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Geometric euclidean and fractal characterization of sickle cells

Caracterización geométrica euclidiana y fractal de células falciformes




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Geometric euclidean and fractal characterization of sickle cells. (2020). NOVA, 18(33), 43-62. https://doi.org/10.22490/https://doi.org/10.22490/24629448.3696

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NOVA by http://www.unicolmayor.edu.co/publicaciones/index.php/nova is distributed under a license creative commons non comertial-atribution-withoutderive 4.0 international.

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Javier Rodríguez
    Martha Castillo
      Ana Lucia Oliveros
        Yolanda Soracipa
          Signed Prieto

            Javier Rodríguez,

            MD. Director del Grupo Insight. Bogotá, Colombia


            Martha Castillo,

            Directora grupo Eritron. Docente, Universidad Mayor de Cundinamarca. Bogotá


            Ana Lucia Oliveros,

            Investigadora grupo Eritron. Docente, Universidad Mayor de Cundinamarca. Bogotá, Colombia.


            Yolanda Soracipa,

            Investigadora Grupo Insight. Bogotá, Colombia.


            Signed Prieto,

            Investigadora Grupo Insight. Bogotá, Colombia.


            Introduction: Recent studies propose new methodologies that allow the recognition of the different alterations in the shape of red blood cells, establishing mathematical and geometric comparison patterns in the context of fractal and Euclidean geometry. Objective: to characterize the shape of sickle cells using a methodology designed in the context of fractal and Euclidean geometry. Methodology: 30 images of sickle cells were obtained in peripheral blood smears. The sickle cells were delineated and superimposed two Kp grids of 5 x 5 pixels and Kg of 10 x 10 pixels, to calculate the space occupied by these cells and the fractal dimension by means of the Box Counting method. Results: the spaces occupied by the sickle cells varied with the superposition of the Kp grid between 36 and 56; the surface of sickle cells varied between 969 and 1872 pixels, and the proportions between the surface and the values ​​of the Kp grid varied between 23.1 and 39.6. Conclusions: The present study reveals the possibility of making more precise characterizations in sickle cells, from the occupation spaces of the sickle cell by superposing the Kp grid and the proportions between the surface and the Kp grid, and not by the values of the fractal dimension, contributing in this way in the design of methodologies that improve the recognition of this type of cells.


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            1. Mandelbrot B. The Fractal Geometry of Nature. San Francisco: Freeman Ed; 1972. p. 341-348.

            2. Peitgen H, Jurgens H, Saupe D. Limits and self similarity. In: Chaos and Fractals: New Frontiers of Science. N.Y. Springer-Verlag; 1992. p. 129-172.

            3. Bruce T. M. Spatial Aggregation and Neutral Models in Fractal Landscapes The American Naturalist 1992; 139(1): 32-57.

            4. Rodríguez J, Prieto S, Correa C, Bernal P, Puerta G, Vitery S, et al. Theoretical generalization of normal and sick coronary arteries with fractal dimensions and the arterial intrinsic mathematical harmony. BMC Medical Physics. 2010; 10:1-6.

            5. Rodríguez J, Prieto S, Soracipa Y, Correa C, Forero G, Cifuentes R, Aguirre G, Salamanca A, Bernal H. Generalización diagnóstica fractal de la morfología cardiaca ventricular izquierda: anormalidades moderadas y severas a partir del ventriculograma. Universitas Médica. 2018; 59(1):1-9.

            6. Correa C, Rodríguez J, Prieto S, Álvarez L, Ospino B, Munévar A, et al. Geometric diagnosis of erythrocyte morphophysiology. J. Med. Med. Sci 2012; 3(11): 715-720.

            7. Rodríguez J. Prieto S, Correa S, Mejía M, Ospino B, Munevar Á, et al. Simulación de estructuras eritrocitarias con base en la geometría fractal y euclidiana. Arch Med (Manizales) 2014; 14(2): 276-284.

            8. Rodríguez J, Moreno N, Alfonso D, Méndez M, Flórez A. Caracterización Geométrica de la morfología del Equinocito. Arch Med. 2017;13(1):1–5.

