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Cytotoxic T-Cell Markers and Cytokines in Human Papillomavirus 16
Sakineh Abbasi, Shahrzad Sharifpour Vajari,
Volume 15, Issue 5 (Dec 2021 & Jan 2022)
Abstract
Background and Aim: Cervical cancer is the fourth main cause of mortality among women, and annually about half a million new cases are detected in developed countries. Based on oncological studies, human papillomavirus (HPV) is classified into two categories: high-risk type and low-risk type, and most cases are related to the high-risk type of human papillomavirus. HPV 16 and 18 are among the more dangerous ones in this type of cancer. Human papillomavirus is a small group of uncoated viruses with double-stranded DNA that belong to the papillomaviridae family.
Materials and Methods: In this review study, more than 200 articles related to human papillomavirus and immune system function against this virus were reviewed from 2015 to 2020 and among them, 34 articles related to markers and cytokines in cervical cancer were chosen from Google Scholar, Scopus, and PubMed.
Results: One of In-vitro methods in markers detection , is using vectors to infect dendritic cells to present antigen, increase the expression of markers and mature T cell, which leads to the identification of a variety of markers and cytoklines such as PD, PDL, CD, MHC, FASL, IFN, IL, TLR associated with cervical cancer.
Conclusion: Cervical cancer prevention can reduce the economic as well as the social burden of having the disease in the community.  Important cytokines expressed when exposed to HPV include IL-6 and IL-8. Several agonist epitopes with enhanced binding power to the human leukocyte antigen (HLA-A2) A2 class I antigen have been described to enhance cytotoxic T lymphocyte responses and to be used in the development of effective HPV vaccines; this is because it has already been shown that different epitopes of 16 HPVs, such as E6 and E7, are able to elicit human cytotoxic T lymphocyte (CTL) responses by binding to HLA-A2.
The Implementation Challenges of Health Information Systems in Iran’s Southeastern Hospitals: From Managers’ Perspective
Reza Abbasi, Fatemeh Rangraz Jeddi, Shima Anvari, Reza Khajouei,
Volume 16, Issue 3 (Aug 2022)
Abstract
Background and Aim: Hospital managers are one of the key decision-makers in the implementation of health information systems. This study aimed to determine the implementation challenges of health information systems based on the hospital managers’ perspective.
Materials and Methods: This descriptive-analytical study was conducted in 2019 on the hospital managers of three provinces (Kerman, Yazd, Sistan and Baluchestan). Data were collected using a self-administrated questionnaire. The face validity of this questionnaire was approved by experts in health informatics and health information management and its reliability was confirmed by Cronbach’s alpha (α=96.7%). Data were analyzed using SPSS. To investigate the relationship between the mean of each challenge with demographic variables, Pearson, Independent T-test, and ANOVA tests were used.
Results: In this study, the factors related to ignoring the hospital manager’s needs in system selection (1.333 out of 2 points), hardware purchase cost, insufficient user training to using the system (1.238), inadequate manpower and health informatics specialists (1.19), software purchase cost, insufficient financial resources (1.142), high cost of system launching, the lack of integration and interoperability among information systems, lack of support from health care professionals (1.047), and lack of management experience in choosing the best system (one out of 2) had the highest scores (out of 2 points). Also, personnel training costs to work with the system (-0.092) and Lack of improvement in work processes (-0.047) obtained the lowest scores. Data analysis showed that managers with clinical backgrounds considered financial and human challenges more important than non-clinical managers (P<0.031).
Conclusion: The hospital managers believed that financial, human, technical, managerial, and organizational factors are the most important challenges in implementing health information systems in Iran’s hospitals respectively. The health policy-makers and planners at large and small levels can address many of the challenges before implementing systems by focusing on identified priorities.

Design and Usability Evaluation of Chronic Obstructive Pulmonary Disease Self-Care Application Based on Android
Marjan Ghazi Saeedi, Mohammad Amin Abbasi Eslamloo, Kobra Darabiyan, Elham Ataei,
Volume 17, Issue 3 (8-2023)
Abstract
Background and Aim: Despite the continuous progress in medicine, COPD (Chronic Obstructive Pulmonary Disease) is still a progressive, incurable and chronic respiratory condition that limits the patients’ functions in various dimensions, and significantly reduce their quality of life. In this way, self-care of patients and the use of related tools have a significant effect in disease control and treatment. The purpose of this research was design and development of an android-based application for COPD.
