Precision electronic medicine in the brain

An Author Correction to this article was published on 24 October 2019

This article has been updated

Abstract

Periodically throughout history developments from adjacent fields of science and technology reach a tipping point where together they produce unparalleled advances, such as the Allen Brain Atlas and the Human Genome Project. Today, research focused at the interface between the nervous system and electronics is not only leading to advances in fundamental neuroscience, but also unlocking the potential of implants capable of cellular-level therapeutic targeting. Ultimately, these personalized electronic therapies will provide new treatment modalities for neurodegenerative and neuropsychiatric illness;Work from our laboratory powerful control of prosthetics for restorative function in degenerative diseases, trauma and amputation; and even augmentation of human cognition. Overall, we believe that emerging advances in tissue-like electronics will enable minimally invasive devices capable of establishing a stable long-term cellular neural interface and providing long-term treatment for chronic neurological conditions.

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Fig. 1: Unidirectional and bidirectional neurostimulation approaches.
Fig. 2: Challenges affecting neural interfaces.
Fig. 3: Schematic representation of syringe-injectable mesh electronic implant in a human brain.

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  • 24 October 2019

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.

References

  1. 1.

    Herrington, T. M., Cheng, J. J. & Eskandar, E. N. Mechanisms of deep brain stimulation. J. Neurophysiol. 115, 19–38 (2016).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  2. 2.

    Miocinovic, S., Somayajula, S., Chitnis, S. & Vitek, J. L. History, applications, and mechanisms of deep brain stimulation. JAMA Neurol. 70, 163–171 (2013).

    PubMed  Article  PubMed Central  Google Scholar 

  3. 3.

    Kringelbach, M. L., Jenkinson, N., Owen, S. L. F. & Aziz, T. Z. Translational principles of deep brain stimulation. Nat. Rev. Neurosci. 8, 623–635 (2007).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  4. 4.

    Kook, G., Lee, S. W., Lee, H. C., Cho, I.-J. & Lee, H. J. Neural probes for chronic applications. Micromachines (Basel) 7, 179 (2016).

    Article  Google Scholar 

  5. 5.

    Wellman, S. M. et al. A materials roadmap to functional neural interface design. Adv. Funct. Mater. 28, 1701269 (2018).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  6. 6.

    Fattahi, P., Yang, G., Kim, G. & Abidian, M. R. A review of organic and inorganic biomaterials for neural interfaces. Adv. Mater. 26, 1846–1885 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  7. 7.

    Hong, G., Viveros, R. D., Zwang, T. J., Yang, X. & Lieber, C. M. Tissue-like neural probes for understanding and modulating the brain. Biochemistry 57, 3995–4004 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  8. 8.

    Hong, G., Yang, X., Zhou, T. & Lieber, C. M. Mesh electronics: a new paradigm for tissue-like brain probes. Curr. Opin. Neurobiol. 50, 33–41 (2018).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  9. 9.

    Yang, X. et al. Bioinspired neuron-like electronics. Nat. Mater. 18, 510–517, https://doi.org/10.1038/s41563-019-0292-9 (2019).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Sun, F. T. & Morrell, M. J. Closed-loop neurostimulation: the clinical experience. Neurotherapeutics 11, 553–563 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  11. 11.

    Parastarfeizabadi, M. & Kouzani, A. Z. Advances in closed-loop deep brain stimulation devices. J. Neuroeng. Rehabil. 14, 79 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  12. 12.

    Gilja, V. et al. Clinical translation of a high-performance neural prosthesis. Nat. Med. 21, 1142–1145 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  13. 13.

    Aflalo, T. et al. Decoding motor imagery from the posterior parietal cortex of a tetraplegic human. Science 348, 906–910 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  14. 14.

    Zeng, F.-G., Rebscher, S., Harrison, W., Sun, X. & Feng, H. Cochlear implants: system design, integration, and evaluation. IEEE Rev. Biomed. Eng. 1, 115–142 (2008).

    PubMed  PubMed Central  Article  Google Scholar 

  15. 15.

    Hadjinicolaou, A. E., Meffin, H., Maturana, M. I., Cloherty, S. L. & Ibbotson, M. R. Prosthetic vision: devices, patient outcomes and retinal research. Clin. Exp. Optom. 98, 395–410 (2015).

