Tissue Engineering. Part B, Reviews最新文献

Developing an in vitro model of gingival connective tissue that mimics the original structure and composition of gingiva for clinical grafting is relevant for personalized treatment of missing gingiva. Using tissue engineering techniques allows bypassing limitations encountered with existing solutions to increase oral soft tissue volume. This review aims to systematically analyze the different currently existing cellularized materials and technologies used to engineer gingival substitutes for in vivo applications. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed. An electronic search on PubMed, Scopus, Web of Science, and Cochrane Library databases was conducted to identify suitable studies. In vivo studies about gingival substitutes and grafts containing oral cells compared with a control to investigate the graft remodeling were included. Risk of bias in the included studies was assessed using the Systematic Review Center for Laboratory animal Experimentation (SYRCLE) 10-item checklist. Out of 631 screened studies, 19 were included. Animal models were mostly rodents, and the most used implantation was subcutaneous. According to the SYRCLE tool, low-to-unclear risk of bias was prevalent. Studies checked vascularization and extracellular remodeling up to 60 days after implantation of the cellularized biomaterial. Cells used were mostly fibroblasts and stem cells from oral origin. Grafts presenting vascularization potential after implantation were produced by tissue engineering technologies including cell seeding or embedding for 14, cell sheets for 2, microsphere for 1, and extrusion 3D bioprinting for 2. Components used to build the scaffold containing the cells are all naturally derived and are mainly fibrin, gelatin, collagen, agarose, alginate, fibroin, guar gum, hyaluronic acid, and decellularized extracellular matrix. The most recurring crosslinking method was using chemicals. All studies except one reported vascularization of the graft after implantation, and some detailed extracellular matrix remodeling. Current solutions are not efficient enough. By assessing the relevant studies on the subject, this systematic review showed that a diversity of cellularized biomaterials substituting gingival connective tissue enables vascularization and extracellular remodeling. Taking the results of this review into account could help improve current bio-inks used in 3D bioprinting for in vivo applications compensating for gingival loss.

The dura mater, the furthest and strongest layer of the meninges, is crucial for protecting the brain and spinal cord. Its biomechanical behavior is vital, as any alterations can compromise biological functions. In recent decades, interest in the dura mater has increased due to the need for hermetic closure of dural defects prompting the development of several substitutes. Collagen-based dural substitutes are common commercial options, but they lack the complex biological and structural elements of the native dura mater, impacting regeneration and potentially causing complications like wound/postoperative infection and cerebrospinal fluid (CSF) leakage. To face this issue, recent tissue engineering approaches focus on creating biomimetic dura mater substitutes. The objective of this review is to discuss whether mimicking the mechanical properties of native tissue or ensuring high biocompatibility and bioactivity is more critical in developing effective dural substitutes, or if both aspects should be systematically linked. After a brief description of the properties and architecture of the native cranial dura, we describe the advantages and limitations of biomimetic dura mater substitutes to better understand their relevance. In particular, we consider biomechanical properties' impact on dura repair's effectiveness. Finally, the obstacles and perspectives for developing the ideal dural substitute are explored.

This research is dedicated to uncovering the evolving trends, progressive developments, and principal research themes in tissue engineering and regenerative medicine for rotator cuff injuries, which spans the past two decades. This article leverages visualization methodology to provide a clear and comprehensive portrayal of the dynamic landscape within the field. We compiled 758 research entries centered on the application of tissue engineering and regenerative medicine in treating rotator cuff injuries, drawing from the Web of Science Core Collection database and covering the period from 2003 to 2023. Analytical tools such as VOSviewer, CiteSpace, and GraphPad Prism were used. We conducted comprehensive analyses to discern the general characteristics, historical evolution, key literature, and pivotal keywords within this research field. This comprehensive analysis enabled us to identify emerging focal points and current trends in the application of tissue engineering and regenerative medicine for addressing rotator cuff injuries. The compilation of 758 articles in this study indicates a consistent upward trajectory in publications concerning tissue engineering and regenerative medicine for rotator cuff injuries. The scholarly contributions from the United States, China, and South Korea have notable influence on the progression of this research area. The analysis delineated ten specific research subdomains, including fatty infiltration, tears, tissue engineering, shoulder pain, tendon repair, extracellular matrix (ECM), and platelet-rich plasma growth factors. Noteworthy is the recurrent mention of keywords such as "mesenchymal stem cells," "repair," and "platelet-rich plasma" throughout past two decades, highlighting their critical role in the evolution of the relevant field. This bibliometric analysis meticulously examines 758 publications, offering an in-depth exploration of the developments in tissue engineering and regenerative medicine for rotator cuff injuries between 2003 and 2023. The study effectively constructs a knowledge map, delineating the progressive contours of research in this domain. By pinpointing prevailing trends and emerging hotspots, the study furnishes crucial insights, setting a direction for forthcoming explorations and providing guidance for future researchers in this evolving field.

