Electrospun Aligned Nanofiber Yarns Constructed Biomimetic M-Type Interface Integrated into Precise Co-Culture System as Muscle-Tendon Junction-on-a-Chip for Drug Development.

The incorporation of engineered muscle-tendon junction (MTJ) with organ-on-a-chip technology provides promising in vitro models for the understanding of cell-cell interaction at the interface between muscle and tendon tissues. However, developing engineered MTJ tissue with biomimetic anatomical interface structure remains challenging, and the precise co-culture of engineered interface tissue is further regarded as a remarkable obstacle. Herein, an interwoven waving approach is presented to develop engineered MTJ tissue with a biomimetic "M-type" interface structure, and further integrated into a precise co-culture microfluidic device for functional MTJ-on-a-chip fabrication. These multiscale MTJ scaffolds based on electrospun nanofiber yarns enabled 3D cellular alignment and differentiation, and the "M-type" structure led to cellular organization and interaction at the interface zone. Crucially, a compartmentalized co-culture system is integrated into an MTJ-on-a-chip device for the precise co-culture of muscle and tendon zones using their medium at the same time. Such an MTJ-on-a-chip device is further served for drug-associated MTJ toxic or protective efficacy investigations. These results highlight that these interwoven nanofibrous scaffolds with biomimetic "M-type" interface are beneficial for engineered MTJ tissue development, and MTJ-on-a-chip with precise co-culture system indicated their promising potential as in vitro musculoskeletal models for drug development and biological mechanism studies.

MSDEnet: Multi-scale detail enhanced network based on human visual system for medical image segmentation.

In medical image segmentation, accuracy is commonly high for tasks involving clear boundary partitioning features, as seen in the segmentation of X-ray images. However, for objects with less obvious boundary partitioning features, such as skin regions with similar color textures or CT images of adjacent organs with similar Hounsfield value ranges, segmentation accuracy significantly decreases. Inspired by the human visual system, we proposed the multi-scale detail enhanced network. Firstly, we designed a detail enhanced module to enhance the contrast between central and peripheral receptive field information using the superposition of two asymmetric convolutions in different directions and a standard convolution. Then, we expanded the scale of the module into a multi-scale detail enhanced module. The difference between central and peripheral information at different scales makes the network more sensitive to changes in details, resulting in more accurate segmentation. In order to reduce the impact of redundant information on segmentation results and increase the effective receptive field, we proposed the channel multi-scale module, adapted from the Res2net module. This creates independent parallel multi-scale branches within a single residual structure, increasing the utilization of redundant information and the effective receptive field at the channel level. We conducted experiments on four different datasets, and our method outperformed the common medical image segmentation algorithms currently being used. Additionally, we carried out detailed ablation experiments to confirm the effectiveness of each module.

CAENet: Contrast adaptively enhanced network for medical image segmentation based on a differentiable pooling function.

Pixel differences between classes with low contrast in medical image semantic segmentation tasks often lead to confusion in category classification, posing a typical challenge for recognition of small targets. To address this challenge, we propose a Contrastive Adaptive Augmented Semantic Segmentation Network with a differentiable pooling function. Firstly, an Adaptive Contrast Augmentation module is constructed to automatically extract local high-frequency information, thereby enhancing image details and accentuating the differences between classes. Subsequently, the Frequency-Efficient Channel Attention mechanism is designed to select useful features in the encoding phase, where multifrequency information is employed to extract channel features. One-dimensional convolutional cross-channel interactions are adopted to reduce model complexity. Finally, a differentiable approximation of max pooling is introduced in order to replace standard max pooling, strengthening the connectivity between neurons and reducing information loss caused by downsampling. We evaluated the effectiveness of our proposed method through several ablation experiments and comparison experiments under homogeneous conditions. The experimental results demonstrate that our method competes favorably with other state-of-the-art networks on five medical image datasets, including four public medical image datasets and one clinical image dataset. It can be effectively applied to medical image segmentation.

Research on multi-path dense networks for MRI spinal segmentation.

Accurate and robust segmentation of anatomical structures from magnetic resonance images is valuable in many computer-aided clinical tasks. Traditional codec networks are not satisfactory because of their low accuracy of edge segmentation, the low recognition rate of the target, and loss of detailed information. To address these problems, this study proposes a series of improved models for semantic segmentation and progressively optimizes them from the three aspects of convolution module, codec unit, and feature fusion. Instead of the standard convolution structure, we apply a new type of convolution module for the feature extraction. The networks integrate a multi-path method to obtain richer-detail edge information. Finally, a dense network is utilized to strengthen the ability of the feature fusion and integrate more different-level information. The evaluation of the Accuracy, Dice coefficient, and Jaccard index led to values of 0.9855, 0.9185, and 0.8507, respectively. These metrics of the best network increased by 1.0%, 4.0%, and 6.1%, respectively. Boundary F1-Score reached 0.9124 indicating that the proposed networks can segment smaller targets to obtain smoother edges. Our methods obtain more key information than traditional methods and achieve superiority in segmentation performance.

Growth/differentiation factor-5 induces osteogenic differentiation of human ligamentum flavum cells through activation of ERK1/2 and p38 MAPK.

Ossification of ligamentum flavum (OLF) is a pathological ectopic ossification in the spinal ligament, leading to spinal canal stenosis, but little was known about its pathogenesis. A previous study has found growth/differentiation factor (GDF)-5 expression at ossified sites of the ligaments from OLF patients. This study aimed to investigate the osteogenic effects of GDF-5 on cultured human ligamentum flavum cells (LFCs). LFCs were isolated from human spinal ligamentum flavum, and treated with or without recombinant human (rh) GDF-5. Alkaline phosphatase (ALP) activity was measured. Expression of osteocalcin was assessed by reverse transcriptase-PCR, Western blotting and immunofluorescence. Matrix mineralization was assessed by alizarin red staining. Activation of mitogen-activated protein kinases (MAPK) ERK1/2, p38 and JNK were detected by Western blotting. We found that rhGDF-5 treatment increased ALP activity and osteocalcin expression in a time- and dose-dependent manner, and induced mineralized nodule form. In addition, rhGDF-5 challenge mediated the ERK1/2 and p38 activation but not JNK. Inhibiting this activation pharmacologically, using U0126, a ERK1/2 inhibitor, or SB203580, a p38 inhibitor, resulted in significantly lower ALP activity and osteocalcin protein expression. The present study shows that rhGDF-5 induces osteogenic differentiation of human LFCs through activation of ERK1/2 and p38 MAPK. These findings give some new insight into the pathogenesis of OLF.