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Annals of Computer Science and Information Systems, Volume 9

Position Papers of the 2016 Federated Conference on Computer Science and Information Systems

Automated 3D immunofluorescence analysis of Dorsal Root Ganglia for the investigation of neural circuit alterations: a preliminary study.

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DOI: http://dx.doi.org/10.15439/2016F569

Citation: Position Papers of the 2016 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 9, pages 65–70 ()

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Abstract. Diabetic polyneuropathy is a major complication of diabetes mellitus, causing severe alterations of the neural circuits between spinal nerves and spinal cord. The analysis of 3D confocal images of dorsal root ganglia in diabetic mice, where different fluorescent markers are used to identify different types of nociceptors, can help understanding the unknown mechanisms of this pathology. Nevertheless, due to the inherent challenges of 3D confocal imaging, a thorough and comprehensive visual investigation is very difficult. In this work we introduce a tool, 3DRG, that provides a fully-automated segmentation and 3D rendering of positively labeled nociceptors in a dorsal root ganglion, as well a quantitative characterisation of its immunopositivity to each fluorescent marker. Our preliminary experiments on 3D confocal images of entire dorsal root ganglia from healthy and diabetic mice provided very interesting insights about the effects of the pathology on two different types of nociceptors.

References

  1. D. Ziegler and V. Fonseca, “From guideline to patient: a review of recent recommendations for pharmacotherapy of painful diabetic neuropathy,” Journal of Diabetes and its Complications, vol. 29, no. 1, pp. 146 – 156, 2015. http://dx.doi.org/10.1016/j.jdiacomp.2014.08.008.
  2. H. Kamiya, K. E. Weixian Zhang and, J. Wahren, and A. A. Sima, “C-peptide reverses nociceptive neuropathy in type 1 diabetes,” Diabetes, vol. 55, no. 12, pp. 3581 – 3587, 2006. http://dx.doi.org/10.2337/db06-0396.
  3. Z.-Z. Kou, C.-Y. Li, J.-C. Hu, J.-B. Yin, D.-L. Zhang, Z.-Y. Wu, T. Ding, J. Qu, Y.-H. Liao, H. Li, and Y.-Q. Li, “Alterations in the neural circuits from peripheral afferents to the spinal cord: possible implications for diabetic polyneuropathy in streptozotocin-induced type 1 diabetic rats,” Front Neur Circuits, vol. 8, no. 6, 2014. http://dx.doi.org/10.3389/fncir.2014.00006.
  4. E. S. Krames, “The role of the dorsal root ganglion in the development of neuropathic pain,” Pain Medicine, vol. 15, no. 10, pp. 1669–1685, 2014. http://dx.doi.org/10.1111/pme.12413.
  5. R. Wang, A. Rossomando, D. W. Sah, M. H. Ossipov, T. King, and F. Porreca, “Artemin induced functional recovery and reinnervation after partial nerve injury,” Pain, vol. 155, no. 3, pp. 476–484, 2014. http://dx.doi.org/10.1016/j.pain.2013.11.007.
  6. K. Zuiderveld, “Contrast limited adaptive histogram equalization,” in Graphics Gems IV. Academic Press Professional, Inc., 1994, pp. 474–485. [Online]. Available: http://dl.acm.org/citation.cfm?id=180895.180940
  7. K.-S. Chuang, H.-L. Tzeng, S. Chen, J. Wu, and T.-J. Chen, “Fuzzy c-means clustering with spatial information for image segmentation,” Computerized Medical Imaging and Graphics, vol. 30, no. 1, pp. 9 – 15, 2006. http://dx.doi.org/10.1016/j.compmedimag.2005.10.001.
  8. T. Atherton and D. Kerbyson, “Size invariant circle detection,” Image and Vision Computing, vol. 17, no. 11, pp. 795 – 803, 1999. http://dx.doi.org/10.1016/S0262-8856(98)00160-7.
  9. J. Chambers, W. Cleveland, B. Kleiner, and P. Tukey, “Graphical Methods for Data Analysis,” The Wadsworth Statistics/Probability Series. Boston, MA: Duxury, 1983. http://dx.doi.org/10.2307/2531418.
  10. S. Akkina, C. Patterson, and D. Wright, “{GDNF} rescues nonpeptidergic unmyelinated primary afferents in streptozotocintreated diabetic mice,” Experimental Neurology, vol. 167, no. 1, pp. 173 – 182, 2001. http://dx.doi.org/10.1006/exnr.2000.7547.