ORIGINAL_ARTICLE
A New Look at Three Potential Mechanisms Proposed for the Carcinogenesis of 5G Radiation
https://jbpe.sums.ac.ir/article_46931_45f164c24668b413a262bbcda4087fea.pdf
2020-12-01
675
678
10.31661/jbpe.v0i0.2008-1157
J J
Bevelacqua
bevelresou@aol.com
1
PhD, Bevelacqua Resources, Richland, WA 99352, USA
AUTHOR
A R
Mehdizadeh
alireza.mehdizadeh@gmail.com
2
PhD, Department of Medical Physics and Engineering, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
AUTHOR
S M J
Mortazavi
mortazavismj@gmail.com
3
PhD, Department of Medical Physics and Engineering, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
LEAD_AUTHOR
EU 5G Appeal – Scientists warn of potential serious health effects of 5G. [cited 2020 July 19] Available from: https://www.jrseco.com/european-union-5g-appeal-scientists-warn-of-potential-serious-health-effects-of-5g/.
1
Soldani D. Fighting Pandemics By Exploiting 5G, AI and Bigdata Enabled Technologies: How 5G could help us to stem Covid-19 outbreaks. Journal of Telecommunications and the Digital Economy. 2020;8(2):146-58. doi: 10.18080/jtde.v8n2.257.
2
Hardell L, Carlberg M. [Comment] Health risks from radiofrequency radiation, including 5G, should be assessed by experts with no conflicts of interest. Oncology Letters. 2020;20(4):1. doi: 10.3892/ol.2020.11876.
3
Rafferty S, O’Connor C, Murphy M. “Fake News’-5G mobile phones and skin cancer: A global analysis of concerns on social media. Skin Research and Technology. 2020. doi: 10.1111/srt.12912. PubMed PMID: 32674224.
4
Mehdizadeh AR, Mortazavi SMJ. 5G Technology: Why Should We Expect a shift from RF-Induced Brain Cancers to Skin Cancers?. J Biomed Phys Eng. 2019;9(5): 505-6. doi: 10.31661/jbpe.v0i0.1225. PubMed PMID: 31750263. PubMed PMCID: PMC6820018.
5
Broad WJ. The 5G health hazard that Isn’t. The New York Times; 2019. Available from: https://www. nytimes. com/2019/07/16/science/5g-cellphones-wireless-cancer. html.
6
Kostoff RN, Heroux P, Aschner M, Tsatsakis A. Adverse health effects of 5G mobile networking technology under real-life conditions. Toxicology Letters. 2020;323:35-40. doi: 10.1016/j.toxlet.2020.01.020. PubMed PMID: 31991167.
7
Russell CL. 5 G wireless telecommunications expansion: Public health and environmental implications. EnvironMental Research. 2018;165:484-95. doi: 10.1016/j.envres.2018.01.016. PMID: 29655646.
8
Yakymenko I, Tsybulin O, Sidorik E, Henshel D, Kyrylenko O, Kyrylenko S. Oxidative mechanisms of biological activity of low-intensity radiofrequency radiation. Electromagnetic Biology and Medicine. 2016;35(2):186-202. doi: 10.3109/15368378.2015.1043557. PubMed PMID: 26151230.
9
Klaunig JE, Kamendulis LM. The role of oxidative stress in carcinogenesis. Annu. Rev. Pharmacol. Toxicol. 2004;44:239-67. doi: 10.1146/annurev.pharmtox.44.101802.121851. PubMed PMID: 14744246.
10
Nathan C, Ding A. SnapShot: reactive oxygen intermediates (ROI). Cell. 2010;140(6):951. doi: 10.1016/j.cell.2010.03.008. PubMed PMID: 20303882.
11
Pham-Huy LA, He H, Pham-Huy C. Free radicals, antioxidants in disease and health. International Journal of BioMedical Science: IJBS. 2008;4(2):89-96. PubMed PMID: 23675073. PubMed PMCID: PMC3614697.
12
Singh AK, Pandey P, Tewari M, Pandey HP, Gambhir IS, Shukla HS. Free radicals hasten head and neck cancer risk: A study of total oxidant, total antioxidant, DNA damage, and histological grade. Journal of Postgraduate Medicine. 2016;62(2):96-101. doi: 10.4103/0022-3859.180555. PubMed PMID: 27089108. PubMed PMCID: PMC4944358.
13
Lobo V, Patil A, Phatak A, Chandra N. Free radicals, antioxidants and functional foods: Impact on human health. Pharmacognosy Reviews. 2010;4(8):118-26. doi: 10.4103/0973-7847.70902. PubMed PMID: 22228951. PubMed PMCID: PMC3249911.
14
Betzalel N, Ishai PB, Feldman Y. The human skin as a sub-THz receiver–Does 5G pose a danger to it or not? Environmental Research. 2018;163:208-16. doi: 10.1016/j.envres.2018.01.032. PubMed PMID: 29459303.
15
Tripathi SR, Miyata E, Ishai PB, Kawase K. Morphology of human sweat ducts observed by optical coherence tomography and their frequency of resonance in the terahertz frequency region. Scientific Reports. 2015;5:9071. doi: 10.1038/srep09071.
16
Bevelacqua JJ. Contemporary health physics: problems and solutions. John Wiley & Sons; 2009.
17
ORIGINAL_ARTICLE
Study of Photoneutron Production for the 18 MV Photon Beam of the Siemens Medical linac by Monte Carlo Simulation
Background: Considering the importance of photoneutron production in linear accelerators, it is necessary to describe and measure the photoneutrons produced around modern linear accelerators. Objective: The aim of the present research is to study photoneutron production for the 18 MV photon beam of a Siemens Primus Plus medical linear accelerator.Material and Methods: This study is an experimental study. The main components of the head of Siemens Primus Plus linac were simulated using MCNPX 2.7.0 code. The contribution of different components of the linac in photoneutron production, neutron source strength, neutron source strength and photon and electron spectra were calculated for the flattening filter and flattening filter free cases for the 18 MV photon beam, and was scored for three fields of 5 × 5 cm2, 10 × 10 cm2 and 20 × 20 cm2 in size. Results: The results show that the primary collimator has the largest contribution to production of neutrons. Moreover, the photon fluence for the flattening filter free case is 8.62, 6.51 and 4.62 times higher than the flattening filter case for the three fields, respectively. The electron fluences for the flattening filter free case are 4.62, 2.93 and 2.79 times higher than with flattening filter case for the three fields under study, respectively. In addition to these cases, by increasing the field size, the contribution of neutron production related to the jaws is reduced, so that when the field size increases from 5 × 5 cm2 to 20 × 20 cm2, a 17.93% decrease in photoneutron production was observed. Conclusion: In all of the accelerators, the neutron strength also increases with increasing energy. The calculated neutron strength was equal to 0.83×1012 neutron Gy −1 at the isocenter.
https://jbpe.sums.ac.ir/article_45721_1c40bbcdc476d4b05d7cc60b1cd605b2.pdf
2020-12-01
679
690
10.31661/jbpe.v0i0.939
Neutron Contamination
Particle Accelerators
18 MV Photon Beam
Monte Carlo Method
Electrons
Neutron Source Strength
Proton Spectrum
H
Dowlatabadi
h_d_ph@yahoo.com
1
PhD, Physics Department, School of Sciences, Payame Noor University of Mashhad, Mashhad, Iran
LEAD_AUTHOR
A A
Mowlavi
ebrahimi.mahdy@gmail.com
2
PhD, Physics Department, School of Sciences, Hakim Sabzevari University, Sabzevar, Iran
AUTHOR
M
Ghorbani
mhdghorbani@gmail.com
3
PhD, Biomedical Engineering and Medical Physics Department, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
AUTHOR
S
Mohammadi
mohammadis@pnum.ac.ir
4
PhD, Physics Department, School of Sciences, Payame Noor University of Mashhad, Mashhad, Iran
AUTHOR
C
Knaup
courtney.knaup@usoncology.com
5
PhD, Comprehensive Cancer Centers of Nevada, Las Vegas, Nevada, USA
AUTHOR
Vega-Carrillo HR, Martinez-Ovalle SA, Lallena AM, Mercado GA, Benites-Rengifo JL. Neutron and photon spectra in LINACs. Appl Radiat Isot. 2012;71 Suppl:75-80. doi: 10.1016/j.apradiso.2012.03.034. PubMed PMID: 22494894.
1
Alfuraih A, Chin M, Spyrou N. Measurements of the photonuclear neutron yield of 15 MV medical linear accelerator. Journal of Radioanalytical and Nuclear Chemistry. 2008;278:681-4.
2
Naseri A, Mesbahi A. A review on photoneutrons characteristics in radiation therapy with high-energy photon beams. Rep Pract Oncol Radiother. 2010;15:138-44. doi: 10.1016/j.rpor.2010.08.003. PubMed PMID: 24376940.PubMed PMCID: PMC3863143.
3
Ghiasi H, Mesbahi A. Monte Carlo characterization of photoneutrons in the radiation therapy with high energy photons: a Comparison between simplified and full Monte Carlo models. International Journal of Radiation Research. 2010;8:187.
4
Akkurt I, Adler JO, Annand JR, Fasolo F, Hansen K, Isaksson L, et al. Photoneutron yields from tungsten in the energy range of the giant dipole resonance. Phys Med Biol. 2003;48:3345-52. PubMed PMID: 14620062.
5
ICRP. The 2007 Recommendations of the International Commission on Radiological Protection. Oxford: Ann ICRP; 2007. p. 1-332.
6
Becker J. Simulation of neutron production at a medical linear accelerator. Institute of Experimental Physics University of Hamburg, MSc Diploma Thesis. 2007:28-30.
7
Nyandoto P, Muhonen T, Joensuu H. Second cancer among long-term survivors from Hodgkin’s disease. Int J Radiat Oncol Biol Phys. 1998;42:373-8. PubMed PMID: 9788418.
8
Kleinerman RA, Boice Jr JD, Storm HH, Sparen P, Andersen A, Pukkala E, et al. Second primary cancer after treatment for cervical cancer. An international cancer registries study. Cancer. 1995;76:442-52. PubMed PMID: 8625126.
9
Chaturvedi AK, Engels EA, Gilbert ES, Chen BE, Storm H, Lynch CF, et al. Second cancers among 104,760 survivors of cervical cancer: evaluation of long-term risk. J Natl Cancer Inst. 2007;99:1634-43. doi: 10.1093/jnci/djm201. PubMed PMID: 17971527.
10
Boice Jr JD, Day N, Andersen A, Brinton L, Brown R, Choi N, et al. Second cancers following radiation treatment for cervical cancer. An international collaboration among cancer registries. J Natl Cancer Inst. 1985;74:955-75.
11
Zabihinpoor S, Hasheminia M. Calculation of neutron contamination from medical linear accelerator in treatment room. Adv Studies Theor Phys. 2011;5:421-8.
12
Martinez-Ovalle SA, Barquero R, Gomez-Ros JM, Lallena AM. Ambient neutron dose equivalent outside concrete vault rooms for 15 and 18 MV radiotherapy accelerators. Radiat Prot Dosimetry. 2012;148:457-64. doi: 10.1093/rpd/ncr208. PubMed PMID: 21750004.
13
Ma A, Awotwi-Pratt J, Alghamdi A, Alfuraih A, Spyrou N. Monte Carlo study of photoneutron production in the Varian Clinac 2100C linac. Journal of Radioanalytical and Nuclear Chemistry. 2007;276:119-23.
14
Mesbahi A. A Monte Carlo study on neutron and electron contamination of an unflattened 18-MV photon beam. Appl Radiat Isot. 2009;67:55-60. doi: 10.1016/j.apradiso.2008.07.013. PubMed PMID: 18760613.
15
Pena J, Franco L, Gomez F, Iglesias A, Pardo J, Pombar M. Monte Carlo study of Siemens PRIMUS photoneutron production. Phys Med Biol. 2005;50:5921-33. doi: 10.1088/0031-9155/50/24/011. PubMed PMID: 16333164.
16
Dowlatabadi H, Mowlavi AA, Ghorbani M. Monte Carlo Simulation of Siemens Primus plus Linac for 6 and 18 MV Photon Beams. J Biomed Phys Eng. 2017;7:333-46. PubMed PMID: 29616199. PubMed PMCID: PMC5809928.
17
McGinley PH, Landry JC. Neutron contamination of x-ray beams produced by the Varian Clinac 1800. Phys Med Biol. 1989;34:777.
18
Mao XS, Kase KR, Liu JC, Nelson WR, Kleck JH, Johnsen S. Neutron sources in the Varian Clinac 2100C/2300C medical accelerator calculated by the EGS4 code. Health Phys. 1997;72:524-9. PubMed PMID: 9119676.
19
International Atomic Energy Agency. Radiation protection in the design of radiotherapy facilities: Internat. Geneva: Atomic Energy Agency; 2006.
20
Kry SF, Titt U, Ponisch F, Vassiliev ON, Salehpour M, Gillin M, et al. Reduced neutron production through use of a flattening-filter-free accelerator. Int J Radiat Oncol Biol Phys. 2007;68:1260-4. doi: 10.1016/j.ijrobp.2007.04.002. PubMed PMID: 17637397.
21
Lin JP, Chu TC, Lin SY, Liu MT. The measurement of photoneutrons in the vicinity of a Siemens Primus linear accelerator. Appl Radiat Isot. 2001;55(3):315-21. PubMed PMID: 11515653.
22
Becker J, Brunckhorst E, Schmidt R. Photoneutron production of a Siemens Primus linear accelerator studied by Monte Carlo methods and a paired magnesium and boron coated magnesium ionization chamber system. Phys Med Biol. 2007;52(21):6375-87. PubMed PMID: 17951849.
23
Followill DS, Stovall MS, Kry SF, Ibbott GS. Neutron source strength measurements for Varian, Siemens, Elekta, and General Electric linear accelerators. J Appl Clin Med Phys. 2003;4(3):189-94. PubMed PMID: 12841788.PubMed PMCID: PMC5724447.
24
Mohammadi N, Miri-Hakimabad H, Rafat-Motavlli L, et al. Neutron spectrometry and determination of neutron contamination around the 15 MV Siemens Primus LINAC. J Radioanal Nucl Chem. 2015;304:1001-8. doi: 10.1007/s10967-015-3944-5.
25
McCall RC. Neutron yields of medical electron accelerators. Report SLAC-PUB 4480; United States: Stanford Linear Accelerator Center; 1987.
