Chondroprotective effects of Apolipoprotein D in knee osteoarthritis mice through the PI3K/AKT/mTOR signaling pathway.

BACKGROUND: Because the pathophysiology of osteoarthritis (OA) has not been fully elucidated, targeted treatments are lacking. In this study, we assessed the role and underlying mechanism apolipoprotein D (APOD) on the development of OA. METHODS: To establish an in vitro OA model, we extracted primary chondrocytes from the cartilage of C57BL/6 mice and stimulated the chondrocytes with IL-1ß. After APOD intervention or incubation with an overexpressing plasmid, we detected inflammatory-related markers using RT-qPCR, Western blotting, and ELISA. To detect apoptosis and autophagy-related markers, we used flow cytometry, immunofluorescence, and transmission electron microscopy (TEM). Finally, we measured the level of oxidative stress. We also used RNA-seq to identify the APOD-regulated downstream signaling pathways. We used an in vivo mice OA model of the anterior cruciate ligament transection (ACLT) and administered intra-articular adenovirus overexpressing APOD. To examine cartilage damage severity, we used immunohistochemical analysis (IHC), micro-CT, scanning electron microscopy (SEM), and Safranin O-fast green staining. RESULTS: Our results showed that APOD inhibited chondrocyte inflammation, degeneration, and apoptosis induced by IL-1ß. Additionally, APOD reversed autophagy inhibition and oxidative stress and also blocked activation of the PI3K/AKT/mTOR signaling pathway induced by IL-1ß. Finally, overexpression of the APOD gene through adenovirus was sufficient to mitigate OA progression. CONCLUSIONS: Our findings revealed that APOD had a chondroprotective role in OA progression by the PI3K/AKT/mTOR signaling pathway.

Automated measurement and grading of knee cartilage thickness: a deep learning-based approach.

Background: Knee cartilage is the most crucial structure in the knee, and the reduction of cartilage thickness is a significant factor in the occurrence and development of osteoarthritis. Measuring cartilage thickness allows for a more accurate assessment of cartilage wear, but this process is relatively time-consuming. Our objectives encompass using various DL methods to segment knee cartilage from MRIs taken with different equipment and parameters, building a DL-based model for measuring and grading knee cartilage, and establishing a standardized database of knee cartilage thickness. Methods: In this retrospective study, we selected a mixed knee MRI dataset consisting of 700 cases from four datasets with varying cartilage thickness. We employed four convolutional neural networks-UNet, UNet++, ResUNet, and TransUNet-to train and segment the mixed dataset, leveraging an extensive array of labeled data for effective supervised learning. Subsequently, we measured and graded the thickness of knee cartilage in 12 regions. Finally, a standard knee cartilage thickness dataset was established using 291 cases with ages ranging from 20 to 45 years and a Kellgren-Lawrence grading of 0. Results: The validation results of network segmentation showed that TransUNet performed the best in the mixed dataset, with an overall dice similarity coefficient of 0.813 and an Intersection over Union of 0.692. The model's mean absolute percentage error for automatic measurement and grading after segmentation was 0.831. The experiment also yielded standard knee cartilage thickness, with an average thickness of 1.98 mm for the femoral cartilage and 2.14 mm for the tibial cartilage. Conclusion: By selecting the best knee cartilage segmentation network, we built a model with a stronger generalization ability to automatically segment, measure, and grade cartilage thickness. This model can assist surgeons in more accurately and efficiently diagnosing changes in patients' cartilage thickness.

APOD acts on fibroblast-like synoviocyte and chondrocyte to alleviate the process of osteoarthritis in vitro.

