{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T12:32:49Z","timestamp":1768825969452,"version":"3.49.0"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,6,4]],"date-time":"2024-06-04T00:00:00Z","timestamp":1717459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,6,4]],"date-time":"2024-06-04T00:00:00Z","timestamp":1717459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100003141","name":"Consejo Nacional de Ciencia y Tecnolog\u00eda","doi-asserted-by":"publisher","award":["48059"],"award-info":[{"award-number":["48059"]}],"id":[{"id":"10.13039\/501100003141","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Imaging"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In this study, we propose a novel method for quantifying tortuosity in 3D voxelized objects. As a shape characteristic, tortuosity has been widely recognized as a valuable feature in image analysis, particularly in the field of medical imaging. Our proposed method extends the two-dimensional approach of the Slope Chain Code (SCC) which creates a one-dimensional representation of curves. The utility of 3D tortuosity (<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\tau _{3D}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:msub>\n                    <mml:mi>\u03c4<\/mml:mi>\n                    <mml:mrow>\n                      <mml:mn>3<\/mml:mn>\n                      <mml:mi>D<\/mml:mi>\n                    <\/mml:mrow>\n                  <\/mml:msub>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula>) as a shape descriptor was investigated by characterizing brain structures. The results of the <jats:inline-formula><jats:alternatives><jats:tex-math>$$\\tau _{3D}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:msub>\n                    <mml:mi>\u03c4<\/mml:mi>\n                    <mml:mrow>\n                      <mml:mn>3<\/mml:mn>\n                      <mml:mi>D<\/mml:mi>\n                    <\/mml:mrow>\n                  <\/mml:msub>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> computation on the central sulcus and the main lobes revealed significant differences between Alzheimer\u2019s disease (AD) patients and control subjects, suggesting its potential as a biomarker for AD. We found a <jats:inline-formula><jats:alternatives><jats:tex-math>$$p&lt;0.05$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:mi>p<\/mml:mi>\n                    <mml:mo>&lt;<\/mml:mo>\n                    <mml:mn>0.05<\/mml:mn>\n                  <\/mml:mrow>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> for the left central sulcus and the four brain lobes.<\/jats:p>","DOI":"10.1186\/s12880-024-01312-6","type":"journal-article","created":{"date-parts":[[2024,6,4]],"date-time":"2024-06-04T11:45:34Z","timestamp":1717501534000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["3D Tortuosity computation as a shape descriptor and its application to brain structure analysis"],"prefix":"10.1186","volume":"24","author":[{"given":"Maria-Julieta","family":"Mateos","sequence":"first","affiliation":[]},{"given":"Ernesto","family":"Bribiesca","sequence":"additional","affiliation":[]},{"given":"Adolfo","family":"Guzm\u00e1n-Arenas","sequence":"additional","affiliation":[]},{"given":"Wendy","family":"Aguilar","sequence":"additional","affiliation":[]},{"given":"Jorge\u00a0A.","family":"Marquez-Flores","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,6,4]]},"reference":[{"key":"1312_CR1","doi-asserted-by":"publisher","first-page":"399","DOI":"10.1145\/356859.356862","volume":"13","author":"SN Srihari","year":"1981","unstructured":"Srihari SN. Representation of Three-Dimensional Digital Images. ACM Comput Surv (CSUR). 1981;13:399\u2013424.","journal-title":"ACM Comput Surv (CSUR)."},{"key":"1312_CR2","doi-asserted-by":"publisher","first-page":"783","DOI":"10.1148\/radiographics.19.3.g99ma13783","volume":"19","author":"JK Udupa","year":"1999","unstructured":"Udupa JK. Three-dimensional visualization and analysis methodologies: A current perspective. Radiographics. 