26
ORIGINAL_ARTICLE
Clinical Experience of Intensity Modulated Radiotherapy Pre-Treatment Quality Assurance for Carcinoma Head and Neck Patients with EPID and IMatriXX in Rural Center
Background: Radiation therapy techniques as Intensity Modulated Radiotherapy (IMRT), rapid arc have been used for treatment of cancer with high accuracy. Objective: Verification of planned and delivered dose distribution is important, therefore current study aims to analyse quality assurance (QA) results of IMRT by Electronic Portal Imaging Device (EPID) and IMatriXX in head and neck Carcinoma (Ca H&N) patients.Material and Methods: In this experimental study, performance of an EPID and IMatriXX was assessed with dose measurements using ionization chamber. Calibrated IMatriXX and EPID are used for pre-treatment patient specific quality assurance (PSQA), for 122 patients’ plans of Ca H&N with IMRT treatment technique on linear accelerator. Dose images were acquired and compared with gamma evaluation (3% / 3 mm) and three scalar parameters, named average γ (γavg), maximum γ (γmax) and area gamma <1, were analyzed in the region of interest. Results: The γ correlation comparisons yielded average correlation of 0.990 and 0.982 for IMatriXX and EPID respectively. Maximum value of gamma is 0.998, while minimum gamma is 0.872 for IMatriXX and 0.953 for EPID. For students, unpaired ‘t’ test analysis is applied for comparison to two data sets. P-value was set at 0.005 which, for this study, was computed 0.001, showing good correlation between measured data with IMatriXX and EPID. Conclusion: The EPID and IMatriXX have significantly improved dosimetric properties, resulting in more sensitive, accurate measurements before actual treatment. The result shows EPID can be replaced with other dosimetry method and ionization chamber measurements. Portal imager is an efficient, accurate and sensitive dosimetry tool and is also the basis of pre-treatment quality assurance protocol.
https://jbpe.sums.ac.ir/article_47070_61a187c6c035b5a3dc882b2ebf5a2faf.pdf
2020-12-01
691
698
10.31661/jbpe.v0i0.2004-1102
Radiotherapy
Intensity-Modulation
Calibration
Dosimetry
quality control
M
More
mahendra.moremv@gmail.com
1
PhD Candidate, Department of Radiotherapy and Oncology, Rural Medical College, Pravara Institute of Medical Sciences (PIMS), Loni, India
LEAD_AUTHOR
V
Jain
2
MD, Department of Radiotherapy and Oncology, Rural Medical College, Pravara Institute of Medical Sciences (PIMS), Loni, India
AUTHOR
O P
Gurjar
ominbarc@gmail.com
3
PhD, Government Cancer Hospital, Mahatma Gandhi Memorial Medical College, Indore, India
AUTHOR
El-Mohri Y, Antonuk LE, Yorkston J, Jee KW, et al. Relative dosimetry using active matrix flat-panel imager (AMFPI) technology. Med Phys. 1999;26(8):1530-41. doi: 10.1118/1.598649. PubMed PMID: 10501053.
1
McCurdy BM, Luchka K, Pistorius S. Dosimetric investigation and portal dose image prediction using an amorphous silicon electronic portal imaging device. Med Phys. 2001;28(6):911-24. doi: 10.1118/1.1374244. PubMed PMID: 11439488.
2
Grein EE, Lee R, Luchka K. An investigation of a new amorphous silicon electronic portal imaging device for transit dosimetry. Med Phys. 2002;29(10):2262-8. doi: 10.1118/1.1508108. PubMed PMID: 12408300.
3
Greer PB, Popescu CC. Dosimetric properties of an amorphous silicon electronic portal imaging device for verification of dynamic intensity modulated radiation therapy. Med Phys. 2003;30(7):1618-27. doi: 10.1118/1.1582469. PubMed PMID: 12906179.
4
Warkentin B, Steciw S, Rathee S, Fallone BG. Dosimetric IMRT verification with a flat-panel EPID. Med Phys. 2003;30(12):3143-55. doi: 10.1118/1.1625440. PubMed PMID: 14713081.
5
McDermott LN, Louwe RJ, Sonke JJ, Van Herk MB, Mijnheer BJ. Dose-response and ghosting effects of an amorphous silicon electronic portal imaging device. Med Phys. 2004;31(2):285-95. doi: 10.1118/1.1637969. PubMed PMID: 15000614.
6
Louwe RJ, McDermott LN, Sonke JJ, et al. The long-term stability of amorphous silicon flat panel imaging devices for dosimetry purposes. Med Phys. 2004;31(11):2989-95. doi: 10.1118/1.1803751. PubMed PMID: 15587651.
7
Reiner BI, Siegel EL, Siddiqui K. Evolution of the digital revolution: a radiologist perspective. J Digit Imaging. 2003;16(4):324-30. doi: 10.1007/s10278-003-1743-y. PubMed PMID: 14747936. PubMed PMCID: PMC3044070.
8
Georg D, Kroupa B, Winkler P, Pötter R. Normalized sensitometric curves for the verification of hybrid IMRT treatment plans with multiple energies. Med Phys. 2003;30(6):1142-50. doi: 10.1118/1.1576951. PubMed PMID: 12852539.
9
Stock M, Kroupa B, Georg D. Interpretation and evaluation of the gamma index and the gamma index angle for the verification of IMRT hybrid plans. Phys Med Biol. 2005;50(3):399-411. doi: 10.1088/0031-9155/50/3/001. PubMed PMID: 15773719.
10
Jursinic PA, Nelms BE. A 2-D diode array and analysis software for verification of intensity modulated radiation therapy delivery. Med Phys. 2003;30(5):870-9. doi: 10.1118/1.1567831. PubMed PMID: 12772995.
11
Létourneau D, Gulam M, Yan D, Oldham M, Wong JW. Evaluation of a 2D diode array for IMRT quality assurance. Radiother Oncol. 2004;70(2):199-206. doi: 10.1016/j.radonc.2003.10.014. PubMed PMID: 15028408.
12
Wiezorek T, Banz N, Schwedas M, et al. Dosimetric quality assurance for intensity-modulated radiotherapy feasibility study for a filmless approach. Strahlenther Onkol. 2005;181(7):468-74. doi: 10.1007/s00066-005-1381-z. PubMed PMID: 15995841.
13
Childress NL, Bloch C, White RA, Salehpour M, Rosen II. Detection of IMRT delivery errors using a quantitative 2D dosimetric verification system. Med Phys. 2005;32(1):153-62. doi: 10.1118/1.1829171. PubMed PMID: 15719966.
14
Steciw S, Warkentin B, Rathee S, Fallone BG. Three-dimensional IMRT verification with a flat-panel EPID. Med Phys. 2005;32(2):600-12. doi: 10.1118/1.1843471. PubMed PMID: 15789607.
15
Wendling M, Louwe RJ, McDermott LN, Sonke JJ, et al. Accurate two-dimensional IMRT verification using a back-projection EPID dosimetry method. Med Phys. 2006;33(2):259-73. doi: 10.1118/1.2147744. PubMed PMID: 16532930.
16
Low DA, Harms WB, Mutic S, Purdy JA. A technique for the quantitative evaluation of dose distributions. Med Phys. 1998;25(5):656-61. doi: 10.1118/1.598248. PubMed PMID: 9608475.
17
Kim YL, Chung JB, Kim JS, Lee JW, Choi KS. Comparison of the performance between portal dosimetry and a commercial two-dimensional array system on pretreatment quality assurance for volumetric-modulated arc and intensity-modulated radiation therapy. Journal of the Korean Physical Society. 2014;64(8):1207-12. doi: 10.3938/jkps.64.1207.
18
Winkler P, Zurl B, Guss H, Kindl P, Stuecklschweiger G. Performance analysis of a film dosimetric quality assurance procedure for IMRT with regard to the employment of quantitative evaluation methods. Phys Med Biol. 2005;50(4):643-54. doi: 10.1088/0031-9155/50/4/006. PubMed PMID: 15773625.
19
Van Zijtveld M, Dirkx ML, De Boer HC, Heijmen BJ. Dosimetric pre-treatment verification of IMRT using an EPID; clinical experience. Radiotherapy and Oncology. 2006;81(2):168-75. doi: 10.1016/j.radonc.2006.09.008.
20
Budgell GJ, Perrin BA, Mott JH, Fairfoul J, Mackay RI. Quantitative analysis of patient-specific dosimetric IMRT verification. Phys Med Biol. 2005;50(1):103-19. doi: 10.1088/0031-9155/50/1/009. PubMed PMID: 15715426.
21
Partridge M, Symonds-Tayler JR, Evans PM. IMRT verification with a camera-based electronic portal imaging system. Phys Med Biol. 2000;45(12):N183-96. doi: 10.1088/0031-9155/45/12/402. PubMed PMID: 11131208.
22
Louwe RJ, Damen EM, Van Herk M, et al. Three-dimensional dose reconstruction of breast cancer treatment using portal imaging. Med Phys. 2003;30(9):2376-89. doi: 10.1118/1.1589496. PubMed PMID: 14528960.
23
Miften M, Olch A, Mihailidis D, Moran J, et al. Tolerance limits and methodologies for IMRT measurement-based verification QA: Recommendations of AAPM Task Group No. 218. Med Phys. 2018;45(4):e53-83. doi: 10.1002/mp.12810. PubMed PMID: 29443390.
24
Herzen J, Todorovic M, Cermers F, et al. Dosimetric evaluation of a 2D pixel ionization chamber for implementation in clinical routine. Phys Med Biol. 2007;52(4):1197-208. doi: 10.1088/0031-9155/52/4/023.
25
Chang J, Ling CC. Using the frame averaging of aS500 EPID for IMRT verification. J Appl Clin Med Phys. 2003;4(4):287-99. doi: 10.1120/jacmp.v4i4.2499. PubMed PMID: 14604418. PubMed PMCID: PMC5724460.
26
ORIGINAL_ARTICLE
Effect of Silicone Rubber-Lead (SR-Pb) Thickness on Dose Reduction and Image Quality as Gonad Shield
Background: Some organs in the body are sensitive to radiation such as eyes, breast, and gonads. Protection of sensitive organs against radiation is necessary. Recently, many sensitive organ shields have been developed from different materials. Objective: The aim of this study is to evaluate the dose reduction and image quality from implementation of Silicone Rubber-Lead (SR-Pb) as an alternative gonad shield in digital radiography (DR).Material and Methods: In this experimental study, the SR-Pb gonad shields with various thicknesses of 2, 4, 6, 8, and 10 mm were synthesized. This study used the Pb percentage of 5 wt%. An anthropomorphic phantom was used in abdomen plain examinations. The results obtained from the use of the SR-Pb was compared with standard gonad shield, i.e. lead apron. To measure the dose reduction, the Piranha detector was used. The image quality assessment was evaluated with the signal-to-noise ratio (SNR) and the contrast-to-noise ratio (CNR). Results: This study showed the dose reduction was significant for all SR-Pb thicknesses, and incrementally increased with the increase of the SR-Pb thickness. The minimum and maximum of dose reduction were 22.8% for 2 mm and 66.9% for 10 mm SR-Pb, respectively. Conclusion: Compared to the reference image without gonad shield, the SNR and CNR do not significantly change. Hence, the SR-Pb is probably to be used as an alternative gonad shield.
https://jbpe.sums.ac.ir/article_46953_755c30ba75c43fd1c6dfbf80b797278d.pdf
2020-12-01
699
706
10.31661/jbpe.v0i0.1912-1007
Lead
Gonads
Imaging
Radiation protection
F
Zahroh
1
MSc, Department of Physics, Faculty of Sciences and Mathematics, Diponegoro University, Semarang, Indonesia
AUTHOR
C
Anam
2
PhD, Department of Physics, Faculty of Sciences and Mathematics, Diponegoro University, Semarang, Indonesia
AUTHOR
H
Sutanto
herisutanto@live.undip.ac.id
3
PhD, Department of Physics, Faculty of Sciences and Mathematics, Diponegoro University, Semarang, Indonesia
LEAD_AUTHOR
Y
Irdawati
4
MSc, Department of Physics, Faculty of Sciences and Mathematics, Diponegoro University, Semarang, Indonesia
AUTHOR
Z
Arifin
5
MSc, Department of Physics, Faculty of Sciences and Mathematics, Diponegoro University, Semarang, Indonesia
AUTHOR
Y
Kartikasari
6
MSc, Health Polytechnic of Semarang, Ministry of Health, Indonesia
AUTHOR
Indonesian Ministry of Healthy. Indonesia Basic Health Research (RISKESDAS). Publication of Indonesian Ministry of Health; 2018. Available from: http://labdata.litbang.depkes.go.id/menu-download.
1
GBD Mortality and Caused of Death Collaborators. Global, regional, and national age–sex specific all-cause and cause-specific mortality for 240 causes of death, 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet. 2015;385(9963):117-71. doi: 10.1016/S0140-6736(14)61682-2. PubMed PMID: 25530442. PubMed PMCID: PMC4340604.
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Marsh RM, Silosky M. Patient Shielding in Diagnostic Imaging: Discontinuing a Legacy Practice. AJR Am J Roentgenol. 2019;212(4):755-7. doi: 10.2214/AJR.18.20508. PubMed PMID: 30673332.
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Dauer LT, Casciotta KA, Erdi YE, Rothenberg LN. Radiation dose reduction at a price: the effectiveness of a male gonadal shield during helical CT scans. BMC Med Imaging. 2007;7:5. doi: 10.1186/1471-2342-7-5. PubMed PMID: 17367529. PubMed PMCID: PMC1831769.
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Karami V, Zabihzadeh M, Gholami M. Gonad Shielding for Patients Undergoing Conventional Radiological Examinations: Is There Cause for Concern? Jentashapir J Health Res. 2016;7(2): 31170. doi: 10.17795/jjhr-31170.
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MacKay M, Hancy C, Crowe A, D’Rozario R, Ng CKC. Attitudes of medical imaging technologists on use of gonad shielding in general radiography. Radiographer. 2012;59(2):35-39. doi: 10.1002/j.2051-3909.2012.tb00172.x.
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Bardo DME, Black M, Schenk K, Zaritzky MF. Location of the ovaries in girls from newborn to 18 years of age: reconsidering ovarian shielding. Pediatr Radiol. 2009;39(3):253-9. doi: 10.1007/s00247-008-1094-4. PubMed PMID: 19130048.
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Fawcett S, Gomez A, Barter S, Ditchfield M, Set P. More harm than good? The anatomy of misguided shielding of the ovaries. Br J Radiol. 2012;85:e442-7. doi: 10.1259/bjr/25742247. PubMed PMID: 22096220. PubMed PMCID: PMC3587098.
9
Frantzen MJ, Robben S, Postma AA, Zoetelief J, Wildberger JE, Kemerink GJ. Gonad shielding in pediatrics pelvic radiography: Disadvantages prevail over benefit. Insights into Imaging. 2012;3(1):23-32. doi: 10.1007/s13244-011-0130-3. PubMed PMID: 22695996. PubMed PMCID: PMC3292647.
10
Karami V, Zabihzadeh M, Shams N, Gholami M. Radioprotection to the Gonads in Pediatric Pelvic Radiography: Effectiveness of Developed Bismuth Shield. Int J Pediatr. 2017;5(42):5153-66. doi: 10.22038/ijp.2017.23.116.1939.
11
Aral N, Nergis FB, Candan C. The X-ray attenuation and the flexural properties of lead-free coated fabrics. J Ind Text. 2016;47(2):252-68. doi: 10.1177/1528083716644287.
12
Wozniak AI, Ivanov VS, Zhdanovich OA, Nazarov VI, Yegorov AS. Modern approaches to polymer materials protecting from ionizing radiation. Oriental Journal of Chemistry. 2017;33(5):2148-63. doi: 10.13005/ojc/330502.
13
Irdawati Y, Sutanto H, Anam C, Fujibuchi T, Zahroh F, Dougherty G. Development of a novel artifact-free eye shield based on silicon rubber-lead composition in the CT examination of the head. J Radiol Prot. 2019;39(4):991-1005. doi: 10.1088/1361-6498/ab2f3e. PubMed PMID: 31272094.
14
Sutanto H, Irdawati Y, Anam C, Fujibuchi T, Dougherty G, Hidayanto E, Arifin Z, Soedarsono JW, Bahrudin. An artifact-free thyroid shield in CT examination: a phantom study. Biomed Phys Eng Express. 2020;6(1):15-29. doi: 10.1088/2057-1976/ab6ed1.
15
Tiwari A, HiharaLH, Rawlins JW. Intelligent Coatings for Corrosion Control. Amsterdam: Elsevier; 2015. p. 585-602.
16
Davey E, England A. AP versus PA positioning in lumbar spine computed radiography: Image quality and individual organ doses. Radiography. 2015;21(2):188-96. doi: 10.1016/j.radi.2014.11.003.
