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طراحی، پیاده‌سازی و ارزیابی کاربردپذیری سامانۀ ثبت جراحی‌های تعویض مفاصل زانو و لگن Design, Implementation, and Applicability Evaluation of Hip and Knee Arthroplasty Registry 1 1 مقدمه: آرتروپلاستی جزء اعمال جراحی مهم و روبه افزایش در ارتوپدی است که موفقیت آن به طور قابل ملاحظه‌ای باعث کاهش درد و افزایش توانایی بیمار می‌شود. مطالعه حاضر با هدف طراحی و پیاده‌سازی و ارزیابی کابرد‌پذیری نرم‌افزاری مناسب برای راه‌اندازی نظام ‌‌ثبت آرتروپلاستی مفاصل زانو و لگن انجام گردید. روش: مرور مقالات، دستورالعمل‌ها، فرم‌ها و گزارش‌های منتشرشده توسط جوامع ارتوپدی برای به دست آوردن حداقل داده‌های مورد نیاز، سپس بررسی سامانه‌های مشابه، مطالعۀ پرونده‌های بیماران و مصاحبه با متخصصین ارتوپدی برای دست یافتن به نیازمندی‌های اطلاعاتی و عملکردی سیستم مطلوب و بررسی روایی داده‌های استخراج شده انجام گرفت. مدل‌سازی و تکامل سیستم با پیروی از چرخۀ حیات طراحی و تولید سیستم‌های اطلاعاتی-رویکرد شی‌گراء صورت گرفت. یک نمونۀ اولیه با زبان پایتون و پایگاه‌ دادۀ PostgreSQL‌، ایجاد و از لحاظ قابلیت ‌استفاده و میزان رضایت کاربران مورد ارزیابی قرار گرفت. آنالیز داده‌های کیفی به روش تماتیک و کمّی با آمارهای توصیفی انجام گردید. نتایج: با بررسی ۹ رجیستری آرتروپلاستی کشورهای پیشرو و استخراج مجموعۀ حداقل داده‌ای و فرآیندهای کاری آن‌ها، اقلام داده‌ای موردنیاز (در پنج دسته شامل ۳۹ آیتم) و فرآیندهای کاری مناسب استخراج و در طراحی، مدل‌سازی و پیاده‌سازی سامانه مورد استفاده قرار گرفتند. کاربران وجود سیستم را مفید و کاربردی ارزیابی کردند. همین‌طور، میزان رضایت کاربران از سیستم رضایت‌بخش بود. نتیجه­ گیری: عملیاتی‌کردن این سیستم و ثبت اطلاعات آرتروپلاستی، به جامعۀ پزشکی و سیاست‌گذاران بهداشتی و درمانی کمک‌ خواهد کرد تا با داشتن اطلاعات به‌روز، تصمیمات و سیاست‌گذاری‌های بهتری در راستای مراقبت از این بیماران انجام دهند. 2 Introduction: Arthroplasty is a major orthopedic operation with an increasing rate. The success of this operation can significantly reduce patients’ pain and disabilities. This study aimed to design a registry system for hip and knee arthroplasties. Method: A comprehensive search was conducted to retrieve minimum data set from articles, guidelines, forms and reports published by orthopedic societies. Then, orthopedists were interviewed and medical records were evaluated for system requirements. After thematic analysis of the qualitative data, the intended system’ requirements were extracted. A system was designed following the "Information System Development Life Cycle and Object-Oriented" approach. The system prototype was developed by Python programming language and PostgreSQL Data Base Management System. Then, the usability of the system and user satisfaction were tested. Quantitative data were analyzed using descriptive statistics and through thematic and quantitative approaches. Results: The required dataset and processes were extracted based on evaluating nine arthroplasty registries of pioneer countries as well as our local needs and requirements. The result was a minimum dataset comprising of 39 elements in 5 groups. They were used for developing the arthroplasty registry forms for hip and knee. The system was considered applicable and useful by potential users. Conclusion: An arthroplasty registry system was developed successfully. This system can provide a ground base for healthcare policymakers as well as the members of orthopedic society for planning a good quality care for arthroplasties. 163 177 2018/08/31 1397/6/9 2019/07/15 1398/4/24 سجاد صمدی اوانسر Sajad Samadi Avansar دانشجوی کارشناسی ارشد انفورماتیک پزشکی، گروه فناوری اطلاعات سلامت، دانشگاه علوم پزشکی ارومیه، ارومیه، ایران ایران sajjad_samadi@yahoo.com زهرا نیازخانی Zahra Niazkhani دکتری تخصصی انفورماتیک پزشکی، دانشیار، مرکز تحقیقات نفرولوژی و پیوند کلیه، دانشگاه علوم پزشکی ارومیه، ارومیه، ایران ایران niazkhani.z@umsu.ac.ir فردین میرزاطلوعی Fardin Mirza Tolouei دکتری تخصصی ارتوپدی، فلوشیپ آرتروپلاستی، استاد، دانشکده پزشکی، دانشگاه علوم پزشکی ارومیه، ارومیه، ایران ایران fardin_tolouei@yahoo.com احمد رضا افشار Ahmad Reza Afshar دکتری تخصصی ارتوپدی، فلوشیپ دست، استاد، دانشکده پزشکی، دانشگاه علوم پزشکی ارومیه، ارومیه، ایران ایران afshar_ah@umsu.ac.ir حبیب الله پیرنژاد Habibollah Pirnejad دکتری تخصصی انفورماتیک پزشکی، دانشیار، مرکز تحقیقات ایمنی بیمار، دانشگاه علوم پزشکی ارومیه، ارومیه، ایران ایران pirnejad.h@umsu.ac.ir Arthroplasty Registry Hip Arthroplasty Knee Arthroplasty Orthopedics Operation Information Technology آرتروپلاستی رجیستری ‌ نظام ثبت جراحی مفصل زانو و لگن سیستم‌های اطلاعاتی ثبت جراحی‌های مفاصل 1. Parno A, Sayehmiri K, Parno M, Khandan M, Poursadeghiyan M, Maghsoudipour M, et al. The prevalence of occupational musculoskeletal disorders in Iran: A meta-analysis study. Work 2017;58(2):203-14. doi: 10.3233/WOR-172619. ##2. Fazli B, Ansari H, Noorani M, Jafari SM, Sharifpoor Z, Ansari S. The Prevalence of Musculoskeletal Disorders and its Predictors among Iranians’ Housewives. International Journal of Epidemiologic Research 2016;3(1):53-62.##3. Bloom DE, Boersch-Supan A, McGee P, Seike A. Population aging: facts, challenges, and responses. Program on the Global Demography of Aging 2011; 1-10.##4. Morrey BF, An KN, Berry DJ, Kitaoka HB, Pagnano MW. Joint Replacement Arthroplasty: Basic Science, Hip, Knee, and Ankle. 4th ed. New York, Wolters Kluwer Health/Lippincott Williams & Wilkins; 2011.##5. Steiner C, Andrews R, Barrett M, Weiss A. HCUP projections: mobility/orthopedic procedures 2003 to 2012. [cited 2018, Nov 25]. Available from: https://www.hcup-us.ahrq.gov/reports/projections/2012-03.pdf##6. Inacio MCS, Paxton EW, Graves SE, Namba RS, Nemes S. Projected increase in total knee arthroplasty in the United States - an alternative projection model. Osteoarthritis Cartilage 2017;25(11):1797-803. doi: 10.1016/j.joca.2017.07.022.##7. Dayton MR, Krivchenia GB, Anthony EA, Sporer SP, Bozic KJ. Third AJRR Annual Report on Hip and Knee Arthroplasty Data. American Joint Replacement Registry; 2016. 8. Danial Z, Motamedi MH, Mirhashemi S, Kazemi A, Mirhashemi AH. Ageing in Iran. Lancet 2014;384(9958):1927. doi: 10.1016/S0140-6736(14)62278-9.##9. Kurtz S, Mowat F, Ong K, Chan N, Lau E, Halpern M. Prevalence of primary and revision total hip and knee arthroplasty in the United States from 1990 through 2002. J Bone Joint Surg Am 2005;87(7):1487-97. doi:10.2106/JBJS.D.02441 ##10. AHRQ Methods for Effective Health Care. In: rd, Gliklich RE, Dreyer NA, Leavy MB. Registries for Evaluating Patient Outcomes: A User's Guide. Rockville (MD): Agency for Healthcare Research and Quality (US); 2014.##11. Delaunay C. Registries in orthopaedics. Orthopaedics & Traumatology: Surgery & Research. 2015;101(1, Supplement):S69-S75. doi.org/10.1016/j.otsr.2014.06.029##12. Malchau H, Herberts P, Söderman P, Odén A. Prognosis of Total Hip Replacement, Update and Validation of Results from the Swedish National Hip Arthroplasty Registry 1979-1998. Scientific Exhibition presented at the 67th Annual Meeting of the American Academy of Orthopaedic Surgeons; 2015 Mar 15-19; Orlando, USA: Department of Orthopaedics, Göteborg University, Sweden; 2000. p 1-18.##13. Network of Orthopaedic Registries of Europe (NORE). Arthroplasty registries; 2017 [cited 2017 Oct 28]. Available from: https://www.efort.org/about-us/nore/research/.##14. Hassenpflug J. The German arthroplasty register EPRD. 13th EFORT Annual Congress; 2012 May 23-25; Berlin: EFORT Head Office; 2012. ##15. Etkin CD, Springer BD. The American Joint Replacement Registry-the first 5 years. Arthroplast Today. 2017;3(2):67-9. doi: 10.1016/j.artd.2017.02.002##16. Robertsson O, Ranstam J, Sundberg M, A WD, Lidgren L. The Swedish Knee Arthroplasty Register: a review. Bone Joint Res 2014;3(7):217-22. doi: 10.1302/2046-3758.37.2000289.##17. Torre M, Romanini E, Zanoli G, Carrani E, Luzi I, Leone L, et al. Monitoring Outcome of Joint Arthroplasty in Italy: Implementation of the National Registry. Joints 2017;5(2):70-8. doi: 10.1055/s-0037-1603899##18. Lucht U. The Danish Hip Arthroplasty Register. Acta Orthop Scand 2000;71(5):433-9. doi:10.1080/000164700317381081##19. Finnish Arthroplasty Register; 2017 [cited 2017 Nov 20]. Available from: http://www.thl.fi/far/.##20. Puolakka TJ, Pajamaki KJ, Halonen PJ, Pulkkinen PO, Paavolainen P, Nevalainen JK. The Finnish Arthroplasty Register: report of the hip register. Acta Orthop Scand 2001;72(5):433-41. doi:10.1080/000164701753532745##21. Norwegian National Advisory Unit on Arthroplasty and Hip Fractures [cited 2017 Nov 10]. Available from: http://nrlweb.ihelse.net/eng/.##22. Havelin LI, Engesaeter LB, Espehaug B, Furnes O, Lie SA, Vollset SE. The Norwegian Arthroplasty Register: 11 years and 73,000 arthroplasties. Acta Orthop Scand 2000;71(4):337-53. doi:10.1080/000164700317393321##23. Tomlinson M, Harrison M. The New Zealand Joint Registry: report of 11-year data for ankle arthroplasty. Foot Ankle Clin 2012;17(4):719-23. doi: 10.1016/j.fcl.2012.08.011. ##24. National Joint Registry. the National Joint Registry for England, Wales, Northern Ireland and the Isle of Man [cited 2017 Nov 20]. Available from: http://www.njrcentre.org.uk.##25. Prime MS, Palmer J, Khan WS. The National Joint Registry of England and Wales. Orthopedics 2011;34(2):107-10. doi: 10.3928/01477447-20101221-21. ##26. Marakas G, O'Brien JA, Introduction to Information Systems. 16th ed. London: McGraw-Hill Education; 2016.##27. Dennis A, Wixom BH, Roth RM. Systems Analysis and Design. 6th ed. Danvers: John Wiley & Sons; 2015. ##28. Lewis C. Using the "Thinking-Aloud" Method In Cognitive Interface Design. Yorktown Heights, N.Y.: IBM T.J. Watson Research Center; 1982. ##29. Chin JP, Diehl VA, Norman KL. Development of an instrument measuring user satisfaction of the ##human-computer interface. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems; 1988 May 15 – 19; Washington, D.C., USA: ACM; 1988. p. 213-8. doi:10.1145/57167.57203##30. Svensson-Ranallo PA, Adam TJ, Sainfort F. A framework and standardized methodology for developing minimum clinical datasets. AMIA Jt Summits Transl Sci Proc 2011;2011:54-8. ##31. Abellan Van Kan G, Sinclair A, Andrieu S, Olde Rikkert M, Gambassi G, Vellas B. The Geriatric Minimum Data Set for clinical trials (GMDS). J Nutr Health Aging 2008;12(3):197-200. doi: 10.1007/BF02982620##32. Sheykhotayefeh M, Safdari R, Ghazisaeedi M, Khademi SH, Farajolah SS, Maserat E, et al. Development of a Minimum Data Set (MDS) for C-Section Anesthesia Information Management System (AIMS). Anesth Pain Med 2017;7(2):e44132. doi: 10.5812/aapm.44132.##33. Davey CJ, Slade SV, Shickle D. A proposed minimum data set for international primary care optometry: a modified Delphi study. Ophthalmic Physiol Opt 2017;37(4):428-39. doi: 10.1111/opo.12372.##34. Choquet R, Maaroufi M, de Carrara A, Messiaen C, Luigi E, Landais P. A methodology for a minimum data set for rare diseases to support national centers of excellence for healthcare and research. J Am Med Inform Assoc 2015;22(1):76-85. doi: 10.1136/amiajnl-2014-002794.