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بررسی مسیرهای سیگنالی دخیل در تقابل عفونت ویروس‌های فرصت‌طلب با سلول‌های لنفوسیت T کمکی Investigating the Signaling Pathways Involved in Fighting Opportunistic Virus Infection with Helper T Lymphocyte Cells 1 1 مقدمه: افرادی که سیستم ایمنی ضعیفی دارند سریع‌تر به عفونت‌های فرصت‌طلب (OIs) مبتلا  می‌شوند. یافتن ارتباط ویروس‌ها و مسیر‌های سیگنالی مرتبط با عفونت‌های این ویروس‌ها مثل اپشتین بار ویروس (EBV‌) و سایتو مگالو ویروس (‌‌CMV‌) نقش قابل ملاحظه‌ای در بررسی ارتباط آن‌ها با سلول‌های لنفوسیت T کمکی دارند. در این مطالعه با استفاده از آنالیز بیوانفورماتیک به بررسی و کاندید نمودن ژن‌های موجود در مسیرهای وابسته به عفونت ویروس‌های فرصت طلب با سلول‌های لنفوسیت  T کمکی پرداخته شد. روش: در این مطالعه با مراجعه به پایگاه داده GEO دیتاست مناسب برای آنالیز انتخاب گردید. این دیتاست شامل پروفایل بیان ژنی در عفونت ویروس‌های EBV و  CMVبود. کلاسترهای ژنی با بیان بالا و پایین دسته‌بندی شدند. برای ارزیابی دقیق‌تر داده از پایگاه‌های داده غنی همچون Enrichr، STRING و Networkanalyst استفاده شد. در نهایت ژن‌های کاندید شده جدا و ارتباط پروتئینی آن‌ها نیز سنجیده شد. نتایج: 964 ژن با بیان بالا و 837 ژن با بیان پایین در مسیرهای پیشرفت عفونت ویروسی‌های فرصت طلب با لنفوسیت‌ها نقش دارند. مسیر‌های چرخه سلولی، استرس اکسیداتیو، سنتز RNA و TGFB به صورت بارزی مشاهده شدند. نتیجه‌گیری: مطالعه حاضر نشان داد که پروتئین‌ها و ژن‌های مهمی در تقویت التهاب ویروس های فرصت‌طلب همچون اپشتین بار و سایتومگالو ویروس نقش عمده‌ای داشته که از میان آن‌ها CDK2, CCNB1, GSK3B, SRC و SMAD3 نقش بارزتری را نشان دادند. 2 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. 201 213 2023/05/12 1402/2/22 2023/12/10 1402/9/19 پریسا حسن زاده Parisa Hassanzadeh کارشناس ارشد زیست‌شناسی، گروه زیست‌شناسی، دانشکده علوم پایه، دانشگاه آزاد اسلامی، واحد اسلامشهر، دانشگاه آزاد اسلامی، اسلامشهر، تهران، ایران ایران pari.hasanzadeh1397@gmail.com مهناز محمدی Mahnaz Mohammadi دکتری زیست شناسی، استادیار، گروه زیست‌شناسی، دانشکده علوم پایه، دانشگاه آزاد اسلامی، واحد اسلامشهر، دانشگاه آزاد اسلامی، اسلامشهر، تهران، ایران ایران mh_mohamadi@yahoo.com صبا طاهری Saba Taheri دکتری زیست شناسی، استادیار، گروه زیست‌شناسی، دانشکده علوم پایه، دانشگاه آزاد اسلامی، واحد اسلامشهر، دانشگاه آزاد اسلامی، اسلامشهر، تهران، ایران ایران sabataheri@yahoo.com Epstein Barr Virus Gene Expression Profile Bioinformatics Analysis Cytomegalovirus Opportunistic Viruses اپشتین بار ویروس پروفایل بیان ژن آنالیز بیوانفورماتیک سایتو مگالو ویروس ویروس‌های فرصت طلب 1. 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امکان‌سنجی اجرای پزشکی از راه دور در شهرستان‌های دارای جاذبه گردشگری استان کرمان Feasibility Study of Telemedicine Implementation in Tourist Cities of Kerman Province 1 1 مقدمه: پژوهش حاضر به منظور امکان‌سنجی اجرای پزشکی از راه دور در دو شهرستان استان کرمان، با جاذبه گردشگری انجام شد. روش: این پژوهش توصیفی- مقطعی در سال 1400، انجام شد. داده‌ها با پرسشنامه استاندارد «ابزار سنجش تله مدیسین» مؤسسه مدیکیر آمریکا جمع‌آوری شدند. با نرم‌افزار G-power حجم نمونه 68 نفر تعیین شد. پژوهشگران با مراجعه حضوری به بیمارستان‌های تحت مطالعه، داده‌های مورد نیاز را جمع‌آوری نموده، سپس داده‌ها با نرم‌افزارSPSS  نسخه 26 و روش‌های آمار توصیفی و تحلیلی، تجزیه و تحلیل شدند. نتایج: یافته‌های دموگرافیک نشان داد که اکثر افراد مطالعه زنان (78/7%)، سن کمتر از 30 سال (37/3%) و سابقه کار کمتر از 5 سال (40/0%)، مدرک تحصیلی لیسانس (88/0%) و بالینی (57/3%) بودند. با توجه به ‌p-value، تفاوت معنی‌داری بین خدمات پزشکی از راه دور از لحاظ اهمیت، وجود دارد. به ترتیب، درمان و مراقبت از راه دور، مشاوره با متخصصان خارج از بیمارستان، خدمات پرستاری از راه دور، ارجاع بیماران و تجویز دارو، دارای بالاترین اولویت بودند. ضمن این که تغییر فرآیند‌ها و ساختار بیمارستان، پایین‌ترین اولویت را داشت. کمبود کادر فنی، مشکلات هزینه‌های اولیه موانع اساسی به‌کارگیری پزشکی از راه دور و نگرش کارکنان و رقابت نیز کم اهمیت ترین موانع بیان شدند. نتیجه‌گیری: با توجه به موقعیت گردشگری شهرستان‌های مورد بررسی و به منظور استقرار پزشکی از راه دور، لازم است مسئولان بیمارستانی و شهرستانی، تلاش و سرمایه‌گذاری جهت رفع مشکلات هزینه‌های اولیه و کادر فنی انجام دهند. 2 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. 