17
McCaffrey JP, Shen H, Downton B, Mainegra-Hing E. Radiation attenuation by lead and nonlead materials used in radiation shielding garment. Med Phys. 2007;34(2):530-7. doi: 10.1118/1.2426404. PubMed PMID: 17388170.
18
Tsai YS, Liu YS, Chuang MT, Wang CK, Lai CS, Tsai HM, Lin CJ, Lu CH. Shielding during x-ray examination of pediatric female patients with developmental dysplasia of the hip. J Radiol Prot. 2014;34(4):801-10. doi: 10.1088/0952-4746/34/4/801. PubMed PMID: 25325378.
19
Jang JS, Yang HJ, Koo HJ, Kim SH, Park CR, Yoon SH, Shin SY, Do KH. Image quality assessment with dose reduction using high kVp and additional filtration for abdominal digital radiography. Physica Medica. 2018;50:46-51. doi: 10.1016/j.ejmp.2018.05.007. PubMed PMID: 29891093.
20
Hess R, Neitzel U. Optimizing Image Quality and Dose for Digital Radiography of Distal Pediatric Extremities Using the Contrast-to-Noise Ratio. Fortschr Röntgenstr. 2012;184(7):643-9. doi: 10.1055/s-0032-1312727. PubMed PMID: 22618480.
21
Mraity HAAB, England A, Cassidy S, Eachus P, Dominguez A, Hogg P. Development and validation of a visual grading scale for assessing image quality of AP pelvis radiographic images. Br J Radiol. 2016;89(1061):1-27. doi: 10.1259/bjr.20150430. PubMed PMID: 26943836. PubMed PMCID: PMC4985444.
22
Kaplan SL, Magill D, Felice MA, Xiao R, Ali S, Zhu X. Female gonadal shielding with automatic exposure control increases radiation risks. Pediatr Radiol. 2018;48:227-34. doi: 10.1007/s00247-017-3996-5.
23
AAPM. Protocol for the radiation safety surveys of diagnostic radiological equipment. New York: Inc. Report no 25; 1988.
24
ORIGINAL_ARTICLE
Radioprotective Effects of Zinc and Selenium on Mice Spermatogenesis
Background: Spermatogenesis system is one of the most radiosensitive organs in the body. A usual therapeutic dose of radiation such as the conventional 2 Gy in each fraction of radiotherapy and lower doses seen in diagnostic radiology or a radiation disaster affect the process of spermatogenesis potently. Selenium and zinc are two important elements playing key roles in the development of sperms and also have radioprotective effects. Objective: In this study aims to evaluate the radioprotective effect of zinc and selenium against radiation-induced mice testis injury.Material and Methods: In this experimental study, 30 mice were divided equally into 6 groups, including control selenium treated, zinc treated, radiation, radiation + selenium, radiation + zinc. Treatments started from 2 days before irradiation with 2 Gy cobalt-60 gamma rays. After 37 days, all mice were killed for histopathological evaluations. Results: Results showed that exposure to radiation caused a potent effect on spermatogenesis system. Treatment with selenium reversed these radiation effects potently, while zinc had some limited protective effects. Zinc treatment itself caused a detrimental effect on epididymis and, in combination with radiation, it leads to more damage to seminiferous tubules. Conclusion: In contrast to previous studies that proposed zinc to protect spermatogenesis against various toxic agents, results of this study showed that although zinc may protect from some parameters, it potentiates radiation damage on seminiferous tubules and has a detrimental effect on the epididymis. By contrast, zinc and selenium could alleviate radiation-induced toxicity on the most of the evaluated parameters.
https://jbpe.sums.ac.ir/article_45715_f886c260449abe35652c7dfe9d05f2ee.pdf
2020-12-01
707
712
10.31661/jbpe.v0i0.957
Radiation
Spermatogenesis
zinc
Selenium
Seminiferous Tubules
Epididymis
H
Bagheri
1
MSc, Radiation and Wave Research Center, Aja University of Medical Sciences, Tehran, Iran
AUTHOR
A
Salajegheh
2
MSc, Radiation and Wave Research Center, Aja University of Medical Sciences, Tehran, Iran
AUTHOR
A
Javadi
3
MD, Department of Pathology, Imam Hossein Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
AUTHOR
P
Amini
4
MSc, Department of Radiology, Faculty of Paramedical, Tehran University of Medical Sciences, Tehran, Iran
AUTHOR
B
Shekarchi
5
MD, Radiation and Wave Research Center, Aja University of Medical Sciences, Tehran, Iran
AUTHOR
D
Shabeeb
6
PhD, Department of Physiology, College of Medicine, University of Misan, Misan, Iraq
AUTHOR
A
Eleojo Musa
7
MSc, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences (International Campus), Tehran, Iran
AUTHOR
M
Najafi
najafi_masoud@yahoo.com
8
PhD, Medical Technology Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran
LEAD_AUTHOR
Marjault HB, Allemand I. Consequences of irradiation on adult spermatogenesis: Between infertility and hereditary risk. Mutat Res. 2016;770:340-8. doi: 10.1016/j.mrrev.2016.07.004. PubMed PMID: 27919340.
1
Moreno SG, Dutrillaux B, Coffigny H. High sensitivity of rat foetal germ cells to low dose-rate irradiation. Int J Radiat Biol. 2001;77:529-38. doi: 10.1080/09553000010030211. PubMed PMID: 11304444.
2
Trasler JM. Epigenetics in spermatogenesis. Mol Cell Endocrinol. 2009;306:33-6. doi: 10.1016/j.mce.2008.12.018. PubMed PMID: 19481683.
3
Coleman CN, Blakely WF, Fike JR, et al. Molecular and cellular biology of moderate-dose (1–10 Gy) radiation and potential mechanisms of radiation protection: report of a workshop at Bethesda, Maryland, December 17–18, 2001. Radiat Res. 2003;159:812-34.
4
Holdcraft RW, Braun RE. Hormonal regulation of spermatogenesis. Int J Androl. 2004;27:335-42. doi: 10.1111/j.1365-2605.2004.00502.x. PubMed PMID: 15595952.
5
Hidiroglou M, Knipfel JE. Zinc in mammalian sperm: a review. J Dairy Sci. 1984;67:1147-56. doi: 10.3168/jds.S0022-0302(84)81416-2. PubMed PMID: 6378991.
6
Emami S, Hosseinimehr SJ, Taghdisi SM, Akhlaghpoor S. Kojic acid and its manganese and zinc complexes as potential radioprotective agents. Bioorg Med Chem Lett. 2007;17:45-8. doi: 10.1016/j.bmcl.2006.09.097. PubMed PMID: 17049858.
7
Veldwijk MR, Herskind C, Sellner L, et al. Normal-tissue radioprotection by overexpression of the copper-zinc and manganese superoxide dismutase genes. Strahlenther Onkol. 2009;185:517-23. doi: 10.1007/s00066-009-1973-0. PubMed PMID: 19652935.
8
Floersheim GL, Floersheim P. Protection against ionising radiation and synergism with thiols by zinc aspartate. Br J Radiol. 1986;59:597-602. doi: 10.1259/0007-1285-59-702-597. PubMed PMID: 3518853.
9
Moslemi MK, Tavanbakhsh S. Selenium–vitamin E supplementation in infertile men: effects on semen parameters and pregnancy rate. Int J Gen Med. 2011;4:99-104. doi: 10.2147/ijgm.s16275.
10
Amini P, Kolivand S, Saffar H, Rezapoor S, Motevaseli E, Najafi M, et al. Protective effect of Selenium-L-methionine on radiation-induced acute pneumonitis and lung fibrosis in rat. Curr Clin Pharmacol. 2018. doi: 10.2174/1574884714666181214101917.
11
Verma P, Kunwar A, Priyadarsini KI. Effect of Low-Dose Selenium Supplementation on the Genotoxicity, Tissue Injury and Survival of Mice Exposed to Acute Whole-Body Irradiation. Biol Trace Elem Res. 2017;179:130-9. doi: 10.1007/s12011-017-0955-9.
12
Boran C, Ozkan KU. The effect of zinc therapy on damaged testis in pre-pubertal rats. Pediatr Surg Int. 2004;20:444-8. doi: 10.1007/s00383-004-1173-z.
13
Ayhanci A, Yaman S, Appak S, Gunes S. Hematoprotective effect of seleno-L-methionine on cyclophosphamide toxicity in rats. Drug Chem Toxicol. 2009;32:424-8. doi: 10.1080/01480540903130682. PubMed PMID: 19793036.
14
Khan S, Adhikari JS, Rizvi MA, Chaudhury NK. Radioprotective potential of melatonin against 60 Co γ-ray-induced testicular injury in male C57BL/6 mice. J Biomed Sci. 2015;22:61. doi: 10.1186/s12929-015-0156-9.
15
Mahdavi M, Mozdarani H. Protective effects of famotidine and vitamin C against radiation induced cellular damage in mouse spermatogenesis process. International Journal of Radiation Research. 2011;8:223.
16
Songthaveesin C, Saikhun J, Kitiyanant Y, Pavasuthipaisit K. Radio-protective effect of vitamin E on spermatogenesis in mice exposed to gamma-irradiation: a flow cytometric study. Asian Journal of Andrology. 2004;6:331-6.
17
Shaban NZ, Zahran AMA, El-Rashidy FH, Kodous ASA. Protective role of hesperidin against γ-radiation-induced oxidative stress and apoptosis in rat testis. Journal of Biological Research-Thessaloniki. 2017;24:5.
18
Sieber F, Muir SA, Cohen EP, Fish BL, et al. Dietary selenium for the mitigation of radiation injury: effects of selenium dose escalation and timing of supplementation. Radiat Res. 2011;176:366-74. PubMed PMID: 21867430. PubMed PMCID: PMC3237945.
19
Sieber F, Muir SA, Cohen EP, North PE, et al. High-dose selenium for the mitigation of radiation injury: a pilot study in a rat model. Radiat Res. 2009;171:368-73. doi: 10.1667/0033-7587-171.3.368.
20
Puspitasari IM, Abdulah R, Yamazaki C, Kameo S, Nakano T, Koyama H. Updates on clinical studies of selenium supplementation in radiotherapy. Radiat Oncol. 2014;9:125. doi: 10.1186/1748-717X-9-125. PubMed PMID: 24885670. PubMed PMCID: PMC4073179.
21
Dani V, Dhawan DK. Radioprotective role of zinc following single dose radioiodine (131I) exposure to red blood cells of rats. Indian J Med Res. 2005;122:338-42. PubMed PMID: 16394327.
22
ORIGINAL_ARTICLE
Correlation between Kidney Function and Sonographic Texture Features after Allograft Transplantation with Corresponding to Serum Creatinine: A Long Term Follow-Up Study
Background: The ability to monitor kidney function after transplantation is one of the major factors to improve care of patients. Objective: Authors recommend a computerized texture analysis using run-length matrix features for detection of changes in kidney tissue after allograft in ultrasound imaging. Material and Methods: A total of 40 kidney allograft recipients (28 male, 12 female) were used in this longitudinal study. Of the 40 patients, 23 and 17 patients showed increased serum creatinine (sCr) (increased group) and decreased sCr (decreased group), respectively. Twenty run-length matrix features were used for texture analysis in three normalizations. Correlations of texture features with serum creatinine (sCr) level and differences between before and after follow-up for each group were analyzed. An area under the receiver operating characteristic curve (Az) was measured to evaluate potential of proposed method. Results: The features under default and 3sigma normalization schemes via linear discriminant analysis (LDA) showed high performance in classifying decreased group with an Az of 1. In classification of the increased group, the best performance gains were determined in the 3sigma normalization schemes via LDA with an Az of 0.974 corresponding to 95.65% sensitivity, 91.30% specificity, 93.47% accuracy, 91.67% PPV, and 95.45% NPV. Conclusion: Run-length matrix features not only have high potential for characterization but also can help physicians to diagnose kidney failure after transplantation.
https://jbpe.sums.ac.ir/article_45714_2684ef6011de09de22c1d8d0cc59f9fd.pdf
2020-12-01
713
726
10.31661/jbpe.v0i0.928
Decision making
Computer-Assisted
Kidney Transplantation
Pattern Recognition System
Ultrasonography
A
Abbasian Ardakani
a.ardekani@live.com
1
PhD, Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
AUTHOR
A
Sattar
alimajer2002@hotmail.com
2
MD, Department of Vascular and Interventional Radiology, School of Medicine, Namazi Hospital, Shiraz University of Medical Sciences, Shiraz, Iran
AUTHOR
J
Abolghasemi
abolghasemi1347@yahoo.com
3
PhD, Department of Biostatistics, School of Public Health, Iran University of Medical Sciences, Tehran, Iran
AUTHOR
A
Mohammadi
ar.mohammadi@jbpe.org
4
MD, Department of Vascular and Interventional Radiology, School of Medicine, Namazi Hospital, Shiraz University of Medical Sciences, Shiraz, Iran
LEAD_AUTHOR
Centers for Disease Control and Prevention. National Chronic Kidney Disease Fact Sheet: general information and national estimates on chronic kidney disease in the United States, 2010. Atlanta, Department of Health and Human Services, Centers for Disease Control and Prevention; 2010.
1
Matas AJ, Smith JM, Skeans MA, Lamb KE, Gustafson SK, Samana CJ, et al. OPTN/SRTR 2011 Annual Data Report: kidney. Am J Transplant. 2013;13 Suppl 1:11-46. doi: 10.1111/ajt.12019. PubMed PMID: 23237695.
2
Forbes JM, Hewitson TD, Becker GJ, Jones CL. Ischemic acute renal failure: long-term histology of cell and matrix changes in the rat. Kidney Int. 2000;57:2375-85. doi: 10.1046/j.1523-1755.2000.00097.x.
3
Simmonds MJ. Using genetic variation to predict and extend long-term kidney transplant function. Transplantation. 2015;99:2038-48. doi: 10.1097/tp.0000000000000836.
4
Merhi B, Bayliss G, Gohh RY. Role for urinary biomarkers in diagnosis of acute rejection in the transplanted kidney. World J Transplant. 2015;5:251-60. doi: 10.5500/wjt.v5.i4.251. PubMed PMID: 26722652. PubMed PMCID: PMC4689935.
5
Wymer DC. Imaging the Chronic Kidney Disease Patient: Clinical Approaches, Utility and Complications. Chronic Renal Disease. 2015:890-6. doi: 10.1016/b978-0-12-411602-3.00075-5.
6
Castellano G, Bonilha L, Li LM, Cendes F. Texture analysis of medical images. Clin Radiol. 2004;59:1061-9. doi: 10.1016/j.crad.2004.07.008. PubMed PMID: 15556588.
7
Abbasian Ardakani A, Gharbali A, Saniei Y, Mosarrezaii A, Nazarbaghi S. Application of Texture Analysis in Diagnosis of Multiple Sclerosis by Magnetic Resonance Imaging. Glob J Health Sci. 2015;7:68-78. doi: 10.5539/gjhs.v7n6p68. PubMed PMID: 26153164. PubMed PMCID: PMC4803872.
8
Raja KB, Madheswaran M, Thyagarajah K. Texture pattern analysis of kidney tissues for disorder identification and classification using dominant Gabor wavelet. Machine Vision and Applications. 2010;21:287-300.
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Hafizah WM, Supriyanto E, Yunus J, editors. Feature extraction of kidney ultrasound images based on intensity histogram and gray level co-occurrence matrix. Sixth Asia Modelling Symposium; Bali, Indonesia: IEEE; 2012. doi: 10.1109/ams.2012.47.
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Attia MW, Abou-Chadi F, Moustafa HE-D, Mekky N. Classification of ultrasound kidney images using PCA and neural networks. International Journal of Advanced Computer Science and Applications. 2015;6:53-7. doi: 10.14569/IJACSA.2015.060407.