Materials and Methods: This research was a descriptive developmental type with a practical approach. Initially, based on the study of library resources, guidelines, and the examination of the medical records of COPD inpatients in Firouzabadi Hospital, a questionnaire was designed to identify the information requirements, data items and features of the application. Then it was reviewed and finalized by a sample of 10 (randomized and convenience sampling) internal and lung specialist doctors in Firouzabadi and Hazrat Rasool hospitals affiliated to Iran University of Medical Sciences. The data was analyzed using descriptive statistics, and then scenario tables and UML diagrams were illustrated to show the overall flow of the application. The application was designed and developed using the Java programming language in the Android Studio 2021 platform. After installing the application on the mobile phones of ten COPD patients of the internal and pulmonary clinic of Firouzabadi Shahre Rey Hospital, and using it for a week, the opinions of the patients about the usability of the application were collected through the QUIS questionnaire, and analyzed. 
Results: Application sections were determined following experts’ survey, personal information items, clinical information, disease management, reporting, and training points, which were provided to patients after design for use and evaluation. At the end of the research, the results of the evaluation of the usability and satisfaction level of the application showed that from the patients’ point of view, the application is at a good level with an average score of 1.8 (out of 10 points).
Conclusion: The developed self-care application can be used to increase awareness, help to manage the disease, increase the level of quality of life, and reduce the complications and disease burden for patients with COPD.
 

An Analysis on the Publishing Process and Co-occurrence Structure of Articles in the Field of Knowledge Tree
Reza Dehkhodaei, Mazyar Karamali, Mohammad Mohammadian, Mohammadkarim Bahadori, Mohsen Abbasifarajzadeh,
Volume 17, Issue 6 (2-2024)
Abstract
Background and Aim: Considering the importance of knowledge management in the current era and the emphasis on the implementation of knowledge management in the health system in the knowledge management system of the Ministry of Health, Treatment and Medical Education, and since it is one of the first steps in the implementation of knowledge management, Drawing the knowledge tree of the organization, the purpose of the current research is to review the process of publishing the knowledge tree and examine it in the field of health.
Materials and Methods: The current research is a type of applied and descriptive review study that was carried out with the method of scientometrics and co-occurrence analysis of keywords. For this purpose, the term “Knowledge tree” OR “knowledge trees” was searched in Scopus reference database. For data analysis, the analyzes provided by the database itself were used, and VOSviewer software was used to visualize the data.
Results: The growth of scientific productions related to the field of the tree of knowledge in general in the mentioned base has started since 1979 and has had a growing trend until 2023. In the field of knowledge tree, among the authors Yang, Y, among the organizations, machine intelligence institute, iona college, and among the countries, China, America, and England have been at the top of the most productive in this field. Among the subjects, the most related articles are primarily related to the field of computer science (32.2 percent) and then to the field of engineering (22.1 percent) and mathematics (10.1 percent), which is significant. that medicine is in the sixth place and this is a sign of the weakness of producing resources in this field and the clustering resulting from the co-occurrence of keywords led to the identification of five clusters respectively with the titles of data mining and information processing, artificial intelligence and expert systems. , knowledge structure and decision support systems, semantics and knowledge representation, and finally learning and teaching systems.
Conclusion: The study of the thematic structure of scientific productions in the field of Knowledge Tree showed that the field of health has a weakness in the production of resources in this field. Therefore, it is necessary for future researches to pay special attention to the development and explanation of this concept and modeling its drawing, especially in the health system, in order to identify and prevent diseases.
Strategic Analysis of the Financing System of Iran Health Insurance Organization
Ali Mohammad Mosadeghrad, Mahdiyeh Heydari, Mahya Abbasi, Mahdi Abbasi,
Volume 18, Issue 2 (5-2024)
Abstract
Background and Aim: Health insurance organizations play an important role in increasing people’s access to health services and protecting them financially against catastrophic health costs. Iran Health Insurance Organization (IHIO) is one of the largest public health insurance organizations in Iran, which faces many challenges. The purpose of this research is the strategic analysis of health financing performance of IHIO.