##35. Sadoghi P, Leithner A, Labek G. Overcoming boundaries of worldwide joint arthroplasty registers: the European Arthroplasty Register minimal dataset. J Arthroplasty 2013;28(8):1327-8. doi: 10.1016/j.arth.2013.02.023.##36. Juniper EF, Guyatt GH, Streiner DL, King DR. Clinical impact versus factor analysis for quality of life questionnaire construction. J Clin Epidemiol 1997;50(3):233-8. doi.org/10.1016/S0895-4356(96)00377-0##37. Serra-Sutton V, Allepuz A, Espallargues M, Labek G, Pons JM. Arthroplasty registers: a review of international experiences. Int J Technol Assess Health Care 2009;25(1):63-72. doi: 10.1017/S0266462309090096. ##38. Zendehdel K. The National Program for Disease Registries and Health Outcomes Tehran 2014 [cited 2018, Nov 25]. Available from: http://registry.behdasht.gov.ir/uploads/Objectives.html.##39. Gundtoft PH, Varnum C, Pedersen AB, Overgaard S. The Danish Hip Arthroplasty Register. Clin Epidemiol 2016;8:509-14. doi: 10.2147/CLEP.S99498##1. Parno A, Sayehmiri K, Parno M, Khandan M, Poursadeghiyan M, Maghsoudipour M, et al. The prevalence of occupational musculoskeletal disorders in Iran: A meta-analysis study. Work 2017;58(2):203-14. doi: 10.3233/WOR-172619. ##2. Fazli B, Ansari H, Noorani M, Jafari SM, Sharifpoor Z, Ansari S. The Prevalence of Musculoskeletal Disorders and its Predictors among Iranians’ Housewives. International Journal of Epidemiologic Research 2016;3(1):53-62.##3. Bloom DE, Boersch-Supan A, McGee P, Seike A. Population aging: facts, challenges, and responses. Program on the Global Demography of Aging 2011; 1-10.##4. Morrey BF, An KN, Berry DJ, Kitaoka HB, Pagnano MW. Joint Replacement Arthroplasty: Basic Science, Hip, Knee, and Ankle. 4th ed. New York, Wolters Kluwer Health/Lippincott Williams & Wilkins; 2011.##5. Steiner C, Andrews R, Barrett M, Weiss A. HCUP projections: mobility/orthopedic procedures 2003 to 2012. [cited 2018, Nov 25]. Available from: https://www.hcup-us.ahrq.gov/reports/projections/2012-03.pdf##6. Inacio MCS, Paxton EW, Graves SE, Namba RS, Nemes S. Projected increase in total knee arthroplasty in the United States - an alternative projection model. Osteoarthritis Cartilage 2017;25(11):1797-803. doi: 10.1016/j.joca.2017.07.022.##7. Dayton MR, Krivchenia GB, Anthony EA, Sporer SP, Bozic KJ. Third AJRR Annual Report on Hip and Knee Arthroplasty Data. American Joint Replacement Registry; 2016. 8. Danial Z, Motamedi MH, Mirhashemi S, Kazemi A, Mirhashemi AH. Ageing in Iran. Lancet 2014;384(9958):1927. doi: 10.1016/S0140-6736(14)62278-9.##9. Kurtz S, Mowat F, Ong K, Chan N, Lau E, Halpern M. Prevalence of primary and revision total hip and knee arthroplasty in the United States from 1990 through 2002. J Bone Joint Surg Am 2005;87(7):1487-97. doi:10.2106/JBJS.D.02441 ##10. AHRQ Methods for Effective Health Care. In: rd, Gliklich RE, Dreyer NA, Leavy MB. Registries for Evaluating Patient Outcomes: A User's Guide. Rockville (MD): Agency for Healthcare Research and Quality (US); 2014.##11. Delaunay C. Registries in orthopaedics. Orthopaedics & Traumatology: Surgery & Research. 2015;101(1, Supplement):S69-S75. doi.org/10.1016/j.otsr.2014.06.029##12. Malchau H, Herberts P, Söderman P, Odén A. Prognosis of Total Hip Replacement, Update and Validation of Results from the Swedish National Hip Arthroplasty Registry 1979-1998. Scientific Exhibition presented at the 67th Annual Meeting of the American Academy of Orthopaedic Surgeons; 2015 Mar 15-19; Orlando, USA: Department of Orthopaedics, Göteborg University, Sweden; 2000. p 1-18.##13. Network of Orthopaedic Registries of Europe (NORE). Arthroplasty registries; 2017 [cited 2017 Oct 28]. Available from: https://www.efort.org/about-us/nore/research/.##14. Hassenpflug J. The German arthroplasty register EPRD. 13th EFORT Annual Congress; 2012 May 23-25; Berlin: EFORT Head Office; 2012. ##15. Etkin CD, Springer BD. The American Joint Replacement Registry-the first 5 years. Arthroplast Today. 2017;3(2):67-9. doi: 10.1016/j.artd.2017.02.002##16. Robertsson O, Ranstam J, Sundberg M, A WD, Lidgren L. The Swedish Knee Arthroplasty Register: a review. Bone Joint Res 2014;3(7):217-22. doi: 10.1302/2046-3758.37.2000289.##17. Torre M, Romanini E, Zanoli G, Carrani E, Luzi I, Leone L, et al. Monitoring Outcome of Joint Arthroplasty in Italy: Implementation of the National Registry. Joints 2017;5(2):70-8. doi: 10.1055/s-0037-1603899##18. Lucht U. The Danish Hip Arthroplasty Register. Acta Orthop Scand 2000;71(5):433-9. doi:10.1080/000164700317381081##19. Finnish Arthroplasty Register; 2017 [cited 2017 Nov 20]. Available from: http://www.thl.fi/far/.##20. Puolakka TJ, Pajamaki KJ, Halonen PJ, Pulkkinen PO, Paavolainen P, Nevalainen JK. The Finnish Arthroplasty Register: report of the hip register. Acta Orthop Scand 2001;72(5):433-41. doi:10.1080/000164701753532745##21. Norwegian National Advisory Unit on Arthroplasty and Hip Fractures [cited 2017 Nov 10]. Available from: http://nrlweb.ihelse.net/eng/.##22. Havelin LI, Engesaeter LB, Espehaug B, Furnes O, Lie SA, Vollset SE. The Norwegian Arthroplasty Register: 11 years and 73,000 arthroplasties. Acta Orthop Scand 2000;71(4):337-53. doi:10.1080/000164700317393321##23. Tomlinson M, Harrison M. The New Zealand Joint Registry: report of 11-year data for ankle arthroplasty. Foot Ankle Clin 2012;17(4):719-23. doi: 10.1016/j.fcl.2012.08.011. ##24. National Joint Registry. the National Joint Registry for England, Wales, Northern Ireland and the Isle of Man [cited 2017 Nov 20]. Available from: http://www.njrcentre.org.uk.##25. Prime MS, Palmer J, Khan WS. The National Joint Registry of England and Wales. Orthopedics 2011;34(2):107-10. doi: 10.3928/01477447-20101221-21. ##26. Marakas G, O'Brien JA, Introduction to Information Systems. 16th ed. London: McGraw-Hill Education; 2016.##27. Dennis A, Wixom BH, Roth RM. Systems Analysis and Design. 6th ed. Danvers: John Wiley & Sons; 2015. ##28. Lewis C. Using the "Thinking-Aloud" Method In Cognitive Interface Design. Yorktown Heights, N.Y.: IBM T.J. Watson Research Center; 1982. ##29. Chin JP, Diehl VA, Norman KL. Development of an instrument measuring user satisfaction of the ##human-computer interface. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems; 1988 May 15 – 19; Washington, D.C., USA: ACM; 1988. p. 213-8. doi:10.1145/57167.57203##30. Svensson-Ranallo PA, Adam TJ, Sainfort F. A framework and standardized methodology for developing minimum clinical datasets. AMIA Jt Summits Transl Sci Proc 2011;2011:54-8. ##31. Abellan Van Kan G, Sinclair A, Andrieu S, Olde Rikkert M, Gambassi G, Vellas B. The Geriatric Minimum Data Set for clinical trials (GMDS). J Nutr Health Aging 2008;12(3):197-200. doi: 10.1007/BF02982620##32. Sheykhotayefeh M, Safdari R, Ghazisaeedi M, Khademi SH, Farajolah SS, Maserat E, et al. Development of a Minimum Data Set (MDS) for C-Section Anesthesia Information Management System (AIMS). Anesth Pain Med 2017;7(2):e44132. doi: 10.5812/aapm.44132.##33. Davey CJ, Slade SV, Shickle D. A proposed minimum data set for international primary care optometry: a modified Delphi study. Ophthalmic Physiol Opt 2017;37(4):428-39. doi: 10.1111/opo.12372.##34. Choquet R, Maaroufi M, de Carrara A, Messiaen C, Luigi E, Landais P. A methodology for a minimum data set for rare diseases to support national centers of excellence for healthcare and research. J Am Med Inform Assoc 2015;22(1):76-85. doi: 10.1136/amiajnl-2014-002794.##35. Sadoghi P, Leithner A, Labek G. Overcoming boundaries of worldwide joint arthroplasty registers: the European Arthroplasty Register minimal dataset. J Arthroplasty 2013;28(8):1327-8. doi: 10.1016/j.arth.2013.02.023.##36. Juniper EF, Guyatt GH, Streiner DL, King DR. Clinical impact versus factor analysis for quality of life questionnaire construction. J Clin Epidemiol 1997;50(3):233-8. doi.org/10.1016/S0895-4356(96)00377-0##37. Serra-Sutton V, Allepuz A, Espallargues M, Labek G, Pons JM. Arthroplasty registers: a review of international experiences. Int J Technol Assess Health Care 2009;25(1):63-72. doi: 10.1017/S0266462309090096. ##38. Zendehdel K. The National Program for Disease Registries and Health Outcomes Tehran 2014 [cited 2018, Nov 25]. Available from: http://registry.behdasht.gov.ir/uploads/Objectives.html.##39. Gundtoft PH, Varnum C, Pedersen AB, Overgaard S. The Danish Hip Arthroplasty Register. Clin Epidemiol 2016;8:509-14. doi: 10.2147/CLEP.S99498 ##

شناسایی بیماری آلزایمر در سالمندان با استفاده از مدل راه رفتن و دوربین کینکت Detection of Alzheimer’s Disease in Elder People Using Gait Analysis and Kinect Camera 1 1 مقدمه: آنالیز الگوی راه رفتن با استفاده از ابزارهای نوین فناوری برای شناسایی بیماری آلزایمر، مورد توجه محققین در دهه اخیر قرار گرفته است. بنابراین در این مطالعه از داده‌های اسکلتی دوربین کینکت برای آنالیز الگوی راه رفتن به منظور شناسایی بیماری آلزایمر در سالمندان استفاده شد. روش: در این مطالعه توسعه­ای-کاربردی به‌صورت تجربی، ثبت نمونه‌های راه رفتن در یک مسیر بیضی شکل برای 12 نفر از زنان سالمند با بیماری آلزایمر و 12 نفر سالمند زن سالم، به وسیله دوربین کینکت صورت گرفت. پس از استخراج ویژگی‌های راه رفتن، آنالیز توصیفی برای مقایسه ویژگی‌ها در میان گروه سالم و بیمار انجام شد. سپس یک کلاسه‌بند ماشین بردار پشتیبان برای شناسایی سالمندان با بیماری آلزایمر طراحی شد. نتایج: مقایسه ویژگی‌های استخراجی از راه رفتن به وسیله داده‌های اسکلتی دوربین کینکت، حکایت از تطبیق نتایج با یافته‌های پیشین حاصل از سیستم‌های مبتنی بر انواع دیگر سنسورها برای تفکیک دو گروه سالمندان سالم و بیمار دارد. نتایج تفکیک افراد سالمند سالم و بیمار شرکت‌کننده در پژوهش حاضر به وسیله کلاسه‌بند ماشین بردار پشتیبان نیز دارای صحت، حساسیت، دقت و Specificity به ترتیب 91/25%، 93/44%، 90/94% و 93/57% برای شناسایی بیماری است. نتیجه‌گیری: در کنار آنالیز آمار توصیفی ویژگی­های راه رفتن، با تکیه بر روش‌های مبتنی بر یادگیری ماشین مانند ماشین‌بردار پشتیبان می‌توان به شناسایی سالمندان با بیماری آلزایمر به وسیله ویژگی‌های استخراجی به دست آمده از داده‌های اسکلتی راه رفتن سالمندان با نتایج قابل قبولی دست یافت. 2 Introduction: Gait analysis through using modern technology for detection of Alzheimer's disease has found special attention by researchers over the last decade. In this study, skeletal data recorded with a Kinect camera, were used to analyze gait for the purpose of detecting Alzheimer's disease in elders. Method: In this applied-developmental experimental study, using a Kinect camera, data were collected for 12 elderly women with Alzheimer's disease and 12 healthy elderly women walking in an oval path. After extracting various features of gait, descriptive analysis was performed to compare the features between the healthy and patient groups. Then, a support vector machine classifier was designed to detect elderly people with Alzheimer's disease. Results: The comparison of extracted features from skeletal data of gait using Kinect camera in this study indicate that the results are matched with previous findings from systems based on other types of sensors. The accuracy, sensitivity, precision and specificity of system designed in the present study for classifying elders with Alzheimer's disease and healthy elders were 91.25%, 93.4484%, 90.5945% and 93.581% respectively. Conclusion: In addition to descriptive analysis of gait, by using machine learning methods such as support vector machine classifier, elderly people with Alzheimer's disease can be detected based on features extracted from skeletal data of Elderly people. 