ORIGINAL_ARTICLE
Editorial
https://jbpe.sums.ac.ir/article_43374_d4e34820209dd266384fd1caa1a456c7.pdf
2019-02-01
1
2
10.31661/jbpe.v0i0.1129
B
Tahayori
tahayori@gmail.com
1
Department of Medical Physics, School of Medicine, Shiraz University of Medical Science, Shiraz, Iran
LEAD_AUTHOR
ORIGINAL_ARTICLE
Parametrization of Pedestrian Injuries and its Utilisation in Proving Traffic Accidents Course Using Injury Signatures and Contact Signatures
Background: The paper points out the present limited possibility of using the verbal description of injuries for the needs of experts from the field of road transportation as relevant criminalistics traces, as well as the options of the FORTIS system that creates a new area for a deeper interdisciplinary approach in the field of expert evidence. Further a description of how to create injury signatures and contact signatures and the possibilities of their evaluation and mutual comparison based on the proven individual attributes are described.Objective: To evaluate pedestrian injuries by the new proper FORTIS system and to show FORTIS valuability in the assessment of mechanical violence and mechanism of injuries.Material and Methods: Cases of traffic injuries including photodocumentation, graphic schemes, medical files and autopsy protocols processed by the new FORTIS forensic system.Results: A collision between a pedestrian and a vehicle represents a matrix of physical violence having an effect on the pedestrian´s body and a matrix of the pedestrian´s body´s response to this violence. The analysis of individual cases shows the valuability of the FORTIS system.Conclusion: It is apparent that for the needs of traffic accidents analysts the FORTIS system has more options for being used in forensic medicine, as it covers not only a field of evaluation of traffic injuries but also all kinds of injuries and accidents (rail accidents, air accidents, violent crimes, etc.).
https://jbpe.sums.ac.ir/article_43373_d63210660ebf3efa7c7b507b7efffb14.pdf
2019-02-01
3
16
10.31661/jbpe.v0i0.850
Pedestrian
parametrization of injuries
collision analysis
Forensic medicine
accident case analysis
J
Mandelík
1
The University of Security Management in Kosice, ul. Kukučínova 17, 040 01 Kosice, Slovak Republic
LEAD_AUTHOR
N
Bobrov
nikita.bobrov@upjs.sk
2
Department of Forensic Medicine, Faculty of Medicine of University Hospital of P. J. Šafárik and University Hospital of L. Pasteur in Košice, Trieda SNP 1, 040 11 Kosice, Slovak Republic
AUTHOR
Z
Nevolná
3
Hospital of Horná Orava with Health Care Center in Trstená, Mieru 549/16, 028 01 Trstená, Slovak Republic
AUTHOR
Moser A, Steffan H, Kasanický G. The pedestrian model in PC-crash–The introduction of a multi body system and its validation. SAE transactions. 1999:794-802.
1
Bobrov N, Longauer F, Szabo M, Mandelík J, Mandelíková Z. Standardization of Injury Parameters at Pedestrians Traffic Accidents in Forensic Medicine. Proceedings to the 80th Anniversary of Pasteur Hospital Foundation; 2004. p. 62-66 (in Slovak).
2
Mandelík J. Pedestrian´s injuri parametrisation and its usage in description of traffic accidents. University of Žilina: PhD. Thesis; 2006. (in Slovak)
3
Bobrov N, Mandelík J, Havaj P. The possibilities of medico-legal evaluation of injuries and its usage in the interdisciplinary traffic accident solving. VŠBM Košice. 2017;185:21-9, 225. (in Slovak)
4
Bobrov N, Ginelliová A, Mandelík J, Longauer F, Mátyás T. The evaluation of soft tissue injury extent at polytrauma in pedestrian traffic accident cases. Folia Societatis Medicinae Legalis Slovacae. 2012;2(1):13-17. (in Slovak)
5
Bobrov N, Ginelliová A, Mandelík J. The injury quantification in traffic accident cases: the injury signature. Proceedings of the 4th Czech and Slovak Congress of Forensic Medicine with international participation; 2014. p. 41-45 (in Slovak)
6
Mandelík J, Bobrov N. Parametrization of Injuries by the FORTIS System and its Utilisation at Solving Traffic Accidents with Pedestrians by the Police. American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS). 2017;36(1):294-305.
7
Mandelík J, Bundzel M. Application of neural network in order to recognise individuality of course of vehicle and pedestrian body contacts during accidents. International Journal of Crashworthiness. 2018:1-14.
8
ORIGINAL_ARTICLE
Evaluation of Lung Density and Its Dosimetric Impact on Lung Cancer Radiotherapy: A Simulation Study
Background: The dosimetric parameters required in lung cancer radiation therapy are taken from a homogeneous water phantom; however, during treatment, the expected results are being affected because of its inhomogeneity. Therefore, it becomes necessary to quantify these deviations.Objective: The present study has been undertaken to find out inter- and intra- lung density variations and its dosimetric impact on lung cancer radiotherapy using Monte Carlo code FLUKA and PBC algorithms.Material and Methods: Density of 100 lungs was recorded from their CT images along with age. Then, after PDD calculated by FLUKA MC Code and PBC algorithm for virtual phantom having density 0.2 gm/cm3 and 0.4 gm/cm3 (density range obtained from CT images of 100 lungs) using Co-60 10 x10 cm2 beams were compared.Results: Average left and right lung densities were 0.275±0.387 and 0.270±0.383 respectively. The deviation in PBC calculated PDD were (+)216%, (+91%), (+)45%, (+)26.88%, (+)14%, (-)1%, (+)2%, (-)0.4%, (-)1%, (+)1%, (+)4%, (+)4.5% for 0.4 gm/cm3 and (+)311%, (+)177%, (+)118%, (+)90.95%, (+)72.23%, (+)55.83% ,(+)38.85%, (+)28.80%, (+)21.79%, (+)15.95%, (+)1.67%, (-) 2.13%, (+)1.27%, (+)0.35%, (-)1.79%, (-)2.75% for 0.2 gm/cm3 density mediums at depths of 1mm, 2mm, 3mm, 4mm, 5mm, 6 mm, 7 mm, 8mm, 9mm,10mm, 15mm, 30mm, 40mm, 50mm, 80mm and 100 mm, respectively.Conclusion: Large variations in inter- and intra- lung density were recorded. PBC overestimated the dose at air/lung interface as well as inside lung. The results of Monte Carlo simulation can be used to assess the performance of other treatment planning systems used in lung cancer radiotherapy.
https://jbpe.sums.ac.ir/article_43375_4cba0f79c21e1fd1f01316bb0d6b66b3.pdf
2019-02-01
17
28
10.31661/jbpe.v9i1Feb.430
PBC
Monte Carlo Code FLUKA
Variation in Lung Density
Virtual Phantom
Computed Tomography
T
Raj Verma
teerth05kashi@gmail.com
1
King George Medical University, UP, Lucknow, India
LEAD_AUTHOR
N
Kumar Painuly
2
King George Medical University, UP, Lucknow, India
AUTHOR
S
Prasad Mishra
mishrasp05@gmail.com
3
Dr.Ram Manohar Lohia Institute of Medical Sciences, Lucknow, India
AUTHOR
S A
Yoganathan
yoganathansa@yahoo.co.in
4
Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, India
AUTHOR
N
Singh
navinkgmu@gmail.com
5
King George Medical University, UP, Lucknow, India
AUTHOR
M L B
Bhatt
drmlbhatt@yahoo.com
6
King George Medical University, UP, Lucknow, India
AUTHOR
N
Jamal
7
King George Medical University, UP, Lucknow, India
AUTHOR
Fu W, Dai J, Hu Y. The influence of lateral electronic disequilibrium on the radiation treatment planning for lung cancer irradiation. Biomed Mater Eng. 2004;14:123-6. PubMed PMID: 14757959.
1
Tada T, Minakuchi K, Sakamoto H, Fukuda H, Bun M, Nakajima T. Inhomogeneity correction in radiotherapy for lung cancer in multicenter clinical trials. Radiat Med. 2002;20:191-4. PubMed PMID: 12296435.
2
Thomas SJ. A modified power-law formula for inhomogeneity corrections in beams of high-energy x rays. Med Phys. 1991;18:719-23. doi.org/10.1118/1.596665. PubMed PMID: 1921876.
3
Orton CG, Chungbin S, Klein EE, Gillin MT, Schultheiss TE, Sause WT. Study of lung density corrections in a clinical trial (RTOG 88-08). Radiation Therapy Oncology Group. Int J Radiat Oncol Biol Phys. 1998;41:787-94. doi.org/10.1016/S0360-3016(98)00117-5. PubMed PMID: 9652839.
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Papanikolaou N, Battista JJ, Boyer AL, Kappas C, Klein E, Mackie TR, et al. Tissue inhomogeneity corrections for megavoltage photon beams. AAPM Task Group. 2004;65:1-142.
5
Abdul Haneefa K, Cyriac TS, Musthafa M, Ganapathi Raman R, Hridya V, Siddhartha A, et al. FLUKA Monte Carlo for Basic Dosimetric Studies of Dual Energy Medical Linear Accelerator. Journal of Radiotherapy. 2014;2014.
6
Battistoni G, Cerutti F, Fasso A, Ferrari A, Muraro S, Ranft J, et al., editors. The FLUKA code: Description and benchmarking. Hadronic Shower Simulation Workshop (AIP Conference Proceedings Volume 896); 2007.
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Ferrari A, Sala PR, Fasso A, Ranft J. FLUKA: A multi-particle transport code (Program version 2005). 2005.
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Botta F, Mairani A, Hobbs RF, Vergara Gil A, Pacilio M, Parodi K, et al. Use of the FLUKA Monte Carlo code for 3D patient-specific dosimetry on PET-CT and SPECT-CT images. Phys Med Biol. 2013;58:8099-120. doi.org/10.1088/0031-9155/58/22/8099. PubMed PMID: 24200697. PubMed PMCID: 4037810.
9
Taleei R, Shahriari M, AGHAMIRI SMR. Evaluation of FLUKA Code in Simulation and Design of X-ray Tubes for X-ray Profile. 2006.
10
Thomas SJ. Relative electron density calibration of CT scanners for radiotherapy treatment planning. Br J Radiol. 1999;72:781-6. doi.org/10.1259/bjr.72.860.10624344. PubMed PMID: 10624344.
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McKenzie A. Cobalt-60 gamma-ray beams. BJR supplement/BIR. 1995;25:46-61.
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Speigel M. Probability and Statistics. Schaum’s Outline Series in Mathematics. McGraw-Hill, New York; 1975
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Fujisaki T, Kikuchi K, Saitoh H, Tohyama N, Myojoyama A, Osawa A, et al. Effects of density changes in the chest on lung stereotactic radiotherapy. Radiat Med. 2004;22:233-8. PubMed PMID: 15468943.
14
AC03205248 A. Absorbed dose determination in external beam radiotherapy: an international code of practice for dosimetry based on standards of absorbed dose to water: Internat. Atomic Energy Agency; 2000.
15
Praveenkumar RD, Santhosh KP, Augustine A. Estimation of inhomogenity correction factors for a Co-60 beam using Monte Carlo simulation. J Cancer Res Ther. 2011;7:308-13. doi.org/10.4103/0973-1482.87030. PubMed PMID: 22044813.
16
Verhaegen F, Seuntjens J. Monte Carlo modelling of external radiotherapy photon beams. Phys Med Biol. 2003;48:R107-64. doi.org/10.1088/0031-9155/48/21/R01. PubMed PMID: 14653555.
17
Joshi CP, Darko J, Vidyasagar PB, Schreiner LJ. Dosimetry of interface region near closed air cavities for Co-60, 6 MV and 15 MV photon beams using Monte Carlo simulations. J Med Phys. 2010;35:73-80. doi.org/10.4103/0971-6203.62197. PubMed PMID: 20589116. PubMed PMCID: 2884308.
18
Robinson D. Inhomogeneity correction and the analytic anisotropic algorithm. J Appl Clin Med Phys. 2008;9:2786. PubMed PMID: 18714283.
19
Tachibana M, Noguchi Y, Fukunaga J, Hirano N, Yoshidome S, Hirose T. Influence on dose calculation by difference of dose calculation algorithms in stereotactic lung irradiation: comparison of pencil beam convolution (inhomogeneity correction: batho power law) and analytical anisotropic algorithm. Nihon Hoshasen Gijutsu Gakkai Zasshi. 2009;65:1064-72. doi.org/10.6009/jjrt.65.1064. PubMed PMID: 19721315.
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Miura H, Masai N, Oh R-J, Shiomi H, Yamada K, Usmani MN, et al. Dosimetric Impact of Tumor Position and Lung Density Variations in Lung Stereotactic Body Radiotherapy. International Journal of Medical Physics, Clinical Engineering and Radiation Oncology. 2014;2014.
21
Aarup LR, Nahum AE, Zacharatou C, Juhler-Nottrup T, Knoos T, Nystrom H, et al. The effect of different lung densities on the accuracy of various radiotherapy dose calculation methods: implications for tumour coverage. Radiother Oncol. 2009;91:405-14. doi.org/10.1016/j.radonc.2009.01.008. PubMed PMID: 19297051.
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Dela Cruz CS, Tanoue LT, Matthay RA. Lung cancer: epidemiology, etiology, and prevention. Clin Chest Med. 2011;32:605-44. doi.org/10.1016/j.ccm.2011.09.001. PubMed PMID: 22054876. PubMed PMCID: 3864624.
