A critical review on the 3D bioprinting in large bone defects regeneration

Shahrezaie, Mostafa; Zamanian, Ali; Sahranavard, Melika; Shahrezaee, Mohammad Hossein

doi:10.1016/j.bprint.2023.e00327

Cited by 2 publications

(4 citation statements)

References 120 publications

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“…At the same time there is an increasing production of reactive oxygen species (ROS), which subsequently contribute the secretion of matrix metalloproteinases (MMP) and ADAMTS (A Disintegrin and Metalloproteinase with Thrombospondin motifs). , Both oxidative stress and inflammation can be involved in the development of osteoporosis by preventing the differentiation of osteoblasts, inducing the differentiation and activity of osteoclasts, enhancing apoptotic osteocytes, and increasing the expression of RANKL and the RANKL/OPG-ratio . However, during the inflammation collagen II is degraded, which is the main component of hyaline cartilage, and this impairs cartilage function and increases osteoclastic activation, which reduces bone healing. , …”

Section: Osteochondral Unitmentioning

confidence: 99%

Section: Osteochondral Unitmentioning

confidence: 99%

“…113 However, during the inflammation collagen II is degraded, which is the main component of hyaline cartilage, and this impairs cartilage function and increases osteoclastic activation, which reduces bone healing. 114,115 The most important studied growth factors involved in osteochondral defects are transforming growth factor (TGFβ), bone morphology factor (BMP), insulin growth factor (IGF), and fibroblast growth factor (FGF), 9 which are involved in bone and cartilage formation and regeneration. The role of vasculature in bone and osteochondral development, growth and repair are well documented.…”

Section: Introductionmentioning

confidence: 99%

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Materials Suitable for Osteochondral Regeneration

Novotná,

Franková

2024

ACS Omega

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Osteochondral defects affect articular cartilage, calcified cartilage, and subchondral bone. The main problem that they cause is a different behavior of cell tissue in the osteochondral and bone part. Articular cartilage is composed mainly of collagen II, glycosaminoglycan (GAG), and water, and has a low healing ability due to a lack of vascularization. However, bone tissue is composed of collagen I, proteoglycans, and inorganic composites such as hydroxyapatite. Due to the discrepancy between the characters of these two parts, it is difficult to find materials that will meet all the structural and other requirements for effective regeneration. When designing a scaffold for an osteochondral defect, a variety of materials are available, e.g., polymers (synthetic and natural), inorganic particles, and extracellular matrix (ECM) components. All of them require the accurate characterization of the prepared materials and a number of in vitro and in vivo tests before they are applied to patients. Taken in concert, the final material needs to mimic the structural, morphological, chemical, and cellular demands of the native tissue. In this review, we present an overview of the structure and composition of the osteochondral part, especially synthetic materials with additives appropriate for healing osteochondral defects. Finally, we summarize in vitro and in vivo methods suitable for evaluating materials for restoring osteochondral defects.

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Section: Osteochondral Unitmentioning

confidence: 99%

Section: Osteochondral Unitmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Materials Suitable for Osteochondral Regeneration

Novotná,

Franková

2024

ACS Omega

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“…The ability to incorporate cells directly into scaffolds during fabrication, a process known as 3D bioprinting, stands out as a unique feature that is nearly impossible to achieve with other scaffold fabrication methods. Additionally, its specific design capabilities, on-demand fabrication, low cost, and the potential for high structural complexity make it a highly desirable method in tissue engineering [3,4]. 3D printing technology has vast applications across multiple areas of tissue engineering, including the fabrication of tissues such as skin, cartilage, liver, lungs, dental, neuronal tissue, and the pancreas [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

A practical machine learning approach for predicting the quality of 3D (bio)printed scaffolds

Rafieyan,

Ansari,

Vasheghani-Farahani

2024

Biofabrication

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3D (Bio)printing is a highly effective method for fabricating tissue engineering scaffolds, renowned for their exceptional precision and control. Artificial intelligence (AI) has become a crucial technology in this field, capable of learning and replicating complex patterns that surpass human capabilities. However, the integration of AI in tissue engineering is often hampered by the lack of comprehensive and reliable data. This study addresses these challenges by providing one of the most extensive datasets on 3D-printed scaffolds. It provides the most comprehensive open-source dataset and employs various AI techniques, from unsupervised to supervised learning. This dataset includes detailed information on 1,171 scaffolds, featuring a variety of biomaterials and concentrations—including 60 biomaterials such as natural and synthesized biomaterials, crosslinkers, enzymes, etc.—along with 49 cell lines, cell densities, and different printing conditions. We used over 40 machine learning and deep learning algorithms, tuning their hyperparameters to reveal hidden patterns and predict cell response, printability, and scaffold quality. The clustering analysis using KMeans identified five distinct ones. In classification tasks, algorithms such as XGBoost, Gradient Boosting, Extra Trees Classifier, Random Forest Classifier, and LightGBM demonstrated superior performance, achieving higher accuracy and F1 scores. A fully connected neural network with six hidden layers from scratch was developed, precisely tuning its hyperparameters for accurate predictions. To promote future research, the developed dataset and the associated code are publicly available on www.github.com/saeedrafieyan/MLATE.

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A critical review on the 3D bioprinting in large bone defects regeneration

Cited by 2 publications

References 120 publications

Materials Suitable for Osteochondral Regeneration

Materials Suitable for Osteochondral Regeneration

A practical machine learning approach for predicting the quality of 3D (bio)printed scaffolds

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