11
Subramanya M, Kumar V, Mukherjee S, Saini M. SVM-based CAC system for B-mode kidney ultrasound images. J Digit Imaging. 2015;28:448-58. doi: 10.1007/s10278-014-9754-4.
12
Pujari RM, Hajare VD, editors. Analysis of ultrasound images for identification of Chronic Kidney Disease stages. First International Conference on Networks & Soft Computing (ICNSC2014); Guntur, India: IEEE. 2014. p. 19-20. doi: 10.1109/cnsc.2014.6906704.
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Chen C-J, Pai T-W, Fujita H, Lee C-H, Chen Y-T, Chen K-S, editors. Stage diagnosis for Chronic Kidney Disease based on ultrasonography. 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD); Xiamen, China: IEEE; 2014. doi: 0.1109/FSKD.2014.6980889.
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Raja KB, Madheswaran M, Thyagarajah K. Ultrasound kidney image analysis for computerized disorder identification and classification using content descriptive power spectral features. J Med Syst. 2007;31:307-17. doi: 10.1007/s10916-007-9068-x. PubMed PMID: 17918683.
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Raja KB, Madheswaran M, Thyagarajah K. A hybrid fuzzy-neural system for computer-aided diagnosis of ultrasound kidney images using prominent features. J Med Syst. 2008;32:65-83. doi: 10.1007/s10916-007-9109-5.
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National Heart L, Institute B. Classification of overweight and obesity by BMI, waist circumference, and associated disease risks. Retrieved March. 2015;12:2015.
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Parthipun A, Pilcher J. Renal transplant assessment: sonographic imaging. Ultrasound Clinics. 2010;5:379-99. doi: 10.1016/j.cult.2010.08.004.
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Fletcher JT, Nankivell BJ, Alexander SI. Chronic allograft nephropathy. Pediatr Nephrol. 2009;24:1465-71.
19
Peters B, Stegmayr B, Mölne J, Haux SB, Hadimeri H. High Resistive Index in Transplant Kidneys Is a Possible Predictor for Biopsy Complications. Transplant Proc. 2016;48:2714-17. doi: 10.1016/j.transproceed.2016.07.016. PubMed PMID: 27788806.
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Liu J, Blumfield A, Trivedi M, Mishra N, Mohammed O, Lin J, et al. Does Assessing Preimplantation Kidney Biopsy and Pump Resistive Index Values Predict Kidney Allograft Survival? Transplantation. 2018;102:S32. doi: 10.1097/01.tp.0000542583.11359.05.
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Yang J, Wang F, Pan W, Ruan L, Ai H. Correlation between ultrasound elastography parameters and renal function after kidney transplantation. Int J Clin Exp Med. 2017;10:3211-7.
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Ghonge NP, Mohan M, Kashyap V, Jasuja S. Renal Allograft Dysfunction: Evaluation with Shear-wave Sonoelastography. Radiology. 2018;288:146-52. doi: 10.1148/radiol.2018170577. PubMed PMID: 29634441.
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Gao J, Rubin JM, Weitzel W, Lee J, Dadhania D, Kapur S, et al. Comparison of Ultrasound Corticomedullary Strain with Doppler Parameters in Assessment of Renal Allograft Interstitial Fibrosis/Tubular Atrophy. Ultrasound Med Biol. 2015;41:2631-9. doi: 10.1016/j.ultrasmedbio.2015.06.009. PubMed PMID: 26219696.
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Sommerer C, Scharf M, Seitz C, Millonig G, Seitz HK, Zeier M, et al. Assessment of renal allograft fibrosis by transient elastography. Transpl Int. 2013;26:545-51. doi: 10.1111/tri.12073. PubMed PMID: 23383606.
25
Abbasian Ardakani A, Mohammadi A, Khalili Najafabad B, Abolghasemi J. Assessment of Kidney Function After Allograft Transplantation by Texture Analysis. Iran J Kidney Dis. 2017;11:157-64. PubMed PMID: 28270649.
26
Wybraniec MT, Bożentowicz-Wikarek M, Chudek J, Mizia-Stec K. Pre-procedural renal resistive index accurately predicts contrast-induced acute kidney injury in patients with preserved renal function submitted to coronary angiography. The International Journal of Cardiovascular Imaging. 2017;33:595-604.
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Komuro K, Yokoyama N, Shibuya M, Soutome K, Hirose M, Yonezawa K, et al. Associations between increased renal resistive index and cardiovascular events. J Med Ultrasonics. 2016;43:263-70. doi: 10.1007/s10396-015-0680-y. PubMed PMID: 27033870.
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Yazici B, Oral A, Gokalp C, Akgün A, Toz H, Ozbek SS, et al. Evaluation of renal transplant scintigraphy and resistance index performed within 2 days after transplantation in predicting long-term graft function. Clin Nucl Med. 2015;40:548-52. doi: 10.1097/rlu.0000000000000789.
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Peng L, Zhong T, Fan Q, Liu Y, Wang X, Wang L. Correlation analysis of renal ultrasound elastography and clinical and pathological changes in patients with chronic kidney disease. Clin Nephrol. 2017;87:293-300. doi: 10.5414/CN108866. PubMed PMID: 28332473.
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Bota S, Bob F, Sporea I, Sirli R, Popescu A. Factors that influence kidney shear wave speed assessed by acoustic radiation force impulse elastography in patients without kidney pathology. Ultrasound Med Biol. 2015;41:1-6. doi: 10.1016/j.ultrasmedbio.2014.07.023. PubMed PMID: 25438855.
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36
ORIGINAL_ARTICLE
Medical Image Fusion using bi-dimensional empirical mode decomposition (BEMD) and an Efficient Fusion Scheme
Background: Medical image fusion is being widely used for capturing complimentary information from images of different modalities. Combination of useful information presented in medical images is the aim of image fusion techniques, and the fused image will exhibit more information in comparison with source images. Objective: In the current study, a BEMD-based multi-modal medical image fusion technique is utilized. Moreover, Teager-Kaiser energy operator (TKEO) was applied to lower BIMFs. The results were compared to six routine methods. Material and Methods: In this study, which is of experimental type, an image fusion technique using bi-dimensional empirical mode decomposition (BEMD), Teager-Kaiser energy operator (TKEO) as a local feature selection and Hierarchical Model And X (HMAX) model is presented. BEMD fusion technique can preserve much functional information. In the process of fusion, we adopt the fusion rule of TKEO for lower bi-dimensional intrinsic mode functions (BIMFs) of two images and HMAX visual cortex model as a fusion rule for higher BIMFs, which are verified to be more appropriate for human vision system. Integrating BEMD and this efficient fusion scheme can retain more spatial and functional features of input images. Results: We compared our method with IHS, DWT, LWT, PCA, NSCT and SIST methods. The simulation results and fusion performance show that the presented method is effective in terms of mutual information, quality of fused image (QAB/F), standard deviation, peak signal to noise ratio, structural similarity and considerably better results compared to six typical fusion methods. Conclusion: The statistical analyses revealed that our algorithm significantly improved spatial features and diminished the color distortion compared to other fusion techniques. The proposed approach can be used for routine practice. Fusion of functional and morphological medical images is possible before, during and after treatment of tumors in different organs. Image fusion can enable interventional events and can be further assessed.
https://jbpe.sums.ac.ir/article_44643_fbea03ead9cd2c4381eba020aee02c59.pdf
2020-12-01
727
736
10.31661/jbpe.v0i0.830
Image Fusion
Empirical Mode Decomposition
Diagnostic Imaging
Image Processing, Computer-Assisted
Multimodal Imaging
M
Mozaffarilegha
m_mozaffari@ymail.com
1
PhD, Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
AUTHOR
A
Yaghobi Joybari
dryaghobii@yahoo.com
2
MD, Department of Radiation Oncology, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
AUTHOR
A
Mostaar
mostaar@sbmu.ac.ir
3
PhD, Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
LEAD_AUTHOR
Wang L, Li B, Tian L-F. Multi-modal medical image fusion using the inter-scale and intra-scale dependencies between image shift-invariant shearlet coefficients. Information Fusion. 2014;19:20-8.
1
Wang L, Li B, Tian L-F. EGGDD: An explicit dependency model for multi-modal medical image fusion in shift-invariant shearlet transform domain. Information Fusion. 2014;19:29-37.
2
Wang Q, Li S, Qin H, Hao A. Robust multi-modal medical image fusion via anisotropic heat diffusion guided low-rank structural analysis. Information Fusion. 2015;26:103-21.
3
Singh R, Khare A. Fusion of multimodal medical images using Daubechies complex wavelet transform-A multiresolution approach. Information Fusion. 2014;19:49-60.
4
Polo A, Cattani F, Vavassori A, Origgi D, Villa G, Marsiglia H, et al. MR and CT image fusion for postimplant analysis in permanent prostate seed implants. Int J Radiat Oncol Biol Phys. 2004;60:1572-9. doi: 10.1016/j.ijrobp.2004.08.033. PubMed PMID: 15590189.
5
Javed U, Riaz MM, Ghafoor A, Ali SS, Cheema TA. MRI and PET image fusion using fuzzy logic and image local features. Scientific World Journal. 2014;2014:708075. doi: 10.1155/2014/708075. PubMed PMID: 24574912. PubMed PMCID: PMC3916105.
6
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59
ORIGINAL_ARTICLE
Detection of Active Plaques in Multiple Sclerosis using 3 and 12 Directional Diffusion-weighted Imaging: Comparison with Gadolinium-enhanced MR Imaging
Background: Multiple Sclerosis (MS), distinguished by aggravating the function of central nervous system because of inflammatory demyelination. The most sensitive method for MS diagnosis is Magnetic resonance imaging (MRI). To distinguish inactive and active MS lesions, contrast-enhanced T1-weighted imaging (CE T1WI) is being used as a gold standard. There are some contraindications in gadolinium based contrast agents (GBCAs) usage. Moreover, diffusion-weighted imaging (DWI) can discover diffusion changes involved inflammatory lesions. Objective: The current research aims at investigating if typical DWI (3 directional) and 12 directional DWI could be a substitute for CE T1WI in order to show active lesions of MS.Material and Methods: In this cross-sectional study, 138 patients with CNS symptoms were examined. For all patients, along with CE T1WI, 3 & 12 directional DWI were performed. Intraclass correlation coefficient (ICC), receiver operating characteristic (ROC), the sensitivity versus specificity plot and the area under the curve (AUC) were calculated. Results: There was a contrast enhancement in CE T1WI for 114 patients (82.6%); in addition, hyper-intense lesions on DWI 3 and DWI 12 were shown in 107 (77.5%) and 117 patients (84.7%) in order. Sensitivity, specificity and AUC were 94.7%, 62.5% and 84% for DWI 12. Moreover, the results were 86%, 62.5 and 79% for the sensitivity, specificity and AUC for DWI 3 respectively. Conclusion: In spite of lower sensitivity of 12 directional DWI compared to CE T1WI, it could be used as a diagnostic sequence in differentiating enhanced lesions from non-enhanced ones when CE-MRI is a worry.
https://jbpe.sums.ac.ir/article_44628_d7291f5383e07b1d2f6895456b8cac7c.pdf
2020-12-01
737
744
10.31661/jbpe.v0i0.925
Demyelinating diseases
Magnetic Resonance Imaging
Diffusion tensor imaging
Gadolinium
Multiple Sclerosis
G H
Meftahi
meftahi_gh@yahoo.com
1
PhD, Neuroscience Research Centre, Baqiyatallah University of Medical Sciences, Tehran, Iran
AUTHOR
G
Pirzad Jahromi
g_pirzad_jahromi@yahoo.com
2
PhD, Neuroscience Research Centre, Baqiyatallah University of Medical Sciences, Tehran, Iran
LEAD_AUTHOR
A
Azari
aliazari8089@yahoo.com
3
BSc, Radiology Department, School of Paramedical Sciences, Shiraz University of Medical Sciences, Shiraz, Iran
AUTHOR
P
Ghaemmaghami
ghammaghami@gmail.com
4
PhD, Biostatistics Department, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
AUTHOR
Cunnusamy K, Baughman EJ, Franco J, Ortega SB, Sinha S, Chaudhary P, et al. Disease exacerbation of multiple sclerosis is characterized by loss of terminally differentiated autoregulatory CD8+ T cells. Clin Immunol. 2014;152(1-2):115-26. doi: 10.1016/j.clim.2014.03.005. PubMed PMID: 24657764. PubMed PMCID: PMC4024444.
1
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12
Thomsen HS, Morcos SK, Almén T, Bellin MF, Bertolotto M, Bongartz G, et al. Nephrogenic systemic fibrosis and gadolinium-based contrast media: updated ESUR Contrast Medium Safety Committee guidelines. Eur Radiol. 2013;23(2):307-18. doi: 10.1007/s00330-012-2597-9. PubMed PMID: 22865271.
13
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14
Lo CP, Kao HW, Chen SY, Chu CM, Hsu CC, Chen YC, et al. Comparison of diffusion-weighted imaging and contrast-enhanced T1-weighted imaging on a single baseline MRI for demonstrating dissemination in time in multiple sclerosis. BMC Neurol. 2014;14:100. doi: 10.1186/1471-2377-14-100. PubMed PMID: 24885357. PubMed PMCID: PMC4036427.
15
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17
Kingsley PB, Monahan WG. Selection of the optimum b factor for diffusion-weighted magnetic resonance imaging assessment of ischemic stroke. Magn Reson Med. 2004;51(5):996-1001. doi: 10.1002/mrm.20059. PubMed PMID: 15122682.
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21
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22
Suzuki M, Kudo K, Sasaki M, Takahashi S, Takahashi J, Fujima N, et al. Detection of active plaques in multiple sclerosis using susceptibility-weighted imaging: comparison with gadolinium-enhanced MR imaging. Magn Reson Med Sci. 2011;10(3):185-92. doi: 10.2463/mrms.10.185. PubMed PMID: 21960001.
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24
Eisele P, Szabo K, Griebe M, Rossmanith C, Förster A, Hennerici M, et al. Reduced diffusion in a subset of acute MS lesions: a serial multiparametric MRI study. Am J Neuroradiol. 2012;33(7):1369-73. doi: 10.3174/ajnr.A2975. PubMed PMID: 22576893.
25
Rosso C, Remy P, Creange A, Brugieres P, Cesaro P, Hosseini H. Diffusion-weighted MR imaging characteristics of an acute strokelike form of multiple sclerosis. Am J Neuroradiol. 2006;27(5):1006-8. PubMed PMID: 16687533.