    PubMed  Article  PubMed Central  Google Scholar 

  16. 16.

    Lewis, P. M., Ackland, H. M., Lowery, A. J. & Rosenfeld, J. V. Restoration of vision in blind individuals using bionic devices: a review with a focus on cortical visual prostheses. Brain Res. 1595, 51–73 (2015).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  17. 17.

    Rosin, B. et al. Closed-loop deep brain stimulation is superior in ameliorating parkinsonism. Neuron 72, 370–384 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  18. 18.

    Lo, M.-C. & Widge, A. S. Closed-loop neuromodulation systems: next-generation treatments for psychiatric illness. Int. Rev. Psychiatry 29, 191–204 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  19. 19.

    Ezzyat, Y. et al. Closed-loop stimulation of temporal cortex rescues functional networks and improves memory. Nat. Commun. 9, 365 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  20. 20.

    Kellaway, P. The part played by electric fish in the early history of bioelectricity and electrotherapy. Bull. Hist. Med. 20, 112–137 (1946).

    CAS  PubMed  Google Scholar 

  21. 21.

    Ramirez-Zamora, A. et al. Evolving applications, technological challenges and future opportunities in neuromodulation: proceedings of the Fifth Annual Deep Brain Stimulation Think Tank. Front. Neurosci. 11, 734 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  22. 22.

    Cagnan, H., Denison, T., McIntyre, C. & Brown, P. Emerging technologies for improved deep brain stimulation. Nat. Biotechnol. https://doi.org/10.1038/s41587-019-0244-6 (2019).

    CAS  PubMed  Article  Google Scholar 

  23. 23.

    van Dijk, K. J. et al. A novel lead design enables selective deep brain stimulation of neural populations in the subthalamic region. J. Neural Eng. 12, 046003 (2015).

    PubMed  Article  Google Scholar 

  24. 24.

    Lein, E. S. et al. Genome-wide atlas of gene expression in the adult mouse brain. Nature 445, 168–176 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25.

    McIntyre, C. C., Chaturvedi, A., Shamir, R. R. & Lempka, S. F. Engineering the next generation of clinical deep brain stimulation technology. Brain Stimul. 8, 21–26 (2015).

    PubMed  Article  Google Scholar 

  26. 26.

    Fisher, B. et al. Battery longevity comparison of two commonly available dual channel implantable pulse generators used for subthalamic nucleus stimulation in Parkinson’s disease. Stereotact. Funct. Neurosurg. 96, 151–156 (2018).

    PubMed  Article  Google Scholar 

  27. 27.

    Park, K. et al. Battery life matters in deep brain stimulation. Stereotact. Funct. Neurosurg. 96, 65–66 (2018).

    PubMed  Article  Google Scholar 

  28. 28.

    Helmers, A. K. et al. Comparison of the battery life of nonrechargeable generators for deep brain stimulation. Neuromodulation 21, 593–596 (2018).

    PubMed  Article  Google Scholar 

  29. 29.

    Cicchetti, F. & Barker, R. A. The glial response to intracerebrally delivered therapies for neurodegenerative disorders: is this a critical issue? Front. Pharmacol. 5, 139 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  30. 30.

    Buhlmann, J., Hofmann, L., Tass, P. A. & Hauptmann, C. Modeling of a segmented electrode for desynchronizing deep brain stimulation. Front. Neuroeng. 4, 15 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  31. 31.

    Alonso, F., Latorre, M. A., Göransson, N., Zsigmond, P. & Wårdell, K. Investigation into deep brain stimulation lead designs: a patient-specific simulation study. Brain Sci. 6, 39 (2016).

    PubMed Central  Article  Google Scholar 

  32. 32.

    Teplitzky, B. A., Zitella, L. M., Xiao, Y. & Johnson, M. D. Model-based comparison of deep brain stimulation array functionality with varying number of radial electrodes and machine learning feature sets. Front. Comput. Neurosci. 10, 58 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  33. 33.

    Little, S. et al. Bilateral adaptive deep brain stimulation is effective in Parkinson’s disease. J. Neurol. Neurosurg. Psychiatry 87, 717–721 (2016).

    PubMed  Article  PubMed Central  Google Scholar 

  34. 34.