The pathogenesis of osteoarthritis (OA) is still unclear, leading to the lack of targeted treatment. We aimed to probe into the effect of apolipoprotein D (APOD), the key gene from our previous study through bioinformatics analysis, on fibroblast-like synoviocyte (FLS) and chondrocytes in vitro to confirm its potential roles on the delay of OA progression. Primary FLS and chondrocytes were extracted from synovium and cartilage of OA patients and stimulated with interleukin 1ß (IL-1ß) in vitro. After APOD intervention, viability and proliferation of FLS and chondrocytes were detected. Subsequently, the inflammatory factors of the two cells were detected by quantitative reverse-transcription polymerase chain reaction, enzyme-linked immunosorbent assay, and western blot, and the apoptosis and autophagy-related substances were determined at the same time. Finally, the oxidation level in FLS and chondrocytes were detected. APOD reversed the change of gene expression stimulated by IL-1ß in FLS and chondrocytes. APOD alleviated the proliferation of FLS while promoted proliferation of chondrocytes, and reduced the expression of inflammatory factors. Moreover, APOD promoted apoptosis of FLS and autography of chondrocytes, while reduced apoptosis of chondrocytes. Finally, decrease level of reactive oxygen species (ROS) in both cells were observed after APOD intervention, as well as the increased expression of antioxidant-related genes. APOD had effects on the proliferation of FLS and chondrocytes through apoptosis and autography as well as the reduction of oxidative stress, delaying the progress of OA.

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.

Visual Cascaded-Progressive Convolutional Neural Network (C-PCNN) for Diagnosis of Meniscus Injury.

OBJECTIVE: The objective of this study is to develop a novel automatic convolutional neural network (CNN) that aids in the diagnosis of meniscus injury, while enabling the visualization of lesion characteristics. This will improve the accuracy and reduce diagnosis times. METHODS: We presented a cascaded-progressive convolutional neural network (C-PCNN) method for diagnosing meniscus injuries using magnetic resonance imaging (MRI). A total of 1396 images collected in the hospital were used for training and testing. The method used for training and testing was 5-fold cross validation. Using intraoperative arthroscopic diagnosis and MRI diagnosis as criteria, the C-PCNN was evaluated based on accuracy, sensitivity, specificity, receiver operating characteristic (ROC), and evaluation performance. At the same time, the diagnostic accuracy of doctors with the assistance of cascade- progressive convolutional neural networks was evaluated. The diagnostic accuracy of a C-PCNN assistant with an attending doctor and chief doctor was compared to evaluate the clinical significance. RESULTS: C-PCNN showed 85.6% accuracy in diagnosing and identifying anterior horn injury, and 92% accuracy in diagnosing and identifying posterior horn injury. The average accuracy of C-PCNN was 89.8%, AUC = 0.86. The diagnosis accuracy of the attending physician with the aid of the C-PCNN was comparable to that of the chief physician. CONCLUSION: The C-PCNN-based MRI technique for diagnosing knee meniscus injuries has significant practical value in clinical practice. With a high rate of accuracy, clinical auxiliary physicians can increase the speed and accuracy of diagnosis and decrease the number of incorrect diagnoses.

SDMT: Spatial Dependence Multi-Task Transformer Network for 3D Knee MRI Segmentation and Landmark Localization.

Knee segmentation and landmark localization from 3D MRI are two significant tasks for diagnosis and treatment of knee diseases. With the development of deep learning, Convolutional Neural Network (CNN) based methods have become the mainstream. However, the existing CNN methods are mostly single-task methods. Due to the complex structure of bone, cartilage and ligament in the knee, it is challenging to complete the segmentation or landmark localization alone. And establishing independent models for all tasks will bring difficulties for surgeon's clinical using. In this paper, a Spatial Dependence Multi-task Transformer (SDMT) network is proposed for 3D knee MRI segmentation and landmark localization. We use a shared encoder for feature extraction, then SDMT utilizes the spatial dependence of segmentation results and landmark position to mutually promote the two tasks. Specifically, SDMT adds spatial encoding to the features, and a task hybrided multi-head attention mechanism is designed, in which the attention heads are divided into the inter-task attention head and the intra-task attention head. The two attention head deal with the spatial dependence between two tasks and correlation within the single task, respectively. Finally, we design a dynamic weight multi-task loss function to balance the training process of two task. The proposed method is validated on our 3D knee MRI multi-task datasets. Dice can reach 83.91% in the segmentation task, and MRE can reach 2.12 mm in the landmark localization task, it is competitive and superior over other state-of-the-art single-task methods.

MsgeCNN: Multiscale geometric embedded convolutional neural network for ONFH segmentation and grading.