26
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28
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29
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30
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31
ORIGINAL_ARTICLE
Lactobacillus Acidophilus and Lactobacillus Casei Exposed to Wi-Fi Radiofrequency Electromagnetic Radiation Show Enhanced Growth and Lactic Acid Production
Background: Lactobacillus acidophilus and Lactobacillus casei are gram-positive probiotics and members of the genus Lactobacillus. These bacteria are widely applicable in food and dairy industries. Increasing bacterial load and decreasing fermentation time make them more profitable for manufacturers. Objective: This study was aimed at assessing the biological effects of short-term exposure of L. acidophilus and L. casei to 2.4 GHz Wi-Fi radiofrequency electromagnetic fields (RF-EMF) generated by a Wi-Fi router on the lactic acid production and proliferation of these probiotic bacteria.Material and Methods: This experimental study was performed on pure culture strains of L. acidophilus and L. casei, first direct vat sets (DVS) were activated in MRS broth for 24 hours then transferred to new culture mediums. Afterward, these mediums were exposed to RF-EMF for 15, 30, 45 and 60 minutes. The control samples were sham-exposed. After 72 hours of incubation on MRS agar cell counts were enumerated. Results: Exposure for 30, 45 and 60 minutes significantly increased the growth of L. acidophilus and L. casei. No difference was found between the growth of the samples exposed to RF-EMF for 15 minutes compared to that of sham-exposed bacteria. In addition, lactic acid concentration in L. acidophilus medium was amplified after 15, 30 and 45 minutes of exposure. However, in L. casei samples, only 30 and 60 min exposures could stimulate the production of lactic acid. Conclusion: L. acidophilus and L. casei probiotic bacteria exposed for a short time to radiofrequency electromagnetic radiation (RF-EMF) generated by a widely used commercial Wi-Fi router show significantly increased proliferation and lactic acid production.
https://jbpe.sums.ac.ir/article_46829_b4cef0aa0a3f752f01f2e9d3120cba0e.pdf
2020-12-01
745
750
10.31661/jbpe.v0i0.1056
Radiation
Electromagnetic
Probiotics
Lactobacillus acidophilus
Lactobacillus casei
S
Amanat
1
MSc, Student Research Committee, Larestan University of Medical Sciences, Larestan, Iran
AUTHOR
S M
Mazloomi
2
PhD, Nutrition Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
AUTHOR
H
Asadimehr
3
BSc, Department of Clinical Nutrition, School of Nutrition & Food Sciences, Shiraz University of Medical Sciences, Shiraz, Iran
AUTHOR
F
Sadeghi
4
MSc, Department of Clinical Nutrition, School of Nutrition & Food Sciences, Shiraz University of Medical Sciences, Shiraz, Iran
AUTHOR
F
Shekouhi
5
MSc, Department of Medical Physics, Shiraz University of Medical Sciences, Shiraz, Iran
AUTHOR
S M J
Mortazavi
mortazavismj@gmail.com
6
PhD, Department of Medical Physics, Shiraz University of Medical Sciences, Shiraz, Iran
LEAD_AUTHOR
FAO/WHO. Health and Nutrition Properties of Probiotics in Food including Powder Milk with Live Lactic Acid Bacteria. Evaluation of Health and Nutritional Properties of Probiotics in Food including Powder Milk with Live Lactic Acid Bacteria; Córdoba, Argentina: FAO/WHO; 2001. p. 1-4.
1
Iqbal MZ, Qadir MI, Hussain T, Janbaz KH, Khan YH, Ahmad B. Probiotics and their beneficial effects against various diseases. Pak J Pharm Sci. 2014;27(2):405-415. PubMed PMID: 24577933.
2
Baroutkoub A, Mehdi RZ, Beglarian R, Hassan J, Zahra S, Mohammad MS. Effects of probiotic yoghurt consumption on the serum cholesterol levels in hypercholestromic cases in Shiraz, Southern Iran. Sci Res Essays. 2010;5(16):2206-9.
3
Mohammadi Sartang M, Mazloomi SM, Tanideh N, Rezaian Zadeh A. The effects of probiotic soymilk fortified with omega-3 on blood glucose, lipid profile, haematological and oxidative stress, and inflammatory parameters in streptozotocin nicotinamide-induced diabetic rats. J Diabetes Res. 2015. doi: 10.1155/2015/696372. PubMed PMID: 26347893. PubMed PMCID: PMC4548139.
4
Kechagia M, Basoulis D, Konstantopoulou S, Dimitriadi D, Gyftopoulou K, Skarmoutsou N, et al. Health benefits of probiotics: a review. ISRN Nutrition. 2013. doi: 10.5402/2013/481651. PubMed PMID: 24959545. PubMed PMCID: PMC4045285.
5
Dholiya K, Patel D, Kothari V. Effect of low power microwave on microbial growth, enzyme activity, and aflatoxin production. Res in Biotech. 2012;3(4):28-34.
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Kushwah P, Mishra T, Kothar V. Effect of Microwave Radiation on Growth, Enzyme Activity (Amylase and Pectinase), and/or Exopolysaccharide Production in Bacillus subtilis, Streptococcus mutans, Xanthomonas campestris and Pectobacterium carotovora. Int J Microbiol Res. 2013:645-653. doi: 10.9734/BMRJ/2013/5036.
7
Grundler W, Keilmann F, Putterlik V, Strube D. Resonant-like dependence of yeast growth rate on microwave frequencies. Br J Cancer. 1982;5:206. PubMed PMID: 7039651. PubMed PMCID: PMC2149285.
8
Vrhovac I, Hrascan R, Franekic J. Effect of 905 MHz microwave radiation on colony growth of the yeast Saccharomyces cerevisiae strains FF18733, FF1481 and D7. Radiol Oncol. 2010;44(2):131-4. doi: 10.2478/v10019-010-0019-7. PubMed PMID: 22933904. PubMed PMCID: PMC3423682.
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Raval S, Chaudhari V, Gosai H, Kothari V. Effect of low power microwave radiation on pigment production in bacteria. Microbiol Res. 2014;5(1). doi: 10.4081/mr.2014.5511.
11
Fang Y, Hu J, Xiong S, Zhao S. Effect of low-dose microwave radiation on Aspergillus parasiticus. Food Control. 2011;22(7):1078-84. doi: 10.1016/j.foodcont.2011.01.004.
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Kang Y, Kato S. Thermal and non-thermal germicidal effects of microwave radiation on microbial agents. Indoor Built Environ. 2014;23(8):1080-91. doi: 10.1177/1420326X13490180.
13
Grundler W, Keilmann F, Fröhlich H. Resonant growth rate response of yeast cells irradiated by weak microwaves. Phys lett A. 1977;62(6):463-6. doi: 10.1016/0375-9601(77)90696-X.
14
Ahmed LT, Majeed AD, Shaima’a AS. The effect of mobile waves on the growth of pathogenic fungi. Int J Curr Microbiol Appl Sci. 2015;4:838-842.
15
Diem E, Schwarz C, Adlkofer F, Jahn O, Rüdiger H. Non-thermal DNA breakage by mobile-phone radiation (1800MHz) in human fibroblasts and in transformed GFSH-R17 rat granulosa cells in vitro. MRGTEM. 2005;583(2):178-83. doi: 10.1016/j.mrgentox.2005.03.006. PubMed PMID: 15869902.
16
Shamis Y, Taube A, Mitik-Dineva N, Croft R, Crawford RJ, Ivanova EP. Specific electromagnetic effects of microwave radiation on Escherichia coli. Appl Environ Microbiol. 2011;77(9):3017-22. doi: 10.1128/AEM.01899-10. PubMed PMID: 21378041. PubMed PMCID: PMC3126418.
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Cohen I, Cahan R, Shani G, Cohen E, Abramovich A. Effect of 99 GHz continuous millimeter wave electro-magnetic radiation on E. coli viability and metabolic activity. Int J Radiat Biol. 2010;86(5):390-9. doi: 10.3109/09553000903567912. PubMed PMID: 20397844.
18
Calabrò E, Condello S, Currò M, Ferlazzo N, et al. Modulation of heat shock protein response in SH-SY5Y by mobile phone microwaves. World J Biol Chem. 2012;3(2):34-40. doi: 10.4331/wjbc.v3.i2.34. PubMed PMID: 22371824. PubMed PMCID: PMC3286792.
19
Lin H, Chen X, Yu L, Xu W, Wang P, Zhang X, et al. Screening of Lactobacillus rhamnosus strains mutated by microwave irradiation for increased lactic acid production. Afr J Microbiol Res. 2012;6(31):6055-65. doi: 10.5897/AJMR12.434.
20
Taheri M, Mortazavi SMJ, Moradi M, Mansouri S, Nouri F, 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(3):115. PubMed PMID: 26396967. PubMed PMCID: PMC4576872.
21
Carta R, Desogus F. Possible non-thermal microwave effects on the growth rate of pseudomonas aeruginosa and staphylococcus aureus. Int Rev Chem Eng. 2012;4(4):392-8.
22
ORIGINAL_ARTICLE
Efficacy of Periapical Radiography and Three Cone-Beam Computed Tomography Systems for Detection of Peri-Implant Dehiscence Defects: An in- Vitro Study
Background: Early detection of peri-implant bone defects is highly important because these defects eventually lead to gingival recession, bone loss and implant failure. Objective: This study aimed to assess and compare the efficacy of periapical radiography and three CBCT systems for the detection of peri-implant dehiscence defects.Material and Methods: In this vitro study, 124 titanium implants were placed in bovine ribs. The bone pieces were then mounted in boxes in the form of mandible and red dental wax was used to simulate the soft tissue. Crestal bone defects with 2, 3, and 4 mm depth were created in the ribs using a round bur. Periapical and CBCT images were then obtained. Images were investigated by two oral and maxillofacial radiologists twice with a two-week interval. The results were analyzed using chi-square, Kappa coefficient, Cochrane’s Q and McNemar tests as well as the receiver operating characteristic (ROC) curve. Results: The two observers showed good agreement in detection of sound and defective samples on periapical radiographs and CBCT scans. The level of agreement was low in detection of two samples with 2 mm defects on CBCT scans taken with Planmeca and NewTom 3G systems at the time of second assessment. NewTom 3G had the highest sensitivity (68.9%, 74.2% and 86.3%, respectively) and specificity (100% for all three) compared to other systems for detection of 2, 3 and 4 mm crestal bone defects. Conclusion: The inter-observer agreement increased with increase in depth of defects. NewTom 3G had the highest accuracy for detection of crestal bone defects.
https://jbpe.sums.ac.ir/article_47012_73a73361562373858f503d6be6d0e892.pdf
2020-12-01
751
760
10.31661/jbpe.v0i0.2008-1162
Surgical Wound Dehiscence
Cone Beam Computed Tomography
Radiography, Dental
V
Akheshteh
v.akheshteh@gmail.com
1
MSD, Department of Oral and Maxillofacial Radiology, Alborz University of Medical Sciences, Tehran, Iran
AUTHOR
A
Eskandarloo
dr.eskandarloo@gmail.com
2
MSD, Department of Oral and Maxillofacial Radiology, School of Dentistry, Hamadan University of Medical Sciences, Hamedan, Iran
LEAD_AUTHOR
S
Saati
s.saati_dds@yahoo.com
3
MSD, Department of Oral and Maxillofacial Radiology, Hamadan University of Medical Sciences, Hamedan, Iran
AUTHOR
M R
Jamalpour
jamalpour@umsha.ac.ir
4
MSD, Department of Oral and Maxillofacial Surgery, Hamadan University of Medical Sciences, Hamedan, Iran
AUTHOR
N
Mohammad Gholi Mezerji
mezerji@gmail.com
5
PhD, Department of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamedan, Iran
AUTHOR
Juodzbalys G, Kubilius M. Clinical and Radiological Classification of the Jawbone Anatomy in Endosseous Dental Implant Treatment. J Oral Maxillofac Res. 2013;4(2):e2. doi: 10.5037/jomr.2013.4202. PubMed PMID: 24422030. PubMed PMCID: PMC3886111.
1
De-Azevedo-V SL, Vaconcelos KD. Detection of periimplant fenestration and dehiscence with the use of two scan modes and the smallest voxel sizes of cone-beam computed tomography device. Oral Surg Oral Med Oral Pathol Oral Radiol. 2013;115(11):121-6. doi: 10.1016/j.oooo.2012.10.003. PubMed PMID: 23217543.
2
Jones AA, Cochran DL. Consequences of implant design. Dent Clin North Am. 2006;50:339-60. doi: 10.1016/j.cden.2006.03.008. PubMed PMID: 16818019.
3
Eskandarloo A, Saati S, Purabdolahi Ardakani M, Jamalpour M, Mezerji N, Akheshteh V. Diagnostic accuracy of three cone beam computed tomography systems and periapical radiography for detection of fenestration around dental implants. Contemp Clin Dent. 2018;9(3)4:376-81. doi: 10.4103/ccd.ccd_103_18. PubMed PMID: 30166830. PubMed PMCID: PMC6104358.
4
Winkler S, Morris HF, Spray JR. Stability of implants and natural teeth as determined by the Periotest over 60 months of function. J Oral Implantol. 2001;27:198-203. doi: 10.1563/1548-1336(2001)0272.3.CO;2. PubMed PMID: 12500879.
5
King GN, Hermann JS, Schoolfield JD, Buser D, Cochran DL. Influence of the size of the microgap on crestal bone levels in non-submerged dental implants: a radiographic study in the canine mandible. J Periodontol. 2002;73:1111-17. doi: 10.1902/jop.2002.73.10.1111. PubMed PMID: 12416767.
6
Chung WE, Rubenstein JE, Phillips KM, Raigrodski AJ. Outcomes assessment of patients treated with osseointegrated dental implants at the University of Washington Graduate Prosthodontic Program, 1998 to 2000. Int J Oral Maxillofac Implants. 2009;24(5):927-35. PubMed PMID: 19865634.
7
Smith DE, Zarb GA. Criteria for success of osseointegrated endosseous implants. J Prosthet Dent. 1989;62:567-72. doi: 10.1016/0022-3913(89)90081-4. PubMed PMID: 2691661.
8
Kamburgolu K, Gulsahi A, Genç Y, Paksoy CS. A comparison of peripheral marginal bone loss at dental implants measured with conventional intraoral film and digitized radiographs. J Oral Implantol. 2012;38:211-19. doi: 10.1563/AAID-JOI-D-09-00147. PubMed PMID: 20712442.
9
Bagis N, Kolsuz ME. Comparison of intraoral radiography and cone-beam computed tomography for the detection of periodontal defects: an in vitro study. BMC Oral Health. 2015;15(64):1-8. doi: 10.1186/s12903-015-0046-2. PubMed PMID: 26016804. PubMed PMCID: PMC4446848.
10
Eskandarloo A, Arabi R, Bidgoli M, Yousefi F, Poorolajal J. Association between Marginal Bone Loss and Bone Quality at Dental Implant Sites Based on Evidence from Cone Beam Computed Tomography and Periapical Radiographs. Contemp Clin Dent. 2019;10(1):36-41. doi: 10.4103/ccd.ccd_185_18. PubMed PMID: 32015639. PubMed PMCID: PMC6974999.
11
Razavi T, Palmer RM, Davies J. Accuracy of measuring the cortical bone thickness adjacent to dental implants using cone beam computed tomography. Clin Oral Impl. 2010;21(5):718-25. doi: 10.1111/j.1600-0501.2009.01905.x. PubMed PMID: 20636726.
12
Shokri A , Eskandarloo A, Norouzi M, Majidi G, Aliyaly A. Diagnostic accuracy of cone-beam computed tomography scans with high- and low-resolution modes for the detection of root perforations. Imaging Sci Dent. 2018;48(1):11-9. doi: 10.5624/isd.2018.48.1.11. PubMed PMID: 29581945. PubMed PMCID: PMC5863015.
13
De Smet E, Jacobs R, Gijbels F, Naert I. The accuracy and reliability of radiographic methods for the assessment of marginal bone level around oral implants. Dentomaxillofac Radiol. 2002;31(3):176-81. doi: 10.1038/sj/dmfr/4600694. PubMed PMID: 12058265.
14
Wakoh M, Harada T, Otonari T, Otonari-Yamamoto M, Ohkubo M, Kousuge Y, et al. Reliability of linear distance measurement for dental implant length with standardized periapical radiographs. Bull Tokyo Dent Coll. 2006;47(3):105-15. doi: 10.2209/tdcpublication.47.105. PubMed PMID: 17344618.
15
Shokri A, Jamalpour M, Eskandarloo A, Godiny M, Amini P, Khavid A. Performance of Cone Beam Computed Tomography Systems in Visualizing the Cortical Plate in 3D Image Reconstruction: An In Vitro Study. Open Dent J. 2018;12:586-95. doi: 10.2174/1874210601812010586. PubMed PMID: 30288182. PubMed PMCID: PMC6142658.