Materials and Methods: This qualitative research was conducted using interpretive phenomenology method through using semi-structured interviews with 25 managers and employees of IHIO. In addition, relevant documents and archival data of IHIO were collected and analyzed. Thematic analysis method was used to analyze the data. 
Results: Overall, 19 strengths, 24 weaknesses, 14 opportunities, 37 threats and 43 solutions were identified for the health financing system of IHIO. Increasing the coverage of health services, correcting the information databases of the insured and electronic prescribing were the most important strengths, and inappropriate pooling of financial resources, incomplete risk pooling, high administrative costs, and inefficient control were the most important weaknesses of IHIO. The most important opportunities for IHIO include the government’s support for universal health coverage and emphasis on primary health care, legal support for consolidating health insurance funds and improving the health technology assessment system in the country. The main threats to IHIO include political and economic unstability, low health insurance premiums, decisions without scientific support and insufficient enforcement of laws. Finally, solutions such as modernizing the tax system, increasing the health literacy of the community, reducing bureaucracy, increasing transparency and accountability, and reforming the monitoring and evaluation system were identified to strengthen the performance of the financing system of IHIO.
Conclusion: Iran’s health insurance organization is facing numerous structural, contexual and process challenges that have reduced its productivity. Decrease in revenues, increase in costs and decrease in efficiency have caused problems in the financing performance of this organization. Recognizing the weaknesses and challenges of financing performance and applying corrective interventions is the first step in strengthening the sustainability of health financing of IHIO.

Proposing a Model for Managerial Competencies of Hospital Managers in Iran
Kourosh Abbasiyan, Mohammad Alimoradnori, Mohammad Bagher Karami,
Volume 18, Issue 2 (5-2024)
Abstract
Background and Aim: Managers, as the main decision-makers in facing various internal and external organizational problems, play a significant and determining role in the success or even failure of an organization. If competent and experienced managers are positioned at the top of organizations, the success of these organizations in achieving their goals will be guaranteed and an organization can achieve maximum efficiency with minimal resources. The aim of this study was to design a model of managerial competencies for hospital managers.
Materials and Methods: This qualitative research was conducted from year 2020 to 2022. After reviewing studies related to the topic, the extracted competencies were given to 19 experts consisted of relevant academic faculty members and managers with experience in the healthcare system and hospitals. Eventually, a managerial competency model was formulated through the use of the Delphi method and expert panel discussions. Collected data were analyzed in Excel software.
Results: The developed model in this research for the concept of hospital managers’ competencies includes 33 managerial competencies of hospital managers in four main management functions (planning, organizing, leadership and control) and managerial roles, which starts from literature review and performing two Delphi steps and implementing two expert panel plans. In the competency of hospital managers model, the planning dimension consists 4 components, organizing consists 4 components, leadership consists 12 components, control consists 4 components, and managerial roles consists 9 components. Strategic thinking, which is a subset of planning, has the highest weight (0.495) and highest rank among other components, and continuous improvement, which is a subset of managerial roles, has the lowest weight (0.033) and lowest rank among other components.
Conclusion: This study proposes an exclusive and comprehensive model, utilizing practical techniques as a suitable solution for evaluating the managerial competencies of hospital managers. The proposed framework in this study can serve as a standard performance assessment tool for evaluating managers.

Investigation of Artificial Intelligence Techniques for the Management of Cataract Disease: A Systematic Review
Zahra Karbasi, Michaeel Motaghi Niko, Maryam Zahmatkeshan,
Volume 18, Issue 3 (7-2024)
Abstract
Background and Aim: Cataracts are recognized as the cause of 51% of blindness worldwide. Following the promising initial results of artificial intelligence systems in eye diseases, AI algorithms have been applied in the diagnosis of cataracts, grading the severity of cataracts, intraocular lens calculations, and even as an assistive tool in cataract surgery. This study presents a systematic review of AI techniques in the management of cataract disease.
Materials and Methods: This systematic review study was conducted to investigate artificial intelligence techniques to manage cataract disease until November 11, 2023, and based on PRISMA guidelines. We retrieved all relevant articles published in English through a systematic search of PubMed, Scopus, and Web of Science online databases.