214 222 2023/05/122023/03/14 1401/12/23 2023/12/102023/10/31 1402/8/9 مهدیه شجاعی باغینی Mahdie ShojaeiBaghini استادیار، مرکز تحقیقات انفورماتیک پزشکی، پژوهشکده آینده‌پژوهی در سلامت، دانشگاه علوم پزشکی کرمان، کرمان، ایران ایران mahdiehsh@gmail.com فاطمه میرزاپور استبرق Fateme MirzapourEstabragh کارشناسی فناوری اطلاعات سلامت، دانشکده مدیریت و اطلاع‌رسانی پزشکی، دانشگاه علوم پزشکی کرمان، کرمان، ایران ایران realfatememirzapour@gmail.com فاطمه ساوس Fateme Savas کارشناسی فناوری اطلاعات سلامت، دانشکده مدیریت و اطلاع‌رسانی پزشکی، دانشگاه علوم پزشکی کرمان، کرمان، ایران ایران realfatememirzapour1@gmail.com Feasibility Telemedicine Tourism امکان‎سنجی پزشکی از راه دور گردشگری 1. Baran Z, Karaca Ş. Next-Generation Technologies in Health Tourism. InGlobal Perspectives on the Opportunities and Future Directions of Health Tourism. IGI Global; 2023. p. 138-64.##2. Wong BKM, Sa’aid Hazley SA. 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ترکیب سلسله مراتبی شبکه‌های عصبی مصنوعی به منظور بهبود تشخیص بیماری آریتمی قلبی Improving the Diagnosis of Arrhythmia using a Combination of Neural Networks in a Hierarchical Way 1 1 مقدمه: بیماری‌های قلبی یکی از شایع‌ترین انواع بیماری‌ها هستند، که باعث مرگ و میر بسیاری از افراد می‌شوند. آریتمی‌ها، نوع نامنظمی در ضربان قلب هستند که موجب می‌شوند قلب به طور غیرطبیعی سریع (تاکی‌کاردی) یا آهسته (برادی‌کاردی) کار کند؛ لذا شناسایی و کلاس‌بندی آریتمی‌های قلبی با استفاده از سیگنال ECG از اهمیت بالایی برخوردار است. هدف از این پژوهش ارائه یک مدل مبتنی بر داده‌کاوی با هدف بهبود تشخیص آریتمی قبلی است. روش: این مطالعه به شیوه توصیفی-تحلیلی و از پایگاه داده UCI استفاده شده است. این پایگاه داده شامل 452 نمونه و 279 ویژگی است. نمونه‌ها برای تشخیص و شناسایی نوع آریتمی قلبی در 5 دسته کلی طبقه‌بندی شده‌اند. الگوریتم استفاده شده در این پژوهش ترکیبی از شبکه‌های عصبی به صورت سلسله مراتبی (ترکیب سیستم‌های خبره) است. نتایج: در تمامی شبکه‌ها، 70% از نمونه‌ها برای آموزش و 30% آن‌ها به منظور آزمون استفاده شده است. پس از مدل‌سازی و مقایسه مدل‌های تولید شده و ثبت نتایج، دقت پیش‌بینی بیماری آریتمی قلبی در زمان عدم ترکیب یادگیری 89/5% و پس از ترکیب خبره‌ها به روش سلسله مراتبی 93/5% به دست آمد. نتیجه‌گیری: نتایج حاصل از این پژوهش نشان می‌دهد که روش پیشنهادی مبتنی بر ترکیب شبکه‌های عصبی به شکل سلسله مراتبی، که منجر به تخصصی شدن وظیفه هر طبقه بند می‌شود، می‌تواند عملکرد بهتری نسبت به مدل‌های مشابه در تشخیص آریتمی قلبی داشته باشد.   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.   223 237 2023/05/122023/03/142023/05/19 1402/2/29 2023/12/102023/10/312023/12/10 1402/9/19 آتنا عبیدی Atena Abidi گروه کامپیوتر، واحد بوشهر، دانشگاه ازاد اسلامی، بوشهر، ایران ایران abidi18.a@gmail.com زهرا حیدران داروقه امنیه zahra Heydaran Daroogheh Amnyieh گروه برق، واحد دولت آباد، دانشگاه آزاد اسلامی، اصفهان، ایران ایران zahra.heydariyan@gmail.com هانیه جامحمودی Hanieh Jamahmoodi گروه کامپیوتر،واحد مشهد، دانشگاه آزاد اسلامی، مشهد، ایران ایران hanieh_jamahmoodi@gmail.com ستاره سالارنیا Setare Salarniya دانش آموخته کارشناسی کامپیوتر،گروه برق و کامپیوتر، دانشگاه تربت حیدریه، تربت حیدریه، خراسان رضوی، ایران ایران setaresalarniya@gmail.com ایمان ذباح Iman Zabbah گروه کامپیوتر، واحد تربت حیدریه، دانشگاه آزاد اسلامی، تربت حیدریه، ایران ایران imanzabbah@gmail.com Data Mining Neural Networks Cardiac Arrhythmia Diagnosis Mixture of Experts داده‌کاوی شبکه ‌های عصبی تشخیص آریتمی قلبی ترکیب خبره‌ها 1. Dowlatshahi M. Application of fuzzy logic in detecting irregular heartbeat using ECG. Fuzzy Systems and its Applications 2020;3(1):33-61. doi:20.1001.1.27174409.1399.3.1.3.8/DOR##2. Zhou X, Zhu X, Nakamura K, Noro M. Atrial fibrillation detection using convolutional neural networks. 9th International Conference on Awareness Science and Technology (iCAST); 2018 Sep 19-21; Fukuoka, Japan: IEEE; 2018. p. 84-9. doi: 10.1109/ICAwST.2018.8517221##3. Saadi P, Zeinalnezhad M, Movahedi SF. Modeling and Predicting the Risk of Coronary Artery Disease Using Data Mining Algorithms. Journal of Health and Biomedical Informatics 2021; 8(2):193-207. [In Persian]##4. Swapna G, Soman KP, Vinayakumar R. Automated detection of cardiac arrhythmia using deep learning techniques. Procedia Computer Science 2018;132:1192-201. https://doi.org/10.1016/j.procs.2018.05.034##5. Badr M, Al-Otaibi S, Alturki N, Abir T. Detection of heart arrhythmia on electrocardiogram using artificial neural networks. Computational Intelligence and Neuroscience. 