    Starr, P. A. Totally implantable bidirectional neural prostheses: a flexible platform for innovation in neuromodulation. Front. Neurosci. 12, 619 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  35. 35.

    Maling, N., Lempka, S. F., Blumenfeld, Z., Bronte-Stewart, H. & McIntyre, C. C. Biophysical basis of subthalamic local field potentials recorded from deep brain stimulation electrodes. J. Neurophysiol. 120, 1932–1944 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  36. 36.

    Swann, N. C. et al. Adaptive deep brain stimulation for Parkinson’s disease using motor cortex sensing. J. Neural Eng. 15, 046006 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  37. 37.

    Molina, R. et al. Report of a patient undergoing chronic responsive deep brain stimulation for Tourette syndrome: proof of concept. J. Neurosurg. 129, 308–314 (2018).

    PubMed  Article  PubMed Central  Google Scholar 

  38. 38.

    Shute, J. B. et al. Thalamocortical network activity enables chronic tic detection in humans with Tourette syndrome. Neuroimage Clin. 12, 165–172 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  39. 39.

    Bergey, G. K. et al. Long-term treatment with responsive brain stimulation in adults with refractory partial seizures. Neurology 84, 810–817 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  40. 40.

    Miranda, R. A. et al. DARPA-funded efforts in the development of novel brain-computer interface technologies. J. Neurosci. Methods 244, 52–67 (2015).

    PubMed  Article  PubMed Central  Google Scholar 

  41. 41.

    Neely, R. M., Piech, D. K., Santacruz, S. R., Maharbiz, M. M. & Carmena, J. M. Recent advances in neural dust: towards a neural interface platform. Curr. Opin. Neurobiol. 50, 64–71 (2018).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  42. 42.

    Heelan, C., Nurmikko, A. V. & Truccolo, W. FPGA implementation of deep-learning recurrent neural networks with sub-millisecond real-time latency for BCI-decoding of large-scale neural sensors (104 nodes). Conf. Proc. IEEE Eng. Med. Biol. Soc. 2018, 1070–1073 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Shenoy, K. V. & Carmena, J. M. Combining decoder design and neural adaptation in brain-machine interfaces. Neuron 84, 665–680 (2014).

    CAS  Article  Google Scholar 

  44. 44.

    Wheeler, J. J. et al. An implantable 64-channel neural interface with reconfigurable recording and stimulation. in 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 7837–7840 (IEEE, 2015); https://doi.org/10.1109/EMBC.2015.7320208

  45. 45.

    Hamilton, L. et al. Neural signal processing and closed-loop control algorithm design for an implanted neural recording and stimulation system. in 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 7831–7836 (IEEE, 2015); https://doi.org/10.1109/EMBC.2015.7320207

  46. 46.

    Bjune, C. K. et al. Package architecture and component design for an implanted neural stimulator with closed loop control. in 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 7825–7830 (IEEE, 2015); https://doi.org/10.1109/EMBC.2015.7320206

  47. 47.

    Reardon, S. Worldwide brain-mapping project sparks excitement — and concern. Nature 537, 597 (2016).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  48. 48.

    Patil, A. C. & Thakor, N. V. Implantable neurotechnologies: a review of micro- and nanoelectrodes for neural recording. Med. Biol. Eng. Comput. 54, 23–44 (2016).

    PubMed  Article  PubMed Central  Google Scholar 

  49. 49.

    Hong, G. & Lieber, C. M. Novel electrode technologies for neural recordings. Nat. Rev. Neurosci. 19, 199 (2019).

    Google Scholar 

  50. 50.

    Frank, J.A., Antonini, M.-J. & Anikeeva, P. Next-generation interfaces for studying neural function. Nat. Biotechnol. https://doi.org/10.1038/s41587-019-0198-8 (2019).

    CAS  PubMed  Article  Google Scholar 

  51. 51.

    Hubel, D. H. & Wiesel, T. N. Receptive fields of single neurones in the cat’s striate cortex. J. Physiol. (Lond.) 148, 574–591 (1959).

    CAS  Article  Google Scholar 

  52. 52.

    O’Keefe, J. & Dostrovsky, J. The hippocampus as a spatial map. Preliminary evidence from unit activity in the freely-moving rat. Brain Res. 34, 171–175 (1971).