Results: In our initial search, 192 records were identified in the databases, and eventually, 23 articles were selected for review. The results indicated that convolutional neural network algorithms (6 articles), recurrent neural networks (1 article), deep convolutional networks (1 article), support vector machines (2 articles), transfer learning (1 article), decision trees (4 articles), random forests (4 articles), logistic regression (3 articles), Bayesian algorithms (3 articles), XGBoost (3 articles), and K-nearest neighbors clustering algorithms (2 articles) were the artificial neural network and machine learning techniques and algorithms utilized. These techniques were employed in the studies for the diagnosis (70%), management (17%), and prediction (13%) of cataract disease.
Conclusion: Various artificial intelligence and machine learning techniques and algorithms can be effective and efficient in diagnosing, grading, managing, and predicting cataracts with high accuracy. In this study, deep learning techniques and convolutional neural networks have made the greatest contribution to cataract diagnosis. Deep learning techniques, decision trees, and Bayesian algorithms were involved in cataract management. Machine learning algorithms such as logistic regression, random forest, artificial neural network, decision tree, K1-nearest neighbor, XGBoost, and adaptive boosting also played a role in cataract prediction. Just as early prediction, diagnosis, and timely referral can reduce future complications of the disease, the use of systems based on artificial intelligence models that have acceptable accuracy can be effective in supporting the decision-making process of physicians and managing this disease.

Evaluation of Health Readiness Components in Isfahan Hospital Libraries
Fatemeh Abbasi Ghaletaki, Maryam Kazerani, Azam Shahbodaghi,
Volume 19, Issue 1 (4-2025)
Abstract
Background and Aim: Hospital library services are among the basic infrastructures for promoting e-health readiness. This study evaluated the components of e-health readiness in hospital libraries in Isfahan.
Materials and Methods: This is an applied-descriptive survey. The statistical population is 10 government hospital libraries in Isfahan city. The questionnaire completers are the managers of the aforementioned libraries. The research tool is a researcher-made questionnaire that was prepared by a deep and comprehensive review of related literature. The 61-question questionnaire is based on the Likert scale and has four sections: learning readiness (R1), core readiness (R2), social readiness (R3), and technology readiness (R4). To determine the content validity of the questionnaire, the opinions of professors, specialists, and experts were used and its validity was confirmed. Its reliability was confirmed using Cronbach’s alpha of 0.83. Descriptive statistics were used to examine the data.
Results: Isfahan government hospital libraries are in a good state in terms of learning readiness with a score of 3.77. They were in a moderate state in terms of core readiness with a score of 3.49. Social readiness with a score of 2.47 and technology readiness with a score of 2.48 were reported as poor state. “Literacy level of technology and services related to health care” component with a score of 2.9 from the core readiness, “reimbursement policies” component with a score of 1 from the social readiness, “resources training” component with a score of 1.8 from the learning readiness, and “organization access to ICT education” component with a score of 1.35 from the technology readiness were identified as weak components. In general, all government hospital libraries in Isfahan are in a moderate state in terms of e-health readiness.
Conclusion: Hospital libraries face various challenges in joining the e-health category, including a lack of readiness in the technology sector. The lack of appropriate policies for implementing e-health in libraries, lack of users’ skills in using information and communication technology, users’ ignorance of the e-health services needed in the library, lack of professional human resources, and lack of e-health-related training for users are some of the weaknesses of hospital libraries in the e-health readiness sector.

Developing a Predicting Model for Neonatal Hypoxic-Ischemic Encephalopathy Using Data Mining
Atefeh Abbasi, Somayeh Nasiri, Sayyed Mostafa Mostafavi, Abbas Habibolahi,
Volume 19, Issue 4 (11-2025)
Abstract
Background and Aim: Neonatal hypoxic-ischemic encephalopathy (HIE) is a clinical syndrome characterized by impaired brain function resulting from oxygen deprivation and reduced cerebral blood flow. Developing predictive models can serve as valuable tools for physicians in forecasting disease outcomes and facilitating early interventions. The present study was conducted with the aim of constructing a predictive model for neonatal hypoxic-ischemic encephalopathy using data mining algorithms.
