Spinal cord injury (SCI) is a debilitating condition that results from a culmination of acute and chronic damage to neural tissue, specifically the myelin sheath, thus impacting neurons’ abilities to synergistically perform their physiological roles. This review explores the molecular underpinnings of myelination, demyelination, and remyelination, emphasizing the role of oligodendrocyte progenitor cells (OPCs), astrocytes, and microglia in physiological, and pathophysiological, healing. Furthermore, we link these processes with emerging therapeutic strategies currently under investigation in animal and human models, underscoring areas of translational medicine that remain underutilized. The goal of this review is to provide a framework for developing more advanced interventions to restore function and improve outcomes for individuals with SCI.

Contemporary advances in bioengineering and materials science have substantially improved the viability of medical implants. The demand for optimized implant technologies has led to the development of advanced coatings that enhance biocompatibility, antimicrobial activity, and durability. Implant manufacturers and surgeons must anticipate both biological and mechanical challenges when implementing devices for patient use. Key areas of concern include infection, corrosion, wear, immune response, and implant rejection; regulatory and economic considerations must also be addressed. Materials science developments are optimizing the integration of established materials such as biometrics, composites, and nanomaterials, while also advancing fabrication-based innovations including plasma functionalization, anodization, and self-assembled monolayers. Emerging smart and stimuli-responsive surface technologies enable controlled drug delivery and real-time implant status communication. These innovations enhance osseointegration, antimicrobial performance, and overall device functionality across orthopedic, dental, and cardiovascular applications. As implant design continues to shift toward personalized, responsive systems, advanced coating technologies are poised to deliver significantly improved long-term clinical outcomes for patients.

Surface engineering and architectural design represent key frontiers in total ankle arthroplasty (TAA) implant development. This narrative review examines biointegration strategies, focusing on porous structures, surface modification techniques, and emerging smart technologies. Optimal porous architectures with 300–600 µm pore sizes facilitate bone ingrowth and osseointegration, while functionally graded structures address regional biomechanical demands. Surface modification encompasses bioactive treatments (such as calcium phosphate coatings), topographical modifications (including micro/nanotexturing), antimicrobial approaches (utilizing metallic ions or antibiotic incorporation), and wear-resistant technologies (such as diamond-like carbon coatings). Multifunctional approaches combine strategies to simultaneously address infection prevention, enhance osseointegration, and improve wear resistance. Emerging technologies include biodegradable scaffolds, biomimetic surface nanotechnology, and intelligent sensor-based monitoring systems. While many innovations remain in the research stage, they demonstrate the potential to establish TAA as a comprehensive alternative to arthrodesis. Successful implant design requires integrated surface engineering tailored to the ankle joint’s demanding biomechanical and biological environment

Total ankle arthroplasty (TAA) has evolved significantly through advances in alloy selection and manufacturing technologies. This narrative review examines the metallurgical foundations of contemporary TAA implants, analyzing primary alloy systems and their mechanical properties. Cobalt-chromium alloys provide superior mechanical strength and durability but present metal ion release concerns, while titanium alloys (Ti6Al4V) optimize biocompatibility with elastic modulus values (101–113 GPa) closer to bone, despite tribological limitations. Novel β-titanium formulations (Ti-35Nb-7Zr-5Ta, Ti10Mo6Zr4Sn3Nb) eliminate toxic aluminum and vanadium components while achieving lower elastic modulus values (50–85 GPa) that better match cortical bone properties. Manufacturing has transitioned from traditional methods (investment casting, forging, CNC machining) toward additive manufacturing technologies. Selective laser melting and electron beam melting enable patient-specific geometries, controlled porosity, and optimized microstructures, though challenges remain with residual stresses, surface finish requirements, and post-processing needs. Emerging biodegradable materials, composite structures, and hybrid implant designs represent promising future directions for addressing current material limitations. This review provides evidence-based insights for alloy selection and manufacturing approaches, emphasizing the critical role of materials engineering in TAA implant performance and clinical outcomes.

“Spinal cord stimulation (SCS)” is an established therapy for chronic pain and an emerging modality for functional recovery after spinal cord injury (SCI) and other clinical applications. Despite its clinical potential, current implantable SCS systems remain constrained by high power consumption, limited adaptability, device size, and long-term stability. Recent advances in closed-loop neuromodulation and integrated circuit (IC) design are enabling more adaptive, efficient, and miniaturized SCS systems. This narrative review focuses on integrated circuit and system-on-chip (SoC) architectures that enable more advanced closed-loop SCS modalities, including stimulation, sensing, and wireless power/data subsystems. Specifically, the present work examines stimulation drivers and charge-balancing circuits that ensure safe and precise current delivery, wireless power transfer, telemetry methods which maintain reliable energy and data flow in implanted systems, and analog front-end circuits that enable closed-loop biopotential monitoring for adaptive feedback control. It aims to highlight innovations in IC design, energy harvesting, and wireless communication strategies while discussing trade-offs in power efficiency, die area, thermal limits, and biocompatibility. Emerging trends emphasize miniaturized and adaptive neural implants that integrate circuit-level efficiency with therapeutic flexibility, thereby advancing the next generation of closed-loop neuromodulation technologies. Ultimately, innovations in microelectronics are paving the way for enhanced long-term efficacy, safety, and clinical applicability of implantable SCS systems to optimize functional impacts.

Background/Objectives: Posterior cervical decompression and fusion (PCDF) carry perioperative risks and increases postoperative healthcare utilization (HU). Traditional prediction models emphasize comorbidities and surgical factors, yet social determinants of health (SDHs) are known to also affect clinical outcomes. We applied machine learning (ML) to integrate SDHs and clinical variables in predicting 90-day readmission and HU after PCDF. Methods: We conducted a retrospective, single-institution machine learning analysis of adult patients undergoing single or multilevel PCDF (2003–2023). Models were designed using 88 clinical variables and five census-derived Social Vulnerability Index (SVI) scores. Outcomes were 90-day readmission and HU (the unweighted sum of 18 post-discharge components including urgent visits, invasive procedures, non-routine testing, and imaging). Models were trained on 50 repeated 80/20 train–test splits with training-only preprocessing and hyperparameter tuning. Results: Among 1015 patients (mean age of 64.7, 59.0% male, 97.2% with multilevel fusions), 90-day readmission was 15.5% and mean HU score was 13.6 ± 9.0. Regarding readmission, the clinical-only logistic regression model was the best predictor (AUROC 0.65). For HU, clinical-only random forest performed best (MAE: 4.80, R²: 0.326). Adding SVI to the matched clinical-plus-SDH models did not improve prediction for either 90-day readmission or healthcare utilization. Length of stay, year of surgery, and several SVI measures were prominent contributors across both SHAP analyses. Conclusions: ML models integrating SDH and clinical factors available by index discharge modestly predicted readmission and HU after PCDF. However, adding SVI did not meaningfully increase their overall performance, and the models require external validation before clinical use.