2022;2022. https://doi.org/10.1155/2022/1094830##6. Jun TJ, Nguyen HM, Kang D, Kim D, Kim D, Kim YH. ECG arrhythmia classification using a 2-D convolutional neural network. arXiv preprint arXiv:1804.06812. 2018 Apr 18. https://doi.org/10.48550/arXiv.1804.06812##7. Fooladi S, Farsi H, Farsi F. Adaptive Filtering Strategy to Remove Noise from ECG Signals Using Wavelet Transform and Deep Learning Journal of Health and Biomedical Informatics 2020; 7(3): 318-25. [In Persian]##8. Le KH, Pham HH, Nguyen TB, Nguyen TA, Thanh TN, Do CD. Lightx3ecg: A lightweight and explainable deep learning system for 3-lead electrocardiogram classification. Biomedical Signal Processing and Control 2023;85:104963. https://doi.org/10.1016/j.bspc.2023.104963##9. Mitra M, Samanta RK. Cardiac arrhythmia classification using neural networks with selected features. Procedia Technology 2013;10:76-84. https://doi.org/10.1016/j.protcy.2013.12.339##10. 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بهینه‌سازی الگوریتم KNN در راستای تشخیص بیماری‌های انسدادی ریوی Optimizing the KNN Algorithm to Diagnose Obstructive Pulmonary Diseases 1 1 مقدمه: به گزارش سازمان بهداشت جهانی، بیماری‌های ریوی سومین علت مرگ و میر در جهان می‌باشند. این بیماری‌ها ماهیت مزمن داشته، بنابراین تشخیص زودهنگام اهمیت بالایی دارد. تست‌های عملکردی ریوی ابزار مهمی در بررسی و پایش بیماران مبتلا به آسیب‌های تنفسی می‌باشند. هدف از این پژوهش بهینه‌سازی الگوریتم پایه K نزدیک‌ترین همسایه می‌باشد که با دقت بالاتری خودارزیابی و تفسیر نتایج تست اسپیرومتری را تسهیل و تسریع می‌کند. روش: در این پژوهش کاربردی روشی پیشنهاد شده است که محدودیت­ های الگوریتم پایه را با بهینه­ سازی، ارزش­گذاری ویژگی­ ها و رأی­ گیری وزن‌دار بهبود بخشیده و با به کارگیری آن بیماری­ های انسدادی ریوی را بر اساس مجموعه داده تشکیل یافته از تست­ های تنفس­ سنجی و پارامترهای عمومی، در سه دسته آسم، برونشیت مزمن و آمفیزم کلاس ­بندی کرده است. نتایج: در تعیین روش مناسب برای محاسبه فاصله داده ­ها، روش مینوکوفسکی انتخاب شد و با اعمال ضرایب ارزش ویژگی ­ها در این رابطه دقت کلاس‌بندی افزایش یافت. رأی ­گیری وزن‌دار در قسمت نهایی الگوریتم بر اساس کرنل گوسی صورت گرفت که بر این اساس عملکرد ثابتی به ازای تغییر پارامتر تعداد همسایگان به دست آمد. نتایج ارزیابی­ ها در قالب اعتبارسنجی متقابل انجام شد که دقت 95/4 درصد و 93/2 درصد صحت در زمان 3/12 ثانیه به دست آمد. نتیجه‌گیری: بکارگیری الگوریتم­ های یادگیری ماشین می­تواند در تجزیه و تحلیل داده­ های پزشکی مؤثر واقع گردد؛ لذا در این مطالعه از این رویکردها برای ارائه روشی جدید در کلاس­بندی، کمک گرفته شد، به طوری که الگوریتم پیشنهادی توانست روش پایه را بهبود ببخشد و همچنین دقت و عملکرد بهتری نسبت به روش ­های پیشین، داشته باشد.   2 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. 238 259 2023/05/122023/03/142023/05/192023/05/27 1402/3/6 2023/12/102023/10/312023/12/102023/11/6 1402/8/15 شهرزاد پورامیرارسلانی Shahrzad Pouramirarsalani دانشجوی دکتری تخصصی مهندسی پزشکی، دانشکده مهندسی برق و کامپیوتر، دانشگاه تبریز، تبریز، ایران ایران pouramirarsalani@tabrizu.ac.ir نادر وحدانی مناف Nader Vahdani manaf دکتری تخصصی مهندسی پزشکی، استادیار گروه مهندسی پزشکی، مؤسسه آموزش عالی سراج، تبریز، ایران ایران nadervahdani@gmail.com سامان راجبی Saman Rajebi دکتری تخصصی مخابرات، استادیار گروه مهندسی برق، موسسه آموزش عالی سراج، تبریز، ایران ایران s.rajebi@seraj.ac.ir سمیه ماکویی Somaye Makouei دکتری تخصصی مهندسی برق- الکترونیک، دانشیار دانشکده مهندسی برق و کامپیوتر، دانشگاه تبریز، تبریز، ایران ایران makouei@tabrizu.ir Classification Obstructive Pulmonary Diseases Fisher’s Discriminant Ratio K Nearest Neighbor Grasshopper Optimization کلاس بندی بیماری های انسدادی ریوی نرخ جداپذیری فیشر K نزدیک ترین همسایه الگوریتم بهینه سازی ملخ 1. 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Kaplan A, Cao H, FitzGerald JM, Iannotti N, Yang E, Kocks JW, et al. Artificial intelligence/machine learning in respiratory medicine and potential role in asthma and COPD diagnosis. J Allergy Clin Immunol Pract 2021;9(6):2255-61. doi: 10.1016/j.jaip.2021.02.014.