178 196 2018/08/312019/01/6 1397/10/16 2019/07/152019/07/1 1398/4/10 محمود سیف اللهی Mahmoud Seifallahi دانشجوی دکتری مهندسی الکترونیک، گروه مهندسی الکترونیک، دانشکده مهندسی برق و کامپیوتر، دانشگاه سمنان، سمنان، ایران ایران m.seifallahi@semnan.ac.ir هادی سلطانی زاده Hadi Soltanizadeh دکترای مهندسی الکترونیک، استادیار، گروه مهندسی الکترونیک، دانشکده مهندسی برق و کامپیوتر، دانشگاه سمنان، سمنان، ایران ایران h_soltanizadeh@semnan.ac.ir افسون حسنی مهربان Afsoon Hassani Mehraban دکترای کاردرمانی، استاد، گروه کاردرمانی، دانشکده علوم توان‌بخشی، دانشگاه علوم پزشکی ایران، تهران، ایران ایران mehraban.a@iums.ac.ir فاطمه خمسه Fatemeh Khamseh دکترای مغز و اعصاب ، انجمن آلزایمر ایران، تهران، ایران ایران fkhamseh1345@yahoo.com Alzheimer Disease Detection Gait Kinect Camera Classification بیماری آلزایمر شناسایی راه رفتن دوربین کینکت طبقه‌بندی 1. 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بررسی آگاهی و استفاده ارائه‌دهندگان خدمات سلامت از مداخلات سلامت روان الکترونیکی برای افسردگی و موانع به کارگیری این مداخلات Healthcare Providers’ Awareness and Practice on Electronic Mental Health Interventions for Depression and its’ Utilization Barriers 1 1 مقدمه: امروزه پیشرفت در فناوری، ارائه خدمات سلامت را تسهیل نموده و منجر به شکل‌گیری سلامت الکترونیکی شده است. استفاده از مداخلات سلامت روان الکترونیکی می‌تواند منجر به تشخیص و درمان به موقع اختلالات روان از جمله افسردگی شود؛ لذا هدف این مطالعه بررسی آگاهی و استفاده از مداخلات سلامت روان الکترونیکی در میان ارائه‌دهندگان خدمات سلامت و موانع به کارگیری آن‌ها بود. روش: این مطالعه توصیفی در سال 1397 در کرمان انجام گرفت. جامعه پژوهش شامل پزشکان خانواده، متخصصین روانشناس و مراقبین سلامت شاغل در مراکز بهداشتی بودند که به صورت نمونه‌گیری تصادفی منظم انتخاب شدند. ابزار جمع‌آوری داده‌ها پرسشنامه محقق ساخته دو بخشی بود. بخش اول این پرسشنامه شامل اطلاعات جمعیت‌شناختی و بخش دوم سؤالات مربوط به آگاهی، استفاده و موانع استفاده از مداخلات الکترونیکی بود. از آزمون‌های اسپیرمن، تای کندال مجذور کای جهت تجزیه و تحلیل داده‌ها استفاده شد. نتایج: در مجموع 143 نفر در این پژوهش شرکت کردند. %69 از شرکت‌کنندگان تاکنون از مداخلات سلامت روان الکترونیکی استفاده نکرده بودند و تنها %9‌ از شرکت‌کنندگان در خصوص این مداخلات آموزش دیده بودند. بیشترین استفاده شرکت‌کنندگان از این مداخلات، در زمینه‌های آموزش‌های روانشناسی (%24) و حمایت توسط همتایان با استفاده از فروم های (Forums) گفتگو آنلاین (%17) بود. بیشترین موانع پیاده‌سازی، ریسک‌های بالینی (%97)، عدم تمایل بیمار و پزشک نسبت به استفاده از مداخلات الکترونیکی (%88) بود. نتیجه ­گیری: در مجموع آگاهی و استفاده ارائه‌دهندگان خدمات سلامت از مداخلات الکترونیکی سلامت روان کم بود، لذا شناسایی و اولویت‌بندی این موانع می‌تواند در ارتقاء قابلیت‌های این فناوری کمک نماید.   2 Introduction: E-mental health interventions can be effective in the early diagnosis and treatment of mental problems such as depression. Therefore, the present study aimed to evaluate the awareness and practice of health care providers on E-mental health interventions and its’ utilization barriers. Method: This descriptive study was conducted in Kerman in 2018. The research population consisted of family physicians, psychologists and health care providers in health care centers who were selected through systematic random sampling. Data collection tool was a valid and reliable researcher-made questionnaire consisting of two parts of demographic questions and items related to awareness, usage, and barriers of using E-mental health interventions. Spearman, Kendall, and Chi square tests were used to analyze the data. Results: A total number of 143 people participated in this study of whom, 69% had not used E-mental health interventions so far and only 9% had been trained in these interventions. In regard to E-mental health interventions, psychological training (24%) and peers’ support by using online discussion forums (17%) gained the highest rates of use among participants and in regard to barriers of implementing and using these interventions, clinical risks (97%) and unwillingness of patient and physician in using electronic interventions (88%) gained the highest rates. Conclusion: In general, health care providers’ awareness and practice on E-mental health interventions were low. Therefore, identifying and prioritizing these barriers can help to enhance the capabilities of these technologies and to promote their use.     197 206 2018/08/312019/01/62019/05/31 1398/3/10 2019/07/152019/07/12019/07/13 1398/4/22 نازنین جنتی Nazanin Jannati کارشناسی ارشد انفورماتیک پزشکی، مرکز تحقیقات مدیریت ارائه خدمات سلامت، پژوهشکده آینده‌پژوهی در سلامت، دانشگاه علوم پزشکی کرمان، کرمان، ایران ایران nazaninjannati@gmail.com فرزاد سلمانی زاده Farzad Salmanizadeh دانشجوی دکتری تخصصی انفورماتیک پزشکی، مرکز تحقیقات انفورماتیک پزشکی، پژوهشکده آینده‌پژوهی در سلامت، دانشگاه علوم پزشکی کرمان، کرمان ، ایران ایران salmanifarzad@gmail.com Mental health Depression E-mental health electronic interventions Technology Healthcare providers سلامت روان افسردگی سلامت روان الکترونیکی مداخلات الکترونیکی تکنولوژی ارائه دهندگان خدمات سلامت 1. World Health Organization (WHO). Depression and other common mental disorders. Global health estimates; 2017 [cited 2018 Aug 8]. Available from: https://apps.who.int/iris/bitstream/handle/10665/254610/WHO-MSD-MER-2017.2-eng.pdf.##2. Turecki G, Brent DA. Suicide and suicidal behaviour. Lancet 2016;387(10024):1227-39. doi: 10.1016/S0140-6736(15)00234-2.##3. Iran MoHi. [cited 2018 Aug 16]. Available from: http://behdasht.gov.ir/?siteid=1&pageid=1508&newsview=159673.##4. World Health Organization(WHO). Depression; 2018 [cited 2018 Aug 16]. Available from: https://www.who.int/news-room/fact-sheets/detail/depression##5. Sharifi V, Amin-Esmaeili M, Hajebi A, Motevalian A, Radgoodarzi R, Hefazi M, et al. Twelve-month prevalence and correlates of psychiatric disorders in Iran: The Iranian Mental Health Survey, 2011. Arch Iran Med 2015; 18(2): 76-84.##6. Craven MA, Bland R. Better practices in collaborative mental health care: an analysis of the evidence base. Can J Psychiatry 2006;51(6 Suppl 1):7S-72S.##7. Sanchez K, Watt TT. Collaborative care for the treatment of depression in primary care with a low-income, spanish-speaking population: outcomes from a community-based program evaluation. Prim Care Companion CNS Disord 2012;14(6). pii: PCC.12m01385. doi: 10.4088/PCC.12m01385.##8. Noee Z, Sharifi V, Abolhassani F, Amin-Esmaeili M, Mosavineia SJ. Establishment of first community mental health center in Iran: Experience of collaborative care. Journal of Fundamentals of Mental Health 2013;15(1):419-23. [In Persian] doi: 10.22038/jfmh.2013.773##9. Ebert DD, Cuijpers P, Munoz RF, Baumeister H. Prevention of Mental Health Disorders Using Internet- and Mobile-Based Interventions: A Narrative Review and Recommendations for Future Research. Front Psychiatry 2017;8:116. doi: 10.3389/fpsyt.2017.00116. eCollection 2017.##10. Karyotaki E, Riper H, Twisk J, Hoogendoorn A, Kleiboer A, Mira A, et al. Efficacy of self-guided internet-based cognitive behavioral therapy in the treatment of depressive symptoms: a meta-analysis of individual participant data. JAMA Psychiatry 2017;74(4):351-9. doi: 10.1001/jamapsychiatry.2017.0044.##11. Sander L, Rausch L. Effectiveness of Internet-Based Interventions for the Prevention of Mental Disorders: A Systematic Review and Meta-Analysis. JMIR Ment Health 2016;3(3):e38. doi: 10.2196/mental.6061.##12. National Institute for Health and Care Excellence(NIC). Depression in adults: recognition and management; 2009. [cited 16 Aug 2018]. Available from: https://www.nice.org.uk/guidance/cg90##13. Carper MM, McHugh RK, Barlow DH. The dissemination of computer-based psychological treatment: a preliminary analysis of patient and clinician perceptions. Adm Policy Ment Health 2013;40(2):87-95. doi: 10.1007/s10488-011-0377-5.##14. Topooco N, Riper H, Araya R, Berking M, Brunn M, Chevreul K, et al. Attitudes towards digital treatment for depression: A European stakeholder survey. Internet Interv 2017;8:1-9. doi: 10.1016/j.invent.2017.01.001##15. Krog MD, Nielsen MG, Le JV, Bro F, Christensen KS, Mygind A. Barriers and facilitators to using a web-based tool for diagnosis and monitoring of patients with depression: a qualitative study among Danish general practitioners. BMC Health Serv Res 2018;18(1):503. doi: 10.1186/s12913-018-3309-1.##16. Gun SY, Titov N, Andrews G. Acceptability of Internet treatment of anxiety and depression. Australas Psychiatry 2011;19(3):259-64. doi: 10.3109/10398562.2011.562295.##17. Vigerland S, Ljótsson B, Bergdahl Gustafsson F, Hagert S, Thulin U, Andersson G, et al. Attitudes towards the use of computerized cognitive behavior therapy (cCBT) with children and adolescents: A survey among Swedish mental health professionals. Internet Interv 2014;1(3):111-7. doi.org/10.1016/j.invent.2014.06.002##18. Breedvelt JJ, Zamperoni V, Kessler D, Riper H, Kleiboer AM, Elliott I, et al. GPs' attitudes towards digital technologies for depression: an online survey in primary care. Br J Gen Pract 2019;69(680):e164-e170. doi: 10.3399/bjgp18X700721.##19. Barr MS. The need to test the patient-centered medical home. JAMA 2008;300(7):834-5. doi: 10.1001/jama.300.7.834.##20. Carolan M. Health literacy and the information needs and dilemmas of first-time mothers over 35 years. J Clin Nurs 2007;16(6):1162-72. doi:10.1111/j.1365-2702.2007.01600.x##21. Devilly GJ, Borkovec TD. Psychometric properties of the credibility/expectancy questionnaire. J Behav Ther Exp Psychiatry 2000;31(2):73-86. doi:10.1016/s0005-7916(00)00012-4##22. Committee on Hospital Care and Institute for Patient- and Family-Centered Care. Patient- and family-centered care and the pediatrician's role. Pediatrics 2012;129(2):394-404. doi: 10.1542/peds.2011-3084.##23. Farrer L, Gulliver A, Chan JK, Batterham PJ, Reynolds J, Calear A, et al. Technology-based interventions for mental health in tertiary students: systematic review. J Med Internet Res 2013;15(5):e101. doi: 10.2196/jmir.2639.## ##