23
ORIGINAL_ARTICLE
Physical and Dosimetric Aspect of Euromechanics Add-on Multileaf Collimator on Varian Clinac 2100 C/D
Background: Before treatment planning and dose delivery, quality assurance of multi-leaf collimator (MLC) has an important role in intensity-modulated radiation therapy (IMRT) due to the creation of multiple segments from optimization process. Objective: The purpose of this study is to assess the quality control of MLC leaves using EBT3 Gafchromic films. Material and Methods: Leaf Position accuracy and leaf gap reproducibility were checked with Garden fence test. The garden fence test consists of 5 thin bands A) 0.2 Cm width spaced at 2 Cm intervals and B) 1 Cm width spaced at 1 Cm intervals. Each leaf accuracy was analyzed with measuring the full-width half-maximum (FWHM). Maximum and average leaf transmission were measured with gafchromic EBT3 films from Ashland for both 6 MV and 18 MV beams.Results: Leaf positions were found to be in a range between 1.78 – 2.53 mm, instead of nominal 2 mm for the test A and between 9.09 – 10.36 mm, instead of nominal 10 mm for the test B. The Average radiation transmission of the MLC was noted 1.79% and 1.98% of the open 10x10 Cm2 field at isocenter for 6 MV and 18 MV beams, respectively. Maximum radiation transmission was noted 4.1% and 4.4% for 6 MV and 18 MV beams, respectively. Conclusion: In this study, application of gafchromic EBT3 films for the quality assurance of Euromechanics multileaf collimator was studied. Our results showed that the average leaf leakage and positional accuracy of this type of MLC were in the acceptance level based on the Protocols.
https://jbpe.sums.ac.ir/article_43376_4fc4f98e7e4bd2c7900bf3b19918e0df.pdf
2019-02-01
29
36
10.31661/jbpe.v0i0.1045
Multileaf Collimator
Mechanical Test
Garden Fence Test
Leaf Transmission
Leaf End Transmission
S A
Rohani
1
Department of Medical Physics, Tehran University of Medical Sciences, Tehran, Iran
AUTHOR
S R
Mahdavi
srmahdavi@hotmail.com
2
Radiation biology research center & medical Physics department, faculty of medicine, Iran University of Medical Sciences, Tehran, Iran
AUTHOR
A
Mostaar
3
Department of Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
AUTHOR
S
Ueltzhöffer
4
Department of Clinic for Radiotherapy and RadioOncology, Medical Faculty Mannheim of the University of Heidelberg, Heidelberg, Germany
AUTHOR
R
Mohammadi
5
Department of Medical Physics, Iran University of Medical Sciences, Tehran, Iran
AUTHOR
Gh
Geraily
ghazalegraily@yahoo.com
6
Department of Medical Physics, Tehran University of Medical Sciences, Tehran, Iran
LEAD_AUTHOR
Graves MN, Thompson AV, Martel MK, McShan DL, Fraass BA. Calibration and quality assurance for rounded leaf-end MLC systems. Med Phys. 2001;28:2227-33. doi: 10.1118/1.1413517. PubMed PMID: 11764026.
1
Deng J, Pawlicki T, Chen Y, Li J, Jiang SB, Ma CM. The MLC tongue-and-groove effect on IMRT dose distributions. Phys Med Biol. 2001;46:1039-60. PubMed PMID: 11324950.
2
Sharpe MB, Miller BM, Wong JW. Compensation of x-ray beam penumbra in conformal radiotherapy. Med Phys. 2000;27:1739-45. doi: 10.1118/1.1287283. PubMed PMID: 10984219.
3
Yang Y, Xing L. Using the volumetric effect of a finite-sized detector for routine quality assurance of multileaf collimator leaf positioning. Med Phys. 2003;30:433-41. doi: 10.1118/1.1543150. PubMed PMID: 12674244.
4
Bayouth JE, Wendt D, Morrill SM. MLC quality assurance techniques for IMRT applications. Med Phys. 2003;30:743-50. doi: 10.1118/1.1564091. PubMed PMID: 12772980.
5
LoSasso T, Kutcher GJ. Multileaf collimation versus alloy blocks: analysis of geometric accuracy. Int J Radiat Oncol Biol Phys. 1995;32:499-506.doi: 10.1016/0360-3016(94)00455-t . PubMed PMID: 7751191.
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LoSasso T, Chui CS, Kutcher GJ, Leibel SA, Fuks Z, Ling CC. The use of a multi-leaf collimator for conformal radiotherapy of carcinomas of the prostate and nasopharynx. Int J Radiat Oncol Biol Phys. 1993;25:161-70. doi: 10.1016/0360-3016(93)90337-u .PubMed PMID: 8420865.
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Galvin JM, Smith AR, Lally B. Characterization of a multi-leaf collimator system. Int J Radiat Oncol Biol Phys. 1993;25:181-92doi: 10.1016/0360-3016(93)90339-w. PubMed PMID: 8420867.
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Wang L, Li J, Paskalev K, Hoban P, Luo W, Chen L, et al. Commissioning and quality assurance of a commercial stereotactic treatment-planning system for extracranial IMRT. J Appl Clin Med Phys. 2006;7:21-34.doi: 10.1120/jacmp.2027.25368 . PubMed PMID: 16518314; PubMed Central PMCID: PMC5722476.
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Belec J, Patrocinio H, Verhaegen F. Development of a Monte Carlo model for the Brainlab microMLC. Phys Med Biol. 2005;50:787-99. doi: 10.1088/0031-9155/50/5/005. PubMed PMID: 15798255.
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Casanova Borca V, Pasquino M, Russo G, Grosso P, Cante D, Sciacero P, et al. Dosimetric characterization and use of GAFCHROMIC EBT3 film for IMRT dose verification. J Appl Clin Med Phys. 2013;14:4111. doi: 10.1120/jacmp.v14i2.4111. PubMed PMID: 23470940; PubMed Central PMCID: PMC5714357.
11
Farah N, Francis Z, Abboud M. Analysis of the EBT3 Gafchromic film irradiated with 6 MV photons and 6 MeV electrons using reflective mode scanners. Phys Med. 2014;30:708-12. doi: 10.1016/j.ejmp.2014.04.010. PubMed PMID: 24880678.
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International Specialty Products (ISP) [Internet]. Gafchromic™ Dosimetry Media, Type EBT-3. Avilale from: http://www.gafchromic.com/documents/EBT3_Specifications.pdf
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Niroomand-Rad A, Blackwell CR, Coursey BM, Gall KP, Galvin JM, McLaughlin WL, et al. Radiochromic film dosimetry: recommendations of AAPM Radiation Therapy Committee Task Group 55. American Association of Physicists in Medicine. Med Phys. 1998;25:2093-115. doi: 10.1118/1.598407. PubMed PMID: 9829234.
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Chui CS, Spirou S, LoSasso T. Testing of dynamic multileaf collimation. Med Phys. 1996;23:635-41. doi: 10.1118/1.597699. PubMed PMID: 8724734.
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Bhardwaj AK, Kehwar TS, Chakarvarti SK, Oinam AS, Sharma SC. Dosimetric and qualitative analysis of kinetic properties of millennium 80 multileaf collimator system for dynamic intensity modulated radiotherapy treatments. J Cancer Res Ther. 2007;3:23-8.doi: 10.4103/0973-1482.31967 . PubMed PMID: 17998715.
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Venencia CD, Besa P. Commissioning and quality assurance for intensity modulated radiotherapy with dynamic multileaf collimator: experience of the Pontificia Universidad Catolica de Chile. J Appl Clin Med Phys. 2004;5:37-54.doi: 10.1120/jacmp.2021.25275 . PubMed PMID: 15753938; PubMed Central PMCID: PMC5723486.
17
Mamalui-Hunter M, Li H, Low DA. MLC quality assurance using EPID: a fitting technique with subpixel precision. Med Phys. 2008;35:2347-55. doi: 10.1118/1.2919560. PubMed PMID: 18649468.
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Chang J, Obcemea CH, Sillanpaa J, Mechalakos J, Burman C. Use of EPID for leaf position accuracy QA of dynamic multi-leaf collimator (DMLC) treatment. Med Phys. 2004;31:2091-6. doi: 10.1118/1.1760187. PubMed PMID: 15305462.
19
Baghani HR, Aghamiri SM, Mahdavi SR, Robatjazi M, Zadeh AR, Akbari ME, et al. Dosimetric evaluation of Gafchromic EBT2 film for breast intraoperative electron radiotherapy verification. Phys Med. 2015;31:37-42. doi: 10.1016/j.ejmp.2014.08.005. PubMed PMID: 25231546.
20
Falahati F, Nickfarjam A, Shabani M. A feasibility study of IMRT of lung cancer using gafchromic EBT3 film. Journal of Biomedical Physics and Engineering. 2018;8.doi: 10.31661/jbpe.v0i0.791.
21
Devic S, Seuntjens J, Sham E, Podgorsak EB, Schmidtlein CR, Kirov AS, et al. Precise radiochromic film dosimetry using a flat-bed document scanner. Med Phys. 2005;32:2245-53. doi: 10.1118/1.1929253. PubMed PMID: 16121579.
22
Sorriaux J, Kacperek A, Rossomme S, Lee JA, Bertrand D, Vynckier S, et al. Evaluation of Gafchromic® EBT3 films characteristics in therapy photon, electron and proton beams. Phys Med. 2013;29:599-606.doi: 10.1016/j.ejmp.2012.10.001.
23
Antypas C, Floros I, Rouchota M, Armpilia C, Lyra M. MLC positional accuracy evaluation through the Picket Fence test on EBT2 films and a 3D volumetric phantom. J Appl Clin Med Phys. 2015;16:5185. doi: 10.1120/jacmp.v16i2.5185. PubMed PMID: 26103188; PubMed Central PMCID: PMC5690090.
24
Sumida I, Yamaguchi H, Kizaki H, Koizumi M, Ogata T, Takahashi Y, et al. Quality assurance of MLC leaf position accuracy and relative dose effect at the MLC abutment region using an electronic portal imaging device. J Radiat Res. 2012;53:798-806. doi: 10.1093/jrr/rrs038. PubMed PMID: 22843372; PubMed Central PMCID: PMC3430416.
25
Klein EE, Hanley J, Bayouth J, Yin FF, Simon W, Dresser S, et al. Task Group 142 report: quality assurance of medical accelerators. Med Phys. 2009;36:4197-212. doi: 10.1118/1.3190392. PubMed PMID: 19810494.
26
Kirby M, Ryde S, Hall C. Acceptance testing and commissioning of linear accelerators. York, UK: Institute of Physics and Engineering in Medicine. 2006.
27
Losasso T. IMRT delivery performance with a varian multileaf collimator. Int J Radiat Oncol Biol Phys. 2008;71:S85-8. doi: 10.1016/j.ijrobp.2007.06.082. PubMed PMID: 18406945.
28
Li J, Zhang X-Z, Gui L-G, Zhang J, Tang X-B, Ge Y, et al. Clinical Feasibility of Leakage and Transmission Radiation Dosimetry Using Multileaf Collimator of ELEKTA Synergy-S Accelerator During Conventional Radiotherapy. Journal of Medical Imaging and Health Informatics. 2016;6:409-15.doi: 10.1166/jmihi.2016.1706.
29
Das IJ, Desobry GE, McNeeley SW, Cheng EC, Schultheiss TE. Beam characteristics of a retrofitted double-focused multileaf collimator. Med Phys. 1998;25:1676-84. doi: 10.1118/1.598348. PubMed PMID: 9775373.
30
ORIGINAL_ARTICLE
Estimation of Dosimetric Parameters based on KNR and KNCSF Correction Factors for Small Field Radiation Therapy at 6 and 18 MV Linac Energies using Monte Carlo Simulation Methods
Background: Estimating dosimetric parameters for small fields under non-reference conditions leads to significant errors if done based on conventional protocols used for large fields in reference conditions. Hence, further correction factors have been introduced to take into account the influence of spectral quality changes when various detectors are used in non-reference conditions at different depths and field sizes.Objective: Determining correction factors (KNR and KNCSF) recommended recently for small field dosimetry formalism by American Association of Physicists in Medicine (AAPM) for different detectors at 6 and 18 MV photon beams.Methods: EGSnrc Monte Carlo code was used to calculate the doses measured with different detectors located in a slab phantom and the recommended KNR and KNCSF correction factors for various circular small field sizes ranging from 5-30 mm diameters. KNR and KNCSF correction factors were determined for different active detectors (a pinpoint chamber, EDP-20 and EDP-10 diodes) in a homogeneous phantom irradiated to 6 and 18 MV photon beams of a Varian linac (2100C/D). Results: KNR correction factor estimated for the highest small circular field size of 30 mm diameter for the pinpoint chamber, EDP-20 and EDP-10 diodes were 0.993, 1.020 and 1.054; and 0.992, 1.054 and 1.005 for the 6 and 18 MV beams, respectively. The KNCSF correction factor estimated for the lowest circular field size of 5 mm for the pinpoint chamber, EDP-20 and EDP-10 diodes were 0.994, 1.023, and 1.040; and 1.000, 1.014, and 1.022 for the 6 and 18 MV photon beams, respectively.Conclusion: Comparing the results obtained for the detectors used in this study reveals that the unshielded diodes (EDP-20 and EDP-10) can confidently be recommended for small field dosimetry as their correction factors (KNR and KNCSF) was close to 1.0 for all small field sizes investigated and are mainly independent from the electron beam spot size.
https://jbpe.sums.ac.ir/article_43377_78500ca0bbe512917c82be52b247df82.pdf
2019-02-01
37
50
10.31661/jbpe.v9i1Feb.414
Small field radiotherapy
Correction factors
TG155
Monte Carlo
Pinpoint chamber
Diode dosimeter
S A
Rahimi
seyedali.rahimi@modares.ac.ir
1
PhD Candidate, Department of Medical Physics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
AUTHOR
B
Hashemi
bhashemi@modares.ac.ir
2
Associate Professor, Department of Medical Physics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
LEAD_AUTHOR
S R
Mahdavi
srmahdavi@hotmail.com
3
Associate Professor, Department of Medical Physics, Faculty of Medicine, Iran University of Medical Sciences, Tehran, Iran
AUTHOR
Duggan DM, Coffey CW, 2nd. Small photon field dosimetry for stereotactic radiosurgery. Med Dosim. 1998;23:153-9. doi.org/10.1016/S0958-3947(98)00013-2. PubMed PMID: 9783268.
