Role of transient receptor potential ankyrin 1 in idiopathic pulmonary fibrosis: modulation of M2 macrophage polarization.

Idiopathic pulmonary fibrosis (IPF) poses significant challenges due to limited treatment options despite its complex pathogenesis involving cellular and molecular mechanisms. This study investigated the role of transient receptor potential ankyrin 1 (TRPA1) channels in regulating M2 macrophage polarization in IPF progression, potentially offering novel therapeutic targets. Using a bleomycin-induced pulmonary fibrosis model in C57BL/6J mice, we assessed the therapeutic potential of the TRPA1 inhibitor HC-030031. TRPA1 upregulation was observed in fibrotic lungs, correlating with worsened lung function and reduced survival. TRPA1 inhibition mitigated fibrosis severity, evidenced by decreased collagen deposition and restored lung tissue stiffness. Furthermore, TRPA1 blockade reversed aberrant M2 macrophage polarization induced by bleomycin, associated with reduced Smad2 phosphorylation in the TGF-ß1-Smad2 pathway. In vitro studies with THP-1 cells treated with bleomycin and HC-030031 corroborated these findings, highlighting TRPA1's involvement in fibrotic modulation and macrophage polarization control. Overall, targeting TRPA1 channels presents promising therapeutic potential in managing pulmonary fibrosis by reducing pro-fibrotic marker expression, inhibiting M2 macrophage polarization, and diminishing collagen deposition. This study sheds light on a novel avenue for therapeutic intervention in IPF, addressing a critical need in the management of this challenging disease.

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.

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.

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.