Advancing Peripheral Nerve Regeneration: 3D Bioprinting of GelMA-Based Cell-Laden Electroactive Bioinks for Nerve Conduits.

Peripheral nerve injuries often result in substantial impairment of the neurostimulatory organs. While the autograft is still largely used as the "gold standard" clinical treatment option, nerve guidance conduits (NGCs) are currently considered a promising approach for promoting peripheral nerve regeneration. While several attempts have been made to construct NGCs using various biomaterial combinations, a comprehensive exploration of the process science associated with three-dimensional (3D) extrusion printing of NGCs with clinically relevant sizes (length: 20 mm; diameter: 2-8 mm), while focusing on tunable buildability using electroactive biomaterial inks, remains unexplored. In addressing this gap, we present here the results of the viscoelastic properties of a range of a multifunctional gelatin methacrylate (GelMA)/poly(ethylene glycol) diacrylate (PEGDA)/carbon nanofiber (CNF)/gellan gum (GG) hydrogel bioink formulations and printability assessment using experiments and quantitative models. Our results clearly established the positive impact of the gellan gum on the enhancement of the rheological properties. Interestingly, the strategic incorporation of PEGDA as a secondary cross-linker led to a remarkable enhancement in the strength and modulus by 3 and 8-fold, respectively. Moreover, conductive CNF addition resulted in a 4-fold improvement in measured electrical conductivity. The use of four-component electroactive biomaterial ink allowed us to obtain high neural cell viability in 3D bioprinted constructs. While the conventionally cast scaffolds can support the differentiation of neuro-2a cells, the most important result has been the excellent cell viability of neural cells in 3D encapsulated structures. Taken together, our findings demonstrate the potential of 3D bioprinting and multimodal biophysical cues in developing functional yet critical-sized nerve conduits for peripheral nerve tissue regeneration.

Next-generation personalized cranioplasty treatment.

Decompressive craniectomy (DC) is a surgical procedure, that is followed by cranioplasty surgery. DC is usually performed to treat patients with traumatic brain injury, intracranial hemorrhage, cerebral infarction, brain edema, skull fractures, etc. In many published clinical case studies and systematic reviews, cranioplasty surgery is reported to restore cranial symmetry with good cosmetic outcomes and neurophysiologically relevant functional outcomes in hundreds of patients. In this review article, we present a number of key issues related to the manufacturing of patient-specific implants, clinical complications, cosmetic outcomes, and newer alternative therapies. While discussing alternative therapeutic treatments for cranioplasty, biomolecules and cellular-based approaches have been emphasized. The current clinical practices in the restoration of cranial defects involve 3D printing to produce patient-specific prefabricated cranial implants, that provide better cosmetic outcomes. Regardless of the advancements in image processing and 3D printing, the complete clinical procedure is time-consuming and requires significant costs. To reduce manual intervention and to address unmet clinical demands, it has been highlighted that automated implant fabrication by data-driven methods can accelerate the design and manufacturing of patient-specific cranial implants. The data-driven approaches, encompassing artificial intelligence (machine learning/deep learning) and E-platforms, such as publicly accessible clinical databases will lead to the development of the next generation of patient-specific cranial implants, which can provide predictable clinical outcomes. STATEMENT OF SIGNIFICANCE: Cranioplasty is performed to reconstruct cranial defects of patients who have undergone decompressive craniectomy. Cranioplasty surgery improves the aesthetic and functional outcomes of those patients. To meet the clinical demands of cranioplasty surgery, accelerated designing and manufacturing of 3D cranial implants are required. This review provides an overview of biomaterial implants and bone flap manufacturing methods for cranioplasty surgery. In addition, tissue engineering and regenerative medicine-based approaches to reduce clinical complications are also highlighted. The potential use of data-driven computer applications and data-driven artificial intelligence-based approaches are emphasized to accelerate the clinical protocols of cranioplasty treatment with less manual intervention and shorter intraoperative time.