BACKGROUND: The incidence of osteonecrosis of the femoral head (ONFH) is increasing gradually, rapid and accurate grading of ONFH is critical. The existing Steinberg staging criteria grades ONFH according to the proportion of necrosis area to femoral head area. PURPOSE: In the clinical practice, the necrosis region and femoral head region are mainly estimated by the observation and experience of doctor. This paper proposes a two-stage segmentation and grading framework, which can be used to segment the femoral head and necrosis, as well as to diagnosis. METHODS: The core of the proposed two-stage framework is the multiscale geometric embedded convolutional neural network (MsgeCNN), which integrates geometric information into the training process and accurately segments the femoral head region. Then, the necrosis regions are segmented by the adaptive threshold method taking femoral head as the background. The area and proportion of the two are calculated to determine the grade. RESULTS: The accuracy of the proposed MsgeCNN for femoral head segmentation is 97.73%, sensitivity is 91.17%, specificity is 99.40%, dice score is 93.34%. And the segmentation performance is better than the existing five segmentation algorithms. The diagnostic accuracy of the overall framework is 90.80%. CONCLUSIONS: The proposed framework can accurately segment the femoral head region and the necrosis region. The area, proportion, and other pathological information of the framework output provide auxiliary strategies for subsequent clinical treatment.

Implant loosening following THA with S-ROM prosthesis and subtrochanteric osteotomy: Three case reports.

Prosthesis loosening after THA is a rather common complication. For DDH patients with Crowe IV, the surgical risk and complexity is significant. THA with S-ROM prosthesis combined with subtrochanteric osteotomy is a common treatment. However, loosening of a modular femoral prosthesis (S-rom) is uncommon in THA and has a very low incidence. With modular prostheses distal prosthesis looseness are rarely reported. Non-union osteotomy is a common complication of subtrochanteric osteotomy. We report three patients with Crowe IV DDH who developed prosthesis loosening following THA with an S-ROM prosthesis and subtrochanteric osteotomy. We addressed the management of these patients and prosthesis loosening as likely underlying causes.

Scapula chondrosarcoma: A case report.

RATIONALE: Chondrosarcoma is a malignant mesenchymal tumor originating from cartilage. The pelvis, ribs, femur, and humerus are the most frequently affected sites, and scapula involvement is relatively rare. The aim of the present study was to report a case of chondrosarcoma in the scapula. PATIENT CONCERNS: A 42-year-old woman presented with a 3-month history of a painful mass in the right scapula. DIAGNOSES AND INTERVENTION: The patient underwent tumor resection. The post-operative pathological diagnosis was scapula chondrosarcoma. OUTCOMES: Following resection, the patient continued to receive routine follow-up care. There was no recurrence or tumor metastasis at a follow-up of 5 years. CONCLUSIONS: Surgery remains the primary therapy for chondrosarcoma. One of the greatest challenges in the management of chondrosarcoma is to accurately assess tumor grade before surgical intervention. Chemotherapy and radiotherapy have been applied without success. Chemo- and radioresistance have been examined beyond classic phenotypic properties to identify more efficient therapeutic strategies. Therefore, development of future novel therapies is contingent upon elucidating the molecular mechanisms of chondrosarcoma.

Massive rare desmoplastic fibroma of the ilium and ischium in a young adult: A case report.

RATIONALE: Desmoplastic fibroma (DF) is a rare, locally invasive but benign bone tumor. It represents one of the rarest bone diseases, with an incidence of only 0.11% of all primary bone tumors. PATIENT CONCERNS: Herein, a case of massive and unusual DF, with simultaneous involvement of ilium and ischium, is described. A 29-year-old man suffered minor pain in his right hip for 2 years. It worsened after sudden movements, which prevented him from walking normally. Physical examination showed a limitation when the right hip was flexed and a percussion pain on the hip region. A medical imaging examination showed that the right ilium and ischium had a massive bone lesion. The top of acetabular had very little bone left and a fracture was likely at any time. No prominent body weight loss was noted, because there was no extensive invasion to the adjacent soft tissue. DIAGNOSES: DF of the Ilium and Ischium. INTERVENTIONS: The patient underwent a surgery involving curettage and grafting to maintain the stability of the pelvis. OUTCOMES: The definitive pathological diagnosis was DF, without evidence of malignancy. The postoperative recovery course at 3-month follow-up was uneventful. LESSONS: To the authors' knowledge, such a massive DF involving both ilium and ischium has been rarely reported. Young patients require appropriate and timely treatment modalities.