Anterior Cruciate Ligament: Structure, Injuries and Regenerative Treatments

Topographically and Chemically Enhanced Textile Polycaprolactone Scaffolds for Tendon and Ligament Tissue Engineering

The use of tissue engineering to address the shortcomings of current procedures for tendons and ligaments is promising, but it requires a suitable scaffold that meets various mechanical, degradation-related, scalability-related, and biological requirements. Macroporous textile scaffolds made from appropriate fiber material have the potential to fulfill the first three requirements. This study aimed to investigate the biocompatibility, sterilizability, and functionalizability of a multilayer braided scaffold. These macroporous scaffolds with dimensions similar to those of the human anterior cruciate ligament consist of fibers with appropriate tensile strength and degradation behavior melt-spun from Polycaprolactone (PCL). Two different cross-sectional geometries resulting in significantly different specific surface areas and morphologies were used at the fiber level, and a Chitosan-graft-PCL (CS-g-PCL) surface modification was applied to the melt-spun substrates for the first time. All scaffolds elicited a positive cell response, and the CS-g-PCL modification provided a platform for incorporating functionalization agents such as drug delivery systems for growth factors, which were successfully released in therapeutically effective quantities. The fiber geometry was found to be a variable that could be manipulated to control the amount released. Therefore, scaled, surface-modified textile scaffolds are a versatile technology that can successfully address the complex requirements of tissue engineering for ligaments and tendons, as well as other structures.

Application of machine learning-based multi-sequence MRI radiomics in diagnosing anterior cruciate ligament tears

OBJECTIVE

To compare the diagnostic power among various machine learning algorithms utilizing multi-sequence magnetic resonance imaging (MRI) radiomics in detecting anterior cruciate ligament (ACL) tears. Additionally, this research aimed to create and validate the optimal diagnostic model.

METHODS

In this retrospective analysis, 526 patients were included, comprising 178 individuals with ACL tears and 348 with a normal ACL. Radiomics features were derived from multi-sequence MRI scans, encompassing T1-weighted imaging and proton density (PD)-weighted imaging. The process of selecting the most reliable radiomics features involved using interclass correlation coefficient (ICC) testing, t tests, and the least absolute shrinkage and selection operator (LASSO) technique. After the feature selection process, five machine learning classifiers were created. These classifiers comprised logistic regression (LR), support vector machine (SVM), K-nearest neighbors (KNN), light gradient boosting machine (LightGBM), and multilayer perceptron (MLP). A thorough performance evaluation was carried out, utilizing diverse metrics like the area under the receiver operating characteristic curve (ROC), specificity, accuracy, sensitivity positive predictive value, and negative predictive value. The classifier exhibiting the best performance was chosen. Subsequently, three models were developed: the PD model, the T1 model, and the combined model, all based on the optimal classifier. The diagnostic performance of these models was assessed by employing AUC values, calibration curves, and decision curve analysis.

RESULTS

Out of 2032 features, 48 features were selected. The SVM-based multi-sequence radiomics outperformed all others, achieving AUC values of 0.973 and 0.927, sensitivities of 0.933 and 0.857, and specificities of 0.930 and 0.829, in the training and validation cohorts, respectively.

CONCLUSION

The multi-sequence MRI radiomics model, which is based on machine learning, exhibits exceptional performance in diagnosing ACL tears. It provides valuable insights crucial for the diagnosis and treatment of knee joint injuries, serving as an accurate and objective supplementary diagnostic tool for clinical practitioners.

Effective automatic detection of anterior cruciate ligament injury using convolutional neural network with two attention mechanism modules

BACKGROUND

To develop a fully automated CNN detection system based on magnetic resonance imaging (MRI) for ACL injury, and to explore the feasibility of CNN for ACL injury detection on MRI images.

METHODS

Including 313 patients aged 16 - 65 years old, the raw data are 368 pieces with injured ACL and 100 pieces with intact ACL. By adding flipping, rotation, scaling and other methods to expand the data, the final data set is 630 pieces including 355 pieces of injured ACL and 275 pieces of intact ACL. Using the proposed CNN model with two attention mechanism modules, data sets are trained and tested with fivefold cross-validation.