1
Ding GX, Duggan DM, Coffey CW. Commissioning stereotactic radiosurgery beams using both experimental and theoretical methods. Phys Med Biol. 2006;51:2549-66. doi.org/10.1088/0031-9155/51/10/013. PubMed PMID: 16675869.
2
Das IJ, Ding GX, Ahnesjö A. Small fields: nonequilibrium radiation dosimetry. Medical physics. 2008;35:206-15. doi.org/10.1118/1.2815356.
3
Czarnecki D, Zink K. Monte Carlo calculated correction factors for diodes and ion chambers in small photon fields. Phys Med Biol. 2013;58:2431-44. doi.org/10.1088/0031-9155/58/8/2431. PubMed PMID: 23514734.
4
Andreo P, Burns DT, Hohlfeld K, Huq MS, Kanai T, Laitano F, et al. Absorbed dose determination in external beam radiotherapy: an international code of practice for dosimetry based on standards of absorbed dose to water. IAEA TRS. 2000.
5
Almond PR, Biggs PJ, Coursey B, Hanson W, Huq MS, Nath R, et al. AAPM’s TG-51 protocol for clinical reference dosimetry of high-energy photon and electron beams. Medical physics. 1999;26:1847-70. doi.org/10.1118/1.598691.
6
Kawrakow I, Rogers DW, Walters BR. Large efficiency improvements in BEAMnrc using directional bremsstrahlung splitting. Med Phys. 2004;31:2883-98. doi.org/10.1118/1.1788912. PubMed PMID: 15543798.
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9
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10
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24
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Cranmer-Sargison G, Weston S, Evans JA, Sidhu NP, Thwaites DI. Implementing a newly proposed Monte Carlo based small field dosimetry formalism for a comprehensive set of diode detectors. Med Phys. 2011;38:6592-602. doi.org/10.1118/1.3658572. PubMed PMID: 22149841.
29
da Rosa LA, Cardoso SC, Campos LT, Alves VG, Batista DV, Facure A. Percentage depth dose evaluation in heterogeneous media using thermoluminescent dosimetry. J Appl Clin Med Phys. 2010;11:2947. PubMed PMID: 20160687.
30
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35
Scott AJ, Nahum AE, Fenwick JD. Using a Monte Carlo model to predict dosimetric properties of small radiotherapy photon fields. Med Phys. 2008;35:4671-84. doi.org/10.1118/1.2975223. PubMed PMID: 18975713.
36
Sauer OA, Wilbert J. Functional representation of tissue phantom ratios for photon fields. Med Phys. 2009;36:5444-50. doi.org/10.1118/1.3250867. PubMed PMID: 20095257.
37
ORIGINAL_ARTICLE
Dosimetry of Critical Organs in Maxillofacial Imaging with Cone-beam Computed Tomography
Background: While the benefits of cone-beam computed tomography (CBCT) are well known in maxillofacial imaging, the use of this modality is not risk-free.Objective: The aim of this study was to evaluate the exposure doses received by patients during maxillofacial imaging with CBCT.Methods: Entrance surface dose (ESD) was measured by using thermoluminescent dosimeters (TLDs) attached to the eyes lids, parotid glands and thyroid of 64 patients in two imaging centers (A and B). Phantom dosimetry was performed by a cylindrical poly-methyl methacrylate (PMMA) head-size phantom and an ionization chamber for different exposure parameters. NewTom VGi and Planmeca Promax 3D CBCT scanners were used at centers A and B, respectively.Results: The mean ESD of the eyes, parotid glands and thyroid were 2.57, 2.33 and 0.28 mGy in center A, 0.35, 2.11 and 0.37 mGy in center B, respectively. ESD of the eyes revealed a significant difference in two centers; in center B, it was 86.4% lower than center A. In the phantom dosimetry, the measured doses of NewTom VGi were 2.63 and 2.08 mGy, respectively by changing field of view (FOV) size from 8×8 cm2 (height × diameter) to 6×6 cm2. For Planmeca Promax 3D, it ranged from 0.98 to 3.24 mGy depending on exposure parameters.Conclusion: There is a wide range of radiation doses dependent on the units, patients and selected scan parameters. Inappropriate selection of exposure settings, especially FOV size, can seriously increase patient dose.
https://jbpe.sums.ac.ir/article_43378_614e9ccda7a0a8d8c387ecb433972941.pdf
2019-02-01
51
60
10.31661/jbpe.v9i1Feb.691
Cone-Beam Computed Tomography
Radiation dosimetry
Entrance Surface Dose
Thermoluminescent Dosimetry
Maxillofacial Imaging
dentistry
R
Ghanbarnezhad Farshi
1
Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
AUTHOR
A
Mesbahi
amesbahi2010@gmail.com
2
Medical Physics Department, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
AUTHOR
M
Johari
khatoonm87@yahoo.com
3
Department of Oral & Maxillofacial Radiology, Faculty of Dentistry, Tabriz University of Medical Sciences, Tabriz, Iran
AUTHOR
Ü
Kara
4
Vocational School of Health Services, Suleyman Demirel University, Isparta, Turkey
AUTHOR
N
Gharehaghaji
gharehaghaji@gmail.com
5
Radiology Department, Paramedical Faculty, Tabriz University of Medical Sciences, Tabriz, Iran
LEAD_AUTHOR
Heydarheydari S, Haghparast A, Eivazi MT. A Novel Biological Dosimetry Method for Monitoring Occupational Radiation Exposure in Diagnostic and Therapeutic Wards: From Radiation Dosimetry to Biological Effects. J Biomed Phys Eng. 2016;6:21-6. PubMed PMID: 27026951. PubMed PMCID: 4795325.
1
Khoshdel-Navi D, Shabestani-Monfared A, Deevband MR, Abdi R, Nabahati M. Local-Reference Patient Dose Evaluation in Conventional Radiography Examinations in Mazandaran, Iran. J Biomed Phys Eng. 2016;6:61-70. PubMed PMID: 27672626. PubMed PMCID: 5022756.
2
Janbabanezhad Toori A, Shabestani-Monfared A, Deevband MR, Abdi R, Nabahati M. Dose Assessment in Computed Tomography Examination and Establishment of Local Diagnostic Reference Levels in Mazandaran, Iran. J Biomed Phys Eng. 2015;5:177-84. PubMed PMID: 26688796. PubMed PMCID: 4681462.
3
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4
Nemtoi A, Czink C, Haba D, Gahleitner A. Cone beam CT: a current overview of devices. Dentomaxillofac Radiol. 2013;42:20120443. doi.org/10.1259/dmfr.20120443. PubMed PMID: 23818529. PubMed PMCID: 3922261.
5
Lavanya R, Babu DB, Waghray S, Chaitanya NC, Mamatha B, Nithika M. A Questionnaire Cross-Sectional Study on Application of CBCT in Dental Postgraduate Students. Pol J Radiol. 2016;81:181-9. doi.org/10.12659/PJR.895688. PubMed PMID: 27158283. PubMed PMCID: 4846182.
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7
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12
Cordasco G, Portelli M, Militi A, Nucera R, Lo Giudice A, Gatto E, et al. Low-dose protocol of the spiral CT in orthodontics: comparative evaluation of entrance skin dose with traditional X-ray techniques. Prog Orthod. 2013;14:24. doi.org/10.1186/2196-1042-14-24. PubMed PMID: 24325970. PubMed PMCID: 4384968.
13
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14
Signorelli L, Patcas R, Peltomaki T, Schatzle M. Radiation dose of cone-beam computed tomography compared to conventional radiographs in orthodontics. J Orofac Orthop. 2016;77:9-15. doi.org/10.1007/s00056-015-0002-4. PubMed PMID: 26747662.
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Nikneshan S, Aghamiri MR, Moudi E, Bahemmat N, Hadian H. Dosimetry of Three Cone Beam Computerized Tomography Scanners at Different Fields of View in Terms of Various Head and Neck Organs. Iran J Radiol. 2016;13:e34220. doi.org/10.5812/iranjradiol.34220. PubMed PMID: 27853498. PubMed PMCID: 5107245.
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Soares MR, Batista WO, Antonio Pde L, Caldas LV, Maia AF. Study of effective dose of various protocols in equipment cone beam CT. Appl Radiat Isot. 2015;100:21-6. doi.org/10.1016/j.apradiso.2015.01.012. PubMed PMID: 25665897.
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Kadesjo N, Benchimol D, Falahat B, Nasstrom K, Shi XQ. Evaluation of the effective dose of cone beam CT and multislice CT for temporomandibular joint examinations at optimized exposure levels. Dentomaxillofac Radiol. 2015;44:20150041. doi.org/10.1259/dmfr.20150041. PubMed PMID: 26119344. PubMed PMCID: 4628419.
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Chambers D, Bohay R, Kaci L, Barnett R, Battista J. The effective dose of different scanning protocols using the Sirona GALILEOS((R)) comfort CBCT scanner. Dentomaxillofac Radiol. 2015;44:20140287. doi.org/10.1259/dmfr.20140287. PubMed PMID: 25358865. PubMed PMCID: 4614170.
19
Batista WO, Soares MR, de Oliveira MV, Maia AF, Caldas LV. Assessment of protocols in cone-beam CT with symmetric and asymmetric beams usingeffective dose and air kerma-area product. Appl Radiat Isot. 2015;100:16-20. doi.org/10.1016/j.apradiso.2015.01.014. PubMed PMID: 25620114.
20
Pauwels R, Zhang G, Theodorakou C, Walker A, Bosmans H, Jacobs R, et al. Effective radiation dose and eye lens dose in dental cone beam CT: effect of field of view and angle of rotation. Br J Radiol. 2014;87:20130654. doi.org/10.1259/bjr.20130654. PubMed PMID: 25189417. PubMed PMCID: 4170857.
21
Hofmann E, Schmid M, Sedlmair M, Banckwitz R, Hirschfelder U, Lell M. Comparative study of image quality and radiation dose of cone beam and low-dose multislice computed tomography--an in-vitro investigation. Clin Oral Investig. 2014;18:301-11. doi.org/10.1007/s00784-013-0948-9. PubMed PMID: 23460022.
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28
Sina S, Zeinali B, Karimipoorfard M, Lotfalizadeh F, Sadeghi M, Zamani E, et al. Investigation of the entrance surface dose and dose to different organs in lumbar spine imaging. J Biomed Phys Eng. 2014;4:119-26. PubMed PMID: 25599058. PubMed PMCID: 4289519.
29
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32
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33
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34
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35
ORIGINAL_ARTICLE
Mother’s Exposure to Electromagnetic Fields before and during Pregnancy is Associated with Risk of Speech Problems in Offspring
Background: Rapid advances in technology, especially in the field of telecommunication, have led to extraordinary levels of mothers’ exposures to radiofrequency electromagnetic fields (RF-EMFs) prior to or during pregnancy. Objective: The main goal of this study was to answer this question whether exposure of women to common sources of RF-EMFs either prior to or during pregnancy is related to speech problems in the offspring. Materials and Methods: In this study, mothers of 110 three-to-seven-year-old children with speech problems and 75 healthy children (control group) were interviewed. These mothers were asked whether they had exposure to different sources of EMFs such as mobile phones, mobile base stations, Wi-Fi, cordless phones, laptops and power lines. Chi square test was used to analyze the differences observed between the control and exposed groups. Results: Statistically significant associations were found between the use of cordless phone and offspring speech problems for both before pregnancy and during pregnancy maternal exposures (P=0.005 and P=0.014, respectively). However, due to high rate of mobile phone use in both groups, this study failed to show any link between mobile phone use and speech problems in offspring. Furthermore, significant associations were observed between living in the vicinity of power lines and speech problems again for both before pregnancy and during pregnancy maternal exposures (P=0.003 and P=0.002, respectively). However, exposure to other sources of non-ionizing radiation was not linked to speech problems. Moreover, exposure to ionizing radiation (e.g. radiography before and during pregnancy) was not associated with the occurrence of speech problems. Conclusion: Although this study has some limitations, it leads us to this conclusion that higher-than-ever levels of maternal exposure to electromagnetic fields could be linked to offspring speech problems.
https://jbpe.sums.ac.ir/article_43379_c7ca0b40e549acfb69897a22510d71bb.pdf
2019-02-01
61
68
10.31661/jbpe.v0i0.676
Speech Problem
Exposure
Pregnancy
Ionizing radiation
Non-Ionizing Radiation
Electromagnetic Fields
S
Zarei
1
Department of Speech Therapy, School of Rehabilitation Sciences, Shiraz University of Medical Sciences, Shiraz, Iran
AUTHOR
M
Vahab
2
Department of Speech Therapy, School of Rehabilitation Sciences, Shiraz University of Medical Sciences, Shiraz, Iran
AUTHOR
M M
Oryadi-Zanjani
3
Department of Speech Therapy, School of Rehabilitation Sciences, Shiraz University of Medical Sciences, Shiraz, Iran
AUTHOR
N
Alighanbari
4
Occupational Health Engineering Department, School of Public Health, Shiraz University of Medical Sciences, Shiraz, Iran
AUTHOR
S M J
Mortazavi
mortazavismj@gmail.com
5
Medical Physics and Medical Engineering Department, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
LEAD_AUTHOR
Peart KN [Internet]. Cell phone use in pregnancy may cause behavioral disorders in offspring. [cited 15 March 2012]. Available from: https://news.yale.edu/2012/03/15/cell-phone-use-pregnancy-may-cause-behavioral-disorders-offspring
1
Mortazavi SM, Rahimi S, Talebi A, Soleimani A, Rafati A. Survey of the Effects of Exposure to 900 MHz Radiofrequency Radiation Emitted by a GSM Mobile Phone on the Pattern of Muscle Contractions in an Animal Model. J Biomed Phys Eng. 2015;5(3):121-32.
