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The repair of abdominal wall defects often requires the use of polypropylene (PP) as the main material. After a PP mesh is implanted in the body, contact with the intestine can cause adhesions between the intestine and the mesh, leading to serious complications such as intestinal fistula. In this study, we used electrostatic spinning technology to coat one side of PP meshes with an electrostatically spun isolating layer of acellular dermal matrix (ADM)/silk fibroin (SF) hybrid material. These meshes were used to repair abdominal wall defects in model rats and were compared with polycaprolactone (PCL) composite polypropylene meshes and PP meshes. The results showed that the adhesion score and area of ADM/SF-PP meshes were smaller than those of PCL-PP and PP meshes. Immunohistochemical assessment revealed that the ADM/SF meshes could effectively reduce the inflammatory response at the contact surface between the meshes and abdominal organs. The tissues regenerated on the abdominal side were rich in new blood vessels. Furthermore, the ADM/SF meshes could effectively reduce the expression levels of the inflammation-related factors IL-6 and TNF-α. The expression levels of tissue regeneration-related factors, such as VEGF and PAX-7, were also higher after ADM/SF-PP mesh-mediated repair than after PCL-PP mesh and PP mesh repair. Thus, ADM/SF-PP meshes can effectively reduce the inflammatory response at the contact surface between the meshes and abdominal organs and quickly promote regeneration of abdominal surface tissue to prevent and reduce abdominal adhesion and support restoration of the abdominal wall. • AMD and SF were successfully mixed for electrospinning. • ADM/SF electrospun membranes promote cell proliferation. • ADM/SF-PP composite meshes could effectively reduce adhesion. • ADM/SF-PP meshes promoted vascular proliferation in the repair area and reduced inflammatory response. [ABSTRACT FROM AUTHOR]

Deep learning is widely used in Bearing Fault Diagnosis (BFD). Nonetheless, practical industrial production often generates a large amount of industrial noise. These noises exhibit randomness and complexity, which puts forward higher requirements for diagnosis algorithms. Certain studies have tackled the issue of anti-interference in high-noise environments (S N R ≤ 0 dB) by increasing the complexity of the model. However, due to the excessive number of parameters and computation, such models cannot be deployed on low-end edge devices. Balancing resource consumption and accuracy has become a major challenge in BFD modeling research. To solve the above problems, this paper proposes a new Transformer architecture model called LTFAFormer. The LTFAFormer is capable of achieving high-precision diagnostics on low-end edge devices and shows greater noise resistance. In terms of processing sequence information, Transformer has proven to be superior to other solutions. However, when dealing with longer sensor signal data containing complex noise, the traditional self-attention mechanism not only cannot effectively extract fault features, but also generates more computational complexity than CNN. To address this issue, we propose a novel time–frequency dual-channel parallel attention mechanism. Our approach enhances the feature extraction capability of the model by expanding the attention computation scale and reduces the computational resource consumption of the model by optimizing the model structure. To validate the effectiveness of LTFAFormer, we present two cases to demonstrate that LTFAFormer has higher prediction accuracy while satisfying lightweight. Especially in high-noise environments, LTFAFormer has stronger robustness. In this paper provides a new set of feasible strategies for the practical deployment of BFD models in practical industrial environments. • The LTFAFormer, a lightweight and noise-robust model, is ideal for low-end devices. • Added a Dual-Channel Attention Module to expand traditional self-attention scale. • Developed a Cross-fusion mechanism for attention to enhance feature fusion. • Optimized the model's embedding module based on sensor signal traits. • Our model outperforms others in bearing fault diagnosis and is robust in noise. [Display omitted] [ABSTRACT FROM AUTHOR]

Highlights • Bioimaging in acidic lysosomes for analytes is of great significance. • A pH-independent probe for sensing HOCl at lysosomal pH 5.0 was developed. • The new probe was successfully applied to visualize HOCl in living systems. Abstract As acidic organelles (pH 4.0–5.5) in living cells, lysosomes play key roles in numerous cellular biological processes, and associate with diverse diseases. Small molecular probes based fluorescence imaging techniques capable of providing information on target analytes in lysosomes are highly appreciated. Herein, for the first time we propose a new strategy of constructing pH-independent rhodamine based spiro-ring platform for lysosomal imaging applications. As a proof of concept, by incorporating lysosome specific moiety morpholine, a thio-lactone rhodamine analogue HN4MS for HOCl with high sensitivity, high selectivity and fast response at lysosomal pH value was developed. Moreover, possessing ability of imaging in lysosome organelles, the new probe was successfully applied to visualize exogenous and endogenous HOCl in living cells and tissues, which verified the present avenue for expansion of organelle targetable imaging tools. [ABSTRACT FROM AUTHOR]