تأثیر آموزش پیمایش مدیریت رفتار از طریق سلامت همراه به مادران بر کاهش نشانگان ضدیت و بیش جنبشی کودکان مبتلا به اختلال نافرمانی مقابله‌ای: یک مطالعه آزمایشی تک نمونه The Effect of Training Flow Chart Behavioral Management Program by Mobile to Mothers of Children with Oppositional Defiant Disorder for Reducing Oppositional and Hyperactivity Symptoms: one-single sample experimental study 1 1 مقدمه: ضدیت و بیش جنبشی از جمله رفتارهای کودکان مبتلا به اختلال نافرمانی مقابله‌ای می‌باشند. با توجه به تغییرات زندگی‌، مسافت و مشغله زیاد والدین فرصت مراجعه حضوری به درمانگر وجود ندارد؛ لذا این پژوهش با هدف تعیین اثربخشی آموزش پیمایش مدیریت رفتار از طریق سلامت همراه به مادران بر کاهش نشانه‌های ضدیت و بیش جنبشی کودکان با اختلال نافرمانی مقابله‌ای انجام شد. روش: روش پژوهش از نوع طرح‌های تک آزمودنی AB است. این پژوهش در شهر تهران و در سال 1397 اجرا شد. در این پژوهش، 4 مادر که نمرات بالایی به رفتار ضدیت و بیش جنبشی فرزندشان داده بودند، برای آموزش انتخاب شدند. مادران، برنامه آموزشی مدیریت رفتار را در 8 جلسه به صورت 6 جلسه مجازی و از طریق تلفن همراه و 2 جلسه حضوری توجیهی دریافت کردند. داده‌های پژوهش با استفاده از جداول، درصد بهبودی و شاخص تغییر پایا اندازه‌گیری شدند. نتایج: یافته‌ها نشان داد که درصد بهبودی کل برای هر سه کودک در متغیر اول ضدیت 61/43 و در متغیر دوم بیش جنبشی 69/74 به دست آمد. بعد از پیگیری یک‌ماهه، درصد بهبودی کل در هر دو متغیر افزایش پیدا کرد (75/77 و 79/47). بعد از پیگیری یک‌ماهه اثرات درمان همچنان ادامه داشت. در نتیجه هر دو فرضیه از نظر بالینی معنادار است. نتیجه‌گیری: با توجه به یافته‌های به دست آمده می‌توان نتیجه گرفت که این آموزش کم هزینه، در دسترس و می‌تواند مکملی برای ارائه خدمات بهداشت روان حضوری به مادران با فرزند مشکل‌دار باشد. 2 Introduction: Opposition and hyperactivity are among behaviors of children with Oppositional Defiant Disorder (ODD). Nowadays, due to the changes in life style and busy life, direct referring to health provider is difficult. Therefore, the present study was done to examine the effectiveness of training behavior management flowchart by mobile to mothers for reducing oppositional and hyperactivity symptoms in children with oppositional defiant disorder. Method: This research was done in Tehran/ Iran in 2018 and through single case experiment by A-B model. Four mothers whose children had gained high scores of ODD were selected for training in eighth sessions of 60-90 minutes. Mothers received the training program of behavior management for six virtual sessions through mobile phone and two in person sessions. Data were analyzed using chart, improve percent and RCI. Results: The overall improvement was 61.43% for defiant and 69.74% for hyperactivity. In one-month follow up, both mentioned variables improved (75.77% and 79.47% respectively). According to the results, the two hypotheses were clinically significant. Conclusion: The mobile health technology is a low cost, available, and suitable method for mental health services and can be used as a complementary health service for mothers who have a child with oppositional defiant disorder. 207 217 2018/08/312019/01/62019/05/312019/04/7 1398/1/18 2019/07/152019/07/12019/07/132019/06/9 1398/3/19 زهرا جمالی Zahra Jamali کارشناس ارشد روان‌شناسی‌، گروه روان‌شناسی و علوم تربیتی، دانشکده علوم انسانی، دانشگاه خاتم، تهران، ایران ایران anna_khodabakhshi@yahoo.com آناهیتا خدابخشی کولایی Anahita Khodabakhshi Koolaee دکترای مشاوره، استادیار، گروه روان‌شناسی و علوم تربیتی، دانشکده علوم انسانی، دانشگاه خاتم، تهران، ایران ایران a.khodabakhshid@khatam.ac.ir Behavioral management Mobile health Mother Child Oppositional Defiant Disorder (ODD) مدیریت رفتار سلامت همراه مادر کودک اختلال نافرمانی مقابله‌ای 1. Morshed N, Davoodi I, Babamiri M. Effectiveness of group play therapy on symptoms of oppositional defiant among children. J Educ Community Health 2015;2(3):12-8. doi: 10.20286/jech-02032##2. Shafipour SZ, Sheikhi A, Mirzaei M, KazemnezhadLeili E. Parenting styles and its relation with children behavioral problems. J Holist Nurs Midwifery 2015;25(2):49-56. [In Persian]##3. American Academy of Child and Adolescent Psychiatry. ODD: A Guide for Families by the American Academy of Child and Adolescent American Academy of Child and Adolescent Psychiatry; 2009 [cited 2019 Dec 3]. Available from: https://www.aacap.org/App_Themes/AACAP/docs/resource_centers/odd/odd_resource_center_odd_guide.pdf##4. Substance A, Mental Health Services A. CBHSQ Methodology Report. DSM-5 Changes: Implications for Child Serious Emotional Disturbance. Rockville (MD): Substance Abuse and Mental Health Services Administration (US); 2016.##5. The American Psychiatric Association (APA). Diagnostic and Statistical Manual of Mental Disorders (DSM–5). American Psychiatric Association; 2013.##6. Hojati Abed E, Mirzaie H, Aahmadi A. Cognitive Behavioral Stress Management Program and Mothers of Children with Oppositional Defiant Disorder. Iranian Rehabilitation Journal 2014;12(3):43-7. [In Persian]##7. Khodabakhshi Koolaee A. Patterns of Family Therapy and Parent Education. 3rd ed. Tehran: Jangal; 2019. [In Persian]##8. Danforth JS. Parent training for families of children with comorbid ADHD and ODD. International Journal of Behavioral Consultation and Therapy 2006; 2(1):45-64.##9. Khodabakhshi Koolaee A, Shahi A, Navidian A, Mosalanejad L. The effect of positive parenting program training in mothers of children with attention deficit hyperactivity on reducing children's externalizing behavior problems. Journal of Fundamentals of Mental Health 2015; 17(3): 135-41.##10. Fox S, Duggan M. Mobile Health 2010. Washington, DC: Pew Internet & American Life Project; 2010 Oct 19. USA.##11. Singh NN. Handbook of Evidence-Based Practices in Intellectual and Developmental Disabilities. USA: Springer; 2016.##12. Maadanipour H, Aslizadeh A. Firmware development work on expanding the use of smart phone applications. Media Studies 2017;11(2):49-64. [In Persian]##13. Rezaee Khoshkozari g, Khodabakhshi Koolaee a. The Effectiveness of Floor time Play on Anxiety ‎in children with Asperger disorder and burden among their mothers (a single case study). Iranian Journal of Pediatric Nursing 2018;4(4):50-9. [In Persian]##14. Gadow KD, Sprafkin J. Child Symptom Inventory. CSI: Checkmate Plus; 1997.##15. Mohamadesmaiel E, Alipour A. A preliminary study on the reliability, validity and cut off points of the Disorders of Children Symptom Inventory-4 (CSI-4). Journal of Exceptional Children 2002;2(3):239-54. [In Persian]##16. Danforth JS. A flow chart of behavior management strategies for families of children with co-occurring attention-deficit hyperactivity disorder and conduct problem behavior. Behav Anal Pract 2016;9(1):64-76. doi: 10.1007/s40617-016-0103-6.##17. Sulik MJ, Blair C, Mills-Koonce R, Berry D, Greenberg M. Early parenting and the development of externalizing behavior problems: longitudinal mediation through children's executive function. Child Dev 2015;86(5):1588-603. doi: 10.1111/cdev.12386.##18. Vahidi A, Mahmodfekheh H. Comparison of the effectiveness of two Methods of mobile education and speech therapy on anxiety in cardiac surgery patients. Journal of Health Literacy 2018; 3(3): 53-62. [In Persian]##19. Ershad Sarabi R, Sadoughi F, Jamshidi Orak R, Bahaadinbeigy K. Role of mobile technology in Iran healthcare system: a review study. Journal of Health and Biomedical Informatics 2018;4(4):313-26. [In Persian]##20. Moghaddasi H, Mehdizadeh H. Mobile health for diagnosis and management of skin lesions. Journal of Health and Biomedical Informatics 2016;3(2):155-65. [In Persian]##21. Mehdipour Y, Khammarnia M, Karimi A, Alipour J, Seyedzaei F, Ebrahimi S. Effective factors in acceptance of mobile health from physicians point of view. Journal of Modern Medical Information Sciences 2017;3(1):30-7. [In Persian]##22. Ebrahimi F, Aghamolaei T, Abedini S, Rafati S. Effect of educational intervention using mobile on life style of women who referred to health centers in Bandar Abbas. Iran J Health Educ Health Promot 2017;5(2):81-9. [In Persian]##23. Xie Y, Dixon JF, Yee OM, Zhang J, Chen YA, Deangelo S, et al. A study on the effectiveness of videoconferencing on teaching parent training skills to parents of children with ADHD. Telemed J E Health 2013;19(3):192-9. doi: 10.1089/tmj.2012.0108.##24. Babinski DE, Mills SL, Bansal PS. The effects of behavioral parent training with adjunctive social skills training for a preadolescent girl with ADHD and borderline personality features. Clinical Case Studies 2018; 17(1):21-37. doi.org/10.1177/1534650117741707##1. Morshed N, Davoodi I, Babamiri M. Effectiveness of group play therapy on symptoms of oppositional defiant among children. J Educ Community Health 2015;2(3):12-8. doi: 10.20286/jech-02032##2. Shafipour SZ, Sheikhi A, Mirzaei M, KazemnezhadLeili E. Parenting styles and its relation with children behavioral problems. J Holist Nurs Midwifery 2015;25(2):49-56. [In Persian]##3. American Academy of Child and Adolescent Psychiatry. ODD: A Guide for Families by the American Academy of Child and Adolescent American Academy of Child and Adolescent Psychiatry; 2009 [cited 2019 Dec 3]. Available from: https://www.aacap.org/App_Themes/AACAP/docs/resource_centers/odd/odd_resource_center_odd_guide.pdf##4. Substance A, Mental Health Services A. CBHSQ Methodology Report. DSM-5 Changes: Implications for Child Serious Emotional Disturbance. Rockville (MD): Substance Abuse and Mental Health Services Administration (US); 2016.##5. The American Psychiatric Association (APA). Diagnostic and Statistical Manual of Mental Disorders (DSM–5). American Psychiatric Association; 2013.##6. Hojati Abed E, Mirzaie H, Aahmadi A. Cognitive Behavioral Stress Management Program and Mothers of Children with Oppositional Defiant Disorder. Iranian Rehabilitation Journal 2014;12(3):43-7. [In Persian]##7. Khodabakhshi Koolaee A. Patterns of Family Therapy and Parent Education. 3rd ed. Tehran: Jangal; 2019. [In Persian]##8. Danforth JS. Parent training for families of children with comorbid ADHD and ODD. International Journal of Behavioral Consultation and Therapy 2006; 2(1):45-64.##9. Khodabakhshi Koolaee A, Shahi A, Navidian A, Mosalanejad L. The effect of positive parenting program training in mothers of children with attention deficit hyperactivity on reducing children's externalizing behavior problems. Journal of Fundamentals of Mental Health 2015; 17(3): 135-41.##10. Fox S, Duggan M. Mobile Health 2010. Washington, DC: Pew Internet & American Life Project; 2010 Oct 19. USA.##11. Singh NN. Handbook of Evidence-Based Practices in Intellectual and Developmental Disabilities. USA: Springer; 2016.##12. Maadanipour H, Aslizadeh A. Firmware development work on expanding the use of smart phone applications. Media Studies 2017;11(2):49-64. [In Persian]##13. Rezaee Khoshkozari g, Khodabakhshi Koolaee a. The Effectiveness of Floor time Play on Anxiety ‎in children with Asperger disorder and burden among their mothers (a single case study). Iranian Journal of Pediatric Nursing 2018;4(4):50-9. [In Persian]##14. Gadow KD, Sprafkin J. Child Symptom Inventory. CSI: Checkmate Plus; 1997.##15. Mohamadesmaiel E, Alipour A. A preliminary study on the reliability, validity and cut off points of the Disorders of Children Symptom Inventory-4 (CSI-4). Journal of Exceptional Children 2002;2(3):239-54. [In Persian]##16. Danforth JS. A flow chart of behavior management strategies for families of children with co-occurring attention-deficit hyperactivity disorder and conduct problem behavior. Behav Anal Pract 2016;9(1):64-76. doi: 10.1007/s40617-016-0103-6.