16
Dehghani M, Montazer Lotf Elahi H. Comparing the accuracy of cone beam computed tomography,digital intraoral radiography and conventional intraoral radiography in the measurement of periodontal bone defects. J Res Dentomaxillofac Sci. 2016;1(1):34-9. doi: 10.29252/jrdms.1.1.34.
17
Patel S, Kanagasingam S, Mannocci F. Cone beam computed tomography (CBCT) in endodontics. Dent Update. 2009;37(6):373-9. doi: 10.12968/denu.2010.37.6.373. PubMed PMID: 20929151.
18
Stavropoulos A, Wenzel A. Accuracy of cone beam dental CT, intraoral digital and conventional film radiography for the detection of periapical lesions. An ex vivo study in pig jaws. Clin Oral Investig. 2007;11(1):101-6. doi: 10.1007/s00784-006-0078-8. PubMed PMID: 17048029.
19
Takeshita WM, Iwaki LCV, Da Silva MC, Tonin RH. Evaluation of diagnostic accuracy of conventional and digital periapical radiography, panoramic radiography, and cone-beam computed tomography in the assessment of alveolar bone loss. Contemp Clin Dent. 2014;5(3):318-23. doi: 10.4103/0976-237X.137930. PubMed PMID: 25191066. PubMed PMCID: PMC4147806.
20
Saati S, Kaveh F, Yarmohammadi SH. Comparison of Cone-Beam computed tomography and Multi slice computed tomography image quality of human dried mandible using 10 anatomical landmarks. J Clin Diagn Res. 2017;11(2):13-6. doi: 10.7860/JCDR/2017/20637.9253. PubMed PMID: 28384972. PubMed PMCID: PMC5376866.
21
Kasraei SH, Shokri A, Poorolajal J, Khajeh S, Rahmani H. Comparison of cone-beam computed tomography and Intraoral radiography in detection of recurrent caries under composite restorations. Braz Dent J. 2017;28(1):85-91. doi: 10.1590/0103-6440201701248. PubMed PMID: 28301024.
22
Van Dessel J, Nicolielo L, Huang Y, Coudyzer W, Salmon B, Lambrichts I, Jacobs R. Accuracy and reliability of different cone-beam computed tomography(CBCT)devices for structural analysis of alveolar bone in comparison with multislice CT and Micro-ct. Eur J Oral implantol. 2017;10(1):95-105. PubMed PMID: 28327698.
23
ORIGINAL_ARTICLE
Comparative Evaluation of Carbon Reinforced Polyetherketone Acetabular Cup using Finite Element Analysis
Background: Patients suffering from osteoarthritis undergo surgery to replace hip joints with hip prosthesis implants. Today most acetabular cups of hip prostheses are made of Ultra-High-Molecular-Weight-Polyethylene. However, these materials acting as acetabular cups of the implant have been recalled since patients have been feeling uncomfortable and abstained from physical activities. A newly introduced material, 30% Carbon Reinforced Polyetherketone, possess better isotropic mechanical properties and lower wear rates. Objective: The research aims to compare the von-Mises stresses and deformation in static and dynamic loading of Ultra-High Molecular-Weight-Polyethylene to 30% Reinforced Carbon Fiber Polyetherketone using Finite Element Analysis.Material and Methods: An analytical study was performed to evaluate material selection and their contact performances of acetabular cups. Four pairs have been analyzed under loading conditions following ASTM F2996-13 and ISO 7206-4 standards. The acetabular cups options are made of 30% Carbon Reinforced Fiber Polyetherketone or Ultra-High-Molecular-Weight-Polyethylene. Besides, the femoral head and steam options are either Alumina Ceramic or Cobalt Chrome Molybdenum. Results: The yield strength of Ultra-High-Molecular-Weight-Polyethylene is considerably small, resulting in the acetabular cup to fail when applied to high loading conditions. Carbon Reinforced Polyetherketone with Alumina Ceramic yielded 65% lower deformation at stumbling phase. Conclusion: Since the study focuses on linear isotropic material properties, Alumina Ceramic dominates a higher elastic modulus than Cobalt Chrome Molybdenum, nominating it the best fit combination for lower von-Mises stresses, acting on the Carbon Reinforced Polyetherketone acetabular cup.
https://jbpe.sums.ac.ir/article_47029_afc1481e3031745eddff9279855a4685.pdf
2020-12-01
761
770
10.31661/jbpe.v0i0.2005-1123
Hip Prosthesis
Carbon Fiber
Acetabular Cup
Dynamic and Static Contact
Finite Element Analysis
Acetabulum
A
Abdal
aabdal@lion.lmu.edu
1
MSc, Department of Mechanical Engineering, Loyola Marymount University, Los Angeles, USA
LEAD_AUTHOR
R
Noorani
2
PhD, Department of Mechanical Engineering, Loyola Marymount University, Los Angeles, USA
AUTHOR
G
Cha
3
PhD, The Aerospace Corporation, 2310 E. El Segundo Blvd., El Segundo, CA 90245, USA
AUTHOR
AIHW. Osteoarthritis Snapshot, What Is Osteoarthritis? Australian Institute of Health and Welfare. 2018 [cited 2018 July 24]. Available from: www.aihw.gov.au/reports/chronic-musculoskeletal-conditions/osteoarthritis/contents/what-is-osteoarthritis.
1
CPMC. Osteoarthritis: Risk Report. Coriell Personalized Medicine Collaborative. 2018 [cited 2018 December 15]. Available from: https://cpmc.coriell.org/v/Report/Demo/Osteoarthritis/DemoNat.
2
Stuart G. CFR PEEK Composite for Surgical Applications. MDDI Online, Medical Device and Diagnostic Industrt. 2017 [cited 2017 Aug 7]. Available from: www.mddionline.com/cfr-peek-composite-surgical-applications.
3
Brockett CL, Carbone S, Fisher J, Jennings LM. PEEK and CFR-PEEK as alternative bearing materials to UHMWPE in a fixed bearing total knee replacement: an experimental wear study. Wear. 2017;374:86-91. doi: 10.1016/j.wear.2016.12.010.
4
Dickinson AS, Taylor AC, Browne M. The influence of acetabular cup material on pelvis cortex surface strains, measured using digital image correlation. Journal of Biomechanics. 2012;45(4):719-23. doi: 10.1016/j.jbiomech.2011.11.042.
5
Wang A, Lin R, Stark C, Dumbleton JH. Suitability and limitations of carbon fiber reinforced PEEK composites as bearing surfaces for total joint replacements. Wear. 1999;225:724-7. doi: 10.1016/S0043-1648(99)00026-5.
6
ISO. Implants for surgery — Partial and total hip joint prostheses — Part 4: Determination of endurance properties and performance of stemmed femoral components. [cited 2015 September 24]. Available from: https://www.iso.org/standard/42769.html.
7
American Society for Testing and Materials. Standard practice for finite element analysis (FEA) of non-modular metallic orthopaedic hip femoral stems. ASTM International; 2013.
8
Jiang HB. Static and dynamic mechanics analysis on artificial hip joints with different interface designs by the finite element method. Journal of Bionic Engineering. 2007;4(2):123-31. doi: 10.1016/S1672-6529(07)60024-9.
9
El’Sheikh HF, MacDonald BJ, Hashmi MS. Finite element simulation of the hip joint during stumbling: a comparison between static and dynamic loading. Journal of Materials Processing Technology. 2003;143:249-55. doi: 10.1016/S0924-0136(03)00352-2.
10
Shankar S, Prakash L, Kalayarasan M. Finite element analysis of different contact bearing couples for human hip prosthesis. Int J Biomed Eng Technol. 2013;11(1):66-80. doi: 10.1504/IJBET.2013.053712.
11
Wu JS, Hung JP, Shu CS, Chen JH. The computer simulation of wear behavior appearing in total hip prosthesis. Computer Methods and Programs in Biomedicine. 2003;70(1):81-91. doi: 10.1016/S0169-2607(01)00199-7.
12
The Online Materials Information Resource. MatWeb, Victrex Polymer Solutions. [cited 2019 January 15]. Available from: www.matweb.com/search/DataSheet.aspx?MatGUID=e0993de8cfa74798876b7883382af4dd&ckck=1.
13
Bishop NE, Hothan A, Morlock MM. High Friction Moments in Large Hard-on-Hard Hip Replacement Bearings in Conditions of Poor Lubrication. J Orthop Res. 2013;31(5):807-13. doi: 10.1002/jor.22255. PubMed PMID: 23239536.
14
Desai C, Hirani H, Chawla. Life Estimation of Hip Joint Prosthesis. J Inst Eng India Ser C. 2015;96(3):261-7. doi: 10.1007/s40032-014-0159-4.
15
Scholes SC, Unsworth A. The Wear Properties of CFR-PEEK-OPTIMA Articulating against Ceramic Assessed on a Multidirectional Pin-Onplate Machine. Proc Inst Mech Eng H. 2007;221(3):281-9. doi: 10.1243/09544119JEIM224. PubMed PMID: 17539583.
16
Shankar S, Manikandan M. Dynamic Contact Analysis of Total Hip Prosthesis During Stumbling Cycle. Journal of Mechanics in Medicine and Biology. 2014;14(3):1-13. doi: 10.1142/S0219519414500419.
17
Varghese V. Finite Element Based Design of Hip Joint Prosthesis. Department of Biotechnology & Medical Engineering National Institute of Technology Rourkela-769008; 2015. Available from: https://www.researchgate.net/publication/288303844_Finite_element_based_design_of_hip_joint_prosthesis.
18
ORIGINAL_ARTICLE
The Increment of Genoprotective Effect of Melatonin due to “Autooptic” Effect versus the Genotoxicity of Mitoxantrone
Background: Mitoxantrone is a chemotherapy anti-cancer drug, which can have side effects on healthy cells like secondary cancers. On the other side, Melatonin is a hormone that is responsible for the daily rhythm adjustment and has several properties to be anticancer and anti-inflammatory. Recently, it has been shown that all living cells produce ultraweak photon emission (UPE) spontaneously and continuously. The intensity of UPE is in the order of a few, up to 104 photon/(cm2 sec) (or 10−19 to 10−14 W/cm2) measurable by photodetectors. UPEs are produced from diverse natural oxidative and biochemical reactions, especially free radical reactions and the simple cessation of excited molecules. Also, it has been evidenced that UPE has a signaling role at a distance among different cell cultures.Objective: Here, we investigate the effect of UPE among similar cells (i.e. “Autooptic effect”) by using mirrors around the cell plate(s).Material and Methods: In this experimental research, the HepG2 cells were co-treated by melatonin as a genoprotective and silver nanoparticles as a carrier against mitoxantrone’s genotoxicity. Our results are analyzed based on the Comet assay method, and the genoprotective effect of melatonin is investigated in presence of (and without) mirrors against the genotoxicity of mitoxantrone. Additionally, the autooptic effect is investigated in presence of Ag nanoparticles (NPs).Results: The results indicated that Ag NPs with lower concentrations of melatonin made more protection as genoprotective agent, and the same results obtained by increasing access’ cells to drug. Conclusion: The autooptic effect could increase the genoprotective effect of melatonin.
https://jbpe.sums.ac.ir/article_44611_f426539ee5f9baeb8242918bbbca5287.pdf
2020-12-01
771
782
10.31661/jbpe.v0i0.508
Genoprotective effect
Melatonin
genotoxicity
Mitoxantrone
Biophoton
Autooptic effect
Free radicals
M
Zamani
zamani.mansoure@yahoo.com
1
MSc, Department of Pharmacology, Isfahan Pharmaceutical Sciences Research Center, School of Pharmacy and Pharmaceutical Sciences, Isfahan University of Medical Sciences, Isfahan, Iran
LEAD_AUTHOR
M
Etebari
etebari@pharm.mui.ac.ir
2
PhD, Department of Pharmacology, Isfahan Pharmaceutical Sciences Research Center, School of Pharmacy and Pharmaceutical Sciences, Isfahan University of Medical Sciences, Isfahan, Iran
AUTHOR
Sh
Moradi
persintech@gmail.com
3
MSc, Department of Pharmacology, Isfahan Pharmaceutical Sciences Research Center, School of Pharmacy and Pharmaceutical Sciences, Isfahan University of Medical Sciences, Isfahan, Iran
AUTHOR
Alberts DS, Peng YM, Bowden GT, Dalton WS, Mackel C. Pharmacology of mitoxantrone: mode of action and pharmacokinetics. Investigational New Drugs. 1985;3(2):101-7.
1
Dogra S, Awasthi P, Nair M, Barthwal R. Interaction of anticancer drug mitoxantrone with DNA hexamer sequence d-(CTCGAG)2 by absorption, fluorescence and circular dichroism spectroscopy. J Photochem Photobiol B. 2013;123:48-54. doi: 10.1016/j.jphotobiol.2013.03.015. PubMed PMID: 23624101.
2
Mishra S, Mishra RP. A comparison of the in vitro Genotoxicity of Anticancer Drugs Melphalan and Mitoxantrone. American Journal of Biomedical Sciences. 2013;5(3). doi: 10.5099/aj130300171.
3
Blanz J, Mewes K, Ehninger G, Proksch B, Waidelich D, Greger B, et al. Evidence for oxidative activation of mitoxantrone in human, pig, and rat. Drug Metab Dispos. 1991;19:871-80. PubMed PMID: 1686230.
4
Buschini A, Poli P, Rossi C. Saccharomyces cerevisiae as an eukaryotic cell model to assess cytotoxicity and genotoxicity of three anticancer anthraquinones. Mutagenesis. 2003;18:25-36. doi: 10.1093/mutage/18.1.25. PubMed PMID: 12473732.
5
Ferguson LR, Denny WA. Genotoxicity of non-covalent interactions: DNA intercalators. Mutat Res. 2007;623:14-23. doi: 10.1016/j.mrfmmm.2007.03.014. PubMed PMID: 17498749.
6
Kolodziejczyk P, Reszka K, Lown JW. Enzymatic oxidative activation and transformation of the antitumor agent mitoxantrone. Free Radic Biol Med. 1988;5:13-25. doi: 10.1016/0891-5849(88)90058-5. PubMed PMID: 3254299.
7
Galano A, Tan DX, Reiter RJ. Melatonin as a natural ally against oxidative stress: a physicochemical examination. J Pineal Res. 2011;51:1-16. doi: 10.1111/j.1600-079X.2011.00916.x. PubMed PMID: 21752095.
8
Sliwinski T, Rozej W, Morawiec-Bajda A, Morawiec Z, Reiter R, Blasiak J. Protective action of melatonin against oxidative DNA damage: chemical inactivation versus base-excision repair. Mutat Res. 2007;634:220-7. doi: 10.1016/j.mrgentox.2007.07.013. PubMed PMID: 17851115.
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Hardeland R. Neurobiology, pathophysiology, and treatment of melatonin deficiency and dysfunction. The Scientific World Journal. 2012;2012:1-18. doi: 10.1100/2012/640389.
10
Motilva V, Garcia-Maurino S, Talero E, Illanes M. New paradigms in chronic intestinal inflammation and colon cancer: role of melatonin. J Pineal Res. 2011;51:44-60. doi: 10.1111/j.1600-079X.2011.00915.x. PubMed PMID: 21752096.
11
Ferreira SG, Peliciari-Garcia RA, Takahashi-Hyodo SA, Rodrigues AC, Amaral FG, Berra CM, et al. Effects of melatonin on DNA damage induced by cyclophosphamide in rats. Braz J Med Biol Res. 2013;46:278-86. doi: 10.1590/1414-431X20122230. PubMed PMID: 23471360. PubMed PMCID: 3854377.