    PubMed  Article  Google Scholar 

  53. 53.

    Hafting, T., Fyhn, M., Molden, S., Moser, M.-B. & Moser, E. I. Microstructure of a spatial map in the entorhinal cortex. Nature 436, 801–806 (2005).

    CAS  Article  Google Scholar 

  54. 54.

    Georgopoulos, A. P., Schwartz, A. B. & Kettner, R. E. Neuronal population coding of movement direction. Science 233, 1416–1419 (1986).

    CAS  Article  Google Scholar 

  55. 55.

    Normann, R. A. & Fernández, E. Clinical applications of penetrating neural interfaces and Utah Electrode Array technologies. J. Neural Eng. 13, 061003 (2016).

    PubMed  Article  Google Scholar 

  56. 56.

    Hochberg, L. R. et al. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature 485, 372–375 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  57. 57.

    Jun, J. J. et al. Fully integrated silicon probes for high-density recording of neural activity. Nature 551, 232–236 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  58. 58.

    Raducanu, B. C. et al. Time multiplexed active neural probe with 1356 parallel recording sites. Sensors (Basel) 17, 2388 (2017).

    Article  Google Scholar 

  59. 59.

    Stringer, C. et al. Spontaneous behaviors drive multidimensional, brainwide activity. Science 364, 255 (2019).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  60. 60.

    Lacour, S. P., Courtine, G. & Guck, J. Materials and technologies for soft implantable neuroprostheses. Nat. Rev. Mater. 1, 16063 (2016).

    CAS  Article  Google Scholar 

  61. 61.

    Chen, R., Canales, A. & Anikeeva, P. Neural recording and modulation technologies. Nat. Rev. Mater. 2, 16093 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  62. 62.

    Polikov, V. S., Tresco, P. A. & Reichert, W. M. Response of brain tissue to chronically implanted neural electrodes. J. Neurosci. Methods 148, 1–18 (2005).

    PubMed  Article  Google Scholar 

  63. 63.

    Kandel, E. Principles of Neural Science 5th edn (McGraw Hill Professional, 2013).

  64. 64.

    Ghane-Motlagh, B. & Sawan, M. Design and implementation challenges of microelectrode arrays: a review. Mater. Sci. Appl. 4, 483–495 (2013).

    Google Scholar 

  65. 65.

    Garcia, J. A., Pena, J. M., McHugh, S. & Jerusalem, A. A model of the spatially dependent mechanical properties of the axon during its growth. Comput. Model. Eng. Sci. 87, 411–432 (2012).

    Google Scholar 

  66. 66.

    Wang, S. S. H. et al. Functional trade-offs in white matter axonal scaling. J. Neurosci. 28, 4047–4056 (2008).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  67. 67.

    Terem, I. et al. Revealing sub-voxel motions of brain tissue using phase-based amplified MRI (aMRI). Magn. Reson. Med. 80, 2549–2559 (2018).

    CAS  PubMed  Article  Google Scholar 

  68. 68.

    Tyler, W. J. The mechanobiology of brain function. Nat. Rev. Neurosci. 13, 867–878 (2012).

    CAS  PubMed  Article  Google Scholar 

  69. 69.

    Kasthuri, N. et al. Saturated reconstruction of a volume of neocortex. Cell 162, 648–661 (2015).

    CAS  PubMed  Article  Google Scholar 

  70. 70.

    Saxena, T. & Bellamkonda, R. V. Implantable electronics: a sensor web for neurons. Nat. Mater. 14, 1190–1191 (2015).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  71. 71.

    Fu, T.-M., Hong, G., Viveros, R. D., Zhou, T. & Lieber, C. M. Highly scalable multichannel mesh electronics for stable chronic brain electrophysiology. Proc. Natl Acad. Sci. USA 114, E10046–E10055 (2017).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  72. 72.

    Hong, G. et al. Syringe injectable electronics: precise targeted delivery with quantitative input/output connectivity. Nano Lett. 15, 6979–6984 (2015).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  73. 73.

    Hong, G. et al. A method for single-neuron chronic recording from the retina in awake mice. Science 360, 1447–1451 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  74. 74.