1999;19:783\u2013806.","journal-title":"Radiographics."},{"issue":"1","key":"1312_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jvcir.2011.07.008","volume":"23","author":"PY Chiang","year":"2012","unstructured":"Chiang PY, Kuo CCJ. Voxel-based shape decomposition for feature-preserving 3D thumbnail creation. J Vis Commun Image Represent. 2012;23(1):1\u201311.","journal-title":"J Vis Commun Image Represent."},{"issue":"6","key":"1312_CR4","doi-asserted-by":"publisher","first-page":"591","DOI":"10.1007\/s00371-014-0980-z","volume":"30","author":"Z Xie","year":"2014","unstructured":"Xie Z, Xiong Y, Xu K. AB3D: action-based 3D descriptor for shape analysis. Vis Comput. 2014;30(6):591\u2013601.","journal-title":"Vis Comput."},{"issue":"12","key":"1312_CR5","doi-asserted-by":"publisher","first-page":"2169","DOI":"10.1007\/s11517-017-1661-7","volume":"55","author":"E Akar","year":"2017","unstructured":"Akar E, Kara S, Akdemir H, K\u0131r\u0131\u015f A. 3D structural complexity analysis of cerebellum in Chiari malformation type I. Med Biol Eng Comput. 2017;55(12):2169\u201382.","journal-title":"Med Biol Eng Comput."},{"key":"1312_CR6","unstructured":"Weiner R. Webster's New World dictionary of media and communications. New York: Webster's New World. Webster's New World Distributed by Prentice Hall Trade Sales New York, NY, USA; 1990."},{"issue":"1","key":"1312_CR7","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1007\/s11517-009-0559-4","volume":"48","author":"G Dougherty","year":"2010","unstructured":"Dougherty G, Johnson MJ, Wiers MD. Measurement of retinal vascular tortuosity and its application to retinal pathologies. Med Biol Eng Comput. 2010;48(1):87\u201395.","journal-title":"Med Biol Eng Comput."},{"issue":"2","key":"1312_CR8","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1007\/s00371-017-1465-7","volume":"35","author":"J Zhang","year":"2019","unstructured":"Zhang J, Wang CB, Qin H, Chen Y, Gao Y. Procedural modeling of rivers from single image toward natural scene production. Vis Comput. 2019;35(2):223\u201337.","journal-title":"Vis Comput."},{"issue":"9","key":"1312_CR9","doi-asserted-by":"publisher","first-page":"1163","DOI":"10.1109\/TMI.2003.816964","volume":"22","author":"E Bullitt","year":"2003","unstructured":"Bullitt E, Gerig G, Pizer SM, Lin W, Aylward SR. Measuring tortuosity of the intracerebral vasculature from MRA images. IEEE Trans Med Imaging. 2003;22(9):1163\u201371.","journal-title":"IEEE Trans Med Imaging."},{"issue":"1","key":"1312_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ultras.2009.07.011","volume":"50","author":"LH Le","year":"2010","unstructured":"Le LH, Zhang C, Ta D, Lou E. Measurement of tortuosity in aluminum foams using airborne ultrasound. Ultrasonics. 2010;50(1):1\u20135.","journal-title":"Ultrasonics."},{"issue":"1","key":"1312_CR11","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1007\/BF00414653","volume":"211","author":"W Lotmar","year":"1979","unstructured":"Lotmar W, Freiburghaus A, Bracher D. Measurement of vessel tortuosity on fundus photographs. Albrecht Graefes Arch Klin Exp Ophthalmol. 1979;211(1):49\u201357.","journal-title":"Albrecht Graefes Arch Klin Exp Ophthalmol"},{"key":"1312_CR12","unstructured":"Chandrinos KV, Pilu M, Fisher RB, Trahanias P. Image processing techniques for the quantification of atherosclerotic changes. Edinburgh: Department of Artificial Intelligence, University of Edinburgh. Department of Artificial Intelligence. University of Edinburgh 5 Forrest Hill Edinburgh EH1 2QL Scotland, UK; 1998."},{"issue":"2\u20133","key":"1312_CR13","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1016\/S1386-5056(98)00163-4","volume":"53","author":"WE Hart","year":"1999","unstructured":"Hart WE, Goldbaum M, C\u00f4t\u00e9 B, Kube P, Nelson MR. Measurement and classification of retinal vascular tortuosity. Int J Med Inform. 