Tendons and tendon interfaces have a very limited regenerative capacity, rendering their injuries clinically challenging to resolve. Tendons sense muscle-mediated load; however, our knowledge on how loading affects tendon structure and functional adaption remains fragmentary. Here, we provide evidence that the matricellular protein secreted protein acidic and rich in cysteine (SPARC) is critically involved in the mechanobiology of tendons and is required for tissue maturation, homeostasis, and enthesis development. We show that tendon loading at the early postnatal stage leads to tissue hypotrophy and impaired maturation of Achilles tendon enthesis in Sparc−/− mice. Treadmill training revealed a higher prevalence of spontaneous tendon ruptures and a net catabolic adaptation in Sparc−/− mice. Tendon hypoplasia was attenuated in Sparc−/− mice in response to muscle unloading with botulinum toxin A. In vitro culture of Sparc−/− three-dimensional tendon constructs showed load-dependent impairment of ribosomal S6 kinase activation, resulting in reduced type I collagen synthesis. Further, functional calcium imaging revealed that lower stresses were required to trigger mechanically induced responses in Sparc−/− tendon fascicles. To underscore the clinical relevance of the findings, we further demonstrate that a missense mutation (p.Cys130Gln) in the follistatin-like domain of SPARC, which causes impaired protein secretion and type I collagen fibrillogenesis, is associated with tendon and ligament injuries in patients. Together, our results demonstrate that SPARC is a key extracellular matrix protein essential for load-induced tendon tissue maturation and homeostasis.
The combination of computer – aided design (CAD) techniques based on computed tomography (CT) data support the planning of patient – specific implants massively. During the last decade, Geometric Morphometric (GMM) methods utilizing thin – plate splines (TPS) were increasingly used to estimate missing data in clinical cases that are characterized by large skull defects featuring partly and completely missing midsagittal planes. Applying a dedicated reconstruction protocol, GMM methods enable the design of personalized cranial implants that accurately reproduce the bone thickness of the affected area. In this study we applied a TPS based reconstruction approach to design a cranial implant of a clinical case that features the bone thickness distribution in the affected region. This virtual model was subsequently additively manufactured (AM) by fused filament fabrication (FFF) using polyetheretherketone (PEEK). The 3D - printed implant was non – destructively investigated via X-ray microcomputed tomography (XCT). The nominal – actual comparison between the designed virtual model and XCT scan of the final implant showed an average deviation in terms of root mean square (RMS) error of 0.168 mm. Additionally, a pore analysis of the 3D printed implant was carried out showing a porosity of 0.07%.
The combination of computer‐aided design (CAD) techniques based on computed tomography (CT) data to generate patient‐specific implants is in use for decades. However, persisting disadvantages are complicated design procedures and rigid reconstruction protocols, for example, for tailored implants mimicking the patient‐specific thickness distribution of missing cranial bone. In this study we used two different approaches, CAD‐ versus thin‐plate spline (TPS)‐based implants, to reconstruct extensive unilateral and bilateral cranial defects in three clinical cases. We used CT data of three complete human crania that were virtually damaged according to the missing regions in the clinical cases. In total, we carried out 132 virtual reconstructions and quantified accuracy from the original to the generated implant and deviations in the resulting implant thickness as root‐mean‐square error (RMSE). Reconstructions using TPS showed an RMSE of 0.08–0.18 mm in relation to geometric accuracy. CAD‐based implants showed an RMSE of 0.50–1.25 mm. RMSE in relation to implant thickness was between 0.63 and 0.70 mm (TPS) while values for CAD‐based implants were significantly higher (0.63–1.67 mm). While both approaches provide implants showing a high accuracy, the TPS‐based approach additionally provides implants that accurately reproduce the patient‐specific thickness distribution of the affected cranial region.
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