##10. Topalovic M, Das N, Burgel PR, Daenen M, Derom E, Haenebalcke C, et al. Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests. Eur Respir J 2019;53(4):1801660. doi: 10.1183/13993003.01660-2018##11. Hasbi NH, Bade A, Chee FP, Rumaling MI. Pattern recognition for human diseases classification in spectral analysis. Computation 2022;10(6):96. https://doi.org/10.3390/computation10060096##12. Swaminathan S, Qirko K, Smith T, Corcoran E, Wysham NG, Bazaz G, et al. A machine learning approach to triaging patients with chronic obstructive pulmonary disease. PLoS One 2017;12(11):e0188532. https://doi.org/10.3390/computation10060096 ##13. Wu CT, Li GH, Huang CT, Cheng YC, Chen CH, Chien JY, et al. Acute exacerbation of a chronic obstructive pulmonary disease prediction system using wearable device data, machine learning, and deep learning: development and cohort study. JMIR Mhealth Uhealth 2021;9(5):e22591. doi: 10.2196/22591.##14. Ioachimescu OC, Stoller JK. An alternative spirometric measurement. area under the expiratory flow–volume curve. Ann Am Thorac Soc 2020;17(5):582-8. doi: 10.1513/AnnalsATS.201908-613OC.##15. Haider NS, Singh BK, Periyasamy R, Behera AK. Respiratory sound-based classification of chronic obstructive pulmonary disease: a risk stratification approach in machine learning paradigm. Journal of Medical Systems 2019;43:1-3.##16. Hussain A, Choi HE, Kim HJ, Aich S, Saqlain M, Kim HC. Forecast the exacerbation in patients of chronic obstructive pulmonary disease with clinical indicators using machine learning techniques. Diagnostics 2021;11(5):829. doi: 10.3390/diagnostics11050829##17. 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تشخیص هوشمند سرطان پستان از طریق شبکه‌های ترکیبی عمیق با استفاده از تصاویر ترموگرافی Breast Cancer Detection in Thermographic Images Using Hybrid Networks 1 1 مقدمه: سرطان پستان، شایع‌ترین سرطان بدخیم در زنان است و بیشتر از سایر سرطان‌ها باعث مرگ و میر آنان می‌شود. روش ترموگرافی یکی از روش‌های تشخیص سرطان پستان است. مهم‌ترین چالش در تشخیص زود هنگام از روی این تصاویر می‌تواند مربوط به خطای انسانی و یا عدم دسترسی به شخص ماهر باشد. استفاده از روش‌های هوش مصنوعی در پردازش تصاویر در تشخیص زودهنگام و کاهش خطای انسانی می‌تواند مؤثر باشد. هدف کلی این پژوهش معرفی شبکه‌های ترکیبی عمیق برای تشخیص هوشمند سرطان پستان از تصاویر ترموگرافی است. روش: تصاویر ترموگرافی مورد استفاده در این پژوهش از پایگاه داده DMR-IR جمع‌آوری شده است. ابتدا ویژگی‌های اصلی تصاویر توسط شبکه پیچشی عمیق CNN استخراج گردید. سپس در ادامه از دو الگوریتم FCNNs و SVM برای کلاس‌بندی سرطان پستان از تصاویر ترموگرافی استفاده شد. نتایج: نرخ دقت برای الگوریتم‌های CNN_FC و CNN-SVM، به ترتیب 94/2%، 95/0% بود. علاوه بر این، پارامترهای قابلیت اطمینان هم برای این طبقه‌بندی‌کننده‌ها به ترتیب 92/1%، 97/5% و حساسیت برای هر یک از این طبقه‌بندی‌کننده‌ها به ترتیب  95/5%، 94/1% محاسبه شد. نتیجه‌گیری: مدل پیشنهادی مبتنی بر شبکه ترکیبی عمیق دقت مناسبی نسبت به الگوریتم‌‌های مشابه دارد؛ بنابراین می‌تواند پزشکان را در تشخیص زودهنگام سرطان پستان از طریق تصاویر ترموگرافی کمک نموده و خطای انسانی را به حداقل برساند.   2 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. 260 268 2023/05/122023/03/142023/05/192023/05/272023/06/6 1402/3/16 2023/12/102023/10/312023/12/102023/11/62023/10/31 1402/8/9 حانیه رضازاده تمرین Hanieh Rezazadeh Tamrin کارشناسی ارشد مهندسی هسته‌ای، مرکز تحقیقات پرتو پزشکی، دانشگاه آزاد اسلامی واحد تهران مرکز، تهران، ایران ایران maral.88861@gmail.com الهام صنیعی Elham Saniei دکتری مهندسی هسته‌ای، استادیار، مرکز تحقیقات پرتو پزشکی، دانشگاه آزاد اسلامی واحد تهران مرکز، تهران، ایران ایران elhsaniei@gmail.com مهدی صالحی باروق Mehdi Salehi Barough دکتری مهندسی هسته‌ای، استادیار، مرکز تحقیقات پرتو پزشکی، دانشگاه آزاد اسلامی واحد تهران مرکز، تهران، ایران ایران msbarough@gmail.com Diagnosis Breast Cancer Deep Learning Convolutional Neural Network Thermography تشخیص سرطان پستان یادگیری عمیق شبکه عصبی کانولوشنی ترموگرافی 1. Mousavi H, Bagherian R. Health literacy and breast cancer: review paper. Journal of Health Psychology 2019; 8(31): 91-102. https://doi.org/10.30473/hpj.2019.46660.4426##2. Mohamed AA, Berg WA, Peng H, Luo Y, Jankowitz RC, Wu S. A deep learning method for classifying mammographic breast density categories. Medical Physics 2018;45(1):314-21. https://doi.org/10.1002/mp.12683##3. Clady X, Negri P, Milgram M, Poulenard R. Multi-class vehicle type recognition system. Artificial Neural Networks in Pattern Recognition; 2008 Jul 2-4; Paris, France: ANNPR; 2008. p. 228-39.