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Machine Intelligence for Nerve Conduit Design and Production Louisiana State University LSU Digital Commons Department of Biological & Agricultural Faculty Publications Engineering 9-9-2020 Machine intelligence for nerve conduit design and production Caleb E. Stewart Chin Fung Kelvin Kan Brody R. Stewart Henry W. Sanicola III Jangwook P. Jung See next page for additional authors Follow this and additional works at: https://digitalcommons.lsu.edu/bio_engineering_pubs Part of the Biomedical Engineering and Bioengineering Commons Authors Caleb E. Stewart, Chin Fung Kelvin Kan, Brody R. Stewart, Henry W. Sanicola III, Jangwook P. Jung, Olawale A. R. Sulaiman, and Dadong Wang Stewart et al. Journal of Biological Engineering (2020) 14:25 https://doi.org/10.1186/s13036-020-00245-2 REVIEW Open Access Machine intelligence for nerve conduit design and production Caleb E. Stewart1* , Chin Fung Kelvin Kan2, Brody R. Stewart3, Henry W. Sanicola III1, Jangwook P. Jung4, Olawale A. R. Sulaiman5,6* and Dadong Wang7* Abstract Nerve guidance conduits (NGCs) have emerged from recent advances within tissue engineering as a promising alternative to autografts for peripheral nerve repair. NGCs are tubular structures with engineered biomaterials, which guide axonal regeneration from the injured proximal nerve to the distal stump. NGC design can synergistically combine multiple properties to enhance proliferation of stem and neuronal cells, improve nerve migration, attenuate inflammation and reduce scar tissue formation. The aim of most laboratories fabricating NGCs is the development of an automated process that incorporates patient-specific features and complex tissue blueprints (e.g. neurovascular conduit) that serve as the basis for more complicated muscular and skin grafts. One of the major limitations for tissue engineering is lack of guidance for generating tissue blueprints and the absence of streamlined manufacturing processes. With the rapid expansion of machine intelligence, high dimensional image analysis, and computational scaffold design, optimized tissue templates for 3D bioprinting (3DBP) are feasible. In this review, we examine the translational challenges to peripheral nerve regeneration and where machine intelligence can innovate bottlenecks in neural tissue engineering. Keywords: Bioprinting, Data science, Tissue engineering, Computer vision, Nerve regeneration, Machine learning, Artificial intelligence Background—critical challenges in (re)innervation for nerve regeneration. Many challenges exist in clinical Peripheral nerve injuries (PNI) are a common source of research to produce a conduit that meets or exceeds the disability that originate from traumatic, nontraumatic, performance of autografts for treatment of short and and iatrogenic causes [1–3]. Advancements made in tis- long gap nerve injuries. From a clinical research stand- sue engineering have led to the emergence of nerve point, Sun et al. have documented a recent rise in ran- guidance conduits (NGC) that offer a promising replace- domized control trials (RCTs) for peripheral nerve ment for autografts [4]. Nerve guides are tubular bios- repair but suboptimal quality of systematic reviews on tructures designed to house growth factors and neural the subject [5]. They found the number of annual sys- progenitor stem cells in a microenvironment conducive tematic reviews increased from 2004 to 2015 but median scores rated fair in quality throughout this period [5]. Establishing NGC superiority over traditional treatments * Correspondence: [email protected]; [email protected]; [email protected] will be a major challenge considering evidence-based 1Current Affiliation: Department of Neurosurgery, Louisiana State University medicine (EBM) faces similar problems determining the Health Sciences Center, Shreveport, Louisiana, USA effectiveness of standard peripheral nerve repair [5, 6]. 5Ochsner Neural Injury & Regeneration Laboratory, Ochsner Clinic Foundation, New Orleans, LA 70121, USA In particular, PNI categories cover a large domain that 7Quantitative Imaging Research Team, Data 61, Commonwealth Scientific includes nerve types (sensory, motor, both), mechanisms and Industrial Research Organization, Marsfield, NSW 2122, Australia (stretch, crush, percussion, laceration), anatomical Full list of author information is available at the end of the article © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Stewart et al. Journal of Biological Engineering (2020) 14:25 Page 2 of 19 regions (plexuses, nerve root, extremities), and anatom- less probable [24, 25]. This injury is referred to as the ical variants. Tissue engineers need to consider these least severe form of neurotmesis. Grade III injuries re- standards among others when designing nerve conduits cover partially and do not require surgical repair. Grades and scaffolds. This makes a transdisciplinary approach IV and V represent PNIs requiring surgical intervention attractive since experts from the fields of engineering, to restore function. Grade IV are the result of damage to physics, computer science, and medicine can blend their the endoneurium and perineurium. While, Grade V is faculties into a comprehensive biomanufacturing process loss of all three layers ensheathing the nerve [22, 25].. and product. (Reproduced with permission from reference 19. Nerve conduits provide a customizable solution for Copyright Elsevier [18]) both repair options by tailoring conduit features to patient-specific injuries to enhance the repair of acute or Current strategies of NGC Design in Tissue Engineering chronic injuries. Narayan et al. performed a systematic Here we categorize current strategies of NGC design review and meta-analysis of three randomized control with integrating: 1) biological modulation 2) engineering trials (RCTs) comparing conduits with conventional approaches and 3) surgical intervention. nerve repair [7]. Three RCTs showed conduits were sig- nificantly more effective compared to standard end-to- end suture repair for short gap (< 10 mm) sensory nerve Biological modulation injuries [7]. More studies are required to assess the ef- fectiveness of conduits for motor and mixed nerves in- a. Gene delivery cluding cranial nerves and complex nerve plexuses. From a tissue engineering perspective, many novel fea- Gene therapy has developed novel therapeutics to treat tures have been utilized for peripheral nerve regener- peripheral nerve insults [26]. Gene therapeutic strategies ation [8], specifically, neurotrophic factors and extend to but are not limited to eliminating toxic pro- anisotropic gradients, electrical stimulation [9], pluripo- teins at injured sites, activating regenerative phenotypes tent stem cell derived progenitor cells [10, 11], 3DBP in chronic nerve injuries, increasing expression of thera- [12], immuobioengineering [13], nerve differentiation peutic signals in cellular components in nerve regener- strategies, simultaneous vascularization, design ation, increasing sensitivity to cell to cell customization [14], and gene therapy [15–17]. Here we communication, and programming stem cells to differ- briefly reviewed to identify points of intersection be- entiate in a specific manner [26]. Novel studies have im- tween tissue engineering and machine intelligence with proved transduction efficiency and genetic transfer using a particular concentration on peripheral nerve regener- both non-viral and adenovirus methods [27–29]. Adeno- ation. We utilize these examples along with current en- associated viral (AAV) vectors have become a popular gineering challenges encountered in peripheral nerve method for gene delivery in peripheral nerves because of regeneration to postulate where machine intelligence their low risk for immunogenicity, mutagenesis, and can complement biofabrication in product performance, higher titers [30–32]. Different viral serotypes display additive manufacturing (AM) processes, and medical unique transduction profiles and perform better in spe- regulatory compliance. cific neurons [33, 34]. For example, AAV5 is the pre- ferred serotype for treating sensory neurons in rat Classification of nerve injuries models [34]. AAV vectors have been effective tools for In 1951, the Sunderland classification system became identifying the effects of particular genes on regeneration (Fig. 1) the preferred PNI grading system, since it makes and survival of motor and sensory neurons [35–38]. better clinical prognostications and directs appropriate Gene therapy can target Schwann cells whose regen- therapy [19–21]. Sunderland identified
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