##17. Sulik MJ, Blair C, Mills-Koonce R, Berry D, Greenberg M. Early parenting and the development of externalizing behavior problems: longitudinal mediation through children's executive function. Child Dev 2015;86(5):1588-603. doi: 10.1111/cdev.12386.##18. Vahidi A, Mahmodfekheh H. Comparison of the effectiveness of two Methods of mobile education and speech therapy on anxiety in cardiac surgery patients. Journal of Health Literacy 2018; 3(3): 53-62. [In Persian]##19. Ershad Sarabi R, Sadoughi F, Jamshidi Orak R, Bahaadinbeigy K. Role of mobile technology in Iran healthcare system: a review study. Journal of Health and Biomedical Informatics 2018;4(4):313-26. [In Persian]##20. Moghaddasi H, Mehdizadeh H. Mobile health for diagnosis and management of skin lesions. Journal of Health and Biomedical Informatics 2016;3(2):155-65. [In Persian]##21. Mehdipour Y, Khammarnia M, Karimi A, Alipour J, Seyedzaei F, Ebrahimi S. Effective factors in acceptance of mobile health from physicians point of view. Journal of Modern Medical Information Sciences 2017;3(1):30-7. [In Persian]##22. Ebrahimi F, Aghamolaei T, Abedini S, Rafati S. Effect of educational intervention using mobile on life style of women who referred to health centers in Bandar Abbas. Iran J Health Educ Health Promot 2017;5(2):81-9. [In Persian]##23. Xie Y, Dixon JF, Yee OM, Zhang J, Chen YA, Deangelo S, et al. A study on the effectiveness of videoconferencing on teaching parent training skills to parents of children with ADHD. Telemed J E Health 2013;19(3):192-9. doi: 10.1089/tmj.2012.0108.##24. Babinski DE, Mills SL, Bansal PS. The effects of behavioral parent training with adjunctive social skills training for a preadolescent girl with ADHD and borderline personality features. Clinical Case Studies 2018; 17(1):21-37. doi.org/10.1177/1534650117741707 ##

تشخیص بیماری رتینوپاتی دیابت با استفاده از پردازش تصاویر فوندوس شبکیه و تکنیک‌های مورفولوژیک Diagnosis of Diabetic Retinopathy Using Processing of Fundus Images and Morphological Techniques 1 1 مقدمه: رتینوپاتی دیابتی، تأثیر مخرب دیابت بر رگ‌های خونی شبکیه است که تشخیص دیرهنگام آن می‌تواند باعث نابینایی شود. میکروآنوریسم‌ها از علائم اولیه این بیماری هستند که تشخیص زودهنگام آن‌ها باعث درمان به‌موقع و مانع از پیشرفت بیماری می‌گردد. از آن‌جا که این بیماری بدون علامت است و تنها توسط پزشک قابل تشخیص است، بدین‌جهت نیاز است بیماران دیابتی به طور منظم مورد آزمایش قرار گیرند، از طرفی با توجه به این‌که رشد تعداد چشم پزشکان کمتر از رشد جمعیت مبتلا به دیابت است، تشخیص دستی ضایعه‌ها زمان‌بر بوده و هزینه زیادی را به ‌دنبال دارد؛ لذا طراحی سیستم‌های تشخیص خودکار ضروری است. روش: در این مطالعه توصیفی-تحلیلی ابتدا تصاویر فوندوس شبکیه تحت پیش‌پردازش قرار گرفت، سپس نواحی کاندیدای میکروآنوریسم‌ها با استفاده از معیار metric و عملگرهای مورفولوژیک Bottom-hat و Hit-or-Miss تعیین و در مرحله بعد به استخراج ویژگی با استفاده از تجزیه و تحلیل مؤلفه اصلی برای تشخیص میکروآنوریسم‌های‌ واقعی پرداخته شد. برای ارزیابی الگوریتم پیشنهادی از تصاویر پایگاه داده DiaRetDB1 استفاده شد. نتایج: هدف این پژوهش توسعه یک روش خودکار برای تشخیص میکروآنوریسم‌ها است که بتواند به متخصصین چشم در فرآیند غربالگری رتینوپاتی دیابت کمک کند، تا علائم این بیماری را سریع‌تر، آسان‌تر و با هزینه کمتر تشخیص دهند. در ارزیابی روش پیشنهادی، نرخ حساسیت %87/6، نرخ تشخیص %98/7 و دقت %85/7 به‌ دست آمد. نتیجه‌گیری: با توجه به نتایج به دست آمده براساس پارامترهای ارزیابی، روش پیشنهادی از دقیق‌ترین الگوریتم‌های این حوزه است. 2 Introduction: Diabetic retinopathy is the damaging effect of diabetes on retinal blood vessels that can cause blindness when diagnosed late. Microaneurysms are early signs of the disease that their early diagnosis promotes timely treatment and prevents disease progression. Since this disease is asymptomatic and can only be detected by ophthalmologists, diabetic patients should be tested regularly. On the other hand, given the fact that the increase rate of the number of ophthalmologists is less than the growth of the diabetic population, manual diagnosis of the lesion is time consuming and costly, and thus the design of automatic detection systems is essential. Method: In this descriptive analytic study, the fundus images of the retina were subjected to preprocessing. Then, the candidate regions of microanurysms were determined using the metric and morphological operators Bottom-hat and Hit-or-Miss. In the next step, using principal component analysis, the specificity of main feature of real microanurysms diagnosis was extracted. The DiaRetDB1 database images were used to evaluate the proposed algorithm. Results: The purpose of this research was to develop an automated method for the detection of microanurysms that can help ophthalmologists in the process of diabetic retinopathy screening and diagnosing the symptoms faster, easier and at lower cost. In evaluation, the proposed method achieved a sensitivity of 87.6%, specificity of 98.7% and the precision of 85.7%. Conclusion: According to the results obtained based on evaluation parameters, the proposed method is one of the most accurate algorithms in this field. 218 230 2018/08/312019/01/62019/05/312019/04/72018/11/21 1397/8/30 2019/07/152019/07/12019/07/132019/06/92019/07/13 1398/4/22 مائده تاجی Maedeh Taji دانشجوی کارشناسی ارشد مهندسی کامپیوتر-گرایش هوش مصنوعی و رباتیک، گروه علمی مهندسی کامپیوتر و فناوری اطلاعات، دانشگاه پیام‌نور نجف آباد، نجف‌آباد، ایران ایران m.taji2020@gmail.com سعید آیت Saeed Ayat دکتری مهندسی کامپیوتر، دانشیار، گروه علمی مهندسی کامپیوتر و فناوری اطلاعات، دانشگاه پیام نور نجف آباد، نجف آباد، ایران ایران Dr.ayat@pnu.ac.ir Diabetic retinopathy Fundus images Microaneurysms Morphological techniques Principal Component Analysis رتینوپاتی دیابت تصاویر فوندوس شبکیه میکروآنوریسم عملگرهای مورفولوژیک آنالیز مؤلفه اصلی 1. Saleh E, Blaszczynski J, Moreno A, Valls A, Romero-Aroca P, de la Riva-Fernandez S, et al. Learning ensemble classifiers for diabetic retinopathy assessment. Artif Intell Med 2018;85:50-63. doi: 10.1016/j.artmed.2017.09.006.##2. Pourreza HR, Bahreyni Toossi MH, Mehdizadeh A, Pourreza R, Tavakoli M. Automatic Detection of Microaneurysms in Color Fundus Images using a Local Radon Transform Method. Iranian Journal of Medical Physics 2009;6(1):13-20. [In Persian] doi:10.22038/ijmp.2009.7386##3. Tavakoli M, Mehdizadeh A, Pourreza R, Banaee T, Bahreyni Toossi MH, Pourreza HR. Early detection of diabetic retinopathy in fluorescent angiography retinal images using image processing methods. Iranian Journal of Medical Physics 2010;7(4):7-14. [In Persian] doi:10.22038/ijmp.2010.7240##4. Baudoin CE, Lay BJ, Klein JC. Automatic detection of microaneurysms in diabetic fluorescein angiography. Rev Epidemiol Sante Publique 1984;32(3-4):254-61.##5. Spencer T, Phillips RP, Sharp PF, Forrester JV. Automated detection and quantification of microaneurysms in fluorescein angiograms. Graefes Arch Clin Exp Ophthalmol 1992;230(1):36-41. doi:10.1007/bf00166760##6. Spencer T, Olson JA, Mchardy KC, Sharp PF, Forrester JV. An image-processing strategy for the segmentation and quantification of microaneurysms in fluorescein angiograms of the ocular fundus. Computers And Biomedical Research 1996;29(4):284-302. ##7. Saleh MD, Eswaran C. An automated decision-support system for non-proliferative diabetic retinopathy disease based on MAs and HAs detection. Comput Methods Programs Biomed 2012;108(1):186-96. doi: 10.1016/j.cmpb.2012.03.004. ##8. SujithKumar SB, Singh V. Automatic Detection of Diabetic Retinopathy in Non-dilated RGB Retinal Fundus Images. International Journal of Computer Applications 2012; 47(19):26-32. doi: 10.5120/7297-0511##9. Kafieh R, Rabbani H, Hajizadeh F, Ommani M. An accurate multimodal 3-D vessel segmentation method based on brightness variations on OCT layers and curvelet domain fundus image analysis. IEEE Trans Biomed Eng 2013;60(10):2815-23. doi: 10.1109/TBME.2013.2263844.##10. Tavakoli M, Shahri RP, Pourreza H, Mehdizadeh A, Banaee T, Bahreini Toosi MH. A complementary method for automated detection of microaneurysms in fluorescein angiography fundus images to assess diabetic retinopathy. Pattern Recognition 2013;46(10):2740-53. doi.org/10.1016/j.patcog.2013.03.011##11. Adal KM, Sidibe D, Ali S, Chaum E, Karnowski TP, Meriaudeau F. Automated detection of microaneurysms using scale-adapted blob analysis and semi-supervised learning. Comput Methods Programs Biomed 2014;114(1):1-10. doi: 10.1016/j.cmpb.2013.12.009.##12. Manjaramkar A, Kokare M. A Rule Based Expert System for Microaneurysm Detection in Digital Fundus Images. International Conference on Computational Techniques in Information and Communication Technologies (ICCTICT); 2016 Mar 11-13; New Delhi, India: IEEE; 2016.p. 137-40. ##13. DiaRetDB1 V2.1 - Diabetic Retinopathy Database and Evaluation Protocol; 2009 [cited 2018 May 9]. https://www.it.lut.fi/project/imageret/diaretdb1_v2_1/#DOCUMENTATION.%202009.##14. Preece SJ, Claridge E. Monte Carlo modelling of the spectral reflectance of the human eye. Phys Med Biol 2002;47(16):2863-77. doi:10.1088/0031-9155/47/16/303##15. Gonzalez RC, Woods RE. Digital Image Processing. 3th ed. London: Pearson; 2007.##16. Haralick RM, Sternberg SR, Zhuang X. Image analysis using mathematical morphology. IEEE Trans Pattern Anal Mach Intell 1987;9(4):532-50.## ##

بررسی کیفیت کدگذاری پرونده بیماران مبتلا به سرطان بر اساس ICD-10 در بیمارستان‌های دانشگاهی استان هرمزگان The Quality of Coding Medical Records of Cancer Patients Based on ICD-10 in Hospitals of Hormozgan University of Medical Sciences 1 1 مقدمه: این مطالعه‏ با هدف تعیین وضعیت کدهایICD-10 اختصاص یافته به پرونده‏های بیماران مبتلا به سرطان از لحاظ سه ویژگی صحت، کامل بودن و به هنگام بودن انجام شد. روش: در این پژوهش توصیفی- مقطعی، 374 پرونده با کدهای تشخیصی C00-D48 مربوط به سال 1394 به روش نمونه‏گیری طبقه‏ای انتخاب شد. ابزار گردآوری داده‏ ها، فرم محقق ساخته شامل بخش‌های ثبت چکیده‏ اطلاعات بالینی، کدهای استادیاران و کدگذاران، بررسی و مقایسه کدها با هم، ثبت یافته‏های حاصل از کنترل کدها در جداول ICD-10 و خطاهای استخراج شده از کدهای کدگذاران بود. عوامل مؤثر در بروز انواع خطای کدگذاری در سطوح مختلف و تأثیر آن‌ها بر صحت و کامل بودن کدها، دسته‏ بندی شد. نتایج: در 79 پرونده (21/76 درصد) خطای کدگذاری مشاهده شد. تفاوت کدها، در سطح فصل در 30 مورد، به علت عدم رعایت قوانین انتخاب تشخیص اصلی و در 38 مورد از پرونده ‏ها ناشی از مطالعه ناکافی اطلاعات پرونده بود. از لحاظ کامل بودن، در 27 پرونده، کدهای کدگذاران نسبت به کدهای استادیاران دارای نقص بود. از نظر به هنگام بودن، طبق استاندارد سازمان جهانی بهداشت، کدگذاری پرونده‏ ها طی 48 ساعت پس از ترخیص بیماران، انجام می‏‏شد. نتیجه‌گیری: کیفیت کدگذاری تنها وابسته به کدگذار نیست و عوامل دیگری از قبیل نقص در مستندسازی می‏تواند در کیفیت کدگذاری تأثیرگذار باشد؛ بنابراین آموزش ‏های مداوم در راستای رفع اشکالات، برای گروه‏های کدگذاران و مستندسازان به خصوص در زمینه‏ ی سرطان الزامی است. به دنبال آن استفاده از نتایج این مطالعه جهت برنامه‏ ریزی کارگاه ‏های مرتبط توصیه می‏شود.   2 Introduction: The aim of this study was to determine the status of ICD-10 codes assigned to cancer patients' medical records in terms of three attributes of accuracy, completeness, and timeliness. Method: in this cross-sectional descriptive study, 374 medical files with C00-D48 diagnosis codes were selected through stratified sampling. Data gathering tool was a researcher-made checklist consisted of clinical information summary, codes assigned by assistant professors and coders, the review and comparison of the codes, the results obtained from the control of codes by ICD-10 and the errors extracted from coders’ codes. The factors affecting the occurrence of coding errors at different levels and their impact on the accuracy and completeness of the codes were classified. Results: Totally, coding errors were observed in 79 cases (21.74%). differences in codes at the level of the chapter were due to not following the rules for choosing the main diagnosis (30 cases) and inadequate study of the records (38 cases). In terms of completeness, in 27 cases (7.43%), codes assigned by coders were defective compared to assistant professors’ codes. In relation to the timeliness, in accordance with the WHO standard, coding was done within 48 hours of the patient's discharge. Conclusion: According to the results, coding quality is not only dependent on the coders, but also on other factors such as documentation defect. Therefore, continuous training for both coders and documenters is necessary to resolve defects, especially in the field of cancer. It is also recommended to use the results of this study for planning related workshops. 