## 4. Li Z, Liu F, Yang W, Peng S, Zhou J. A survey of convolutional neural networks: analysis, applications, and prospects. IEEE Transactions on Neural Networks and Learning Systems. 2021 Jun 10; IEEE; 2021. p. 6999 - 7019 doi: 10.1109/TNNLS.2021.3084827##5. Desai M, Shah M. An anatomization on breast cancer detection and diagnosis employing multi-layer perceptron neural network (MLP) and Convolutional neural network (CNN). Clinical eHealth 2021;4:1-1. https://doi.org/10.1016/j.ceh.2020.11.002##6. Awotunde JB, Panigrahi R, Khandelwal B, Garg A, Bhoi AK. Breast cancer diagnosis based on hybrid rule-based feature selection with deep learning algorithm. Research on Biomedical Engineering 2023;39(1):115-27.##7. Francis SV, Sasikala M, Saranya S. Detection of breast abnormality from thermograms using curvelet transform based feature extraction. J Med Syst 2014;38(4):23. https://doi.org/10.1007/s10916-014-0023-3##8. Silva LF, Saade DC, Sequeiros GO, Silva AC, Paiva AC, Bravo RS, et al. A new database for breast research with infrared image. Journal of Medical Imaging and Health Informatics 2014;4(1):92-100. https://doi.org/10.1166/jmihi.2014.1226 ##9. Venkatesan R, Li B. Convolutional neural networks in visual computing: a concise guide. CRC Press; 2017. ##10. Taylor L, Nitschke G. Improving deep learning with generic data augmentation. In2018 IEEE Symposium Series on Computational Intelligence (SSCI) Nov 18 Bangalore, India: IEEE; 2018. doi: 10.1109/SSCI.2018.8628742##11. Burges CJ. A tutorial on support vector machines for pattern recognition. Data mining and knowledge discovery. 1998;2(2):121-67.##12. Schaefer G, Závišek M, Nakashima T. Thermography based breast cancer analysis using statistical features and fuzzy classification. Pattern Recognition. 2009;42(6):1133-7. https://doi.org/10.1016/j.patcog.2008.08.007##13. Kapoor P, Prasad SV. Image processing for early diagnosis of breast cancer using infrared images. 2nd International Conference on Computer and Automation Engineering (ICCAE); Feb 26-28; Singapore: IEEE; 2010. p. 564-6. doi: 10.1109/ICCAE.2010.5451827## 14. Acharya UR, Ng EY, Tan JH, Sree SV. Thermography based breast cancer detection using texture features and support vector machine. J Med Syst. 2012;36:1503-10. https://doi.org/10.1007/s10916-010-9611-z##15. Pramanik S, Bhattacharjee D, Nasipuri M. Wavelet based thermogram analysis for breast cancer detection. International Symposium on Advanced Computing and Communication (ISACC); Sep 14-15; Silchar, India: IEEE; 2015. p. 205-12. doi: 10.1109/ISACC.2015.7377343## ##

معرفی یک واکسن چند اپی‌توپی کارآمد در برابر سویه‌های مختلف SARS-CoV-2: واکسینولوژی معکوس Introduction of an Efficient Multiepitopic Vaccine Against Different SARS-CoV-2 Strains: Reverse Vaccinology 1 1 مقدمه: در سال‌های اخیر، COVID-19 به عنوان تهدیدی در زمینه‌ سلامت شناخته شده است. با وجود واکسیناسیون هنوز هم افراد به این بیماری مبتلا می‌شوند چراکه واریانت‌های به‌روزتر دارای جهش‌هایی در ژنوم خود هستند که در اتصال به گیرنده‌های میزبان و فرار از پاسخ‌های سیستم ایمنی نقش دارند. از این رو مهم‌ترین هدف این مطالعه معرفی یک واکسن سریع و کاربردی به کمک ابزارهای بیوانفورماتیکی برای مقابله با این جهش‌های متنوع در سویه‌های مختلف SARS-CoV-2 است. روش: به‌منظور نقشه‌برداری اپی‌توپ‌ها، پروتئین‌ اسپایک 32 واریانت مختلف بازیابی شد. سپس از (Immune Epitope Database) IEDB،‌NetCTL‌  و NetMHCIIpan برای پیش‌بینی اپی‌توپ‌های سلول T و B استفاده شد. واکسن مبتنی ‌بر اپی‌توپ‌های حفاظت‌شده از نظر آنتی‌ژنی، حساسیت‌زایی، سمیت، حلالیت، خواص فیزیکوشیمیایی، پوشش جمعیتی و ساختار ثانویه‌‌ با سرورهای مربوطه بررسی شدند. مدل‌سازی به کمک Robetta‌ و داکینگ با گیرنده‌ شبه (TLR3) Toll ، به ترتیب با استفاده از Cluspro، PatchDock و FireDock انجام شد. نتایج: پس از ارزیابی‌های دقیق، تمامی نتایج کیفیت مطلوب واکسن را تأیید‌ کردند. طبق بررسی‌های بیشتر این ساختار مشابه پروتئین بومی است و برهمکنشی پایدار و قوی بین واکسن و گیرنده وجود دارد. براساس شبیه‌سازی دینامیک مولکولی فشردگی ساختاری و پایداری در اتصال نیز مشاهد شد. علاوه بر این، شبیه‌سازی ایمنی نشان داد که واکسن می‌تواند پاسخ‌های ایمنی مشابه با شرایط واقعی را تحریک نماید. در نهایت، بهینه‌سازی کدون و شبیه‌سازی in silico بیان کارآمد در‌ Escherichia coli را تأیید کرد. نتیجه‌گیری: براساس نتایج به دست آمده واکسن‌ چند اپی‌توپی طراحی شده می‌تواند به عنوان یک کاندیدای پیشگیری کننده در برابر SARS-CoV-2 عمل نماید.   