2
Mortazavi SAR, Mortazavi G, Mortazavi SMJ. Comments on “Radiofrequency electromagnetic fields and some cancers of unknown etiology: An ecological study”. Sci Total Environ. 2017;609:1. doi: 10.1016/j.scitotenv.2017.07.131. PubMed PMID: 28732291.
3
Zarei S, Mortazavi SM, Mehdizadeh AR, Jalalipour M, Borzou S, Taeb S, et al. A Challenging Issue in the Etiology of Speech Problems: The Effect of Maternal Exposure to Electromagnetic Fields on Speech Problems in the Offspring. J Biomed Phys Eng. 2015;5:151-4. PubMed PMID: 26396971; PubMed Central PMCID: PMC4576876.
4
Mokarram P, Sheikhi M, Mortazavi SMJ, Saeb S, Shokrpour N. Effect of Exposure to 900 MHz GSM Mobile Phone Radiofrequency Radiation on Estrogen Receptor Methylation Status in Colon Cells of Male Sprague Dawley Rats. J Biomed Phys Eng. 2017;7:79-86. PubMed PMID: 28451581; PubMed Central PMCID: PMC5401136.
5
Eghlidospour M, Ghanbari A, Mortazavi SMJ, Azari H. Effects of radiofrequency exposure emitted from a GSM mobile phone on proliferation, differentiation, and apoptosis of neural stem cells. Anat Cell Biol. 2017;50:115-23. doi: 10.5115/acb.2017.50.2.115. PubMed PMID: 28713615; PubMed Central PMCID: PMC5509895.
6
Taheri M, Mortazavi SM, Moradi M, Mansouri S, Hatam GR, Nouri F. Evaluation of the Effect of Radiofrequency Radiation Emitted From Wi-Fi Router and Mobile Phone Simulator on the Antibacterial Susceptibility of Pathogenic Bacteria Listeria monocytogenes and Escherichia coli. Dose Response. 2017;15:1559325816688527. doi: 10.1177/1559325816688527. PubMed PMID: 28203122; PubMed Central PMCID: PMC5298474.
7
Mortazavi SAR, Mortazavi SMJ, Paknahad M. The role of electromagnetic fields in neurological disorders. J Chem Neuroanat. 2016;77:78-9. doi: 10.1016/j.jchemneu.2016.04.004. PubMed PMID: 27126876.
8
Mortazavi SM, Rouintan MS, Taeb S, Dehghan N, Ghaffarpanah AA, Sadeghi Z, et al. Human short-term exposure to electromagnetic fields emitted by mobile phones decreases computer-assisted visual reaction time. Acta Neurol Belg. 2012;112:171-5. doi: 10.1007/s13760-012-0044-y. PubMed PMID: 22426673.
9
Mortazavi SM. Subjective Symptoms Related to GSM Radiation from Mobile Phone Base Stations: a cross-sectional study. J Biomed Phys Eng. 2014;4:39-40. PubMed PMID: 25505767; PubMed Central PMCID: PMC4258853.
10
Mortazavi SM, Motamedifar M, Namdari G, Taheri M, Mortazavi AR, Shokrpour N. Non-linear adaptive phenomena which decrease the risk of infection after pre-exposure to radiofrequency radiation. Dose Response. 2014;12:233-45. doi: 10.2203/dose-response.12-055.Mortazavi. PubMed PMID: 24910582; PubMed Central PMCID: PMC4036396.
11
Mortazavi SM, Mahbudi A, Atefi M, Bagheri S, Bahaedini N, Besharati A. An old issue and a new look: electromagnetic hypersensitivity caused by radiations emitted by GSM mobile phones. Technol Health Care. 2011;19:435-43. doi: 10.3233/THC-2011-0641. PubMed PMID: 22129944.
12
Mortazavi SM, Ahmadi J, Shariati M. Prevalence of subjective poor health symptoms associated with exposure to electromagnetic fields among university students. Bioelectromagnetics. 2007;28:326-30. doi: 10.1002/bem.20305. PubMed PMID: 17330851.
13
Mortazavi S. Safety issues of mobile phone base stations. Journal of Biomedical Physics and Engineering. 2013;3.
14
Parsaei H, Faraz M, Mortazavi S. A multilayer perceptron neural network–based model for predicting subjective health symptoms in people living in the vicinity of mobile phone base stations. Ecopsychology. 2017;9:99-105 doi: 10.1089/eco.2017.0011.
15
Mortazavi G, Mortazavi SM. Increased mercury release from dental amalgam restorations after exposure to electromagnetic fields as a potential hazard for hypersensitive people and pregnant women. Rev Environ Health. 2015;30:287-92. doi: 10.1515/reveh-2015-0017. PubMed PMID: 26544100.
16
Mortazavi SA, Taeb S, Mortazavi SM, Zarei S, Haghani M, Habibzadeh P, et al. The Fundamental Reasons Why Laptop Computers should not be Used on Your Lap. J Biomed Phys Eng. 2016;6:279-84. PubMed PMID: 28144597; PubMed Central PMCID: PMC5219578.
17
Paknahad M, Mortazavi SM, Shahidi S, Mortazavi G, Haghani M. Effect of radiofrequency radiation from Wi-Fi devices on mercury release from amalgam restorations. J Environ Health Sci Eng. 2016;14:12. doi: 10.1186/s40201-016-0253-z. PubMed PMID: 27418965; PubMed Central PMCID: PMC4944481.
18
Shekoohi-Shooli F, Mortazavi SM, Shojaei-Fard MB, Nematollahi S, Tayebi M. Evaluation of the Protective Role of Vitamin C on the Metabolic and Enzymatic Activities of the Liver in the Male Rats After Exposure to 2.45 GHz Of Wi-Fi Routers. J Biomed Phys Eng. 2016;6:157-64. PubMed PMID: 27853723; PubMed Central PMCID: PMC5106548.
19
Taheri M, Mortazavi SM, Moradi M, Mansouri S, Nouri F, Mortazavi SA, et al. Klebsiella pneumonia, a Microorganism that Approves the Non-linear Responses to Antibiotics and Window Theory after Exposure to Wi-Fi 2.4 GHz Electromagnetic Radiofrequency Radiation. J Biomed Phys Eng. 2015;5:115-20. PubMed PMID: 26396967; PubMed Central PMCID: PMC4576872.
20
Perez F, Millholland G, Peddinti SV, Thella AK, Rizkalla J, Salama P, et al. Electromagnetic and Thermal Simulations of Human Neurons for SAR Applications. J Biomed Sci Eng. 2016;9:437-44. doi: 10.4236/jbise.2016.99039. PubMed PMID: 27617054; PubMed Central PMCID: PMC5014390.
21
Petitdant N, Lecomte A, Robidel F, Gamez C, Blazy K, Villegier AS. Cerebral radiofrequency exposures during adolescence: Impact on astrocytes and brain functions in healthy and pathologic rat models. Bioelectromagnetics. 2016;37:338-50. doi: 10.1002/bem.21986. PubMed PMID: 27272062.
22
Aldad TS, Gan G, Gao XB, Taylor HS. Fetal radiofrequency radiation exposure from 800-1900 mhz-rated cellular telephones affects neurodevelopment and behavior in mice. Sci Rep. 2012;2:312. doi: 10.1038/srep00312. PubMed PMID: 22428084; PubMed Central PMCID: PMC3306017.
23
Papadopoulou E, Haugen M, Schjølberg S, Magnus P, Brunborg G, Vrijheid M, et al. Maternal cell phone use in early pregnancy and child’s language, communication and motor skills at 3 and 5 years: the Norwegian mother and child cohort study (MoBa). BMC Public Health. 2017;17:685. doi: 10.1186/s12889-017-4672-2.
24
ORIGINAL_ARTICLE
An Efficient Framework for Accurate Arterial Input Selection in DSC-MRI of Glioma Brain Tumors
Introduction: Automatic and accurate arterial input function (AIF) selection has an essential role for quantification of cerebral perfusion hemodynamic parameters using dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI). The purpose of this study is to develop an optimal automatic method for arterial input function determination in DSC-MRI of glioma brain tumors by using a new preprocessing method. Material and Methods: For this study, DSC-MR images of 43 patients with glioma brain tumors were retrieved retrospectively. Our proposed AIF selection framework consisted an effcient pre-processing step, through which non-arterial curves such as tumorous, tissue, noisy and partial-volume affected curves were excluded, followed by AIF selection through agglomerative hierarchical (AH) clustering method. The performance of automatic AIF clustering was compared with manual AIF selection performed by an experienced radiologist, based on curve shape parameters, i.e. maximum peak (MP), full-width-at-half-maximum (FWHM), M (=MP/ (TTP × FWHM)) and root mean square error (RMSE).Results: Mean values of AIFs shape parameters were compared with those derived from manually selected AIFs by two-tailed paired t-test. The results showed statistically insignificant differences in MP, FWHM, and M parameters and lower RMSE, approving the resemblance of the selected AIF with the gold standard. The intraclass correlation coefficient and coefficients of variation percent showed a better agreement between manual AIF and our proposed AIF selection than previously proposed methods.Conclusion: The results of current work suggest that by using efficient preprocessing steps, the accuracy of automatic AIF selection could be improved and this method appears promising for efficient and accurate clinical applications.
https://jbpe.sums.ac.ir/article_43380_6aa716e655e947848fcce92c0e486f4a.pdf
2019-02-01
69
80
10.31661/jbpe.v0i0.899
Perfusion
Dynamic Susceptibility Contrast Enhanced MRI
Arterial Input Function
Cluster Analysis
H
Rahimzadeh
h.rahimzadeh@sbmu.ac.ir
1
Quantitative Medical Imaging Systems Group, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
AUTHOR
A
Fathi Kazerooni
anahita.fathi@gmail.com
2
Quantitative Medical Imaging Systems Group, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
AUTHOR
M R
Deevband
mdeevband@sbmu.ac.ir
3
Department of Bioengineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
AUTHOR
H
Saligheh Rad
h-salighehrad@tums.ac.ir
4
Quantitative Medical Imaging Systems Group, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
LEAD_AUTHOR
Bjornerud A, Emblem KE. A fully automated method for quantitative cerebral hemodynamic analysis using DSC-MRI. J Cereb Blood Flow Metab. 2010;30:1066-78. doi: 10.1038/jcbfm.2010.4. PubMed PMID: 20087370; PubMed Central PMCID: PMC2949177.
1
Shiroishi MS, Castellazzi G, Boxerman JL, D’Amore F, Essig M, Nguyen TB, et al. Principles of T2 *-weighted dynamic susceptibility contrast MRI technique in brain tumor imaging. J Magn Reson Imaging. 2015;41:296-313. doi: 10.1002/jmri.24648. PubMed PMID: 24817252.
2
Hauser T, Schonknecht P, Thomann PA, Gerigk L, Schroder J, Henze R, et al. Regional cerebral perfusion alterations in patients with mild cognitive impairment and Alzheimer disease using dynamic susceptibility contrast MRI. Acad Radiol. 2013;20:705-11. doi: 10.1016/j.acra.2013.01.020. PubMed PMID: 23664398.
3
Schmainda KM, Zhang Z, Prah M, Snyder BS, Gilbert MR, Sorensen AG, et al. Dynamic susceptibility contrast MRI measures of relative cerebral blood volume as a prognostic marker for overall survival in recurrent glioblastoma: results from the ACRIN 6677/RTOG 0625 multicenter trial. Neuro Oncol. 2015;17:1148-56. doi: 10.1093/neuonc/nou364. PubMed PMID: 25646027; PubMed Central PMCID: PMC4490871.
4
Kennan RP, Jäger HR. T2-and T2*-w DCE-MRI: blood perfusion and volume estimation using bolus tracking. Quantitative MRI of the Brain. 2003:365-412.
