Quantitative analysis of local microcirculation changes in early osteonecrosis of femoral head: DCE-MRI findings

AimThis study aims to quantitatively analyze the changes in local microcirculation in early osteonecrosis of the femoral head (ONFH) by dynamic contrast-enhanced (DCE) MRI and to explore the pathophysiological mechanisms of early ONFH.Patients and MethodsWe selected 49 patients (98 hips) aged 21–59 years who were clinically diagnosed with early ONFH. A total of 77 femoral heads were diagnosed with different degrees of necrosis according to the Association Research Circulation Osseous (ARCO) staging system, and 21 femoral heads were judged to be completely healthy. All patients underwent DCE-MRI scanning. Pseudocolor images and time-signal intensity curves were generated by Tissue 4D processing software. The volume transfer constant (Ktrans), extracellular extravascular space, also known as vascular leakage (Ve), and transfer rate constant (Kep) of healthy and different areas of necrotic femoral heads were measured on perfusion parameter maps. The differences and characteristics of these parameters in healthy and different areas of necrotic femoral heads were analyzed.ResultsThe signal accumulation in healthy femoral heads is lower than that of necrotic femoral heads in pseudocolor images. The time-signal intensity curve of healthy femoral heads is along the horizontal direction, while they all have upward trends for different areas of necrotic femoral heads. The mean value of Ktrans of healthy femoral heads was lower than the integration of necrotic, boundary, and other areas (F = 3.133, P = .036). The Kep value of healthy femoral heads was higher than the integration of lesion areas (F = 6.273, P = .001). The mean Ve value of healthy femoral heads was smaller than that of the lesion areas (F = 3.872, P = .016). The comparisons of parameters between different areas and comparisons among healthy areas and lesion areas showed different results.ConclusionONFH is a complex ischemic lesion caused by changes in local microcirculation. It mainly manifests as increased permeability of the vascular wall, blood stasis in the posterior circulation, high intraosseous pressure in the femoral head, and decreased arterial blood flow. The application of DCE-MRI scanning to quantitatively analyze the visual manifestations of microcirculation after early ONFH is an ideal method to study the microcirculation changes of necrotic femoral heads

Adiponectin-Mediated Promotion of CD44 Suppresses Diabetic Vascular Inflammatory Effects

While adiponectin (APN) was known to significantly abolish the diabetic endothelial inflammatory response, the specific mechanisms have yet to be elucidated. Aortic vascular tissues from mice fed normal and high-fat diets (HFD) were analyzed by transcriptome analysis. GO functional annotation showed that APN inhibited vascular endothelial inflammation in an APPL1-dependent manner. We confirmed that activation of the Wnt/β-catenin signaling plays a key role in APN-mediated anti-inflammation. Mechanistically, APN promoted APPL1/reptin complex formation and β-catenin nuclear translocation. Simultaneously, we identified APN promoted the expression of CD44 by activating TCF/LEF in an APPL1-mediated manner. Clinically, the serum levels of APN and CD44 were decreased in diabetes; the levels of these two proteins were positively correlated. Functionally, treatment with CD44 C-terminal polypeptides protected diabetes-induced vascular endothelial inflammation in vivo. Collectively, we provided a roadmap for APN-inhibited vascular inflammatory effects and CD44 might represent potential targets against the diabetic endothelial inflammatory effect

HCTNet: A Hybrid ConvNet-Transformer Network for Retinal Optical Coherence Tomography Image Classification

Automatic and accurate optical coherence tomography (OCT) image classification is of great significance to computer-assisted diagnosis of retinal disease. In this study, we propose a hybrid ConvNet-Transformer network (HCTNet) and verify the feasibility of a Transformer-based method for retinal OCT image classification. The HCTNet first utilizes a low-level feature extraction module based on the residual dense block to generate low-level features for facilitating the network training. Then, two parallel branches of the Transformer and the ConvNet are designed to exploit the global and local context of the OCT images. Finally, a feature fusion module based on an adaptive re-weighting mechanism is employed to combine the extracted global and local features for predicting the category of OCT images in the testing datasets. The HCTNet combines the advantage of the convolutional neural network in extracting local features and the advantage of the vision Transformer in establishing long-range dependencies. A verification on two public retinal OCT datasets shows that our HCTNet method achieves an overall accuracy of 91.56% and 86.18%, respectively, outperforming the pure ViT and several ConvNet-based classification methods