Autologous fat grafting is a common procedure in plastic, reconstructive, and aesthetic surgery. However, it is frequently associated with an unpredictable resorption rate of the graft depending on the engraftment kinetics. This, in turn, is determined by the interaction of the grafted adipose tissue with the tissue at the recipient site. Accordingly, preconditioning strategies have been developed following the principle of exposing these tissues in the pretransplantation phase to stimuli inducing endogenous protective and regenerative cellular adaptations, such as the upregulation of stress-response genes or the release of cytokines and growth factors. As summarized in the present review, these stimuli include hypoxia, dietary restriction, local mechanical stress, heat, and exposure to fractional carbon dioxide laser. Preclinical studies show that they promote cell viability, adipogenesis, and angiogenesis, while reducing inflammation, fibrosis, and cyst formation, resulting in a higher survival rate and quality of fat grafts in different experimental settings. Hence, preconditioning represents a promising approach to improve the outcome of fat grafting in future clinical practice. For this purpose, it is necessary to establish standardized preconditioning protocols for specific clinical applications that are efficient, safe, and easy to implement into routine procedures.

Tissue engineering, a crucial approach in medical research and clinical applications, aims to regenerate damaged organs. By combining stem cells, biochemical factors, and biomaterials, it encounters challenges in designing complex 3D structures. Artificial intelligence (AI) enhances tissue engineering through computational modeling, biomaterial design, cell culture optimization, and personalized medicine. This review explores AI applications in organ tissue engineering (bone, heart, nerve, skin, cartilage), employing various machine learning (ML) algorithms for data analysis, prediction, and optimization. Each section discusses common ML algorithms and specific applications, emphasizing the potential and challenges in advancing regenerative therapies.

The healing process after tendon injury is often accompanied by the formation of peritendinous adhesion, contributing to limb dysfunction and exerting detrimental effects on the individuals, as well as the development of society and economy. With the continuous development of material science, as well as the augmented understanding of tendon healing and the mechanism of peritendinous adhesion formation, materials used for the fabrication of barrier membranes against peritendinous adhesion emerge endlessly. In this article, based on the analysis of the mechanism of adhesion formation, we first review the commonly used natural and synthetic materials, along with their corresponding fabrication strategies, in order to furnish valuable insights for the future optimization and development of antiperitendinous adhesion barrier membranes. This article also discusses the interaction between antiadhesion materials and cells for ameliorating peritendinous adhesion.

Peri-implant gingival tissue integration (GTI) is pivotal in determining the long-term success and functionality of dental implants. To enhance GTI, researchers have increasingly focused during the past decade on unraveling the response of gingival tissues to implant surfaces. This increased focus on soft instead of hard tissue integration has led to the development of various models, including in vitro cell culture systems and in vivo animal models, designed to predict and assess GTI around dental implants. However, inconsistent study outcomes between the different models have created confusion, highlighting the need for a comprehensive review. Therefore, the main objective of this review is to present a comprehensive overview of existing in vitro models, ranging from 2D to 3D, specifically designed to investigate cellular behavior relevant to peri-implant GTI. To facilitate a better comprehension of the utility of these models, the review initiates an elucidation of the histological characteristics of gingival tissues surrounding natural dentition, offering insights into the healing dynamics and histological adaptation processes occurring in gingival tissues adjacent to dental implants. In addition, through a critical evaluation of the strengths and limitations inherent in each model, our aim is to contribute to a more profound understanding of their applicability and effectiveness in GTI research.