Materials and Methods: This applied study was conducted using a descriptive approach. In the first stage, the factors influencing the prediction of neonatal hypoxic-ischemic encephalopathy were identified through expert surveys. In the second stage, data pertaining to 4,000 neonates were collected from the Iman system, available in the database of the Ministry of Health and Medical Education, during the years 2020–2021. Following preprocessing, a dataset comprising 3,962 records with 13 features was extracted. Subsequently, predictive models were developed using algorithms including artificial neural networks, decision tree variants, random forest, support vector machines, logistic regression, and Bayesian networks. Model construction was performed using the Python programming language within the Anaconda environment. Finally, performance evaluation and comparison were carried out using metrics such as accuracy, precision, specificity, F1-score, and the Area Under the Curve (AUC).
Results: The findings of the study revealed that the Area Under the Receiver Operating Characteristic Curve (AUROC) for models developed using logistic regression, artificial neural networks, random forest, Bayesian networks, support vector machines, and decision trees were 86%, 86%, 84%, 82%, 76%, and 74%, respectively. The highest performance was achieved by the logistic regression algorithm, with an accuracy of 81%, sensitivity of 85%, and specificity of 96%. The greatest sensitivity was observed in logistic regression, artificial neural networks, and support vector machines, whereas the naïve Bayesian algorithm demonstrated the lowest performance metrics. In the predictive model for hypoxic-ischemic encephalopathy, the most influential feature was the first-minute Apgar score, while the least influential factor was delivery outside the hospital.
Conclusion: The findings of the present study indicated that the predictive model for neonatal hypoxic-ischemic encephalopathy based on the logistic regression algorithm demonstrated superior performance. It is anticipated that the application of practical data-driven algorithms for neonates with hypoxic-ischemic encephalopathy will play a crucial role in the rapid identification of the condition and the provision of appropriate treatment. Such approaches can enable healthcare professionals to act within the critical window of opportunity, thereby improving the quality of care, preventing disease progression, and reducing the severity of adverse outcomes.

Global Supply-Demand Correlation of Elderly Health Information: An Infodemiological Study (2015-2024)
Shohreh Seyyed-Hosseini, Marzieh Shahbazi, Alireza Davarpanah, Fatemeh Kalteh, Reza Basirian-Jahromi,
Volume 19, Issue 5 (12-2025)
Abstract
Background and Aim: The monitoring of users’ real-time and continuous web searches, in conjunction with the identification of research conducted by experts in a specific field, constitutes the domain of infodemiology. The present study sought to examine the correlation between the demand for health information among users and the scientific output of researchers in elderly health from 2015 to 2024.
Materials and Methods: The present data mining research is of a descriptive-analytical nature, conducted using web mining and scientometrics approaches, employing infodemiology indicators. The web mining section of the study population comprised global user search keywords in the field of elderly health, as examined using Google Trends. In the scientometrics section, the research conducted by researchers in the field of elderly health was analyzed. This research was indexed in the PubMed database from 2015 to 2024. To examine the alignment between users’ information-seeking behavior and researchers’ scientific output, correlation tests were performed using SPSS software.
Results: A rise was observed in the volume of scientific output from researchers and the user search volume index in the field of elderly health on the Google search engine from 2015 to 2024. The monthly mean growth of scientific output from researchers over the ten years was 1439.70. In the user behavior of health information seeking (health information demand), the highest relative search volume index belonged to Ireland, Jamaica, and the United States of America, respectively. The highest number of scientific articles by researchers in the field of elderly health, with 20,480 articles, was related to the year 2021. Also, the average monthly growth of scientific output by researchers in the field of elderly health in this ten-year period (from January 2015 to December 2024) was 1439.70. The investigation revealed a direct and significant relationship (P-value<0.005) between users’ information-seeking behavior and researchers’ scientific output in this field, as determined by applying the Google search engine.
Conclusion: A multitude of factors have the capacity to influence the level of scientific output from researchers in the field of elderly health. In view of the positive relationship that has been observed and the reciprocal relation between the variables of users’ information-seeking behavior and researchers’ scientific output, it can be concluded that the factor of information demand, or users’ internet information-seeking behavior in the web environment, in this area, can be one of the most significant factors. This factor must be given due consideration through rigorous research.

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