1999;53(2\u20133):239\u201352.","journal-title":"Int J Med Inform."},{"key":"1312_CR14","first-page":"181","volume":"60","author":"KG Goh","year":"2001","unstructured":"Goh KG, Hsu W, Li Lee M, Wang H. ADRIS: an automatic diabetic retinal image screening system. Stud Fuzziness Soft Comput. 2001;60:181\u2013210.","journal-title":"Stud Fuzziness Soft Comput."},{"issue":"3","key":"1312_CR15","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1109\/TMI.2007.904657","volume":"27","author":"E Grisan","year":"2008","unstructured":"Grisan E, Foracchia M, Ruggeri A. A novel method for the automatic grading of retinal vessel tortuosity. IEEE Trans Med Imaging. 2008;27(3):310\u20139.","journal-title":"IEEE Trans Med Imaging."},{"issue":"3","key":"1312_CR16","doi-asserted-by":"publisher","first-page":"716","DOI":"10.1016\/j.patcog.2012.09.017","volume":"46","author":"E Bribiesca","year":"2013","unstructured":"Bribiesca E. A measure of tortuosity based on chain coding. Pattern Recog. 2013;46(3):716\u201324.","journal-title":"Pattern Recog."},{"key":"1312_CR17","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.jvcir.2019.03.015","volume":"61","author":"E Bribiesca","year":"2019","unstructured":"Bribiesca E, Bribiesca-Contreras F, Carrillo-Bermejo \u00c1, Bribiesca-Correa G, Hevia-Montiel N. A chain code for representing high definition contour shapes. J Vis Commun Image Represent. 2019;61:93\u2013104.","journal-title":"J Vis Commun Image Represent."},{"key":"1312_CR18","doi-asserted-by":"crossref","unstructured":"Abdalla M, Hunter A, Al-Diri B. Quantifying retinal blood vessels' tortuosity \u2014 Review. 2015 Science and Information Conference (SAI), 687-693. Piscataway, NJ, USA: IEEE Operations Center; 2015.","DOI":"10.1109\/SAI.2015.7237216"},{"key":"1312_CR19","doi-asserted-by":"publisher","first-page":"103406","DOI":"10.1016\/j.compgeo.2019.103406","volume":"120","author":"R Nemati","year":"2020","unstructured":"Nemati R, Shahrouzi JR, Alizadeh R. A stochastic approach for predicting tortuosity in porous media via pore network modeling. Comput Geotech. 2020;120:103406.","journal-title":"Comput Geotech."},{"key":"1312_CR20","doi-asserted-by":"publisher","first-page":"107321","DOI":"10.1016\/j.petrol.2020.107321","volume":"192","author":"AM Lala","year":"2020","unstructured":"Lala AM. A novel model for reservoir rock tortuosity estimation. J Pet Sci Eng. 2020;192:107321.","journal-title":"J Pet Sci Eng."},{"issue":"1","key":"1312_CR21","doi-asserted-by":"publisher","first-page":"36","DOI":"10.3103\/S1060992X15010014","volume":"24","author":"NY Ilyasova","year":"2015","unstructured":"Ilyasova NY. Methods to evaluate the three-dimensional features of blood vessels. Opt Mem Neural Netw. 2015;24(1):36\u201347.","journal-title":"Opt Mem Neural Netw."},{"issue":"1","key":"1312_CR22","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1007\/s00521-014-1657-2","volume":"26","author":"J Malek","year":"2015","unstructured":"Malek J, Azar AT, Tourki R. Impact of retinal vascular tortuosity on retinal circulation. Neural Comput Appl. 2015;26(1):25\u201340.","journal-title":"Neural Comput Appl."},{"issue":"16","key":"1312_CR23","doi-asserted-by":"publisher","first-page":"12453","DOI":"10.1007\/s00521-019-04697-6","volume":"32","author":"S Ramachandran","year":"2020","unstructured":"Ramachandran S, Strisciuglio N, Vinekar A, John R, Azzopardi G. U-COSFIRE filters for vessel tortuosity quantification with application to automated diagnosis of retinopathy of prematurity. Neural Comput Appl. 2020;32(16):12453\u201368.","journal-title":"Neural Comput Appl."},{"key":"1312_CR24","doi-asserted-by":"publisher","unstructured":"Zhang T, Nagy G. Surface tortuosity and its application to analyzing cracks in concrete. Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. Vol.2. Cambridge: IEEE Computer Society 2001 L Street NW, Suite 700 Washington, DC 20036-4928, USA; 2004. p. 851\u20134. https:\/\/doi.org\/10.1109\/ICPR.2004.1334392.","DOI":"10.1109\/ICPR.2004.1334392"},{"issue":"4","key":"1312_CR25","doi-asserted-by":"publisher","first-page":"045015","DOI":"10.1088\/1742-2132\/10\/4\/045015","volume":"10","author":"W Xiao","year":"2013","unstructured":"Xiao W, Xia C, Wei W, Bian Y. Combined effect of tortuosity and surface roughness on estimation of flow rate through a single rough joint. J Geophys Eng. 2013;10(4):045015.","journal-title":"J Geophys Eng."},{"issue":"3","key":"1312_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42979-021-00565-0","volume":"2","author":"E Bribiesca","year":"2021","unstructured":"Bribiesca E. A Measure of Tortuosity for Enclosing Surfaces of Voxel-Based Objects. SN Comput Sci. 2021;2(3):1\u201311.","journal-title":"SN Comput Sci."},{"key":"1312_CR27","doi-asserted-by":"publisher","unstructured":"Mateos MJ, M\u00e1rquez J, Bribiesca E. 3D Tortuosity: a morphological characterization of the central sulcus to differentiate patients with alzheimer's disease and controls. 2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS\/MIC). Boston: IEEE Operations Center 445 Hoes Lane Piscataway, NJ 08854, USA; 2020. p. 1\u20134. https:\/\/doi.org\/10.1109\/NSS\/MIC42677.2020.9507994.","DOI":"10.1109\/NSS\/MIC42677.2020.9507994"},{"issue":"5","key":"1312_CR28","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1016\/S0031-3203(97)00071-X","volume":"31","author":"LJ Latecki","year":"1998","unstructured":"Latecki LJ, Rosenfeld A. Supportedness and tameness differentialless geometry of plane curves. Pattern Recogn. 1998;31(5):607\u201322.","journal-title":"Pattern Recogn."},{"key":"1312_CR29","unstructured":"James RC. Mathematics dictionary. Springer Science & Business Media. New York: Chapman & Hall; 1992."},{"issue":"3","key":"1312_CR30","first-page":"17","volume":"8","author":"LA Wulandhari","year":"2008","unstructured":"Wulandhari LA, Haron H. The evolution and trend of chain code scheme. Graph Vis Image Process. 2008;8(3):17\u201323.","journal-title":"Graph Vis Image Process."},{"key":"1312_CR31","unstructured":"Klette R, Rosenfeld A. Digital geometry: Geometric methods for digital picture analysis. Morgan Kaufmann. San Francisco: Morgan Kaufmann Publishers; 2004."},{"key":"1312_CR32","doi-asserted-by":"crossref","unstructured":"Subba T, Pradhan AK, Rai P. \"Voxelization\" in 3D Searching- A Study. Int J Comput Appl. 2015;132:37\u201340.","DOI":"10.5120\/ijca2015907547"},{"key":"1312_CR33","doi-asserted-by":"publisher","unstructured":"Khilar R, Chitrakala S, SelvamParvathy S. 3D image reconstruction: Techniques, applications and challenges,\" 2013 International Conference on Optical Imaging Sensor and Security (ICOSS). Coimbatore: IEEE Operations Center 445 Hoes Lane Piscataway, NJ 08854, USA; 2013. p. 1\u20136. https:\/\/doi.org\/10.1109\/ICOISS.2013.6678395.","DOI":"10.1109\/ICOISS.2013.6678395"},{"issue":"3","key":"1312_CR34","doi-asserted-by":"publisher","first-page":"353","DOI":"10.3390\/s16030353","volume":"16","author":"J Seo","year":"2016","unstructured":"Seo J, Chae S, Shim J, Kim D, Cheong C, Han TD. Fast contour-tracing algorithm based on a pixel-following method for image sensors. Sensors. 2016;16(3):353.","journal-title":"Sensors."},{"issue":"2","key":"1312_CR35","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1016\/S0262-8856(01)00091-9","volume":"20","author":"M Ren","year":"2002","unstructured":"Ren M, Yang J, Sun H. Tracing boundary contours in a binary image. Image Vis Comput. 2002;20(2):125\u201331.","journal-title":"Image Vis Comput."},{"issue":"1\u20133","key":"1312_CR36","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/j.dam.2003.08.003","volume":"139","author":"D Coeurjolly","year":"2004","unstructured":"Coeurjolly D, Gerard Y, Reveilles JP, Tougne L. An elementary algorithm for digital arc segmentation. Discret Appl Math. 2004;139(1\u20133):31\u201350.","journal-title":"Discret Appl Math."