5
Essig M, Nguyen TB, Shiroishi MS, Saake M, Provenzale JM, Enterline DS, et al. Perfusion MRI: the five most frequently asked clinical questions. American Journal of Roentgenology. 2013;201:W495-W510.
6
Peruzzo D, Bertoldo A, Zanderigo F, Cobelli C. Automatic selection of arterial input function on dynamic contrast-enhanced MR images. Comput Methods Programs Biomed. 2011;104:e148-57. doi: 10.1016/j.cmpb.2011.02.012. PubMed PMID: 21458099.
7
Murase K, Kikuchi K, Miki H, Shimizu T, Ikezoe J. Determination of arterial input function using fuzzy clustering for quantification of cerebral blood flow with dynamic susceptibility contrast-enhanced MR imaging. J Magn Reson Imaging. 2001;13:797-806. PubMed PMID: 11329204.
8
Bleeker EJ, van Osch MJ, Connelly A, van Buchem MA, Webb AG, Calamante F. New criterion to aid manual and automatic selection of the arterial input function in dynamic susceptibility contrast MRI. Magn Reson Med. 2011;65:448-56. doi: 10.1002/mrm.22599. PubMed PMID: 21264935.
9
Mouridsen K, Christensen S, Gyldensted L, Ostergaard L. Automatic selection of arterial input function using cluster analysis. Magn Reson Med. 2006;55:524-31. doi: 10.1002/mrm.20759. PubMed PMID: 16453314.
10
Yin J, Yang J, Guo Q. Evaluating the feasibility of an agglomerative hierarchy clustering algorithm for the automatic detection of the arterial input function using DSC-MRI. PLoS One. 2014;9:e100308. doi: 10.1371/journal.pone.0100308. PubMed PMID: 24932638; PubMed Central PMCID: PMC4059756.
11
Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, et al. The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J Digit Imaging. 2013;26:1045-57. doi: 10.1007/s10278-013-9622-7. PubMed PMID: 23884657; PubMed Central PMCID: PMC3824915.
12
Ostergaard L, Weisskoff RM, Chesler DA, Gyldensted C, Rosen BR. High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. Part I: Mathematical approach and statistical analysis. Magn Reson Med. 1996;36:715-25. PubMed PMID: 8916022.
13
Belliveau JW, Rosen BR, Kantor HL, Rzedzian RR, Kennedy DN, McKinstry RC, et al. Functional cerebral imaging by susceptibility-contrast NMR. Magn Reson Med. 1990;14:538-46. PubMed PMID: 2355835.
14
Yin J, Sun H, Yang J, Guo Q. Comparison of K-means and fuzzy c-means algorithm performance for automated determination of the arterial input function. PLoS One. 2014;9:e85884. doi: 10.1371/journal.pone.0085884. PubMed PMID: 24503700; PubMed Central PMCID: PMC3913570.
15
Chan AA, Nelson SJ. Simplified gamma-variate fitting of perfusion curves. In Biomedical imaging: nano to macro, 2004. IEEE International Symposium on, pp. 1067-1070. IEEE, 2004.
16
Freire L, Roche A, Mangin JF. What is the best similarity measure for motion correction in fMRI time series? IEEE Trans Med Imaging. 2002;21:470-84. doi: 10.1109/TMI.2002.1009383. PubMed PMID: 12071618.
17
Freire L, Mangin JF. Motion correction algorithms may create spurious brain activations in the absence of subject motion. Neuroimage. 2001;14:709-22. doi: 10.1006/nimg.2001.0869. PubMed PMID: 11506543.
18
Emblem KE, Due-Tonnessen P, Hald JK, Bjornerud A. Automatic vessel removal in gliomas from dynamic susceptibility contrast imaging. Magn Reson Med. 2009;61:1210-7. doi: 10.1002/mrm.21944. PubMed PMID: 19253390.
19
Calamante F. Arterial input function in perfusion MRI: a comprehensive review. Prog Nucl Magn Reson Spectrosc. 2013;74:1-32. doi: 10.1016/j.pnmrs.2013.04.002. PubMed PMID: 24083460.
20
Ellinger R, Kremser C, Schocke MF, Kolbitsch C, Griebel J, Felber SR, et al. The impact of peak saturation of the arterial input function on quantitative evaluation of dynamic susceptibility contrast-enhanced MR studies. J Comput Assist Tomogr. 2000;24:942-8. PubMed PMID: 11105716.
21
Yin J, Sun H, Yang J, Guo Q. Automated detection of the arterial input function using normalized cut clustering to determine cerebral perfusion by dynamic susceptibility contrast-magnetic resonance imaging. J Magn Reson Imaging. 2015;41:1071-8. doi: 10.1002/jmri.24642. PubMed PMID: 24753102.
22
Yin J, Yang J, Guo Q. Automatic determination of the arterial input function in dynamic susceptibility contrast MRI: comparison of different reproducible clustering algorithms. Neuroradiology. 2015;57:535-43. doi: 10.1007/s00234-015-1493-9. PubMed PMID: 25633539; PubMed Central PMCID: PMC4412433.
23
Carroll TJ, Rowley HA, Haughton VM. Automatic calculation of the arterial input function for cerebral perfusion imaging with MR imaging. Radiology. 2003;227:593-600.
24
Law M, Young R, Babb J, Rad M, Sasaki T, Zagzag D, et al. Comparing perfusion metrics obtained from a single compartment versus pharmacokinetic modeling methods using dynamic susceptibility contrast-enhanced perfusion MR imaging with glioma grade. AJNR Am J Neuroradiol. 2006;27:1975-82. PubMed PMID: 17032878.
25
ORIGINAL_ARTICLE
Mandibular Trabecular Bone Analysis Using Local Binary Pattern for Osteoporosis Diagnosis
Background: Osteoporosis is a systemic skeletal disease characterized by low bone mineral density (BMD) and micro-architectural deterioration of bone tissue, leading to bone fragility and increased fracture risk. Since Panoramic image is a feasible and relatively routine imaging technique in dentistry; it could provide an opportunistic chance for screening osteoporosis. In this regard, numerous panoramic derived indices have been developed and suggested for osteoporosis screening. Jaw trabecular pattern is one of the main bone strength factors and trabecular bone pattern assessment is important factor in bone quality analysis. Texture analysis applied to trabecular bone images offers an ability to exploit the information present on conventional radiographs. Objective: The purpose of this study was to evaluate the relationship between Jaw trabecular pattern in panoramic image and osteoporosis based on image texture analyzing using local binary pattern.Material and Methods: An experiment is evaluated in this paper based on a real hand-captured database of panoramic radiograph images from osteoporosis and non-osteoporosis person in Namazi Hospital, Shiraz, Iran .An approach is proposed for osteoporosis diagnosis consisting of two steps. First, modified version of local binary patterns is used to extract discriminative features from jaw panoramic radiograph images. Then, classification is done using different classifiers. Results: Comparative results show that the proposed approach provides classification accuracy about 99.6%, which is higher than many state-of-the-art methods. Conclusion: High classification accuracy, low computational complexity, multi-resolution and rotation invariant are among advantages of our proposed approach.
https://jbpe.sums.ac.ir/article_43381_cb06d78824b8626e9974b2a15774226d.pdf
2019-02-01
81
88
10.31661/jbpe.v9i1Feb.743
Osteoporosis
Panoramic
Texture Analysis
Local Binary Pattern
L
Khojastepour
1
Department of Oral and Maxillofacial Radiology, School of Dentistry, Shiraz University of Medical Sciences, Shiraz, Iran
AUTHOR
M
Hasani
2
Department of Oral and Maxillofacial Radiology, School of Dentistry, Shiraz University of Medical Sciences, Shiraz, Iran
AUTHOR
M
Ghasemi
3
Department of Oral and Maxillofacial Radiology, School of Dentistry, Shiraz University of Medical Sciences, Shiraz, Iran
LEAD_AUTHOR
A R
Mehdizadeh
4
Department of Medical Physics, School of Medicine, Shiraz University of Medical Sciences
AUTHOR
F
Tajeripour
5
Department of Computer Engineering, Science and IT, Shiraz University, Shiraz, Iran
AUTHOR
Baylink DJ, Strong DD, Mohan S. The diagnosis and treatment of osteoporosis: future prospects. Mol Med Today. 1999;5:133-40. PubMed PMID: 10203737.
1
White S. Oral radiographic predictors of osteoporosis. Dentomaxillofacial radiology. 2002;31:84-92.
2
Dervis E. Oral implications of osteoporosis. Oral Surg Oral Med Oral Pathol Oral Radiol Endod. 2005;100:349-56. doi: 10.1016/j.tripleo.2005.04.010. PubMed PMID: 16122665.
3
Taguchi A, Asano A, Ohtsuka M, Nakamoto T, Suei Y, Tsuda M, et al. Observer performance in diagnosing osteoporosis by dental panoramic radiographs: results from the osteoporosis screening project in dentistry (OSPD). Bone. 2008;43:209-13. doi: 10.1016/j.bone.2008.03.014. PubMed PMID: 18482878.
4
Pramudito J, Soegijoko S, Mengko T, Muchtadi F, Wachjudi R. Trabecular pattern analysis of proximal femur radiographs for osteoporosis detection. Journal of Biomedical & Pharmaceutical Engineering. 2007;1:45-51.
5
Gaur B, Chaudhary A, Wanjari PV, Sunil M, Basavaraj P. Evaluation of panoramic Radiographs as a Screening Tool of Osteoporosis in Post Menopausal Women: A Cross Sectional Study. J Clin Diagn Res. 2013;7:2051-5. doi: 10.7860/JCDR/2013/5853.3403. PubMed PMID: 24179941; PubMed Central PMCID: PMC3809680.
6
Johari Khatoonabad M, Aghamohammadzade N, Taghilu H, Esmaeili F, Jabbari Khamnei H. Relationship Among Panoramic Radiography Findings, Biochemical Markers of Bone Turnover and Hip BMD in the Diagnosis of Postmenopausal Osteoporosis. Iran J Radiol. 2011;8:23-8. PubMed PMID: 23329912; PubMed Central PMCID: PMC3522411.
7
Horner K, Devlin H. The relationship between mandibular bone mineral density and panoramic radiographic measurements. J Dent. 1998;26:337-43. PubMed PMID: 9611939.
8
Taguchi A, Suei Y, Ohtsuka M, Otani K, Tanimoto K, Ohtaki M. Usefulness of panoramic radiography in the diagnosis of postmenopausal osteoporosis in women. Width and morphology of inferior cortex of the mandible. Dentomaxillofac Radiol. 1996;25:263-7. doi: 10.1259/dmfr.25.5.9161180. PubMed PMID: 9161180.
9
Kanis JA, Johnell O. Requirements for DXA for the management of osteoporosis in Europe. Osteoporos Int. 2005;16:229-38. doi: 10.1007/s00198-004-1811-2. PubMed PMID: 15618996.
10
Oliveira ML, Pedrosa EF, Cruz AD, Haiter-Neto F, Paula FJ, Watanabe PC. Relationship between bone mineral density and trabecular bone pattern in postmenopausal osteoporotic Brazilian women. Clin Oral Investig. 2013;17:1847-53. doi: 10.1007/s00784-012-0882-2. PubMed PMID: 23239088.
11
Roberts MG, Graham J, Devlin H. Image texture in dental panoramic radiographs as a potential biomarker of osteoporosis. IEEE Trans Biomed Eng. 2013;60:2384-92. doi: 10.1109/TBME.2013.2256908. PubMed PMID: 23568478.
12
Khojastehpour L, Mogharrabi S, Dabbaghmanesh MH, Iraji Nasrabadi N. Comparison of the mandibular bone densitometry measurement between normal, osteopenic and osteoporotic postmenopausal women. J Dent (Tehran). 2013;10:203-9. PubMed PMID: 25512746; PubMed Central PMCID: PMC4264091.
13
Yasar F, Akgunlu F. Evaluating mandibular cortical index quantitatively. Eur J Dent. 2008;2:283-90. PubMed PMID: 19212535; PubMed Central PMCID: PMC2634783.
14
Khojastehpour L, Shahidi S, Barghan S, Aflaki E. Efficacy of panoramic mandibular index in diagnosing osteoporosis in women. J Dent (Tehran). 2009;6:11-5.
15
Hildebolt C. Osteoporosis and oral bone loss. Dentomaxillofacial Radiology. 1997;26:3-15.
16
Yasar F, Akgunlu F. The differences in panoramic mandibular indices and fractal dimension between patients with and without spinal osteoporosis. Dentomaxillofac Radiol. 2006;35:1-9. doi: 10.1259/dmfr/97652136. PubMed PMID: 16421256.
17
Mohajery M, Brooks SL. Oral radiographs in the detection of early signs of osteoporosis. Oral Surg Oral Med Oral Pathol. 1992;73:112-7. PubMed PMID: 1603549.
18
Peitgen H-O, Jürgens H, Saupe D. Introduction to fractals and chaos: Springer-Verlag; 1992.
19
Bollen AM, Taguchi A, Hujoel PP, Hollender LG. Case-control study on self-reported osteoporotic fractures and mandibular cortical bone. Oral Surg Oral Med Oral Pathol Oral Radiol Endod. 2000;90:518-24. doi: 10.1067/moe.2000.107802. PubMed PMID: 11027391.
20
Klemetti E, Kolmakov S, Kröger H. Pantomography in assessment of the osteoporosis risk group. Eur J Oral Sci. 1994;102:68-72.