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Tozan-Beceren A. Melatonin protects against acrylamideinduced oxidative tissue damage in rats. Marmara Pharmaceutical Journal. 2012 ;3(16):213-21. doi: 10.12991/201216401.
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Cifra M, Fields JZ, Farhadi A. Electromagnetic cellular interactions. Progress in biophysics and molecular biology. 2011;105(3):223-46. doi: 10.1016/j.pbiomolbio.2010.07.003.
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Salari V, Tuszynski J, Bokkon I, Rahnama M, Cifra M. On the photonic cellular interaction and the electric activity of neurons in the human brain. Journal of Physics: Conference Series. 2011;329:(1). doi: 10.1088/1742-6596/329/1/012006.
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Gurwitsch A. Physikalisches über mitogenetische Strahlen. Development Genes and Evolution. 1924;103:490-8. doi: 10.1007/bf02107498.
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Cifra M, Pospisil P. Ultra-weak photon emission from biological samples: definition, mechanisms, properties, detection and applications. J Photochem Photobiol B. 2014;139:2-10. doi: 10.1016/j.jphotobiol.2014.02.009. PubMed PMID: 24726298.
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Petrash VV, Borovkov EI, Dovgusha VV, Ivanova VA, Egorov YN, Il’ina LV, editors. Autooptic Effect. Doklady Biochemistry and Biophysics. 2004;396:174-6. doi: 10.1023/B:DOBI.0000033522.90775.95.
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Tice RR, Agurell E, Anderson D, Burlinson B, Hartmann A, Kobayashi H, et al. Single cell gel/comet assay: guidelines for in vitro and in vivo genetic toxicology testing. Environ Mol Mutagen. 2000;35:206-21. doi: 10.1002/(SICI)1098-2280(2000)35:33.0.CO;2-J. PubMed PMID: 10737956.
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Devaraj B, Scott R, Roschger P, Inaba H. Ultraweak light emission from rat liver nuclei. Photochemistry and photobiology. 1991;54:289-93. doi: 10.1111/j.1751-1097.1991.tb02018.x.
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Etebari M, Zolfaghari B, Jafarian-Dehkordi A, Rakian R. Evaluation of DNA damage of hydro-alcoholic and aqueous extract of Echium amoenum and Nardostachys jatamansi. J Res Med Sci. 2012;17:782-6. PubMed PMID: 23798947. PubMed PMCID: 3687887.
29
Tavakoli M, Bateni E, Rismanchian M, Fathi M, Doostmohammadi A, Rabiei A, et al. Genotoxicity effects of nano bioactive glass and Novabone bioglass on gingival fibroblasts using single cell gel electrophoresis (comet assay): An in vitro study. Dent Res J (Isfahan). 2012;9:314-20. PubMed PMID: 23087738. PubMed PMCID: 3469899.
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31
McShan D, Ray PC, Yu H. Molecular toxicity mechanism of nanosilver. J Food Drug Anal. 2014;22:116-27. doi: 10.1016/j.jfda.2014.01.010. PubMed PMID: 24673909. PubMed PMCID: 4281024.
32
Poljaková J, Eckschlager T, Hraběta J, Hřebačková J, Smutný S, Frei E, Martínek V, Kizek R, Stiborová M. The mechanism of cytotoxicity and DNA adduct formation by the anticancer drug ellipticine in human neuroblastoma cells. Biochemical Pharmacology. 2009;77(9):1466-79. doi: 10.1016/j.bcp.2009.01.021.
33
Tacar O, Sriamornsak P, Dass CR. Doxorubicin: an update on anticancer molecular action, toxicity and novel drug delivery systems. J Pharm Pharmacol. 2013;65(2):157-70. doi: 10.1111/j.2042-7158.2012.01567.x.
34
Kizek R, Adam V, Hrabeta J, Eckschlager T, Smutny S, Burda JV, Frei E, Stiborova M. Anthracyclines and ellipticines as DNA-damaging anticancer drugs: recent advances. Pharmacology & Therapeutics. 2012;133(1):26-39. doi:10.1016/j.pharmthera.2011.07.006.
35
Ferreira SG, Peliciari-Garcia RA, Takahashi-Hyodo SA, Rodrigues AC, Amaral FG, Berra CM, Bordin S, Curi R, Cipolla-Neto J. Effects of melatonin on DNA damage induced by cyclophosphamide in rats. Braz J Med Biol Res. 2013;46(3):278-86. doi: 10.1590/1414-431X20122230. PubMed PMID: 23471360. PubMed PMCID: PMC3854377.
36
Ma D, Tremblay P, Mahngar K, Akbari-Asl P, Pandey S, Collins J, Hudlicky T. Induction of apoptosis and autophagy in human pancreatic cancer cells by a novel synthetic C-1 analogue of 7-deoxypancratistatin. Am J Biomed Sci. 2011;3(4):278-91. doi: 10.5099/aj110400278.
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38
Franke SI, Prá D, Erdtmann B, Henriques JA, Da Silva J. Influence of orange juice over the genotoxicity induced by alkylating agents: an in vivo analysis. Mutagenesis. 2005;20(4):279-83. doi: 10.1093/mutage/gei034. PubMed PMID: 15956044.
39
Jamil K, Shaik AP. Pesticide induced cytogenetic risk assessment in human lymphocyte culture in vitro. Bull Environ Contam Toxicol. 2005;75(1):7-14. doi: 10.1007/s00128-005-0711-2. PubMed PMID: 16228866.
40
Le Deley MC, Suzan F, Cutuli B, Delaloge S, Shamsaldin A, Linassier C, Clisant S, De Vathaire F, Fenaux P, Hill C. Anthracyclines, mitoxantrone, radiotherapy, and granulocyte colony-stimulating factor: risk factors for leukemia and myelodysplastic syndrome after breast cancer. Journal of clinical oncology. 2007;25(3):292-300. doi: 10.1200/JCO.2006.05.9048.
41
Rivero MT, Vázquez-Gundın F, Goyanes V, Campos A, Blasco M, Gosálvez J, Fernández JL. High frequency of constitutive alkali-labile sites in mouse major satellite DNA, detected by DNA breakage detection-fluorescence in situ hybridization. Mutation Research/Fundamental and Molecular Mechanisms of Mutagenesis. 2001;483(1-2):43-50. doi: 10.1016/s0027-5107(01)00218-4 PubMed PMID: 11600131.
42
Allegra M, Reiter RJ, Tan DX, Gentile C, Tesoriere L, Livrea MA. The chemistry of melatonin’s interaction with reactive species. Journal of Pineal Research. 2003;34(1):1-0. doi: 10.1034/j.1600-079X.2003.02112.x.
43
Hardeland R, Balzer I, Poeggeler B, Fuhrberg B, Una H, Behrmann G, Wolf R, Meyer TJ, Reiter RJ. On the primary functions of melatonin in evolution: mediation of photoperiodic signals in a unicell, photooxidation, and scavenging of free radicals. Journal of Pineal Research. 1995;18(2):104-11. doi: 10.1111/j.1600-079X.1995.tb00147.x.
44
Salari V, Valian H, Bassereh H, Bókkon I, Barkhordari A. Ultraweak photon emission in the brain. Journal of integrative neuroscience. 2015;14(03):419-29. doi: 10.1142/S0219635215300012.
45
ORIGINAL_ARTICLE
Design and Preliminary Evaluation of a New Ankle Foot Orthosis on Kinetics and Kinematics parameters for Multiple Sclerosis Patients
Background: The damage of the central nervous system due to Multiple Sclerosis (MS) leads to many walking disorders in this population. However, current ankle-foot orthoses prescribed for improving walking disorders for these patients are not clinically cost-efficient. Objective: This study aimed to design and fabricate a dynamic ankle foot orthosis and a new spring-damper joint mechanism that could adapt the walking problems of MS patients and evaluate the immediate effect of the new orthosis on the speed, range of motion, moment, total work and ground reaction force during walking.Material and Methods: In this case-series study, after the design and fabrication of a new orthosis, the kinetics and kinematics of walking of four patients with MS were assessed in a case series study. Results: Walking speed improved with the new orthosis in two participants. The sagittal range of motion (ROM) increased for most of the participants. The sagittal moments increased for hip, knee and ankle joints in most of the measurements. The total joint work showed noticeable difference in the ankle joint. The increased values of vertical component of the ground reaction force (VGRF) were negligible and the increase in the impulse of VGRF was noticeable for only one participant. Conclusion: The new orthosis had positive effects kinetic and kinematic parameters of walking such as the increased velocity by two subjects and also a more normal sagittal ROM, moment and work, suggesting the potential usefulness of the new orthotic device for MS population.
https://jbpe.sums.ac.ir/article_47068_47b815e52e7b49ebb8e29a01081b974a.pdf
2020-12-01
783
792
10.31661/jbpe.v0i0.2007-1136
Multiple Sclerosis
Kinetics
Kinematics
Ankle-foot orthosis
Gait
A
Keyvani Hafshejani
azam.keyvani@gmail.com
1
PhD Candidate, Orthotics and Prosthetics Department, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran
AUTHOR
Gh
Aminian
gholamrezaaminian@yahoo.com
2
PhD, Orthotics and Prosthetics Department, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran
LEAD_AUTHOR
M
Azimian
mazimian@yahoo.com
3
MD, MS Clinic, Rofeideh Hospital, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran
AUTHOR
M
Bahramizadeh
mbzoandp@gmail.com
4
PhD, Orthotics and Prosthetics Department, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran
AUTHOR
Z
Safaeepour
z.safaeepour@gmail.com
5
PhD, Orthotics and Prosthetics Department, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran
AUTHOR
A
Biglarian
abiglarian@uswr.ac.ir
6
PhD, Department of Biostatistics and Epidemiology, Social Determinants of Health Research Sciences, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran
AUTHOR
M
Keivani
maryam.keivani@yahoo.com
7
MD, Department of Radiology, Shahrekord University of Medical Sciences, Shahrekord, Iran
AUTHOR
Berto P, Amato M, Bellantonio P, Bortolon F, Cavalla P, Florio C, et al. The direct cost of patients with multiple sclerosis: a survey from Italian MS centres. Neurol Sci. 2011;32(6):1035-41. doi: 10.1007/s10072-011-0578-4. PubMed PMID: 21505911.
1
Amato MP, Battaglia MA, Caputo D, Fattore G, Gerzeli S, Pitaro M, et al. The costs of multiple sclerosis: a cross-sectional, multicenter cost-of-illness study in Italy. J Neurol. 2002;249(2):152-63. doi: 10.1007/pl00007858. PubMed PMID: 11985380.
2
Wallin MT, Culpepper WJ, Nichols E, Bhutta ZA, Gebrehiwot TT, Hay SI, et al. Global, regional, and national burden of multiple sclerosis 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 2019;18(3):269-85. doi: 10.1016/S1474-4422(18)30443-5. PubMed PMID: 30679040. PubMed PMCID: PMC6372756.
3
Gehlsen G, Whaley M. Falls in the elderly: part II, Balance, strength, and flexibility. Arch Phys Med Rehabil. 1990;71:739-41. PubMed PMID: 2403279.
4
Jamali A, Sadeghi-Demneh E, Fereshtenajad N, Hillier S. Somatosensory impairment and its association with balance limitation in people with multiple sclerosis. Gait Posture. 2017;57:224-9. doi: 10.1016/j.gaitpost.2017.06.020. PubMed PMID: 28667904.
5
Kelleher KJ, Spence W, Solomonidis S, Apatsidis D. The characterisation of gait patterns of people with multiple sclerosis. Disabil Rehabil. 2010;32(15):1242-50. doi: 10.3109/09638280903464497. PubMed PMID: 20156050.
6
Klewer J, Pohlau D, Nippert I, Haas J, Kugler J. Problems reported by elderly patients with multiple sclerosis. J Neurosci Nurs. 2001;33(3):167-71. doi: 10.1097/01376517-200106000-00009. PubMed PMID: 11413662.
7
Kohn CG, Baker WL, Sidovar MF, Coleman CI. Walking Speed and Health-Related Quality of Life in Multiple Sclerosis. Patient. 2014;7:55-61. doi: 10.1007/s40271-013-0028-x. PubMed PMID: 24078332.
8
Einarsson U, Gottberg K, Fredrikson S, Koch LV, Holmqvist LW. Activities of daily living and social activities in people with multiple sclerosis in Stockholm County. Clin Rehabil. 2006;20(6):543-51. doi: 10.1191/0269215506cr953oa. PubMed PMID: 16892936.
9
Corry M, While A. The needs of carers of people with multiple sclerosis: A literature review. Scand Caring Sci. 2009;23(3):569-88. doi: 10.1111/j.1471-6712.2008.00645.x. PubMed PMID: 19077062.
10
Dunn J. Impact of mobility impairment on the burden of caregiving in individuals with multiple sclerosis. Expert Rev Pharmacoeconomics Outcomes Res. 2010;10(4):433-40. doi: 10.1586/erp.10.34. PubMed PMID: 20482233.
11
LaRocca NG. Impact of Walking Impairment in Multiple Sclerosis, Perspectives of Patients and Care Partners. Patient. 2011;4(3):189-201. doi: 10.2165/11591150-000000000-00000. PubMed PMID: 21766914.
12
Renfrew L, Paul L, McFadyen A, Rafferty D, Moseley O, Lord AC, et al. The clinical- and cost-effectiveness of functional electrical stimulation and ankle-foot orthoses for foot drop in Multiple Sclerosis: a multicentre randomized trial. Clin Rehabil. 2019;33(7):1150-62. doi: 10.1177/0269215519842254. PubMed PMID: 30974955.
13
Chui K, Jorge M, Yen S-C, Lusardi M. Orthotics and Prosthetics in Rehabilitation. Missouri: Elsevier Health Sciences; 2019. p. 227-32.
14
Swinnen E, Lefeber N, Werbrouck A, Gesthuizen Y, Ceulemans L, Christiaens S, et al. Male and female opinions about orthotic devices of the lower limb: A multicentre, observational study in patients with central neurological movement disorders. NeuroRehabilitation. 2018;42(1):121-30. doi: 10.3233/NRE-172214. PubMed PMID: 29400677.
15
Renfrew L, Lord AC, McFadyen AK, Rafferty D, Hunter R, Bowers R, et al. A comparison of the initial orthotic effects of functional electrical stimulation and ankle-foot orthoses on the speed and oxygen cost of gait in multiple sclerosis. J Rehabil Assist Technol Eng. 2018;5:1-9. doi: 10.1177/2055668318755071.
16
Cattaneo D, Marazzini F, Crippa A, Cardini R. Do static or dynamic AFOs improve balance? Clin Rehabil. 2002;16(8):894-9. doi: 10.1191/0269215502cr547oa. PubMed PMID: 12501952.
17
Hsu JD, Michael J, Fisk J. AAOS Atlas of Orthoses and Assistive Devices E-Book: Elsevier Health Sciences; 2008.
18
Bregman DJ, De Groot V, Van Diggele P, Meulman H, Houdijk H, Harlaar J. Polypropylene ankle foot orthoses to overcome drop-foot gait in central neurological patients: a mechanical and functional evaluation. Prosthet Orthot Int. 2010;34(3):293-304. doi: 10.3109/03093646.2010.495969. PubMed PMID: 20738233.
19
Sheffler LR, Bailey SN, Chae J. Spatiotemporal and kinematic effect of peroneal nerve stimulation versus an ankle-foot orthosis in patients with multiple sclerosis: a case series. PM&R. 2009;1(7):604-11. doi: 10.1016/j.pmrj.2009.04.002. PubMed PMID: 19627953.