231 242 2018/08/312019/01/62019/05/312019/04/72018/11/212019/02/1 1397/11/12 2019/07/152019/07/12019/07/132019/06/92019/07/132019/05/18 1398/2/28 مائده هاشمی پور Maedeh Hashemipour کارشناسی مدارک پزشکی، بیمارستان خاتم‌الانبیاء (ص)، دانشگاه علوم پزشکی هرمزگان، جاسک، بندرعباس، ایران ایران maedeh.hashemi.pur@gmail.com فرید خرمی Farid Khorrami دکترای مدیریت اطلاعات سلامت، استادیار گروه فناوری اطلاعات سلامت، دانشکده پیراپزشکی، دانشگاه علوم پزشکی هرمزگان، بندرعباس، ایران ایران khorrami.farid@gmail.com مهری انصاری Mehri Ansari کارشناسی ارشد فناوری اطلاعات سلامت، مرکز توسعه تحقیقات بالینی کودکان، دانشگاه علوم پزشکی هرمزگان، بندرعباس، ایران ایران mehriansari60@gmail.com طیبه بنی اسدی Tayebeh Baniasadi دانشجوی دکتری تخصصی انفورماتیک پزشکی، گروه مدیریت اطلاعات سلامت، دانشکده پیراپزشکی، دانشگاه علوم پزشکی تهران، تهران، ایران ایران t_baniasadi@yahoo.com نسرین داوری Nasrin Davari دکترای مدیریت اطلاعات سلامت، دانشیار، گروه فناوری اطلاعات سلامت، دانشکده پیراپزشکی، دانشگاه علوم پزشکی هرمزگان، بندرعباس، ایران ایران davarinn@gmail.com مهربان شاهی Mehraban Shahi دکترای مدیریت اطلاعات سلامت، استادیار گروه فناوری اطلاعات سلامت، دانشکده پیراپزشکی، دانشگاه علوم پزشکی هرمزگان، بندرعباس، ایران ایران shahimehraban@gmail.com Coding Classification Cancer Cancer registry Quality کدگذاری طبقه‌بندی سرطان ثبت سرطان کیفیت 1. Esmaeil Akbari M, Mohammadi G. Iranian Female cancer report. Teharan: Shahid Beheshti Medical Sciences University; 2014.##2. Ferlay J, Shin HR, Bray F, Forman D, Mathers C, Parkin DM. Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int J Cancer 2010;127(12):2893-917. doi: 10.1002/ijc.25516.##3. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2018. CA Cancer J Clin 2018;68(1):7-30. doi: 10.3322/caac.21442.##4. Thun MJ, DeLancey JO, Center MM, Jemal A, Ward EM. The global burden of cancer: priorities for prevention. Carcinogenesis 2010;31(1):100-10. doi: 10.1093/carcin/bgp263.##5. Soerjomataram I, Lortet-Tieulent J, Parkin DM, Ferlay J, Mathers C, Forman D, et al. Global burden of cancer in 2008: a systematic analysis of disability-adjusted life-years in 12 world regions. Lancet 2012;380(9856):1840-50. doi: 10.1016/S0140-6736(12)60919-2.##6. Bray F, Jemal A, Grey N, Ferlay J, Forman D. Global cancer transitions according to the human development index (2008-2030): a population-based study. Lancet Oncol 2012;13(8):790-801. doi: 10.1016/S1470-2045(12)70211-5. ##7. Jemal A, Bray F, Center MM, Ferlay J, Ward E, Forman D. Global cancer statistics. CA Cancer J Clin 2011;61(2):69-90. doi: 10.3322/caac.20107.##8. Bray F, Znaor A, Cueva P, Korir A, Swaminathan R, Ullrich A, et al. Planning and developing population-based cancer registration in low-and middle-income settings: IARC. International Agency for Research on Cancer; 2014.##9. Coleman MP, Quaresma M, Berrino F, Lutz JM, De Angelis R, Capocaccia R, et al. Cancer survival in five continents: a worldwide population-based study (CONCORD). Lancet Oncol 2008;9(8):730-56. doi: 10.1016/S1470-2045(08)70179-7.##10. Roshandel G, Partovipour EA. Iranian National Cancer Registry Guideline. In: Center for Non-Communicable Disease Control Co, editor. Tehran: Ministry of Health and Medical Education; 2016.##11. Teppo L, Pukkala E, Lehtonen M. Data quality and quality control of a population-based cancer registry. experience in finland. Acta Oncol 1994;33(4):365-9. doi:10.3109/02841869409098430##12. Bray F, Parkin DM. Evaluation of data quality in the cancer registry: principles and methods. Part I: comparability, validity and timeliness. Eur J Cancer 2009;45(5):747-55. doi: 10.1016/j.ejca.2008.11.032.##13. Curado MP, Edwards B, Shin HR, Storm H, Ferlay J, Heanue M, Boyle P. Cancer incidence in five continents, Volume IX. IARC Press: International Agency for Research on Cancer; 2007.##14. Stausberg J, Lehmann N, Kaczmarek D, Stein M. Reliability of diagnoses coding with ICD-10. Int J Med Inform 2008;77(1):50-7. doi:10.1016/j.ijmedinf.2006.11.005##15. Farzandipour M, Sheikhtaheri A. Accuracy of diagnostic coding based on ICD-10. Feyz 2009;12(4):68-77 [In Persian]##16. Heywood NA, Gill MD, Charlwood N, Brindle R, Kirwan CC. Improving accuracy of clinical coding in surgery: collaboration is key. J Surg Res 2016;204(2):490-5. doi: 10.1016/j.jss.2016.05.023. ##17. Yavari P. Cancers. Tehran: Gap; 2014. [In Persian]##18. Wilson RJ, O'Neil ME, Ntekop E, Zhang K, Ren Y. Coding completeness and quality of relative survival-related variables in the National Program of Cancer Registries Cancer Surveillance System, 1995-2008. J Registry Manag 2014;41(2):65-71.##19. Saghaeiannejad Isfahani S, Garavand A, Faghiri K, Golshani M, Eskandari H, Kafashi M. The survey to determine the accuracy rate of the neoplasm coding in Seyed Al-Shohada Hospital of Isfahan. Journal of Health and Biomedical Informatics 2015;2(2):76-83. [In Persian]##20. Alipour J, Karimi A, Erfannia L, Shahrakipour M, Hayavi Haghighi MH, Kadkhoda A, et al. Reliability of Medical Diagnosis with International Classification of Diseases 10th Version in 2011. Health Inf Manage 2013; 10(1): 1-9. [In Persian]##21. Jahanbakhsh M, Saghaeyannejad Isfahani S. Survey of coder's knowledge about coding guidelines in Isfahan hospitals. Health Information Management 2011; 7(1): 56-63. [In Persian]##22. Hoseini M, Azizi A, Norouzi M. The survey of neoplasm classification systems in educational hospitals of Iran, Tehran, Shahid Beheshti & Jondi Shapor Universities. Jentashapir J Health Res 2011;2(3):1-9.##23. Ahmadi M, Alipour J. Adherance to quality elements for coding of diagnoses and procedures. Hakim Research Journal 2009;12(1):11-8. [In Persian]##24. Benke JR, Lin SY, Ishman SL. Directed educational training improves coding and billing skills for residents. Int J Pediatr Otorhinolaryngol 2013;77(3):399-401. doi: 10.1016/j.ijporl.2012.11.039.##25. Rezayi Mofrad MR, Rangraz Jeddi F, Rangraz Jeddi M. Adequacy of Data on Injuries' External Causes for Coding Based on ICD-10 in Inpatient Medical Records and Emergency Wards of Shahid Beheshti Hospital In Kashan. Payavard Salamat 2011;5(3):10-9. [In Persian]##26. World Health Organization(WHO). ICD-10: International Statistical Classification of Diseases and Related Health Problems; 2011 [cited 2018 Nov 3]. Available from: https://www.who.int/classifications/icd/ICD10Volume2_en_2010.pdf##27. Ahmadi M, Shahmoradi L, Meidani Z, Sheikhtaheri A, Meraji M, et al. ICD-10 Diagnostic Coding Guidelines. Tehran: Heidari; 2016. [In Persian]##1. Esmaeil Akbari M, Mohammadi G. Iranian Female cancer report. Teharan: Shahid Beheshti Medical Sciences University; 2014.##2. Ferlay J, Shin HR, Bray F, Forman D, Mathers C, Parkin DM. Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int J Cancer 2010;127(12):2893-917. doi: 10.1002/ijc.25516.##3. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2018. CA Cancer J Clin 2018;68(1):7-30. doi: 10.3322/caac.21442.##4. Thun MJ, DeLancey JO, Center MM, Jemal A, Ward EM. The global burden of cancer: priorities for prevention. Carcinogenesis 2010;31(1):100-10. doi: 10.1093/carcin/bgp263.##5. Soerjomataram I, Lortet-Tieulent J, Parkin DM, Ferlay J, Mathers C, Forman D, et al. Global burden of cancer in 2008: a systematic analysis of disability-adjusted life-years in 12 world regions. Lancet 2012;380(9856):1840-50. doi: 10.1016/S0140-6736(12)60919-2.##6. Bray F, Jemal A, Grey N, Ferlay J, Forman D. Global cancer transitions according to the human development index (2008-2030): a population-based study. Lancet Oncol 2012;13(8):790-801. doi: 10.1016/S1470-2045(12)70211-5. ##7. Jemal A, Bray F, Center MM, Ferlay J, Ward E, Forman D. Global cancer statistics. CA Cancer J Clin 2011;61(2):69-90. doi: 10.3322/caac.20107.##8. Bray F, Znaor A, Cueva P, Korir A, Swaminathan R, Ullrich A, et al. Planning and developing population-based cancer registration in low-and middle-income settings: IARC. International Agency for Research on Cancer; 2014.##9. Coleman MP, Quaresma M, Berrino F, Lutz JM, De Angelis R, Capocaccia R, et al. Cancer survival in five continents: a worldwide population-based study (CONCORD). Lancet Oncol 2008;9(8):730-56. doi: 10.1016/S1470-2045(08)70179-7.##10. Roshandel G, Partovipour EA. Iranian National Cancer Registry Guideline. In: Center for Non-Communicable Disease Control Co, editor. Tehran: Ministry of Health and Medical Education; 2016.##11. Teppo L, Pukkala E, Lehtonen M. Data quality and quality control of a population-based cancer registry. experience in finland. Acta Oncol 1994;33(4):365-9. doi:10.3109/02841869409098430##12. Bray F, Parkin DM. Evaluation of data quality in the cancer registry: principles and methods. Part I: comparability, validity and timeliness. Eur J Cancer 2009;45(5):747-55. doi: 10.1016/j.ejca.2008.11.032.##13. Curado MP, Edwards B, Shin HR, Storm H, Ferlay J, Heanue M, Boyle P. Cancer incidence in five continents, Volume IX. IARC Press: International Agency for Research on Cancer; 2007.##14. Stausberg J, Lehmann N, Kaczmarek D, Stein M. Reliability of diagnoses coding with ICD-10. Int J Med Inform 2008;77(1):50-7. doi:10.1016/j.ijmedinf.2006.11.005##15. Farzandipour M, Sheikhtaheri A. Accuracy of diagnostic coding based on ICD-10. Feyz 2009;12(4):68-77 [In Persian]##16. Heywood NA, Gill MD, Charlwood N, Brindle R, Kirwan CC. Improving accuracy of clinical coding in surgery: collaboration is key. J Surg Res 2016;204(2):490-5. doi: 10.1016/j.jss.2016.05.023. ##17. Yavari P. Cancers. Tehran: Gap; 2014. [In Persian]##18. Wilson RJ, O'Neil ME, Ntekop E, Zhang K, Ren Y. Coding completeness and quality of relative survival-related variables in the National Program of Cancer Registries Cancer Surveillance System, 1995-2008. J Registry Manag 2014;41(2):65-71.