RESULTS

The performance is evaluated using accuracy, precision, sensitivity, specificity and F1 score of our proposed CNN model, with results of 0.8063, 0.7741, 0.9268, 0.6509 and 0.8436. The average accuracy in the fivefold cross-validation is 0.8064. For our model, the average area under curves (AUC) for detecting injured ACL has results of 0.8886.

CONCLUSION

We propose an effective and automatic CNN model to detect ACL injury from MRI of human knees. This model can effectively help clinicians diagnose ACL injury, improving diagnostic efficiency and reducing misdiagnosis and missed diagnosis.

Magnetic resonance imaging assessments for knee segmentation and their use in combination with machine/deep learning as predictors of early osteoarthritis diagnosis and prognosis

Knee osteoarthritis (OA) is a prevalent and disabling disease that can develop over decades. This disease is heterogeneous and involves structural changes in the whole joint, encompassing multiple tissue types. Detecting OA before the onset of irreversible changes is crucial for early management, and this could be achieved by allowing knee tissue visualization and quantifying their changes over time. Although some imaging modalities are available for knee structure assessment, magnetic resonance imaging (MRI) is preferred. This narrative review looks at existing literature, first on MRI-developed approaches for evaluating knee articular tissues, and second on prediction using machine/deep-learning-based methodologies and MRI as input or outcome for early OA diagnosis and prognosis. A substantial number of MRI methodologies have been developed to assess several knee tissues in a semi-quantitative and quantitative fashion using manual, semi-automated and fully automated systems. This dynamic field has grown substantially since the advent of machine/deep learning. Another active area is predictive modelling using machine/deep-learning methodologies enabling robust early OA diagnosis/prognosis. Moreover, incorporating MRI markers as input/outcome in such predictive models is important for a more accurate OA structural diagnosis/prognosis. The main limitation of their usage is the ability to move them in rheumatology practice. In conclusion, MRI knee tissue determination and quantification provide early indicators for individuals at high risk of developing this disease or for patient prognosis. Such assessment of knee tissues, combined with the development of models/tools from machine/deep learning using, in addition to other parameters, MRI markers for early diagnosis/prognosis, will maximize opportunities for individualized risk assessment for use in clinical practice permitting precision medicine. Future efforts should be made to integrate such prediction models into open access, allowing early disease management to prevent or delay the OA outcome.

Advanced Gene Therapy Strategies for the Repair of ACL Injuries

The anterior cruciate ligament (ACL), the principal ligament for stabilization of the knee, is highly predisposed to injury in the human population. As a result of its poor intrinsic healing capacities, surgical intervention is generally necessary to repair ACL lesions, yet the outcomes are never fully satisfactory in terms of long-lasting, complete, and safe repair. Gene therapy, based on the transfer of therapeutic genetic sequences via a gene vector, is a potent tool to durably and adeptly enhance the processes of ACL repair and has been reported for its workability in various experimental models relevant to ACL injuries in vitro, in situ, and in vivo. As critical hurdles to the effective and safe translation of gene therapy for clinical applications still remain, including physiological barriers and host immune responses, biomaterial-guided gene therapy inspired by drug delivery systems has been further developed to protect and improve the classical procedures of gene transfer in the future treatment of ACL injuries in patients, as critically presented here.