20
Khurana SR, Beranger AG, Felix ER. Perceived Exertion Is Lower When Using a Functional Electrical Stimulation Neuroprosthesis Compared With an Ankle-Foot Orthosis in Persons With Multiple Sclerosis: A Preliminary Study. Am J Phys Med Rehabil. 2017;96(3):133-9. doi: 10.1097/PHM.0000000000000626. PubMed PMID: 27680426.
21
Bulley C, Mercer TH, Hooper JE, Cowan P, Scott S, Van Der Linden ML. Experiences of functional electrical stimulation (FES) and ankle foot orthoses (AFOs) for foot-drop in people with multiple sclerosis. Disabil Rehabil Assist Technol. 2015;10(6):458-67. doi: 10.3109/17483107.2014.913713. PubMed PMID: 24796365.
22
Yamamoto S, Ibayashi S, Fuchi M, Yasui T. Immediate-term effects of use of an ankle–foot orthosis with an oil damper on the gait of stroke patients when walking without the device. Prosthet Orthot Int. 2015;39(2):140-9. doi: 10.1177/0309364613518340. PubMed PMID: 24469429.
23
Alam M, Choudhury IA, Mamat AB. Mechanism and design analysis of articulated ankle foot orthoses for drop-foot. Scientific World Journal. 2014;2014:867869. doi: 10.1155/2014/867869. PubMed PMID: 24892102. PubMed PMCID: PMC4032669.
24
Kaipust JP, Huisinga JM, Filipi M, Stergiou N. Gait variability measures reveal differences between multiple sclerosis patients and healthy controls. Motor Control. 2012;16(2):229-44. doi: 10.1123/mcj.16.2.229. PubMed PMID: 22615327.
25
Windolf M, Gotzen N, Morlock M. Systematic accuracy and precision analysis of video motion capturing systems—exemplified on the Vicon-460 system. J Biomech. 2008;41:2776-80. doi:10.1016/j.jbiomech.2008.06.024.
26
Rogind H, Simonsen H, Era P, Bliddal H. Comparison of Kistler 9861A force platform and Chattecx Balance System for measurement of postural sway: correlation and test-retest reliability. Scand J Med Sci Sports. 2003;13(2):106-14. doi: 10.1034/j.1600-0838.2003.01139.x. PubMed PMID: 12641642.
27
Benedetti M, Piperno R, Simoncini L, Bonato P, Tonini A, Giannini S. Gait abnormalities in minimally impaired multiple sclerosis patients. Mult Scler. 1999;5(5):363-8. doi: 10.1177/135245859900500510. PubMed PMID: 10516781.
28
Neptune RR, Sasaki K. Ankle plantar flexor force production is an important determinant of the preferred walk-to-run transition speed. J Exp Biol. 2005;208(5):799-808. doi: 10.1242/jeb.01435. PubMed PMID: 15755878.
29
Bregman D, Harlaar J, Meskers C, De Groot V. Spring-like Ankle Foot Orthoses reduce the energy cost of walking by taking over ankle work. Gait Posture. 2012;35(1):148-53. doi: 10.1016/j.gaitpost.2011.08.026. PubMed PMID: 22050974.
30
Petrucci MN. Evaluation of gait kinematics and kinetics using a powered ankle-foot orthosis for gait assistance in people with multiple sclerosis. University of Illinois Urbana-Champaign; 2016.
31
ORIGINAL_ARTICLE
Establishment of Diagnostic Reference Levels for Computed Tomography Scanning in Hamadan
Background: New advancements have increased the capabilities of computed tomography as a sectional medical imaging modality. An important note is assessing absorbed dose to patients and minimizing it when performing computed tomography examinations. One approach to control dose is to establish diagnostic reference levels. Objective: This study aimed to investigate diagnostic reference levels of computed tomography in Hamadan.Material and Methods: This work was conducted as an experimental study. Computed tomography dose index (CTDI) was measured using a Piranha quality control kit, head and body CTDI phantoms for brain, lung, abdomen-pelvic and coronary CT angiography examinations. Volume Computed Tomography Dose Index (CTDIvol) was calculated from obtained data and 3rd quartile of that was determined as diagnostic reference levels. Results: Diagnostic reference levels (DRLs) in terms of CTDIvol for brain, lung, abdomen-pelvic and coronary CT angiography were 50/25, 6/73, 22/01 and 32/06 mGy respectively in Hamadan. Difference between displayed CTDIvol and measured CTDIvol is not significance for all examinations (p>0.05). Conclusion: DRLs depend on to many dose affecting parameters in CT. DRL for brain CT is greater than other scan regions. Application of DRLs which resulted from this study can help to optimize radiation dose to the patients while maintaining acceptable diagnostic images quality.
https://jbpe.sums.ac.ir/article_47036_e4efd912078d90825917b78415953119.pdf
2020-12-01
793
800
10.31661/jbpe.v0i0.2004-1099
Tomography, X-Ray Computed
Diagnostic Reference Levels
Radiation Dosage
Phantoms, Imaging
Hamadan
S
Jafari
salman.jafari21@gmail.com
1
PhD, Department of Radiology Technology, School of Paramedicine, Hamadan University of Medical Sciences, Hamadan, Iran
AUTHOR
K
Ghazikhanlu Sani
ghazi1356@gmail.com
2
PhD, Department of Radiology Technology, School of Paramedicine, Hamadan University of Medical Sciences, Hamadan, Iran
LEAD_AUTHOR
M
Karimi
mikarimi9631@gmail.com
3
MSc, Department of Biomedical Physics and Engineering, School Of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
AUTHOR
H
Khosravi
hkhosravi55@gmail.com
4
PhD, Department of Radiology Technology, School of Paramedicine, Hamadan University of Medical Sciences, Hamadan, Iran
AUTHOR
R
Goodarzi
r.goodarzi2009@gmail.com
5
MSc, Department of Radiology Technology, School of Paramedicine, Hamadan University of Medical Sciences, Hamadan, Iran
AUTHOR
M
Pourkaveh
m.poorkaveh@yahoo.com
6
MSc, Department of Radiology Technology, School of Paramedicine, Hamadan University of Medical Sciences, Hamadan, Iran
AUTHOR
Jafari S, Mousavi SR. Computed Tomography: Principles, Design, Artifacts, and Recent Advances, Third Edition. Tehran: Arshadan; 2019.
1
Jafari S. Advanced techniques and dosimetry concepts of CT, 1st ed. Tehran: Arshadan; 2020.
2
Miles K. Perfusion CT for the assessment of tumour vascularity: which protocol? Br J Radiol. 2003;76(1):S36-42. doi: 10.1259/bjr/18486642. PubMed PMID: 15456712.
3
Yee J. CT colonography: techniques and applications. Radiol Clin North Am. 2009;47(1):133-45. doi: 10.1016/j.rcl.2008.11.002. PubMed PMID: 19195539.
4
Tavakoli MB, Jabbari K, Jafari S, Hashemi SM, Akbari M. Comparing the absorbed doses by skin, thyroid, and eyes in CT coronary angiography and conventional angiography. Journal of Isfahan Medical School. 2011;29(159):1703-12.
5
Tavakoli H M, Jabari K, Salman J. SU-E-I-51: Investigation of Absorbed Dose to the Skin, Eyes and Thyroid of Patients during CT Angiography and Comparison with Conventional Angiography. Med Phys. 2012;39(6 Part4):3636. doi: 10.1118/1.4734767. PubMed PMID: 28519522.
6
Tavakoli MB, Faraji R, Sajjadieh A, Jafari S. Determination of the weighted computed tomography dose index in coronary multidetector computed tomography angiography. Journal of Isfahan Medical School. 2016;34(398):1060-65.
7
Ai T, Yang Z, Hou H, Zhan C, Chen C, Lv W, Tao Q, Sun Z, Xia L. Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases. Radiology. 2020;296(2):E32-E40. doi: 10.1148/radiol.2020200642. PubMed PMID: 32101510. PubMed PMCID: PMC7233399.
8
Ye Z, Zhang Y, Wang Y, Huang Z, Song B. Chest CT manifestations of new coronavirus disease 2019 (COVID-19): a pictorial review. Eur Radiol. 2020;30:4381-89. doi: 10.1007/s00330-020-06801-0.
9
Pearce MS, Salotti JA, Little MP, McHugh K, Lee C, Kim KP, Howe NL, Ronckers CM, Rajaraman P, Craft AW. Radiation exposure from CT scans in childhood and subsequent risk of leukaemia and brain tumours: a retrospective cohort study. Lancet. 2012;380(9840):499-505. doi: 10.1016/S0140-6736(12)60815-0. PubMed PMID: 22681860. PubMed PMCID: PMC3418594.
10
Mathews JD, Forsythe AV, Brady Z, Butler MW, Goergen SK, Byrnes GB, Giles GG, Wallace AB, Anderson PR, Guiver TA. Cancer risk in 680000 people exposed to computed tomography scans in childhood or adolescence: data linkage study of 11 million Australians. BMJ. 2013;346:f2360. doi: 10.1136/bmj.f2360.
11
Kalra MK, Maher MM, Toth TL, Hamberg LM, Blake MA, Shepard J-A, Saini S. Strategies for CT radiation dose optimization. Radiology. 2004;230(3):619-28. doi: 10.1148/radiol.2303021726.
12
McCollough CH, Bruesewitz MR, Kofler Jr JM. CT dose reduction and dose management tools: overview of available options. Radiographics. 2006;26(2):503-12. doi: 10.1148/rg.262055138.
13
Najafi M, Deevband MR, Ahmadi M, Kardan MR. Establishment of diagnostic reference levels for common multi-detector computed tomography examinations in Iran. Australas Phys Eng Sci Med. 2015;38(4):603-9. doi: 10.1007/s13246-015-0388-8. PubMed PMID: 26507898.
14
Foley SJ, McEntee MF, Rainford LA. Establishment of CT diagnostic reference levels in Ireland. Br J Radiol. 2012;85(1018):1390-7. doi: 10.1259/bjr/15839549. PubMed PMID: 22595497. PubMed PMCID: PMC3474022.
15
Fukushima Y, Tsushima Y, Takei H, Taketomi-Takahashi A, Otake H, Endo K. Diagnostic reference level of computed tomography (CT) in Japan. Radiat Prot Dosimetry. 2012;151(1):51-7. doi: 10.1093/rpd/ncr441. PubMed PMID: 22147925.
16
Van Der Molen A, Schilham A, Stoop P, Prokop M, Geleijns J. A national survey on radiation dose in CT in The Netherlands. Insights Imaging. 2013;4(3):383-90. doi: 10.1007/s13244-013-0253-9. PubMed PMID: 23673455. PubMed PMCID: PMC3675255.
17
Kanal KM, Butler PF, Sengupta D, Bhargavan-Chatfield M, Coombs LP, Morin RL. US diagnostic reference levels and achievable doses for 10 adult CT examinations. Radiology. 2017;284(1):120-33. doi: 10.1148/radiol.2017161911. PubMed PMID: 28221093.
18
Tavakoli MB, Heydari K, Jafari S. Evaluation of diagnostic reference levels for CT scan in Isfahan. Glob J Med Res Stud. 2014;1:130-4.
19
McNitt-Gray MF. AAPM/RSNA physics tutorial for residents: topics in CT: radiation dose in CT. Radiographics. 2002;22(6):1541-53. doi: 10.1148/rg.226025128. PubMed PMID: 12432127.
20
Brady Z, Ramanauskas F, Cain T, Johnston P. Assessment of paediatric CT dose indicators for the purpose of optimisation. Br J Radiol. 2012;85(1019):1488-98. doi: 10.1259/bjr/28015185. PubMed PMID: 22844033. PubMed PMCID: PMC3500792.
21
Afzalipour R, Abdollahi H, Hajializadeh M, Jafari S, Mahdavi S. Estimation of diagnostic reference levels for children computed tomography: A study in Tehran, Iran. Int J Radiat Res. 2019;17(3):407-13.
22
ORIGINAL_ARTICLE
A Deep Learning Approach to Skin Cancer Detection in Dermoscopy Images
This work proposes a deep learning model for skin cancer detection from skin lesion images. In this analytic study, from HAM10000 dermoscopy image database, 3400 images were employed including melanoma and non-melanoma lesions. The images comprised 860 melanoma, 327 actinic keratoses and intraepithelial carcinoma (AKIEC), 513 basal cell carcinoma (BCC), 795 melanocytic nevi, 790 benign keratosis, and 115 dermatofibroma cases. A deep convolutional neural network was developed to classify the images into benign and malignant classes. A transfer learning method was leveraged with AlexNet as the pre-trained model. The proposed model takes the raw image as the input and automatically learns useful features from the image for classification. Therefore, it eliminates complex procedures of lesion segmentation and feature extraction. The proposed model achieved an area under the receiver operating characteristic (ROC) curve of 0.91. Using a confidence score threshold of 0.5, a classification accuracy of 84%, the sensitivity of 81%, and specificity of 88% was obtained. The user can change the confidence threshold to adjust sensitivity and specificity if desired. The results indicate the high potential of deep learning for the detection of skin cancer including melanoma and non-melanoma malignancies. The proposed approach can be deployed to assist dermatologists in skin cancer detection. Moreover, it can be applied in smartphones for self-diagnosis of malignant skin lesions. Hence, it may expedite cancer detection that is critical for effective treatment.
https://jbpe.sums.ac.ir/article_47034_49d7c4101f329256a416c4eaf869177e.pdf
2020-12-01
801
806
10.31661/jbpe.v0i0.2004-1107
Skin Cancer
Deep Learning
Melanoma
Transfer Learning
Dermoscopy
A
Ameri
aliameri86@gmail.com
1
PhD, Department of Biomedical Engineering, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
LEAD_AUTHOR
Xu YG, Aylward JL, Swanson AM, Spiegelman VS, Vanness ER, Teng JM, et al. Nonmelanoma skin cancers: basal cell and squamous cell carcinomas. Abeloff’s Clinical Oncology. 2020:1052-73. doi: 10.1016/B978-0-323-47674-4.00067-0.
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Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115-8. doi: 10.1038/nature21056. PubMed PMID: 28117445.
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Masood A, Ali Al-Jumaily A. Computer aided diagnostic support system for skin cancer: a review of techniques and algorithms. Int J of Biomed Imaging. 2013:323268. doi: 10.1155/2013/323268. PubMed PMID: 24575126. PubMed PMCID: PMC3885227.
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Burroni M, Corona R, Dell’Eva G, Sera F, Bono R, Puddu P, et al. Melanoma computer-aided diagnosis: reliability and feasibility study. Clin Cancer Res. 2004;10(6):1881-6. doi: 10.1158/1078-0432.ccr-03-0039. PubMed PMID: 15041702.
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Rosado B, Menzies S, Harbauer A, Pehamberger H, Wolff K, Binder M, et al. Accuracy of computer diagnosis of melanoma: a quantitative meta-analysis. Arch of Dermatol. 2003;139(3):361-7. doi: 10.1001/archderm.139.3.361. PubMed PMID: 12622631.
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Ameri A. EMG-based wrist gesture recognition using a convolutional neural network. Teh Univ Med J TUMS Publications. 2019;77(7):434-9.
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Ameri A, Akhaee MA, Scheme E, Englehart K. Regression convolutional neural network for improved simultaneous EMG control. J Neural Eng. 2019;16(3):036015. doi: 10.1088/1741-2552/ab0e2e. PubMed PMID: 30849774.
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Ameri A, Akhaee MA, Scheme E, Englehart K. Real-time, simultaneous myoelectric control using a convolutional neural network. PloS One. 2018;13(9):e0203835. doi: 10.1371/journal.pone.0203835. PubMed PMID: 30212573. PubMed PMCID: PMC6136764
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Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Communications of the ACM. 2017;60(6):84-90. doi: 10.1145/3065386.
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