##19. Saghaeiannejad Isfahani S, Garavand A, Faghiri K, Golshani M, Eskandari H, Kafashi M. The survey to determine the accuracy rate of the neoplasm coding in Seyed Al-Shohada Hospital of Isfahan. Journal of Health and Biomedical Informatics 2015;2(2):76-83. [In Persian]##20. Alipour J, Karimi A, Erfannia L, Shahrakipour M, Hayavi Haghighi MH, Kadkhoda A, et al. Reliability of Medical Diagnosis with International Classification of Diseases 10th Version in 2011. Health Inf Manage 2013; 10(1): 1-9. [In Persian]##21. Jahanbakhsh M, Saghaeyannejad Isfahani S. Survey of coder's knowledge about coding guidelines in Isfahan hospitals. Health Information Management 2011; 7(1): 56-63. [In Persian]##22. Hoseini M, Azizi A, Norouzi M. The survey of neoplasm classification systems in educational hospitals of Iran, Tehran, Shahid Beheshti & Jondi Shapor Universities. Jentashapir J Health Res 2011;2(3):1-9.##23. Ahmadi M, Alipour J. Adherance to quality elements for coding of diagnoses and procedures. Hakim Research Journal 2009;12(1):11-8. [In Persian]##24. Benke JR, Lin SY, Ishman SL. Directed educational training improves coding and billing skills for residents. Int J Pediatr Otorhinolaryngol 2013;77(3):399-401. doi: 10.1016/j.ijporl.2012.11.039.##25. Rezayi Mofrad MR, Rangraz Jeddi F, Rangraz Jeddi M. Adequacy of Data on Injuries' External Causes for Coding Based on ICD-10 in Inpatient Medical Records and Emergency Wards of Shahid Beheshti Hospital In Kashan. Payavard Salamat 2011;5(3):10-9. [In Persian]##26. World Health Organization(WHO). ICD-10: International Statistical Classification of Diseases and Related Health Problems; 2011 [cited 2018 Nov 3]. Available from: https://www.who.int/classifications/icd/ICD10Volume2_en_2010.pdf##27. Ahmadi M, Shahmoradi L, Meidani Z, Sheikhtaheri A, Meraji M, et al. ICD-10 Diagnostic Coding Guidelines. Tehran: Heidari; 2016. [In Persian] ##

ارزیابی و پیش‌بینی تأثیر امواج پارازیت‌ها و استفاده از موبایل در مادران باردار بر میزان آب دور جنین بر مبنای الگوریتم های داده‌کاوی Evaluation and Prediction of the Impact of Parasite Waves and Cell Phone Use by Pregnant Mothers on the Volume of Amniotic Fluid based on Data Mining Algorithms 1 1 مقدمه: امروزه اثرات ناشی از استفاده‌های مکرر از موبایل باعث بروز مشکلاتی گردیده است. این‌ امواج موجب ایجاد اختلالاتی در عملکرد سیستم‌های مختلف بدن انسان و حتی جنین در حال رشد شده است. در این تحقیق با استفاده از تکنیک‌های داده‌کاوی به این سؤال پاسخ داده شد که تأثیر استفاده مادران باردار از موبایل و اینترنت بر کم شدن میزان آب دور جنین چگونه است؟ روش: در فاز اول طراحی پرسشنامه و ارزیابی آن توسط متخصصین زنان، زایمان و مخابرات صورت گرفت. پرسشنامه شامل سؤالاتی مانند میزان مکالمه با تلفن همراه در طول روز، استفاده از اینترنت، وای فای و... است. پس از جمع‌آوری داده و پیش پردازش آن‌ها با الگوریتم Perdictive Apriori  تأثیر ویژگی‌های ذکر شده بر کم شدن میزان آب دور جنین بررسی و قوانین انجمنی مناسب استخراج شد. سپس با استفاده از دسته‌بندی‌های مختلف به پیش‌بینی بروز این خطر پرداخته شد. نتایج: این امر موجب آگاهی زودهنگام پزشک و بیمار شده که می‌تواند با تغییر سبک زندگی در نحوه استفاده مادران از موبایل و بروز این مشکل جلوگیری به عمل آورد. الگوریتم SVM موفق به کسب بهترین نتیجه با دقتی برابر با %80‌ شد. نتیجه‌گیری: مهم‌ترین قانون نشان داد در صورتی که مادران باردار‌، بالای 6 ساعت در روز از تلفن همراه و 4 ساعت به بالا از اینترنت و 8 ساعت به بالا اینترنت با وای فای روشن استفاده نمایند، حتماً آب دور جنین آن‌ها کم خواهد بود. همچنین روش پیشنهادی قابلیت پیش‌بینی بروز این خطر در شرایط کنونی مادران باردار را دارا می‌باشد. 2 Introduction: Nowadays, the effects of radiation and constant use of cell phones have led to some problems. These radiations cause disorders in different systems of human body and even in a growing fetus. The aim of this study was to find the effect of using cell phone and internet by pregnant women on the amount of amniotic fluid. Method: First, a questionnaire was designed and evaluated by obstetricians and telecommunication specialists. The questionnaire includes questions regarding the amount of using cell phone, internet and Wi-Fi during a day. After data collection and preprocessing with predictive Apriori algorithm, the effective factors on the amount of amniotic fluid were investigated and proper associative rules were extracted. The next phase is dedicated to predicting the risks of decreased amniotic fluid by classification algorithms. Results: Early awareness of doctors as well as the patient can cause avoiding this problem through some related life style changes by mothers. SVM algorithm was successful in arriving at the best results with 80% accuracy. Conclusion: The most important rule showed that more than 6 hours cell phone conversation, more than 4 hours internet use and more than 8 hours Wi-Fi use by pregnant women can definitely decrease the amount of amniotic fluid. Also, the proposed method, using different classification algorithms, can predict the risk of decreasing the amount of amniotic fluid in pregnant mothers. 243 254 2018/08/312019/01/62019/05/312019/04/72018/11/212019/02/12018/11/2 1397/8/11 2019/07/152019/07/12019/07/132019/06/92019/07/132019/05/182019/07/13 1398/4/22 زهرا حسینی Zahra Hoseini دانشجوی کارشناسی ارشد مهندسی کامپیوتر، گروه مهندسی کامپیوتر، مؤسسه آموزش عالی زند دانش‌گستر، شیراز، ایران ایران Z.Hosseini7090@yahoo.com فاطمه مویدی Fatemeh Moayedi دکتری مهندسی کامپیوتر، استادیار، گروه مهندسی کامپیوتر، مجتمع آموزش عالی لارستان، لار، ایران ایران fmoayyedi@gmail.com سید ابراهیم دشتی Ebrahim Dashti دکتری مهندسی کامپیوتر، استادیار، گروه مهندسی کامپیوتر، واحد جهرم، دانشگاه آزاد اسلامی، جهرم، ایران ایران sayed.dashty@gmail.com Oligohydramnios Cell phone radiation Association Rule Mining Support Vector Machine Prediction کاهش میزان آب دور جنین‌ امواج موبایل قوانین انجمنی ماشین بردار پشتیبان پیش‌بینی 1. Aggarwal A, Gupta A. Effect of electromagnetic radiations on humans: A study. Technology Symposium (TechSym), IEEE Students; 2011 Jan 14-16; Kharagpur, India: IEEE; 2011. doi: 10.1109/TECHSYM.2011.5783805##2. Bodera P, Stankiewicz W, Zawada K, Antkowiak B, Paluch M, Kieliszek J, et al. Changes in antioxidant capacity of blood due to mutual action of electromagnetic field (1800 MHz) and opioid drug (tramadol) in animal model of persistent inflammatory state. Pharmacol Rep 2013;65(2):421-8. doi: 10.1016/S1734-1140(13)71017-X##3. Cicily TJ, Sherin S, Gopal AK. Effect of hydration therapy on oligohydramnios. Int J Reprod Contracept Obstet Gynecol 2017;6(5):1800-5. doi: http://dx.doi.org/10.18203/2320-1770.ijrcog20171538##4. Asnafi N, Bouzari Z, Mohammadnetadj M. Oligohydramnios and Pregnancy Outcome: Ten-Years Review. International Biological and Biomedical Journal 2015;1(1):23-8. [In Persian]##5. Bhat S. Study of effect of oligohydramnios on maternal and fetal outcome. International Journal of Medical and Dental Sciences 2015;4(1):582-8. https://doi.org/10.18311/ijmds/2015/19779##6. Fayyad U, Piatetsky-Shapiro G, Smyth P. From data mining to knowledge discovery in databases. AI Magazine 1996; 17(3): 37. doi: https://doi.org/10.1609/aimag.v17i3.1230##7. Heynick LN, Merritt JH. Radiofrequency fields and teratogenesis. Bioelectromagnetics. 2003;Suppl 6:S174-86. doi:10.1002/bem.10127##8. Baharara J, Parivar K, Oryan S, Ashraf A. The effects of long-term exposure with simulating cell phone waves on gonads of female Balb/C mouse. J Reprod Infertil 2004;5(3):217-26.##9. Baharara J, Parivar K, Ashraf AR, Majidi B. The effects of mobile phone waves (940MHz) on embryonic development of hematopoiesis system in Balb/C mouse. J Shahrekord Univ Med Sci 2008;10(1):1-8. [In Persian]##10. Baharara J, Hadad F, Shariatzade MA, Amirahmadi M. The effects of cellular phone waves on the frequency micronucleus in newborn and adult Balb/c mouse. Zahedan Journal of Research in Medical Sciences 2011; 13(4): 6-11. [In Persian]##11. Hosseini SE, Zia Z. Effect of cell-phone radiation in pregnancy on serum levels of sexual hormones and dynastic cells in adult female offspring in rats. Journal of Ardabil University of Medical Sciences 2016; 16(1): 23-31. [In Persian]##12. El-Sayed A, Badr HS, Yahia R, Salem SM, Kandil AM. Effects of thirty minute mobile phone irradiation on morphological and physiological parameters and gene expression in pregnant rats and their fetuses. African Journal of Biotechnology 2011;10(84):19670-80. http://dx.doi.org/10.5897/AJB11.1274##13. Balmori A. Electromagnetic pollution from phone masts. Effects on wildlife. Pathophysiology 2009;16(2-3):191-9. doi: 10.1016/j.pathophys.2009.01.007.##14. Bonett DG, Thomas A. Wright. Cronbach's alpha reliability: Interval estimation, hypothesis testing, and sample size planning. Journal of Organizational Behavior 2015; 36(1): 3-15. https://doi.org/10.1002/job.1960##15. Kyriaki M, Eleni T, Efi P, Ourania K, Vassilios S, Lambros V. The EORTC core quality of life questionnaire (QLQ-C30, version 3.0) in terminally ill cancer patients under palliative care: validity and reliability in a Hellenic sample. Int J Cancer 2001;94(1):135-9. doi: 10.1002/ijc.1439##16. Naghibi T, Hoffmann S, Pfister B. A semidefinite programming based search strategy for feature selection with mutual information measure IEEE. Transactions on Pattern Analysis and Machine Intelligence 2015; 37(8): 1529-41. doi: 10.1109/TPAMI.2014.2372791##17. Kamkar I, Gupta SK, Phung D, Venkatesh S. Stable feature selection for clinical prediction: Exploiting ICD tree structure using Tree-Lasso. Journal of Biomedical Informatics 2015;53:277-90. doi.org/10.1016/j.jbi.2014.11.013##18. Mingxia L, Zhang D. Feature selection with effective distance. Neurocomputing 2016; 215: 100-9. doi.org/10.1016/j.neucom.2015.07.155##19. Mahmoodi SA, Mirzaei K, Mahmoodi SM. Using association rules for the detection of risk factors in gastric cancer. Journal of Health and Biomedical Informatics. 2015;1(2):95-103. [In Persian]##20. Agrawal R, Imieliński T, Swami A. Mining association rules between sets of items in large databases. Proceeding SIGMOD '93 Proceedings of the 1993 ACM SIGMOD International Conference on Management Of Data; 1993 May 25 – 28; Washington, D.C., USA: ACM; 1993. p. 207-16. doi:10.1145/170035.170072##21. Ruijuan H. Medical Data mining based on association rules. Computer and Information Science 2010; 3(4): 104-8. doi: 10.5539/cis.v3n4p104##22. Mahmoodi SA, Mirzaei K, Mahmoodi SM. Using Association Rules for the Detection of Risk Factors in Gastric Cancer. Journal of Health and Biomedical Informatics 2015;1(2):95-103. [In Persian]##23. Agrawal R, Srikant R. Fast algorithms for mining association rules. Proceeding VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases; 1994 Sep 12-15; Morgan Kaufmann Publishers Inc. San Francisco, CA, USA: ACM; 1994. p. 487-99.