21
Geraets WG, Van der Stelt PF, Netelenbos CJ, Elders PJ. A new method for automatic recognition of the radiographic trabecular pattern. J Bone Miner Res. 1990;5:227-33. PubMed PMID: 2333781.
22
Link TM, Majumdar S, Lin JC, Augat P, Gould RG, Newitt D, et al. Assessment of trabecular structure using high resolution CT images and texture analysis. J Comput Assist Tomogr. 1998;22:15-24. PubMed PMID: 9448755.
23
Shahidi S, Bahrampour E, Soltanimehr E, Zamani A, Oshagh M, Moattari M, et al. The accuracy of a designed software for automated localization of craniofacial landmarks on CBCT images. BMC Med Imaging. 2014;14:32.
24
Houam L, Hafiane A, Boukrouche A, Lespessailles E, Jennane R, editors. Texture characterization using local binary pattern and wavelets. Application to bone radiographs. Image Processing Theory, Tools and Applications (IPTA), 2012 3rd International Conference on; 2012: IEEE.
25
Ojala T, Pietikainen M, Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell. 2002;24:971-87.
26
Fekri-Ershad S, Tajeripour F. A robust approach for surface defect detection based on one dimensional local binary patterns. Indian Journal of Science and Technology. 2012;5:3197-203.
27
Tajeripour F, Fekri-Ershad S. Developing a novel approach for stone porosity computing using modified local binary patterns and single scale retinex. Arabian Journal for Science and Engineering. 2014;39:875-89.
28
Gao Z, Hong W, Xu Y. Trabecular bone micro-CT images analysis for osteoporosis diagnosis. J Comput Inf Syst. 2012;8:10341-7.
29
ORIGINAL_ARTICLE
Evaluation of Gold Nanoparticle Size Effect on Dose Enhancement Factor in Megavoltage Beam Radiotherapy Using MAGICA Polymer Gel Dosimeter
Background: Gold nanoparticles (GNPs) are among the most promising radiosensitive materials in radiotherapy. Studying the effective sensitizing factors such as nanoparticle size, concentration, surface features, radiation energy and cell type can help to optimize the effect and possible clinical application of GNPs in radiation therapy. In this study, the radiation sensitive polymer gel was used to investigate the dosimetric effect of GNP size in megavoltage (MV) photon beam radiotherapy. Material and Methods: GNPs with the size of 30nm, 50nm and 100nm in diameter were used. Transmission electron microscope (TEM) and dynamic light scattering (DLS) were applied to analyze the size of nanoparticles. The MAGICA polymer gel was synthesized and impregnated with different sizes of GNPs. The samples were irradiated with 6MV photon beam and 24 hours after irradiation, they were read using a Magnetic Resonance Imaging (MRI) scanner. Macroscopic Dose Enhancement Factor (DEF) was measured to compare the effect of GNP size. The MAGICA response of the 6MV x-ray beam was verified comparing Percentage Depth Dose (PDD) curve extracted from polymer gel dosimetry and Treatment Planning System (TPS). Results: MAGICA polymer gel dose response curve was linear in the range of 0 to 10 Gy. DEFs by adding 30nm, 50nm and 100nm GNPs were 1.1, 1.17 and 1.12, respectively. PDD curves of polymer gel dosimeter and treatment planning system were in good agreement. Conclusion: The results indicated a substantial increase in DEF uses a MV photon beam in combination with GNPs of different sizes and it was inconsistent with previous radiobiological studies. The maximum DEF was achieved for 50nm GNPs in comparison with 30nm and 100nm leading to the assumption of self-absorption effect by larger diameters. According to the outcomes of this work, MAGICA polymer gel can be recommended as a reliable dosimeter to investigate the dosimetric effect of GNP size and also a useful method to validate the current radiobiological and simulation studies.
https://jbpe.sums.ac.ir/article_43382_5b937935aba2e04b2e6b86541f166f3c.pdf
2019-02-01
89
96
10.31661/jbpe.v9i1Feb.1019
Gold Nanoparticle
Polymer Gel
Dosimetry
Radiotherapy
Zh
Behrouzkia
1
PhD of Medical Physics, Urmia University of Medical Science, School of Medicine, Urmia, Iran
AUTHOR
R
Zohdiaghdam
2
PhD of Medical Physics, Urmia University of Medical Science, School of Para Medicine, Urmia, Iran
LEAD_AUTHOR
H R
Khalkhali
3
PhDs of Biostatics, Patient Safety Research Center, Urmia University of Medical Sciences, Urmia, Iran
AUTHOR
F
Mousavi
4
MSc in Medical Physics, Urmia University of Medical Science, School of Medicine, Urmia, Iran
AUTHOR
Baskar R, Lee KA, Yeo R, Yeoh KW. Cancer and radiation therapy: current advances and future directions. Int J Med Sci. 2012;9:193-9. doi: 10.7150/ijms.3635. PubMed PMID: 22408567; PubMed Central PMCID: PMC3298009.
1
Kwatra D, Venugopal A, Anant S. Nanoparticles in radiation therapy: a summary of various approaches to enhance radiosensitization in cancer. Translational Cancer Research. 2013;2:330-42.
2
Jain S, Coulter JA, Hounsell AR, Butterworth KT, McMahon SJ, Hyland WB, et al. Cell-specific radiosensitization by gold nanoparticles at megavoltage radiation energies. Int J Radiat Oncol Biol Phys. 2011;79:531-9. doi: 10.1016/j.ijrobp.2010.08.044. PubMed PMID: 21095075; PubMed Central PMCID: PMC3015172.
3
Jeremic B, Aguerri AR, Filipovic N. Radiosensitization by gold nanoparticles. Clinical and Translational Oncology. 2013;15:593-601.
4
Su XY, Liu PD, Wu H, Gu N. Enhancement of radiosensitization by metal-based nanoparticles in cancer radiation therapy. Cancer Biol Med. 2014;11:86-91. doi: 10.7497/j.issn.2095-3941.2014.02.003. PubMed PMID: 25009750; PubMed Central PMCID: PMC4069802.
5
Butterworth KT, McMahon SJ, Currell FJ, Prise KM. Physical basis and biological mechanisms of gold nanoparticle radiosensitization. Nanoscale. 2012;4:4830-8. doi: 10.1039/c2nr31227a. PubMed PMID: 22767423.
6
Hainfeld JF, Dilmanian FA, Slatkin DN, Smilowitz HM. Radiotherapy enhancement with gold nanoparticles. J Pharm Pharmacol. 2008;60:977-85.
7
Zhang XD, Wu D, Shen X, Chen J, Sun YM, Liu PX, et al. Size-dependent radiosensitization of PEG-coated gold nanoparticles for cancer radiation therapy. Biomaterials. 2012;33:6408-19. doi: 10.1016/j.biomaterials.2012.05.047. PubMed PMID: 22681980.
8
Zhang XD, Wu D, Shen X, Liu PX, Yang N, Zhao B, et al. Size-dependent in vivo toxicity of PEG-coated gold nanoparticles. Int J Nanomedicine. 2011;6:2071-81. doi: 10.2147/IJN.S21657. PubMed PMID: 21976982; PubMed Central PMCID: PMC3181066.
9
Chithrani DB, Jelveh S, Jalali F, van Prooijen M, Allen C, Bristow RG, et al. Gold nanoparticles as radiation sensitizers in cancer therapy. Radiat Res. 2010;173:719-28. doi: 10.1667/RR1984.1. PubMed PMID: 20518651.
10
Lechtman E, Chattopadhyay N, Cai Z, Mashouf S, Reilly R, Pignol J. Implications on clinical scenario of gold nanoparticle radiosensitization in regards to photon energy, nanoparticle size, concentration and location. Phys Med Biol. 2011;56:4631-47.doi: 10.1088/0031-9155/56/15/001.
11
Mesbahi A, Jamali F, Garehaghaji N. Effect of photon beam energy, gold nanoparticle size and concentration on the dose enhancement in radiation therapy. Bioimpacts. 2013;3:29-35. doi: 10.5681/bi.2013.002. PubMed PMID: 23678467; PubMed Central PMCID: PMC3648909.
12
Marques T, Schwarcke M, Garrido C, Zucolot V, Baffa O, Nicolucci P, editors. Gel dosimetry analysis of gold nanoparticle application in kilovoltage radiation therapy. Journal of Physics: Conference Series; 2010. doi: 10.1088/1742-6596/250/1/012084.
13
Rahman WN, Wong CJ, Ackerly T, Yagi N, Geso M. Polymer gels impregnated with gold nanoparticles implemented for measurements of radiation dose enhancement in synchrotron and conventional radiotherapy type beams. Australas Phys Eng Sci Med. 2012;35:301-9.doi: 10.1007/s13246-012-0157-x.
14
Fong PM, Keil DC, Does MD, Gore JC. Polymer gels for magnetic resonance imaging of radiation dose distributions at normal room atmosphere. Phys Med Biol. 2001;46:3105-13.doi: 10.1088/0031-9155/46/12/303. PubMed PMID: 11768494.
15
Zahmatkesh M, Kousari R, Akhlaghpour S, Bagheri S. MRI gel dosimetry with methacrylic acid, ascorbic acid, hydroquinone and copper in agarose (MAGICA) gel. Preliminary Proceedings of DOSGEL. 2004;2004.
16
Meesat R, Jay-Gerin JP, Khalil A, Lepage M. Evaluation of the dose enhancement of iodinated compounds by polyacrylamide gel dosimetry. Phys Med Biol. 2009;54:5909-17. doi: 10.1088/0031-9155/54/19/016. PubMed PMID: 19759404.
17
Baldock C, De Deene Y, Doran S, Ibbott G, Jirasek A, Lepage M, et al. Polymer gel dosimetry. Phys Med Biol. 2010;55:R1.
18
Ibbott GS, editor Applications of gel dosimetry. Journal of Physics: conference series; 2004.
19
Leung MK, Chow JC, Chithrani BD, Lee MJ, Oms B, Jaffray DA. Irradiation of gold nanoparticles by x-rays: Monte Carlo simulation of dose enhancements and the spatial properties of the secondary electrons production. Med Phys. 2011;38:624-31. doi: 10.1118/1.3539623. PubMed PMID: 21452700.
20
McMahon SJ, Hyland WB, Muir MF, Coulter JA, Jain S, Butterworth KT, et al. Nanodosimetric effects of gold nanoparticles in megavoltage radiation therapy. Radiother Oncol. 2011;100:412-6. doi: 10.1016/j.radonc.2011.08.026. PubMed PMID: 21924786.
21
Alqathami M, Blencowe A, Yeo UJ, Doran SJ, Qiao G, Geso M. Novel multicompartment 3-dimensional radiochromic radiation dosimeters for nanoparticle-enhanced radiation therapy dosimetry. International Journal of Radiation Oncology* Biology* Physics. 2012;84:e549-e55.doi: 10.1016/j.ijrobp.2012.05.029.
22
ORIGINAL_ARTICLE
Effect of Exercise Training on Heart Rate Variability in Patients with Heart Failure After Percutaneous Coronary Intervention
Background: This study aims to evaluate the effect of exercise training on heart rate variability (HRV) and to determine the correlation between parameters of HRV and the ejection fraction in patients with heart failure after percutaneous coronary intervention. Material and Methods: Fifty patients with left ventricular ejection fraction ≤ 40% undergone percutaneous coronary intervention were randomly allocated in either an exercise training (ET) group or a control group. The ET group performed exercise training for 45 minutes, three times a week for seven weeks. Patients in both groups received a leaflet for daily exercising at home. HRV parameters comprising, the standard deviation of normal R-R intervals (SDNN), the square root of the mean of the squares of successive R-R intervals differences (RMSSD) ,the percentage of successive R-R intervals differing from more than 50 ms (PNN50), using 24-hour Holter electrocardiographic monitoring was measured.Results: After the intervention, the SDNN improved in the ET group (P=0.002), while changes in all remaining HRV indices were insignificant (P≥0.05). The control group showed no significant changes in any HRV parameters (P≥0.05). Changes in SDNN in the ET group were significantly different from the control group (P=0.003). At baseline, our results revealed a significant weak correlation between ejection fraction and SDNN (r =0.279, P=0.047). However, ejection fraction did not correlate significantly with RMSSD and PNN50. Conclusion: Exercise training is safe and feasible in post percutaneous coronary intervention patients, even in those with reduced ejection fraction. In a seven-week period, exercise training was effective in improving HRV in heart failure patients after percutaneous coronary intervention.
https://jbpe.sums.ac.ir/article_43383_1429152d749cdf28928269e6be35e571.pdf
2019-02-01
97
104
10.31661/jbpe.v0i0.842
Exercise Training
Heart Failure
Percutaneous Coronary Intervention
Heart Rate Variability
S
Abolahrari-Shirazi
sa_ahrari@yahoo.com
1
PhD Candidate, Physical Therapy Department, School of Rehabilitation Sciences, Shiraz University of Medical Sciences, Shiraz, Iran
AUTHOR
J
Kojuri
kojurij@yahoo.com
2
Cardiologist, Interventionist, Full professor, Clinical education improvement research center, Shiraz University of Medical Sciences, Shiraz, Iran
AUTHOR
Z
Bagheri
3
PhD of Biostatistics, Department of Biostatistics, Shiraz University of Medical Sciences, Shiraz, Iran
AUTHOR
Z
Rojhani-Shirazi
4
PhD of Physiotherapy, Associated professor, Physical Therapy Department, School of Rehabilitation Sciences, Shiraz University of Medical Sciences, Shiraz, Iran
LEAD_AUTHOR
Kolh P, Windecker S, Alfonso F, Collet JP, Cremer J, Falk V, et al. 2014 ESC/EACTS Guidelines on myocardial revascularization: the Task Force on Myocardial Revascularization of the European Society of Cardiology (ESC) and the European Association for Cardio-Thoracic Surgery (EACTS). Developed with the special contribution of the European Association of Percutaneous Cardiovascular Interventions (EAPCI). Eur J Cardiothorac Surg. 2014;46:517-92. doi: 10.1093/ejcts/ezu366. PubMed PMID: 25173601.