2 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. 269 293 2023/05/122023/03/142023/05/192023/05/272023/06/62023/07/6 1402/4/15 2023/12/102023/10/312023/12/102023/11/62023/10/312023/11/25 1402/9/4 جواد سرومیلی Javad Sarvmeili دانشجوی دکتری بیوتکنولوژی کشاورزی، گروه به‌نژادی و بیوتکنولوژی گیاهی، دانشکده‌ کشاورزی، دانشگاه تبریز، تبریز، ایران ایران javadsarvmeili@yahoo.com بهرام باغبان کهنه روز Bahram Baghban Kohnehrouz دکتری بیولوژی مولکولی و بیوتکنولوژی، دانشیار، گروه به نژادی و بیوتکنولوژی گیاهی، دانشکده کشاورزی، دانشگاه تبریز، تبریز، ایران ایران bahramrouz@yahoo.com اشرف قلی زاده Ashraf Gholizadeh دکتری بیوشیمی، استاد، دانشکده علوم طبیعی و مرکز تحقیقات علوم و فناوری زیستی، دانشگاه تبریز، تبریز، ایران ایران aghz_bioch@yahoo.co.in حمیده افقی Hamideh Ofoghi دکتری بیوتکنولوژی، دانشیار، گروه بیوتکنولوژی، سازمان پژوهش‌های علمی و صنعتی ایران، تهران، ایران ایران ofoghi@irost.ir داریوش شانه بندی Dariush Shanehbandi دکتری ایمونولوژی، استادیار گروه ایمونولوژی، مرکز تحقیقات ایمونولوژی، دانشگاه علوم پزشکی تبریز، تبریز، ایران ایران dariush_shanehbandi@yahoo.com Dynamic Simulation Immunoinformatics Immune Simulation Molecular Docking Multi-epitope Vaccine شبیه‌سازی دینامیکی ایمونوانفورماتیک شبیه‌سازی ایمنی داکینگ مولکولی واکسن چند اپی‌توپی 1. 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مدل مفهومی و نیازمندی‌های فرم گزارش مورد الکترونیکی برای جمع‌آوری حداقل مجموعه داده‌های عوارض واکسن کرونا Conceptual Model and Requirements of An Electronic Case Report Form to Collect a Minimum Data Set of Complications of the COVID-19 Vaccine 1 1 مقدمه: در سال‌های اخیر جمع‌آوری داده‌های ناشی از پایش کارآزمایی‌های بالینی به صورت فرم‌های گزارش مورد الکترونیکی (Electronic case report forms)‌eCRF، به طور فزاینده‌ای مورد استفاده قرار گرفته است، زیرا به دلیل اجتناب از اشتباهات، کاهش طول مدت مطالعات بالینی و کاهش جلوگیری از افزایش هزینه‌های جمع‌آوری داده‌ها، می‌تواند بر موفقیت مطالعه تأثیر بگذارد. بر همین اساس این پژوهش در نظر دارد مدل مفهومی و نیازمندی‌های eCRF پیشنهادی که مبتنی بر پایگاه داده NoSQL است را برای دستیابی به عوارض ناشی از واکسن کرونا ارائه دهد. روش: در این پژوهش با استفاده از روش توصیفی- کاربردی، ابتدا مدل مفهومی سیستم eCRF به منظور جمع‌آوری مجموعه حداقل داده‌های عوارض واکسن کرونا طراحی شد. سپس با استفاده از روش‌های مشاهده، تفکر و تأمل و پرسشنامه، نیازمندی‌های عملکردی و غیرعملکردی سیستم پیشنهادی شناسایی گردید. نتایج: این پژوهش دارای دو بخش اصلی مدل مفهومی پیشنهادی و نیازمندی‌های عملکردی و غیرعملکردی سیستم eCRF است. مدل مفهومی براساس مؤلفه‌ها، ماژول‌ها و اهداف هسته مرکزی eCRF پیشنهاد شد که قابلیت استفاده برای محققین و متخصصین کارآزمایی‌های بالینی را دارد. نیازمندی‌ها نیز برای چهار گروه تمامی ذی‌نفعان بیمار، مراقبان سلامت، کارشناسان بیماری و مدیر اجرائی احصاء شد گردید که از جمله آن می‌توان به مواردی مانند قابلیت ثبت، درج، حذف و ویرایش فرم گزارش مورد اشاره کرد. نتیجه‌گیری: سیستم پیشنهادی با توجه به ثبت عوارض ناشی از واکسن کرونا به صورت یکپارچه، داده‌های طبقه‌بندی شده و معتبر را در اختیار محققین و متخصصین کارآزمایی‌های بالینی قرار می‌دهد؛ بنابراین برخی از ذینفعان eCRF مانند بیماران، مراقبان سلامت، کارشناسان بیماری‌ها و مدیران اجرائی و محققین می‌توانند با تحلیل، تفسیر و اشتراک‌گذاری اطلاعات ثبت شده، به واکسنی با ایمنی و اثربخشی قابل قبول دست یابند.   2 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. 