    Zhou, T. et al. Syringe-injectable mesh electronics integrate seamlessly with minimal chronic immune response in the brain. Proc. Natl Acad. Sci. USA 114, 5894–5899 (2017).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  75. 75.

    Fu, T.-M. et al. Stable long-term chronic brain mapping at the single-neuron level. Nat. Methods 13, 875–882 (2016).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  76. 76.

    Mann, A. et al. Chronic deep brain stimulation in an Alzheimer’s disease mouse model enhances memory and reduces pathological hallmarks. Brain Stimul. 11, 435–444 (2018).

    PubMed  Article  PubMed Central  Google Scholar 

  77. 77.

    Cui, Y., Wei, Q., Park, H. & Lieber, C. M. Nanowire nanosensors for highly sensitive and selective detection of biological and chemical species. Science 293, 1289–1292 (2001).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  78. 78.

    Gao, N. et al. General strategy for biodetection in high ionic strength solutions using transistor-based nanoelectronic sensors. Nano Lett. 15, 2143–2148 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  79. 79.

    Calabresi, P., Picconi, B., Tozzi, A., Ghiglieri, V. & Di Filippo, M. Direct and indirect pathways of basal ganglia: a critical reappraisal. Nat. Neurosci. 17, 1022–1030 (2014).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  80. 80.

    Choi, S. H. et al. Combined adult neurogenesis and BDNF mimic exercise effects on cognition in an Alzheimer’s mouse model. Science 361, eaan8821 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  81. 81.

    Boulanger-Weill, J. et al. Functional interactions between newborn and mature neurons leading to integration into established neuronal circuits. Curr. Biol. 27, 1707–1720.e5 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  82. 82.

    Spitzer, N. C. Electrical activity in early neuronal development. Nature 444, 707–712 (2006).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  83. 83.

    Moore-Kochlacs, C. et al. Principles of high-fidelity, high-density 3-D neural recording. BMC Neurosci. 15, 122 (2014).

    Article  Google Scholar 

  84. 84.

    Guo, L. The pursuit of chronically reliable neural interfaces: a materials perspective. Front. Neurosci. 10, 599 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  85. 85.

    Patolsky, F. et al. Detection, stimulation, and inhibition of neuronal signals with high-density nanowire transistor arrays. Science 313, 1100–1104 (2006).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  86. 86.

    Tian, B. et al. Three-dimensional, flexible nanoscale field-effect transistors as localized bioprobes. Science 329, 830–834 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  87. 87.

    Qing, Q. et al. Free-standing kinked nanowire transistor probes for targeted intracellular recording in three dimensions. Nat. Nanotechnol. 9, 142–147 (2014).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

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Acknowledgements

S.R.P. is supported by the Cure Alzheimer’s Fund and the Henry and Allison McCance Center. C.M.L. acknowledges support of this work by the Air Force Office of Scientific Research (FA9550-14-1-0136) and a National Institutes of Health Director’s Pioneer Award (1DP1EB025835-01).

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Correspondence to Shaun R. Patel or Charles M. Lieber.

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C.M.L. is a co-inventor on patents and patent applications relating to the article that have been filed by the authors’ institution (Harvard University) as follows: ‘Scaffolds comprising nanoelectronic components, tissues, and other applications’, inventors C.M.L., J. Liu, B. Tian, T. Dvir, R. S. Langer and D. S. Kohane; US9,457,128 (issued); describes nanoscale transistors for cell recording. ‘Systems and methods for injectable devices’, inventors C.M.L., J. Liu, Z. Cheng, G. Hong, T.-M. Fu and T. Zhou; 61/975,601 (pending), PCT/US2015/024252 (pending) and 15/301,792 (pending); describes injectable mesh electronics. ‘Techniques and systems for injection and/or connection of electrical devices’, inventors C.M.L., G. Hong, T.-M. Fu and J. Huang; 62/209,255 (pending), PCT/US2016/045587 (issued) and 15/749,617 (pending); describes injection method of mesh electronics. The authors are not involved in efforts related to commercialization of this intellectual property.

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Patel, S.R., Lieber, C.M. Precision electronic medicine in the brain. Nat Biotechnol 37, 1007–1012 (2019). https://doi.org/10.1038/s41587-019-0234-8

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