1
He XM, Chen L, Luo JB, Feng XX, Zhang YB, Chen QJ, et al. Effects of rhBNP after PCI on non-invasive hemodynamic in acute myocardial infarction patients with left heart failure. Asian Pac J Trop Med. 2016;9:791-5. doi: 10.1016/j.apjtm.2016.06.006. PubMed PMID: 27569890.
2
Kelly DJ, Gershlick T, Witzenbichler B, Guagliumi G, Fahy M, Dangas G, et al. Incidence and predictors of heart failure following percutaneous coronary intervention in ST-segment elevation myocardial infarction: the HORIZONS-AMI trial. Am Heart J. 2011;162:663-70. doi: 10.1016/j.ahj.2011.08.002. PubMed PMID: 21982658.
3
Jewiss D, Ostman C, Smart NA. The effect of resistance training on clinical outcomes in heart failure: A systematic review and meta-analysis. Int J Cardiol. 2016;221:674-81. doi: 10.1016/j.ijcard.2016.07.046. PubMed PMID: 27423089.
4
Smart N, Marwick TH. Exercise training for patients with heart failure: a systematic review of factors that improve mortality and morbidity. Am J Med. 2004;116:693-706. doi: 10.1016/j.amjmed.2003.11.033. PubMed PMID: 15121496.
5
Lloyd-Williams F, Mair FS, Leitner M. Exercise training and heart failure: a systematic review of current evidence. Br J Gen Pract. 2002;52:47-55. PubMed PMID: 11791816; PubMed Central PMCID: PMC1314201.
6
van Tol BA, Huijsmans RJ, Kroon DW, Schothorst M, Kwakkel G. Effects of exercise training on cardiac performance, exercise capacity and quality of life in patients with heart failure: a meta-analysis. Eur J Heart Fail. 2006;8:841-50. doi: 10.1016/j.ejheart.2006.02.013. PubMed PMID: 16713337.
7
Haykowsky MJ, Liang Y, Pechter D, Jones LW, McAlister FA, Clark AM. A meta-analysis of the effect of exercise training on left ventricular remodeling in heart failure patients: the benefit depends on the type of training performed. J Am Coll Cardiol. 2007;49:2329-36. doi: 10.1016/j.jacc.2007.02.055. PubMed PMID: 17572248.
8
Hambrecht R, Hilbrich L, Erbs S, Gielen S, Fiehn E, Schoene N, et al. Correction of endothelial dysfunction in chronic heart failure: additional effects of exercise training and oral L-arginine supplementation. J Am Coll Cardiol. 2000;35:706-13. PubMed PMID: 10716474.
9
Piepoli MF, Davos C, Francis DP, Coats AJ, ExTra MC. Exercise training meta-analysis of trials in patients with chronic heart failure (ExTraMATCH). BMJ. 2004;328:189. doi: 10.1136/bmj.37938.645220.EE. PubMed PMID: 14729656; PubMed Central PMCID: PMC318480.
10
Forslund L, Bjorkander I, Ericson M, Held C, Kahan T, Rehnqvist N, et al. Prognostic implications of autonomic function assessed by analyses of catecholamines and heart rate variability in stable angina pectoris. Heart. 2002;87:415-22. PubMed PMID: 11997407; PubMed Central PMCID: PMC1767117.
11
Mäkikallio TH, Huikuri HV, Hintze U, Videbæk J, Mitrani RD, Castellanos A, et al. Fractal analysis and time-and frequency-domain measures of heart rate variability as predictors of mortality in patients with heart failure. Am J Cardiol. 2001;87:178-82.
12
Weber F, Schneider H, von Arnim T, Urbaszek W. Heart rate variability and ischaemia in patients with coronary heart disease and stable angina pectoris; influence of drug therapy and prognostic value. TIBBS Investigators Group. Total Ischemic Burden Bisoprolol Study. Eur Heart J. 1999;20:38-50. PubMed PMID: 10075140.
13
Munk PS, Butt N, Larsen AI. High-intensity interval exercise training improves heart rate variability in patients following percutaneous coronary intervention for angina pectoris. Int J Cardiol. 2010;145:312-4. doi: 10.1016/j.ijcard.2009.11.015. PubMed PMID: 19962772.
14
Tsai MW, Chie WC, Kuo TB, Chen MF, Liu JP, Chen TT, et al. Effects of exercise training on heart rate variability after coronary angioplasty. Phys Ther. 2006;86:626-35. PubMed PMID: 16649887.
15
Beckers PJ, Denollet J, Possemiers NM, Wuyts FL, Vrints CJ, Conraads VM. Combined endurance-resistance training vs. endurance training in patients with chronic heart failure: a prospective randomized study. Eur Heart J. 2008;29:1858-66. doi: 10.1093/eurheartj/ehn222. PubMed PMID: 18515805.
16
Feiereisen P, Delagardelle C, Vaillant M, Lasar Y, Beissel J. Is strength training the more efficient training modality in chronic heart failure? Med Sci Sports Exerc. 2007;39:1910-7. doi: 10.1249/mss.0b013e31814fb545. PubMed PMID: 17986897.
17
Piepoli MF, Conraads V, Corra U, Dickstein K, Francis DP, Jaarsma T, et al. Exercise training in heart failure: from theory to practice. A consensus document of the Heart Failure Association and the European Association for Cardiovascular Prevention and Rehabilitation. Eur J Heart Fail. 2011;13:347-57. doi: 10.1093/eurjhf/hfr017. PubMed PMID: 21436360.
18
Selig SE, Levinger I, Williams AD, Smart N, Holland DJ, Maiorana A, et al. Exercise & Sports Science Australia Position Statement on exercise training and chronic heart failure. J Sci Med Sport. 2010;13:288-94. doi: 10.1016/j.jsams.2010.01.004. PubMed PMID: 20227917.
19
Adamopoulos S, Ponikowski P, Cerquetani E, Piepoli M, Rosano G, Sleight P, et al. Circadian pattern of heart rate variability in chronic heart failure patients. Effects of physical training. Eur Heart J. 1995;16:1380-6. PubMed PMID: 8746907.
20
Larsen AI, Gjesdal K, Hall C, Aukrust P, Aarsland T, Dickstein K. Effect of exercise training in patients with heart failure: a pilot study on autonomic balance assessed by heart rate variability. Eur J Cardiovasc Prev Rehabil. 2004;11:162-7. PubMed PMID: 15187821.
21
Murad K, Brubaker PH, Fitzgerald DM, Morgan TM, Goff DC, Jr., Soliman EZ, et al. Exercise training improves heart rate variability in older patients with heart failure: a randomized, controlled, single-blinded trial. Congest Heart Fail. 2012;18:192-7. doi: 10.1111/j.1751-7133.2011.00282.x. PubMed PMID: 22536936; PubMed Central PMCID: PMC3400715.
22
Nolan J, Batin PD, Andrews R, Lindsay SJ, Brooksby P, Mullen M, et al. Prospective study of heart rate variability and mortality in chronic heart failure: results of the United Kingdom heart failure evaluation and assessment of risk trial (UK-heart). Circulation. 1998;98:1510-6. PubMed PMID: 9769304.
23
Buch AN, Coote JH, Townend JN. Mortality, cardiac vagal control and physical training--what’s the link? Exp Physiol. 2002;87:423-35. PubMed PMID: 12392106.
24
Kingwell BA. Nitric oxide as a metabolic regulator during exercise: effects of training in health and disease. Clin Exp Pharmacol Physiol. 2000;27:239-50. PubMed PMID: 10779120.
25
Townend JN, al-Ani M, West JN, Littler WA, Coote JH. Modulation of cardiac autonomic control in humans by angiotensin II. Hypertension. 1995;25:1270-5. PubMed PMID: 7768573.
26
Kleiger RE, Stein PK, Bigger JT, Jr. Heart rate variability: measurement and clinical utility. Ann Noninvasive Electrocardiol. 2005;10:88-101. doi: 10.1111/j.1542-474X.2005.10101.x. PubMed PMID: 15649244.
27
Enciu EC, Stanciu SM, Matei D, Costache A. Prognostic markers in the pathology of cardiac failure: echocardiography and autonomic nervous system dysfunction. Rom J Morphol Embryol. 2015;56:401-6. PubMed PMID: 26193205.
28
ORIGINAL_ARTICLE
In-silico Evaluation of Rare Codons and their Positions in the Structure of ATP8b1 Gene
Background: Progressive familial intrahepatic cholestases (PFIC) are a spectrum of autosomal progressive liver diseases developing to end-stage liver disease. ATP8B1 deficiency caused by mutations in ATP8B1 gene encoding a P-type ATPase leads to PFIC1. The gene for PFIC1 has been mapped on a 19-cM region of 18q21-q22, and a gene defect in ATP8B1 can cause deregulations in bile salt transporters through decreased expression and/or activity of FXR. Point mutations are the most common, with the majority being missense or nonsense mutations. In addition, approximately 15% of disease-causing ATP8B1 mutations are annotated as splicing disrupting alteration given that they are located at exon-intron borders. Objective: Here, we describe the hidden layer of computational biology information of rare codons in ATP8B1, which can help us for drug design. Methods: Some rare codons in different locations of ATP8b1 gene were identified using several web servers and by in-silico modelling of ATP8b1 in Phyre2 and I-TASSER server, some rare codons were evaluated. Results: Some of these rare codons were located at special positions which seem to have a critical role in proper folding of ATP8b1 protein. Structural analysis showed that some of rare codons are related to mutations in ATP8B1 that are responsible for PFIC1 disease, which may have a critical role in ensuring the correct folding. Conclusion: Investigation of such hidden information can enhance our understanding of ATP8b1 folding. Moreover, studies of these rare codons help us to clarify their role in rational design of new and effective drugs.
https://jbpe.sums.ac.ir/article_43384_c9b367468db70ed60c733988d91aba44.pdf
2019-02-01
105
120
10.31661/jbpe.v9i1Feb.616
Progressive Familial Intrahepatic Cholestasis
Bioinformatics analysis
ATP8b1
Rare codon
M
Zarenezhad
zarenezhad@hotmail.com
1
MD, PhD, Gastroenterohepatology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
LEAD_AUTHOR
S M
Dehghani
dehghanism@gmail.com
2
MD, Gastroenterohepatology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
AUTHOR
F
Ejtehadi
ejtehadif@sums.ac.ir
3
MD, Gastroenterohepatology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
AUTHOR
M R
Fattahi
fattahimr@sums.ac.ir
4
MD, Gastroenterohepatology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
AUTHOR
M
Mortazavi
mortazavi_m@yahoo.com
5
PhD, Department of Biotechnology, Institute of Science and High Technology and Environmental Science, Graduate University of Advanced Technology, Kerman, Iran
AUTHOR
S M B
Tabei
6
MD, Genetic Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
AUTHOR
Jacquemin E. Progressive familial intrahepatic cholestasis. Clinics and research in hepatology and gastroenterology. 2012;36:S26-S35. doi.org/10.1016/S2210-7401(12)70018-9.
1
Sira AM, Sira MM. Progressive familial intrahepatic cholestasis: INTECH Open Access Publisher; 2013.
2
Srivastava A. Progressive familial intrahepatic cholestasis. J Clin Exp Hepatol. 2014;4:25-36. doi.org/10.1016/j.jceh.2013.10.005. PubMed PMID: 25755532. PubMed PMCID: 4017198.
3
Davit-Spraul A, Gonzales E, Baussan C, Jacquemin E. Progressive familial intrahepatic cholestasis. Orphanet J Rare Dis. 2009;4:1. doi.org/10.1186/1750-1172-4-1. PubMed PMID: 19133130. PubMed PMCID: 2647530.
4
Engelmann G, Wenning D, Herebian D, Sander O, Droge C, Kluge S, et al. Two Case Reports of Successful Treatment of Cholestasis With Steroids in Patients With PFIC-2. Pediatrics. 2015;135:e1326-32. doi.org/10.1542/peds.2014-2376. PubMed PMID: 25847799.
5
Groen A, Romero MR, Kunne C, Hoosdally SJ, Dixon PH, Wooding C, et al. Complementary functions of the flippase ATP8B1 and the floppase ABCB4 in maintaining canalicular membrane integrity. Gastroenterology. 2011;141(5):1927-37. e4. https://doi.org/10.1053/j.gastro.2011.07.042.
6
Davit-Spraul A, Gonzales E, Baussan C, Jacquemin E, editors. The spectrum of liver diseases related to ABCB4 gene mutations: pathophysiology and clinical aspects. Seminars in liver disease; 2010: © Thieme Medical Publishers.