294 308 2023/05/122023/03/142023/05/192023/05/272023/06/62023/07/62023/09/28 1402/7/6 2023/12/102023/10/312023/12/102023/11/62023/10/312023/11/252023/12/10 1402/9/19 سید حمزه نصیری خوشرودی Seyyed Hamzeh Nasiri Khoshroudi دانشجوی کارشناسی ارشد انفورماتیک پزشکی، دانشکده علوم پزشکی، دانشگاه تربیت مدرس، تهران، ایران ایران hamze_nasirii@yahoo.com علی اصغر صفائی Ali Asghar Safaei دکترای مهندسی کامپیوتر(نرم افزار)، دانشیار، گروه انفورماتیک پزشکی، دانشکده علوم پزشکی، دانشگاه تربیت مدرس، تهران، ایران ایران aa.safaei@modares.ac.ir حوریه سلیمان جاهی Hoorieh Soleimanjahi دکترای ویروس شناسی پزشکی، استاد، گروه ویروس شناسی، دانشکده علوم پزشکی، دانشگاه تربیت مدرس، تهران، ایران ایران soleim_h@modares.ac.ir Electronic case report form COVID-19 vaccination adverse effects eCRF system requirements فرم گزارش موردی الکترونیکی کرونا عوارض واکسیناسیون نیازمندی‌های سیستم eCRF 1. 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The COVID-19 vaccine development landscape. Nat Rev Drug Discov 2020;19(5):305-6. doi: 10.1038/d41573-020-00073-5.##6. World Health Organization (WHO). Protocol template to be used as template for observational study protocols: cohort event monitoring (CEM) for safety signal detection after vaccination with COVID-19 vaccines; 2021 [cited 2021 Oct 20 ]. Available from: https://www.who.int/publications/i/item/9789240027398##7. Voysey M, Clemens SAC, Madhi SA, Weckx LY, Folegatti PM, Aley PK, et al. Safety and efficacy of the ChAdOx1 nCoV-19 vaccine (AZD1222) against SARS-CoV-2: an interim analysis of four randomised controlled trials in Brazil, South Africa, and the UK. Lancet 2021;397(10269):99-111. doi: 10.1016/S0140-6736(20)32661-1. ##8. Bellary S, Krishnankutty B, Latha M. Basics of case report form designing in clinical research. Perspect Clin Res 2014; 5(4): 159–66. doi: 10.4103/2229-3485.140555##9. Zong N, Wen A, Stone DJ, Sharma DK, Wang C, Yu Y, et al. Developing an FHIR-based computational pipeline for automatic population of case report forms for colorectal cancer clinical trials using electronic health records. JCO Clin Cancer Inform 2020:4:201-9. doi: 10.1200/CCI.19.00116.##10. Le Jeannic A, Quelen C, Alberti C, Durand-Zaleski I. Comparison of two data collection processes in clinical studies: electronic and paper case report forms. BMC Med Res Methodol 2014;14:7. doi: 10.1186/1471-2288-14-7.##11. Chen M, Yuan Y, Zhou Y, Deng Z, Zhao J, Feng F, et al. Safety of SARS-CoV-2 vaccines: a systematic review and meta-analysis of randomized controlled trials. Infect Dis Poverty 2021;10(1):94. doi: 10.1186/s40249-021-00878-5.##12. Chapin-Bardales J, Gee J, Myers T. Reactogenicity following receipt of mRNA-based COVID-19 vaccines. JAMA 2021;325(21):2201-2. doi: 10.1001/jama.2021.5374.##13. Stone Jr CA, Rukasin CR, Beachkofsky TM, Phillips EJ. Immune‐mediated adverse reactions to vaccines. 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تأثیر مداخلات سلامت همراه بر پیامدهای سلامتی زنان: یک مطالعه مروری نظام‌مند بر روی کارآزمایی‌های بالینی کنترل شده The Effect of mHealth Interventions on Women's Health Outcomes: A Systematic Review of Randomized Controlled Trials 1 1 مقدمه: سلامت همراه (Mobile Health/mHealth) فناوری نوظهوری است که پتانسیل زیادی در ارتقاء سلامت افراد دارد. این فناوری به دلیل در دسترس بودن، با فراهم آوردن مراقبت‌های بهداشتی ایمن، هماهنگ و مؤثر می‌تواند انگیزه بیشتری برای ارتقاء سلامتی در زنان ایجاد نماید. باتوجه به اهمیت حوزه سلامتی زنان، مطالعه حاضر با هدف بررسی نظام‌مند تأثیر مداخلات سلامت همراه بر پیامدهای سلامتی زنان انجام شد. روش: مطالعه حاضر به شیوه مرور نظام‌مند بر روی مطالعات کارآزمایی بالینی به زبان انگلیسی انجام شد. پایگاه‌های اطلاعاتی PubMed، Scopus، Science Direct، Cochrane Library، ClinicalTrials.gov مطابق با معیارهای ورود به مطالعه در بازه زمانی ۲۰۱۱ تا ۲۰۲۱ مورد بررسی قرار گرفتند. نتایج: در مجموع ۲۸ مقاله در مطالعه وارد شدند. ابزارهای سلامت همراه در این مطالعه مانند اپلیکیشن‌ها، پیامک، تماس صوتی یا تصویری و ابزارهای پوشیدنی، دارای عملکردهای متعددی مانند ارائه اطلاعات، یادآوری، آموزش و همچنین شامل توصیه‌های عملی برای بهبود سبک زندگی زنان باردار در مناطق مختلف بودند. نتایج مطالعه ۸۲% از پژوهش‌ها نشان داده بود که ابزارهای سلامت همراه می‌تواند در بهبود پیامدهای سلامتی مرتبط با زنان باردار مؤثر باشند و در ۱۸% از مطالعات، نتایج مداخله تفاوتی با روش استاندارد نداشت. نتیجه‌گیری: مداخلات سلامت همراه می‌توانند تأثیر مثبتی بر ارتقاء سلامت زنان باردار از جمله سبک زندگی، دانش و نگرش زنان در مورد سلامت خود و نوزادشان داشته باشند. با این حال در تمامی این مطالعات، ناهمگونی بالا، قطعیت شواهد این یافته‌ها را محدود می‌کند؛ بنابراین مطالعات آینده باید زمینه‌ای را که تحت آن سلامت همراه می‌تواند برای سلامت زنان مؤثرتر باشد را شناسایی کند.   2 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. 