and algorithms referenced in fewer than 50 articles were excluded. Subsequently, annual publication trends were analyzed. A time series model based on artificial neural networks was developed to predict research trends over the next two years and to identify the algorithms expected to receive more research attention.  Results: After applying the exclusion criteria, a total of 2,308 articles were categorized into eight groups: deep learning, artificial neural networks, support vector machines, fuzzy logic, clustering, decision trees, Bayesian methods, and logistic regression.  Additionally, eight time series models were constructed using data from the past seven years, predicting that deep learning and artificial neural networks will lead future research efforts in breast cancer diagnosis.  Conclusion: This study highlights the effectiveness of foresight as a methodological approach for selecting optimal techniques for breast cancer diagnosis. The results indicate that artificial neural networks and deep learning demonstrate superior performance and are likely to be pivotal methodologies for future research in this area. 890 Health Information Systems Identifying Effective Factors in the Electronic Prescribing System of Pharmacies from the Point of view of Selected Pharmacists and Pharmaceutical Technicians in Urmia Mohaghegh-Montazeri Maryam Gozali Elahe Jebraeily Mohamad Ahangar Nematollah M.Sc. Student in Health Information Technology, School of Allied Medical Sciences, Urmia University of Medical Sciences, Urmia, Iran Health and Biomedical Informatics Research Center, Urmia University of Medical Sciences, Urmia, Iran Health and Biomedical Informatics Research Center, Urmia University of Medical Sciences, Urmia, Iran Ph. D in Pharmacology, Professor, Department of Pharmacology, School of Medicine, Guilan University of Medical Sciences, Rasht, Iran 1 12 2024 11 3 257 270 02 10 2024 19 12 2024 Introduction: The emergence of electronic prescriptions has transformed the methods of prescribing and distributing medications in pharmacies. The aim of this study is to explain the factors affecting workflow, patient communication, and medication safety within the electronic prescription system from the perspectives of pharmacists and pharmacy technicians. Method: This research is a descriptive and cross-sectional study conducted in 2023 in selected pharmacies in the city of Urmia. It was carried out in two stages. In the first stage, relevant studies were systematically reviewed and selected based on entry criteria to identify the influencing factors. In the second stage, the two-stage Delphi method was used with purposive and snowball sampling to finalize the identified factors. Results: The results of this study consisted of two stages. In the first stage, the factors affecting workflow, patient communication, and medication safety in pharmacies within the electronic prescription system were identified. In the second stage, the validity of the factors identified in the first stage was determined using a two-stage Delphi method. After two rounds of Delphi, 11 items related to workflow variables, 6 items for patient communication, and 11 items for medication safety were confirmed. Conclusion: This study has identified the factors that can be utilized by health policymakers and developers of electronic prescription systems to improve efficiency and serve as a framework for formulating both long-term and short-term plans. 893 Mobile health Mobile Health Interventions to Facilitate Shared Decision Making: A Systematic Review Hajesmaeel-Gohari Sadrieh Shafiei Elaheh Gholipoor Maryam Ph.D. in Health Information Management, Assistant Professor, Medical Informatics Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran Ph.D. Candidate in Medical Informatics, Medical Informatics Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran MSc in Medical Informatics, Department of Health Information Sciences, Faculty of Management and Health Information Sciences, Kerman University of Medical Sciences, Kerman, Iran 1 12 2024 11 3 271 281 21 10 2024 21 12 2024 Introduction: Shared Decision Making (SDM) is a process in which healthcare providers share evidence with patients, enabling them to make informed decisions based on their values and preferences. Mobile health (mHealth) can support SDM, leading to improved clinical outcomes. Therefore, this study aims to review the literature on mHealth interventions that facilitate SDM. Method: In November 2022, the PubMed, Scopus, and Web of Science databases were searched for papers on mHealth interventions that facilitate SDM. The first author's name, year of publication, country of the study, study objective, type of disease, type of mHealth intervention, and outcomes of the interventions were extracted from the selected studies. Descriptive statistics were used to analyze the data. Results: Of the 32 articles included in the study, most were published in 2021 (25%) and 2022 (21%). The majority of studies (43.5%) were conducted in the United States. Fifteen studies (47%) evaluated participants' satisfaction with using mobile health for shared decision-making, and fourteen studies (43.5%) measured the usability of mobile health. In most studies (84.5%), mobile-based applications were used for shared decision-making. Most studies assessed the impact of shared decision-making on musculoskeletal diseases (18.5%), cancer (15.5%), cardiovascular diseases (15.5%), and mental health disorders (12.5%). Most studies (97%) found that using mobile health had a positive impact on shared decision-making. Conclusion: mHealth assists patients in making informed decisions about their treatment by facilitating SDM. Patients can use this technology at home to overcome barriers posed by a lack of face-to-face visits. The findings of this research can guide healthcare providers in leveraging affordable and accessible technology to engage patients in selecting the best treatment options.