3D-Braided Poly-ε-Caprolactone-Based Scaffolds for Ligament Tissue Engineering

The anterior cruciate ligament (ACL) is the most commonly injured intra-articular ligament of the knee. Due to its limited intrinsical healing potential and vascularization, injuries of the ACL do not heal satisfactorily, and surgical intervention is usually required. The limitations of existing reconstructive grafts and autologous transplants have prompted interest in tissue-engineered solutions. A tissue engineering scaffold for ACL reconstruction must be able to mimic the mechanical properties of the native ligament, provide sufficient porosity to promote cell growth of the neoligament tissue, and be biodegradable. This study investigates long-term biodegradable poly-ε-caprolactone (PCL)-based scaffolds for ACL replacement using the 3D hexagonal braiding technique. The scaffolds were characterized mechanically as well as morphologically. All scaffolds, regardless of their braid geometry, achieved the maximum tensile load of the native ACL. The diameter of all scaffolds was lower than that of the native ligament, making the scaffolds implantable with established surgical methods. The 3D hexagonal braiding technique offers a high degree of geometrical freedom and, thus, the possibility to develop novel scaffold architectures. Based on the findings of this study, the 3D-braided PCL-based scaffolds studied were found to be a promising construct for tissue engineering of the anterior cruciate ligament.

Melt-Spun, Cross-Section Modified Polycaprolactone Fibers for Use in Tendon and Ligament Tissue Engineering

Tissue Engineering is considered a promising route to address existing deficits of autografts and permanent synthetic prostheses for tendons and ligaments. However, the requirements placed on the scaffold material are manifold and include mechanical, biological and degradation-related aspects. In addition, scalable processes and FDA-approved materials should be applied to ensure the transfer into clinical practice. To accommodate these aspects, this work focuses on the high-scale fabrication of high-strength and highly oriented polycaprolactone (PCL) fibers with adjustable cross-sectional geometry and degradation kinetics applying melt spinning technology. Four different fiber cross-sections were investigated to account for potential functionalization and cell growth guidance. Mechanical properties and crystallinity were studied for a 24-week exposure to phosphate-buffered saline (PBS) at 37 °C. PCL fibers were further processed into scaffolds using multistage circular braiding with three different hierarchical structures. One structure was selected based on its morphology and scaled up in thickness to match the requirements for a human anterior cruciate ligament (ACL) replacement. Applying a broad range of draw ratios (up to DR9.25), high-strength PCL fibers with excellent tensile strength (up to 69 cN/tex) could be readily fabricated. The strength retention after 24 weeks in PBS at 37 °C was 83–93%. The following braiding procedure did not affect the scaffolds’ mechanical properties as long as the number of filaments and the braiding angle remained constant. Up-scaled PCL scaffolds resisted loads of up to 4353.88 ± 37.30 N, whilst matching the stiffness of the human ACL (111–396 N/mm). In conclusion, this work demonstrates the fabrication of highly oriented PCL fibers with excellent mechanical properties. The created fibers represent a promising building block that can be further processed into versatile textile implants for tissue engineering and regenerative medicine.

Fibrous Systems as Potential Solutions for Tendon and Ligament Repair, Healing, and Regeneration

Tendon and ligament injuries caused by trauma and degenerative diseases are frequent and affect diverse groups of the population. Such injuries reduce musculoskeletal performance, limit joint mobility, and lower people's comfort. Currently, various treatment strategies and surgical procedures are used to heal, repair, and restore the native tissue function. However, these strategies are inadequate and, in some cases, fail to re-establish the lost functionality. Tissue engineering and regenerative medicine approaches aim to overcome these disadvantages by stimulating the regeneration and formation of neotissues. Design and fabrication of artificial scaffolds with tailored mechanical properties are crucial for restoring the mechanical function of tendons. In this review, the tendon and ligament structure, their physiology, and performance are presented. On the other hand, the requirements are focused for the development of an effective reconstruction device. The most common fiber-based scaffolding systems are also described for tendon and ligament tissue regeneration like strand fibers, woven, knitted, braided, and braid-twisted fibrous structures, as well as electrospun and wet-spun constructs, discussing critically the advantages and limitations of their utilization. Finally, the potential of multilayered systems as the most effective candidates for tendon and ligaments tissue engineering is pointed out.

Deep Learning Approach for Anterior Cruciate Ligament Lesion Detection: Evaluation of Diagnostic Performance Using Arthroscopy as the Reference Standard

BACKGROUND

MRI is the most commonly used imaging method for diagnosing anterior cruciate ligament (ACL) injuries. However, the interpretation of knee MRI is time-intensive and depends on the clinical experience of the reader. An automated detection system based on a deep-learning algorithm may improve interpretation time and reliability.