7
Dröge C, Kluge S, Häussinger D, Kubitz R, Keitel V. Sequencing of ATP8B1, ABCB11 and ABCB4 revealed 135 genetic variants in 374 unrelated patients with suspected intrahepatic cholestasis. Zeitschrift für Gastroenterologie. 2015;53:A3_27.
8
Park JS, Ko JS, Seo JK, Moon JS, Park SS. Clinical and ABCB11 profiles in Korean infants with progressive familial intrahepatic cholestasis. World J Gastroenterol. 2016;22:4901-7. doi.org/10.3748/wjg.v22.i20.4901. PubMed PMID: 27239116. PubMed PMCID: 4873882.
9
Carlton VE, Knisely AS, Freimer NB. Mapping of a locus for progressive familial intrahepatic cholestasis (Byler disease) to 18q21-q22, the benign recurrent intrahepatic cholestasis region. Hum Mol Genet. 1995;4:1049-53. doi.org/10.1093/hmg/4.6.1049. PubMed PMID: 7655458.
10
Fathy M, Kamal M, Al-Sharkawy M, Al-Karaksy H, Hassan N. Molecular characterization of exons 6, 8 and 9 of ABCB4 gene in children with Progressive Familial Intrahepatic Cholestasis type 3. Biomarkers. 2016;21:573-7. doi.org/10.3109/1354750X.2016.1166264. PubMed PMID: 27075526.
11
Kane JF. Effects of rare codon clusters on high-level expression of heterologous proteins in Escherichia coli. Curr Opin Biotechnol. 1995;6:494-500. doi.org/10.1016/0958-1669(95)80082-4. PubMed PMID: 7579660.
12
Nakamura Y, Gojobori T, Ikemura T. Codon usage tabulated from international DNA sequence databases: status for the year 2000. Nucleic Acids Res. 2000;28:292. doi.org/10.1093/nar/28.1.292. PubMed PMID: 10592250. PubMed PMCID: 102460.
13
Chartier M, Gaudreault F, Najmanovich R. Large-scale analysis of conserved rare codon clusters suggests an involvement in co-translational molecular recognition events. Bioinformatics. 2012;28:1438-45. doi.org/10.1093/bioinformatics/bts149. PubMed PMID: 22467916. PubMed PMCID: 3465090.
14
Thanaraj TA, Argos P. Protein secondary structural types are differentially coded on messenger RNA. Protein Sci. 1996;5:1973-83. doi.org/10.1002/pro.5560051003. PubMed PMID: 8897597. PubMed PMCID: 2143259.
15
Zhang Y. I-TASSER server for protein 3D structure prediction. BMC Bioinformatics. 2008;9:40. doi.org/10.1186/1471-2105-9-40. PubMed PMID: 18215316. PubMed PMCID: 2245901.
16
Kaplan W, Littlejohn TG. Swiss-PDB Viewer (Deep View). Brief Bioinform. 2001;2:195-7. doi.org/10.1093/bib/2.2.195. PubMed PMID: 11465736.
17
Theodosiou A, Promponas VJ. LaTcOm: a web server for visualizing rare codon clusters in coding sequences. Bioinformatics. 2012;28:591-2. doi.org/10.1093/bioinformatics/btr706. PubMed PMID: 22199385.
18
Dong H, Nilsson L, Kurland CG. Co-variation of tRNA abundance and codon usage in Escherichia coli at different growth rates. J Mol Biol. 1996;260:649-63. doi.org/10.1006/jmbi.1996.0428. PubMed PMID: 8709146.
19
Wu S, Zhang Y. LOMETS: a local meta-threading-server for protein structure prediction. Nucleic Acids Res. 2007;35:3375-82. doi.org/10.1093/nar/gkm251. PubMed PMID: 17478507. PubMed PMCID: 1904280.
20
Guex N, Peitsch M. Swiss-PdbViewer: a fast and easy-to-use PDB viewer for Macintosh and PC. Protein Data Bank Quaterly Newsletter. 1996;77(7).
21
DeLano WL. The PyMOL molecular graphics system. 2002.
22
Vriend G. WHAT IF: a molecular modeling and drug design program. J Mol Graph. 1990;8:52-6, 29. PubMed PMID: 2268628.
23
Tina KG, Bhadra R, Srinivasan N. PIC: Protein Interactions Calculator. Nucleic Acids Res. 2007;35:W473-6. doi.org/10.1093/nar/gkm423. PubMed PMID: 17584791. PubMed PMCID: 1933215.
24
Sonnhammer EL, Eddy SR, Durbin R. Pfam: a comprehensive database of protein domain families based on seed alignments. Proteins. 1997;28:405-20. doi.org/10.1002/(SICI)1097-0134(199707)28:33.0.CO;2-L. PubMed PMID: 9223186.
25
Widmann M, Clairo M, Dippon J, Pleiss J. Analysis of the distribution of functionally relevant rare codons. BMC Genomics. 2008;9:207. doi.org/10.1186/1471-2164-9-207. PubMed PMID: 18457591. PubMed PMCID: 2391168.
26
Nicolaou M, Andress EJ, Zolnerciks JK, Dixon PH, Williamson C, Linton KJ. Canalicular ABC transporters and liver disease. J Pathol. 2012;226:300-15. doi.org/10.1002/path.3019. PubMed PMID: 21984474.
27
Erlinger S, Arias IM, Dhumeaux D. Inherited disorders of bilirubin transport and conjugation: new insights into molecular mechanisms and consequences. Gastroenterology. 2014;146:1625-38. doi.org/10.1053/j.gastro.2014.03.047. PubMed PMID: 24704527.
28
Bull LN, van Eijk MJ, Pawlikowska L, DeYoung JA, Juijn JA, Liao M, et al. A gene encoding a P-type ATPase mutated in two forms of hereditary cholestasis. Nat Genet. 1998;18:219-24. doi.org/10.1038/ng0398-219. PubMed PMID: 9500542.
29
van der Woerd WL, van Mil SW, Stapelbroek JM, Klomp LW, van de Graaf SF, Houwen RH. Familial cholestasis: progressive familial intrahepatic cholestasis, benign recurrent intrahepatic cholestasis and intrahepatic cholestasis of pregnancy. Best Pract Res Clin Gastroenterol. 2010;24:541-53. doi.org/10.1016/j.bpg.2010.07.010. PubMed PMID: 20955958.
30
Morris AL, Bukauskas K, Sada RE, Shneider BL. Byler disease: early natural history. J Pediatr Gastroenterol Nutr. 2015;60:460-6. doi.org/10.1097/MPG.0000000000000650. PubMed PMID: 25825852.
31
Cheng T, Li Q, Zhou Z, Wang Y, Bryant SH. Structure-based virtual screening for drug discovery: a problem-centric review. AAPS J. 2012;14:133-41. doi.org/10.1208/s12248-012-9322-0. PubMed PMID: 22281989. PubMed PMCID: 3282008.
32
Mortazavi M, Zarenezhad M, Alavian SM, Gholamzadeh S, Malekpour A, Ghorbani M, et al. Bioinformatic Analysis of Codon Usage and Phylogenetic Relationships in Different Genotypes of the Hepatitis C Virus. Hepatitis monthly. 2016;16(10). doi: 10.5812/hepatmon.39196 PMCID: PMC5111459, PMID: 27882066
33
Mortazavi M, Zarenezhad M, Gholamzadeh S, Alavian SM, Ghorbani M, Dehghani R, et al. Bioinformatic Identification of Rare Codon Clusters (RCCs) in HBV Genome and Evaluation of RCCs in Proteins Structure of Hepatitis B Virus. Hepatitis monthly. 2016;16(10). doi: 10.5812/hepatmon.39909, PMCID: PMC5116127, PMID: 27882067
34
Fattahi M, Malekpour A, Mortazavi M, Safarpour A, Naseri N. The characteristics of rare codon clusters in the genome and proteins of hepatitis C virus; a bioinformatics look. Middle East journal of digestive diseases. 2014;6(4):214. PMCID: PMC4208930, PMID: 25349685
35
Mortazavi M, Hosseinkhani S. Design of thermostable luciferases through arginine saturation in solvent-exposed loops. Protein Engineering, Design & Selection. 2011;24(12):893-903. https://doi.org/10.1093/protein/gzr051
36
ORIGINAL_ARTICLE
Investigating the Effect of Air Cavities of Sinuses on the Radiotherapy Dose Distribution Using Monte Carlo Method
Background: Considering that some vital organs exist in the head and neck region, the treatment of tumors in this area is a crucial task. The existence of air cavities, namely sinuses, disrupt the radiotherapy dose distribution. The study aims to analyze the effect of maxillary, frontal, ethmoid and sphenoid sinuses on radiotherapy dose distribution by Monte Carlo method. Material and Methods: In order to analyze the effect of the cavities on dose distribution, the maxillary, frontal, ethmoid and sphenoid sinus cavities were simulated with (3×3.2×2) cm3, (2×2×3.2) cm3, (1×1×1.2) cm3 and (1×1×2) cm3 dimensions.Results: In the analysis of the dose distribution caused by cavities, some parameters were observed, including: inhomogeneity of dose distribution in the cavities, inhomogeneity of dose on the edges of the air cavities and dispersion of the radiations after the air cavity. The amount of the dose in various situations showed differences: before the cavity a 0.64% and a 2.76% decrease, a 12.06% and a 17.17% decrease in the air zone, and a 2.25% and a 5.9% increase after the cavity. Conclusion: The results indicate that a drop in dose before the air cavities and in the air zone occurs due to the lack of scattered radiation. Furthermore, the rise in dose was due to the passage of more radiation from the air cavity and dose deposition after the air cavity. The changes in dose distribution are dependent on the cavity size and depth. As a result, this has to be noted in the treatment planning and MU calculations of the patient.
https://jbpe.sums.ac.ir/article_43385_54be6df9b8aef63c0d4b1d9aee649097.pdf
2019-02-01
121
126
10.31661/jbpe.v9i1Feb.1046
Air Cavities
Monte Carlo Method
Radiotherapy Dose Distribution
F
Seif
sahar_s59@yahoo.com
1
Ph.D of Medical Physics. Assistant professor, Department of Medical Physics and Radiotherapy, Arak university of Medical Sciences and Khansari hospital, Arak, Iran
AUTHOR
M R
Bayatiani
mr_kbi@yahoo.com
2
Ph.D of Medical Physics. Assistant professor, Department of Medical Physics and Radiotherapy, Arak university of Medical Sciences and Khansari hospital, Arak, Iran
LEAD_AUTHOR
S
Hamidi
3
Ph.D of Physics. Associate professor, Department of Physics, Arak University, Arak, Iran
AUTHOR
M
Kargaran
4
Ms.c of Physics, Department of Physics, Arak University, Arak, Iran
AUTHOR
Joshi CP, Darko J, Vidyasagar P, Schreiner LJ. Dosimetry of interface region near closed air cavities for Co-60, 6 MV and 15 MV photon beams using Monte Carlo simulations. Journal of Medical Physics/Association of Medical Physicists of India. 2010;35:73.doi: 10.4103/0971-6203.62197.
1
Miura H, Masai N, Yamada K, Sasaki J, Oh R-J, Shiomi H, et al. Evaluation and commissioning of commercial Monte Carlo dose algorithm for air cavity. International Journal of Medical Physics, Clinical Engineering and Radiation Oncology. 2014;3:9.doi: 10.4236/ijmpcero.2014.31002.
2
Osei EK, Darko J, Mosseri A, Jezioranski J. EGSNRC Monte Carlo study of the effect of photon energy and field margin in phantoms simulating small lung lesions. Med Phys. 2003;30:2706-14. doi: 10.1118/1.1607551. PubMed PMID: 14596309.
3
Chow JC, Grigorov GN. Dosimetry of a small air cavity for clinical electron beams: A Monte Carlo study. Med Dosim. 2010;35:92-100. doi: 10.1016/j.meddos.2009.03.004. PubMed PMID: 19931020.
4
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ORIGINAL_ARTICLE
The Effect of Long-Term X-Ray Exposure on Human Lymphocyte
The aim of the paper is to investigate effects of long term x-ray exposure on the human lymphocyte, reactive lymphocyte parameters and morphology of lymphocytes in x-ray technicians at Kirkuk hospitals. The study included 54 apparently healthy male x-ray technicians were matched with another 54 apparently healthy control to show any alteration in the lymphocytes, reactive lymphocytes and morphology. The investigated samples were divide into two groups depending on the work experience and working hours per day. The samples were tested for hematological parameters by complete blood cells count (CBC). The results showed that strong significant (P<0.0001) increasing was recorded for the reactive lymphocytes in all groups of the diagnostic technicians compared with their controls and significantly increasing of lymphocytes observed for some groups. It was concluded that chronic exposure of x-ray can vary lymphocyte and reactive lymphocyte parameters significantly and working hours per day have discernible effects on lymphocyte morphology.
https://jbpe.sums.ac.ir/article_43372_3a250de903cc1d6e79d3ea0049428c8c.pdf
2019-02-01
127
132
10.31661/jbpe.v0i0.935
X-Ray Exposure
Lymphocyte
Reactive Lymphocyte
A H
Taqi
1
Department of Physics, College of Science, Kirkuk University, Kirkuk, Iraq
LEAD_AUTHOR
K A
Faraj
2
Department of Physics, College of Science, University of Sulaimani, Kurdistan Region-Iraq
AUTHOR
S A
Zaynal
3
Department of Physics, College of Science, Kirkuk University, Kirkuk, Iraq
AUTHOR
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