##24. Scheffer T. Finding association rules that trade support optimally against confidence. In: De Raedt L, Siebes A, editors. Principles of Data Mining and Knowledge Discovery. PKDD 2001. Lecture Notes in Computer Science, vol 2168. Berlin, Heidelberg; Springer; 2001. p. 424-35. doi.org/10.1007/3-540-44794-6_35##25. Shin KS, Lee TS, Kim HJ. An application of support vector machines in bankruptcy prediction model. Expert Systems with Applications. 2005;28(1):127-35. doi.org/10.1016/j.eswa.2004.08.009##26. Parkin DM, Bray F, Ferlay J, Pisani P. Global cancer statistics, 2002. CA Cancer J Clin 2005;55(2):74-108. doi:10.3322/canjclin.55.2.74##27. Cetin H, Naziroglu M, Celik O, Yuksel M, Pastaci N, Ozkaya MO. Liver antioxidant stores protect the brain from electromagnetic radiation (900 and 1800 MHz)-induced oxidative stress in rats during pregnancy and the development of offspring. J Matern Fetal Neonatal Med 2014;27(18):1915-21. doi: 10.3109/14767058.2014.898056##1. Aggarwal A, Gupta A. Effect of electromagnetic radiations on humans: A study. Technology Symposium (TechSym), IEEE Students; 2011 Jan 14-16; Kharagpur, India: IEEE; 2011. doi: 10.1109/TECHSYM.2011.5783805##2. Bodera P, Stankiewicz W, Zawada K, Antkowiak B, Paluch M, Kieliszek J, et al. Changes in antioxidant capacity of blood due to mutual action of electromagnetic field (1800 MHz) and opioid drug (tramadol) in animal model of persistent inflammatory state. Pharmacol Rep 2013;65(2):421-8. doi: 10.1016/S1734-1140(13)71017-X##3. Cicily TJ, Sherin S, Gopal AK. Effect of hydration therapy on oligohydramnios. Int J Reprod Contracept Obstet Gynecol 2017;6(5):1800-5. doi: http://dx.doi.org/10.18203/2320-1770.ijrcog20171538##4. Asnafi N, Bouzari Z, Mohammadnetadj M. Oligohydramnios and Pregnancy Outcome: Ten-Years Review. International Biological and Biomedical Journal 2015;1(1):23-8. [In Persian]##5. Bhat S. Study of effect of oligohydramnios on maternal and fetal outcome. International Journal of Medical and Dental Sciences 2015;4(1):582-8. https://doi.org/10.18311/ijmds/2015/19779##6. Fayyad U, Piatetsky-Shapiro G, Smyth P. From data mining to knowledge discovery in databases. AI Magazine 1996; 17(3): 37. doi: https://doi.org/10.1609/aimag.v17i3.1230##7. Heynick LN, Merritt JH. Radiofrequency fields and teratogenesis. Bioelectromagnetics. 2003;Suppl 6:S174-86. doi:10.1002/bem.10127##8. Baharara J, Parivar K, Oryan S, Ashraf A. The effects of long-term exposure with simulating cell phone waves on gonads of female Balb/C mouse. J Reprod Infertil 2004;5(3):217-26.##9. Baharara J, Parivar K, Ashraf AR, Majidi B. The effects of mobile phone waves (940MHz) on embryonic development of hematopoiesis system in Balb/C mouse. J Shahrekord Univ Med Sci 2008;10(1):1-8. [In Persian]##10. Baharara J, Hadad F, Shariatzade MA, Amirahmadi M. The effects of cellular phone waves on the frequency micronucleus in newborn and adult Balb/c mouse. Zahedan Journal of Research in Medical Sciences 2011; 13(4): 6-11. [In Persian]##11. Hosseini SE, Zia Z. Effect of cell-phone radiation in pregnancy on serum levels of sexual hormones and dynastic cells in adult female offspring in rats. Journal of Ardabil University of Medical Sciences 2016; 16(1): 23-31. [In Persian]##12. El-Sayed A, Badr HS, Yahia R, Salem SM, Kandil AM. Effects of thirty minute mobile phone irradiation on morphological and physiological parameters and gene expression in pregnant rats and their fetuses. African Journal of Biotechnology 2011;10(84):19670-80. http://dx.doi.org/10.5897/AJB11.1274##13. Balmori A. Electromagnetic pollution from phone masts. Effects on wildlife. Pathophysiology 2009;16(2-3):191-9. doi: 10.1016/j.pathophys.2009.01.007.##14. Bonett DG, Thomas A. Wright. Cronbach's alpha reliability: Interval estimation, hypothesis testing, and sample size planning. Journal of Organizational Behavior 2015; 36(1): 3-15. https://doi.org/10.1002/job.1960##15. Kyriaki M, Eleni T, Efi P, Ourania K, Vassilios S, Lambros V. The EORTC core quality of life questionnaire (QLQ-C30, version 3.0) in terminally ill cancer patients under palliative care: validity and reliability in a Hellenic sample. Int J Cancer 2001;94(1):135-9. doi: 10.1002/ijc.1439##16. Naghibi T, Hoffmann S, Pfister B. A semidefinite programming based search strategy for feature selection with mutual information measure IEEE. Transactions on Pattern Analysis and Machine Intelligence 2015; 37(8): 1529-41. doi: 10.1109/TPAMI.2014.2372791##17. Kamkar I, Gupta SK, Phung D, Venkatesh S. Stable feature selection for clinical prediction: Exploiting ICD tree structure using Tree-Lasso. Journal of Biomedical Informatics 2015;53:277-90. doi.org/10.1016/j.jbi.2014.11.013##18. Mingxia L, Zhang D. Feature selection with effective distance. Neurocomputing 2016; 215: 100-9. doi.org/10.1016/j.neucom.2015.07.155##19. Mahmoodi SA, Mirzaei K, Mahmoodi SM. Using association rules for the detection of risk factors in gastric cancer. Journal of Health and Biomedical Informatics. 2015;1(2):95-103. [In Persian]##20. Agrawal R, Imieliński T, Swami A. Mining association rules between sets of items in large databases. Proceeding SIGMOD '93 Proceedings of the 1993 ACM SIGMOD International Conference on Management Of Data; 1993 May 25 – 28; Washington, D.C., USA: ACM; 1993. p. 207-16. doi:10.1145/170035.170072##21. Ruijuan H. Medical Data mining based on association rules. Computer and Information Science 2010; 3(4): 104-8. doi: 10.5539/cis.v3n4p104##22. Mahmoodi SA, Mirzaei K, Mahmoodi SM. Using Association Rules for the Detection of Risk Factors in Gastric Cancer. Journal of Health and Biomedical Informatics 2015;1(2):95-103. [In Persian]##23. Agrawal R, Srikant R. Fast algorithms for mining association rules. Proceeding VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases; 1994 Sep 12-15; Morgan Kaufmann Publishers Inc. San Francisco, CA, USA: ACM; 1994. p. 487-99.##24. Scheffer T. Finding association rules that trade support optimally against confidence. In: De Raedt L, Siebes A, editors. Principles of Data Mining and Knowledge Discovery. PKDD 2001. Lecture Notes in Computer Science, vol 2168. Berlin, Heidelberg; Springer; 2001. p. 424-35. doi.org/10.1007/3-540-44794-6_35##25. Shin KS, Lee TS, Kim HJ. An application of support vector machines in bankruptcy prediction model. Expert Systems with Applications. 2005;28(1):127-35. doi.org/10.1016/j.eswa.2004.08.009##26. Parkin DM, Bray F, Ferlay J, Pisani P. Global cancer statistics, 2002. CA Cancer J Clin 2005;55(2):74-108. doi:10.3322/canjclin.55.2.74##27. Cetin H, Naziroglu M, Celik O, Yuksel M, Pastaci N, Ozkaya MO. Liver antioxidant stores protect the brain from electromagnetic radiation (900 and 1800 MHz)-induced oxidative stress in rats during pregnancy and the development of offspring. J Matern Fetal Neonatal Med 2014;27(18):1915-21. doi: 10.3109/14767058.2014.898056 ##

بررسی خصوصیات دینامیک باقی‌مانده‌های جایگاه اتصال وارفارین-آزاپروپازون در پروتئین آلبومین سرم انسانی Investigating Dynamic Properties of Residues of Warfarin-Azapropazone Binding Site in Human Serum Albumin 1 1 مقدمه: آلبومین سرم انسانی یکی از مهم‌ترین پروتئین‌های خون است که توانایی اتصال به گستره زیادی از مواد مختلف و داروهای مختلفی مانند وارفارین را دارد؛ بنابراین شناخت سرم آلبومین در مطالعات دارویی نقش بسیار مهمی دارد. در این پژوهش به مطالعه دینامیک باقی‌مانده‌های جایگاه اتصال وارفارین سرم آلبومین پرداخته شد. روش: ابتدا ساختار 3 بعدی آلبومین (PDB ID:4G04) از پایگاه داده RCSB دریافت شد. سپس به کمک بسته نرم‌افزاری گرومکس شبیه‌سازی دینامیک مولکولی در مدت 30 نانوثانیه بر روی زنجیره آی این ساختار کریستالوگرافی انجام شد. نتایج: با استفاده از آنالیز RMSD که بر روی باقی‌مانده‌های جایگاه اتصال انجام شد، مشاهده شد که 2 باقی‌مانده آرژینین 186 و 218 دارای تغییرات RMSD زیادی هستند، 2 باقی‌مانده لیزین 185 و 190 تقریباً دارای تغییرات زیادی هستند؛ ولی از آرژینین‌ها کمتر است و باقی‌مانده‌های دیگر مانند گلیسین 189 دارای تغییرات بسیار کمی هستند. تغییرات RMSD باقی‌مانده‌ها با سطح در دسترس حلال رابطه مستقیم دارد. نتیجه‌گیری: نتایج به دست آمده نشان داد که جایگاه اتصال وارفارین در آلبومین می‌تواند وضعیت‌های کنفورماسیونی متنوعی با توجه به دینامیک باقی‌مانده‌های خود بگیرد. این مسئله می‌تواند در طراحی دارو بسیار مهم باشد. در کل بر اساس نتایج، باقی‌مانده‌ها به 3 دسته تقسیم شدند. از میان آن‌ها تریپتوفان 214 که بر اساس مقالات مختلف یکی از مهم‌ترین باقی‌مانده‌های جایگاه اتصال به حساب می‌آید جزء کم تحرک‌ترین باقی‌مانده‌های جایگاه اتصال دسته‌بندی شد. 2 Introduction: Human Serum Albumin (HSA) is one of the most important proteins in blood that can bind a wide range of components and different drugs such as Warfarin and is also circulated in the body by HSA. Therefore, studying HSA is very significant in pharmacology. In this research, dynamic behavior of residues of Warfain binding site of HSA has been investigated. Methods: Firstly, PDB format of HSA was downloaded from RCSB (PDB ID:2BX8) which was in complex with Azapropazone. After that, molecular dynamics simulation was during 30ns by GROMACS package. CHIMERA software was used to discover residues of binding site. Results: After RMSD analysis was done on residues of binding site, it was seen that Arginines 186 and 218 have a wide fluctuations in their RMSD plot. Also, two Lysines 185 & 190 have nearly wide fluctuations of RMSD, this fluctuations were less than Arginine's, however. Other residues such as Glycine 189 have few fluctuations. Fluctuations in RMSD have direct relationship with accessible surface area (ASA) of the residues. Conclusions: The results show that the Warfarin binding site in HSA can have various conformational situations because of the dynamics of its residues.  This problem can be important to drug design. Overall, according to the results in this study, residues have been divided into 3 categories. Among them, Tryptophan 214 according to different articles is one of the most important residues site and based on our result, it stays on category of small mobility.  255 263 2018/08/312019/01/62019/05/312019/04/72018/11/212019/02/12018/11/22019/04/14 1398/1/25 2019/07/152019/07/12019/07/132019/06/92019/07/132019/05/182019/07/132019/06/17 1398/3/27 امیرحسین ساعی نیا Amir Hossein Saeinia دانشجوی کارشناسی علوم آزمایشگاهی، گروه علوم آزمایشگاهی، دانشکده پیراپزشکی، علوم پزشکی تهران، دانشگاه آزاد اسلامی، تهران، ایران ایران saeiamirhossein@yahoo.com محمد تقی زاده Mohammad Taghizadeh دکتری تخصصی بیوانفورماتیک، گروه بیوتکنولوژی میکروبی، دانشکده علوم و فناوری‌های نوین، علوم پزشکی تهران، دانشگاه آزاد اسلامی، تهران، ایران ایران mtaghizadeh@ut.ac.ir مهدی علوی Seyed Mehdi Alavi دکتری تخصصی سلولی- مولکولی، دانشیار، گروه بیوتکنولوژی کشاورزی، پژوهشگاه ملی مهندسی ژنتیک و زیست‌فناوری، تهران، ایران ایران mealavi@yahoo.com Human Serum Albumin Molecular Dynamics simulation Sudlow site I Azapropazone RMSD آلبومین سرم انسانی شبیه‌سازی دینامیک مولکولی جایگاه اتصال سادلو I آزاپروپازون آر ام اس دی 1. 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