},{"key":"1312_CR37","doi-asserted-by":"publisher","unstructured":"Kovalevsky VA. New definition and fast recognition of digital straight segments and arcs,\" [1990] Proceedings. 10th International Conference on Pattern Recognition, vol.2. Atlantic City: IEEE Computer Society Press 10662 Los Vaqueros Circle Los Alamitos, CA 90720-1314, USA; 1990. p. 31\u201334. https:\/\/doi.org\/10.1109\/ICPR.1990.119324.","DOI":"10.1109\/ICPR.1990.119324"},{"issue":"3","key":"1312_CR38","doi-asserted-by":"publisher","first-page":"994","DOI":"10.1523\/JNEUROSCI.23-03-00994.2003","volume":"23","author":"PM Thompson","year":"2003","unstructured":"Thompson PM, Hayashi KM, De Zubicaray G, Janke AL, Rose SE, Semple J, et al. Dynamics of gray matter loss in Alzheimer\u2019s disease. J Neurosci. 2003;23(3):994\u20131005.","journal-title":"J Neurosci."},{"issue":"6","key":"1312_CR39","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1136\/jnnp.73.6.657","volume":"73","author":"G Frisoni","year":"2002","unstructured":"Frisoni G, Testa C, Zorzan A, Sabattoli F, Beltramello A, Soininen H, et al. Detection of grey matter loss in mild Alzheimer\u2019s disease with voxel-based morphometry. J Neurol Neurosurg Psychiatry. 2002;73(6):657\u201364.","journal-title":"J Neurol Neurosurg Psychiatry."},{"key":"1312_CR40","doi-asserted-by":"publisher","first-page":"116343","DOI":"10.1016\/j.neuroimage.2019.116343","volume":"207","author":"MJ Mateos","year":"2020","unstructured":"Mateos MJ, Gastelum-Strozzi A, Barrios FA, Bribiesca E, Alcauter S, Marquez-Flores JA. A novel voxel-based method to estimate cortical sulci width and its application to compare patients with Alzheimer\u2019s disease to controls. NeuroImage. 2020;207:116343.","journal-title":"NeuroImage."},{"issue":"1","key":"1312_CR41","doi-asserted-by":"publisher","first-page":"27","DOI":"10.5566\/ias.v32.p27-43","volume":"32","author":"C Peyrega","year":"2013","unstructured":"Peyrega C, Jeulin D. Estimation of tortuosity and reconstruction of geodesic paths in 3D. Image Anal Stereology. 2013;32(1):27\u201343.","journal-title":"Image Anal Stereology."},{"key":"1312_CR42","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/j.mspro.2014.07.553","volume":"4","author":"S Pardo-Alonso","year":"2014","unstructured":"Pardo-Alonso S, Vicente J, Sol\u00f3rzano E, Rodriguez-Perez M\u00c1, Lehmhus D. Geometrical tortuosity 3D calculations in infiltrated aluminium cellular materials. Procedia Mater Sci. 2014;4:145\u201350.","journal-title":"Procedia Mater Sci."},{"issue":"8","key":"1312_CR43","doi-asserted-by":"publisher","first-page":"2000","DOI":"10.1002\/aic.11812","volume":"55","author":"CJ Gommes","year":"2009","unstructured":"Gommes CJ, Bons AJ, Blacher S, Dunsmuir JH, Tsou AH. Practical methods for measuring the tortuosity of porous materials from binary or gray-tone tomographic reconstructions. AIChE J. 2009;55(8):2000\u201312.","journal-title":"AIChE J."}],"container-title":["BMC Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-024-01312-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12880-024-01312-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-024-01312-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,4]],"date-time":"2024-06-04T11:47:38Z","timestamp":1717501658000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedimaging.biomedcentral.com\/articles\/10.1186\/s12880-024-01312-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,4]]},"references-count":43,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["1312"],"URL":"https:\/\/doi.org\/10.1186\/s12880-024-01312-6","relation":{},"ISSN":["1471-2342"],"issn-type":[{"value":"1471-2342","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,4]]},"assertion":[{"value":"14 February 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 May 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 June 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All studies counted with authorization of their IRB ().","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"130"}}