PURPOSE

To determine the feasibility of using a deep learning approach to detect ACL injuries within the knee joint on MRI.

STUDY TYPE

Retrospective.

POPULATION

In all, 163 subjects with an ACL tear and 245 subjects with an intact ACL. There were 285, 81, and 42 volumes for training, validation, and test sets, respectively.

FIELD STRENGTH/SEQUENCE

2D sagittal proton density-weighted spectral attenuated inversion recovery sequences at 1.5T and 3.0T.

ASSESSMENT

Based on the architecture of 3D DenseNet, we constructed a classification convolutional neural network. We tested this deep learning approach with different inputs and two other algorithms, including VGG16 and ResNet. Then we had both inexperienced radiologists and senior radiologists read the MR images.

STATISTICAL TESTS

Using arthroscopic results as the reference standard, the performance of three different inputs and three different algorithms, the residents and senior radiologists assessed the classification accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC).

RESULTS

The accuracy, sensitivity, specificity, PPV, and NPV of our customized 3D deep learning architecture was 0.957, 0.976, 0.944, 0.940, and 0.976, respectively. The average AUCs were 0.946, 0.859, 0.960 for ResNet, VGG16, and our proposed network, respectively. The diagnostic accuracy of our model, residents, and senior radiologists was 0.957, 0.814, and 0.899, respectively.

DATA CONCLUSION

Our study demonstrated the feasibility of using an automated deep-learning-based detection system to evaluate ACL injury.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY STAGE: 1 J. MAGN. RESON. IMAGING 2020;52:1745-1752.

Cruciate ligament healing and injury prevention in the age of regenerative medicine and technostress: homeostasis revisited

PURPOSE

This clinical concepts paper discusses the essential elements of cruciate ligament recuperation, micro-trauma repair, and remodeling.

METHODS

Cruciate ligament mechanobiology and tissue heterogeneity, anatomy and vascularity, and synovial membrane and fluid functions are discussed in relationship to deficiency-induced inflammatory responses, nervous and immune system function, recuperation, repair and remodeling, and modern threats to homeostasis.

RESULTS

Cruciate ligament surgical procedures do not appreciate the vital linked functions of the central, peripheral, and autonomic nervous systems and immune system function on knee ligament injury recuperation, micro-trauma repair, and remodeling. Enhanced knowledge of these systems could provide innovative ways to decrease primary non-contact knee injury rates and improve outcomes following reconstruction or primary repair.

CONCLUSIONS

Restoration of knee joint homeostasis is essential to cruciate ligament recuperation, micro-trauma repair, and remodeling. The nervous and immune systems are intricately involved in this process. Varying combinations of high-intensity training, under-recovery, technostress, and environmental pollutants (including noise) regularly expose many athletically active individuals to factors that abrogate the environment needed for cruciate ligament recuperation, micro-trauma repair, and remodeling. Current sports training practice, lifestyle psychobehaviors, and environmental factors combine to increase both primary non-contact knee injury risk and the nervous and immune system dysregulation that lead to poor sleep, increased anxiety, and poorly regulated hormone and cytokine levels. These factors may create a worst-case scenario leading to poor ligament recuperation, micro-trauma repair, and remodeling. Early recognition and modification of these factors may decrease knee ligament injury rates and improve cruciate ligament repair or reconstruction outcomes.

LEVEL OF EVIDENCE

V.