            9. Rodríguez J, Soracipa Y, Ovalle A, Castro M, Snejoa N, Quijano B, et al, Geometría fractal aplicada para comparar los espacios ocupados por eritrocitos normales y esferocitos. Archivos de Medicina. 2018; 1:13-23.

            10. Rodríguez J, Escobar S, Abder L, del Río J, Quintero L, Ocampo D. Nueva metodología geométrica para evaluar la morfología del eritrocito normal. Nova 2017; 15(27), 37-43.

            11. Rodríguez J, Soracipa Y, Ovalle A, Castro M, Snejoa N, Quijano B, et al, Geometría fractal aplicada para comparar los espacios ocupados por eritrocitos normales y esferocitos. Archivos de Medicina. 2018; 1:13-23.

            12. Juliania A, Biesa A, Boydstonb C, Taylorb R, Margaret S. Sereno. Navigation performance in virtual environments varies with fractal dimension of landscape. Journal of Environmental Psychology. 2016; 47:115-16.

            13. Carr J. Atlas de la hematología. Bogotá: Panamericana. 2009.

            14. CDS. Información básica sobre la enfermedad de células falciformes. Disponible en: https://www.cdc.gov/ncbddd/spanish/sicklecell/facts.html

            15. Mukhopadhyay R, Gerald Lim H W, Wortis M. Echinocyte Shapes: Bending, Stretching, and Shear Determine Spicule Shape and Spacing. Biophys J 2002; 82(4): 1756–1772.

            16. Campuzano G. Utilidad clínica del extendido de sangre periférica: los eritrocitos. Medicina & laboratorio. 2008; 14(7-8): 311-313.

            17. Miale J.B. Hematología medicina de laboratorio. Barcelona: Editorial reverte S.A. 1985.

            18. Price-Jones, C. (1933). Red Blood Cell Diameters. Oxford University Press, London.

            19. Constantino B. Red Cell Distribution Width, Revisited. Laboratory Medicine. 2013;44(Issue 2):e2–e9.

            20. Diggs L.W., Bibb J. The erythrocyte in sickle cell anemia morphology, size, hemoglobin content, fragility and sedimentation rate. JAMA. 1939;695-701.

            21. Velásquez J, Prieto S, Catalina C, Dominguez D, Cardona DM, Melo M. Geometrical nuclear diagnosis and total paths of cervical cell evolution from normality to cancer. J Cancer Res Ther 2015; 11(Issue 1): 98-104.

            22. Rodríguez J, Prieto S, Correa C, Domínguez D, Pardo J, Mendoza F, Soracipa Y, Olarte N, Cardona D, Mendez L. Clinic application of a cardiac diagnostic method based on dynamic systems theory. Res. J. Cardiol. 2017; 10.

            23. Rodríguez J, Prieto S, Flórez M, Alarcón C, López R, Aguirre G, et al. Physical-mathematical diagnosis of cardiac dynamic on neonatal sepsis: predictions of clinical application. J. Med. Sci 2014; 5(5): 102-108.

            24. Rodríguez J. Dynamical systems applied to dynamic variables of patients from the intensive care unit (ICU): Physical and mathematical mortality predictions on ICU. J. Med. Med. Sci 2015; 6(8):209-220.

            25. Rodríguez J, Bernal P, Álvarez L, Pabón S, Ibáñez S, Chapuel N, et al. Predicción de unión de péptidos de MSP-1 y EBA-140 de plasmodium falciparum al HLA clase II Probabilidad, combinatoria y entropía aplicadas a
            secuencias peptídicas. Inmunología 2010; 29(3):91-99.

            26. Rodríguez J. Método para la predicción de la dinámica temporal de la malaria en los municipios de Colombia. Rev Panam Salud Pública 2010; 27(3):211-8.

            27. Rodríguez J, Prieto S, Correa C, Forero M, Pérez C, Soracipa Y, et al. Teoría de conjuntos aplicada al recuento de linfocitos y leucocitos: predicción de linfocitos T CD4 de pacientes con VIH/SIDA. Inmunología. 2013; 32(2): 50-56.

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