309 325 2023/05/122023/03/142023/05/192023/05/272023/06/62023/07/62023/09/282023/05/8 1402/2/18 2023/12/102023/10/312023/12/102023/11/62023/10/312023/11/252023/12/102023/09/23 1402/7/1 لیلا مجدی پور Leyla Majdipoor کارشناس ارشد انفورماتیک پزشکی، دانشکده علوم پزشکی، دانشگاه تربیت مدرس، تهران، ایران ایران leylamajdipoor2@gmail.com رضوان رحیمی Rezvan Rahimi دکتری تخصصی انفورماتیک پزشکی، استادیار، گروه انفورماتیک پزشکی، دانشکده علوم پزشکی، دانشگاه تربیت مدرس، تهران، ایران ایران R.Rahimi@modares.ac.ir شاداب شاه علی Shadab Shahali دکتری تخصصی بهداشت باروری، استادیار، گروه بهداشت باروری و مامایی، دانشکده علوم پزشکی، دانشگاه تربیت مدرس، تهران، ایران ایران shadab.shahali@modares.ac.ir Mobile Health Digital Health Telehealth Women's Health Systematic Review سلامت همراه سلامت دیجیتال سلامت از راه دور سلامت زنان مرور نظام‌مند 1. Lupton D. Critical perspectives on digital health technologies. Sociology Compass 2014;8(12):1344-59. https://doi.org/10.1111/soc4.12226##2. Mathews SC, McShea MJ, Hanley CL, Ravitz A, Labrique AB, Cohen AB. NPJ Digit Med 2019:2:38. doi: 10.1038/s41746-019-0111-3. ##3. Meskó B, Drobni Z, Bényei É, Gergely B, Győrffy Z. Digital health is a cultural transformation of traditional healthcare. Mhealth 2017; 3: 38. doi: 10.21037/mhealth.2017.08.07##4. Ford EA, Roman SD, McLaughlin EA, Beckett EL, Sutherland JM. The association between reproductive health smartphone applications and fertility knowledge of Australian women. BMC Womens Health 2020;20(1):45. doi: 10.1186/s12905-020-00912-y##5. Rahimi R, Khoundabi B. Investigating the effective factors of using mHealth apps for monitoring COVID-19 symptoms and contact tracing: A survey among Iranian citizens. Int J Med Inform 2021:155:104571. doi: 10.1016/j.ijmedinf.2021.104571.##6. Hussain T, Smith P, Yee LM. Mobile phone–based behavioral interventions in pregnancy to promote maternal and fetal health in high-income countries: systematic review. JMIR Mhealth Uhealth 2020; 8(5): e15111. doi: 10.2196/15111##7. Tehrani H, Gholian Aval M, Abdollahi M. Digital Technology: A New Approach to Improve Maternal Health. Navid No 2020;23:35-42. doi:10.22038/NNJ.2020.47192.1204##8. Munos B, Baker PC, Bot BM, Crouthamel M, de Vries G, Ferguson I, et al. Mobile health: the power of wearables, sensors, and apps to transform clinical trials. Ann N Y Acad Sci 2016;1375(1):3-18. doi: 10.1111/nyas.13117.##9. Sakamoto JL, Carandang RR, Kharel M, Shibanuma A, Yarotskaya E, Basargina M, et al. Effects of mHealth on the psychosocial health of pregnant women and mothers: a systematic review. BMJ Open 2022;12(2):e056807. doi: 10.1136/bmjopen-2021-056807.##10. Ahmadi S, Moghaddam-Banaem LH, Hosseinpour S, Rahimi R. Investigating the impact of mobile health applications in pregnant women lifestyle: a review article. Sixth International Conference on Interdisciplinary Studies in Iranian Food Industry and Nutrition Sciences; 2023 Jan 17; Tehran: Center for the Development Interdisciplinary Studies; 2023. [In Persian]##11. Chan KL, Chen M. Effects of social media and mobile health apps on pregnancy care: meta-analysis. JMIR Mhealth Uhealth 2019;7(1):e11836. doi: 10.2196/11836. ##12. Tobe RG, Haque SE, Ikegami K, Mori R. Mobile-health tool to improve maternal and neonatal health care in Bangladesh: a cluster randomized controlled trial. BMC Pregnancy Childbirth 2018; 18: 102. doi: 10.1186/s12884-018-1714-4##13. Han M, Lee E. Effectiveness of mobile health application use to improve health behavior changes: a systematic review of randomized controlled trials. Healthc Inform Res 2018; 24(3): 207–26. doi: 10.4258/hir.2018.24.3.207##14. Overdijkink SB, Velu AV, Rosman AN, Van Beukering MD, Kok M, Steegers-Theunissen RP. 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