Viscoelastic Behavior of Embroidered Scaffolds for ACL Tissue Engineering Made of PLA and P(LA-CL) After In Vitro Degradation

A rupture of the anterior cruciate ligament (ACL) is the most common knee ligament injury. Current applied reconstruction methods have limitations in terms of graft availability and mechanical properties. A new approach could be the use of a tissue engineering construct that temporarily reflects the mechanical properties of native ligament tissues and acts as a carrier structure for cell seeding. In this study, embroidered scaffolds composed of polylactic acid (PLA) and poly(lactic-co-ε-caprolactone) (P(LA-CL)) threads were tested mechanically for their viscoelastic behavior under in vitro degradation. The relaxation behavior of both scaffold types (moco: mono-component scaffold made of PLA threads, bico: bi-component scaffold made of PLA and P(LA-CL) threads) was comparable to native lapine ACL. Most of the lapine ACL cells survived 32 days of cell culture and grew along the fibers. Cell vitality was comparable for moco and bico scaffolds. Lapine ACL cells were able to adhere to the polymer surfaces and spread along the threads throughout the scaffold. The mechanical behavior of degrading matrices with and without cells showed no significant differences. These results demonstrate the potential of embroidered scaffolds as an ACL tissue engineering approach.

Gene Delivery to Joints by Intra-Articular Injection

Most forms of arthritis are incurable, difficult to treat, and a major cause of disability in Western countries. Better local treatment of arthritis is impaired by the pharmacokinetics of the joint that make it very difficult to deliver drugs to joints at sustained, therapeutic concentrations. This is especially true of biologic drugs, such as proteins and RNA, many of which show great promise in preclinical studies. Gene transfer provides a strategy for overcoming this limitation. The basic concept is to deliver cDNAs encoding therapeutic products by direct intra-articular injection, leading to sustained, endogenous synthesis of the gene products within the joint. Proof of concept has been achieved for both in vivo and ex vivo gene delivery using a variety of vectors, genes, and cells in several different animal models. There have been a small number of clinical trials for rheumatoid arthritis (RA) and osteoarthritis (OA) using retrovirus vectors for ex vivo gene delivery and adeno-associated virus (AAV) for in vivo delivery. AAV is of particular interest because, unlike other viral vectors, it is able to penetrate deep within articular cartilage and transduce chondrocytes in situ. This property is of particular importance in OA, where changes in chondrocyte metabolism are thought to be fundamental to the pathophysiology of the disease. Authorities in Korea have recently approved the world's first arthritis gene therapy. This targets OA by the injection of allogeneic chondrocytes that have been transduced with a retrovirus carrying transforming growth factor-β₁ cDNA. Phase III studies are scheduled to start in the United States soon. Meanwhile, two additional Phase I trials are listed on Clinicaltrials.gov , both using AAV. One targets RA by transferring interferon-β, and the other targets OA by transferring interleukin-1 receptor antagonist. The field is thus gaining momentum and promises to improve the treatment of these common and debilitating diseases.

Baseline cartilage quality is associated with voxel-based T1ρ and T2 following ACL reconstruction: A multicenter pilot study

In this multi-center study, voxel-based relaxometry (VBR), a novel technique to automatically quantify localized cartilage change, was used to investigate T_1ρ and T₂ relaxation times of patients with anterior cruciate ligament (ACL) tears at the time of injury and 6 months after reconstructive surgery. Sixty-four ACL-injured patients from three sites underwent bilateral 3T MR T_1ρ and T₂ mapping; 56 patients returned 6 months after surgery. Cross-sectional and longitudinal VBR comparisons of relaxation times were calculated. Noyes Score (NS) clinical grades of cartilage lesions were noted at both times and correlated with relaxation times. Lastly, patients were divided into two groups based on baseline NS grades in the injured knee. T_1ρ times of each group were assessed with VBR and compared. Results illustrate the feasibility of VBR for efficiently analyzing data from patients at different sites. Significant relaxation time elevations at baseline were observed in the injured knee compared to the uninjured, particularly in the posterolateral tibia (pLT). Longitudinally, a decrease was observed in the pLT and patella, while an increase was noted in the trochlea. Stratifying patients by baseline lesion presence revealed T_1ρ increased more 6 months after surgery in patients with lesions. Such findings propose that the presence of cartilage lesions at baseline are associated with the longitudinal progression of T_1ρ and T₂ after ACL injury, and may contribute to early cartilage degeneration. Furthermore, the speed and localized specificity of automatic VBR analysis may translate well for clinical application, as seen in this multicenter study. © 2016 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 35:688-698, 2017.