Objective To investigate the improvement effects and mechanisms of composite chitosan (CS) hydrogel on traditional polypropylene (PP) mesh for repairing abdominal wall defects. Methods CS hydrogel was prepared via physical cross-linking and then combined with PP mesh to create a CS hydrogel/PP mesh composite. The internal structure and hydrophilicity of the composite were characterized using macroscopic observation, upright metallographic microscope, scanning electron microscopy, and water contact angle measurements. The performance of the composite (experimental group) in resisting cell adhesion and supporting cell infiltration was assessed through fibroblast (NIH-3T3) infiltration experiments and human umbilical vein endothelial cells (HUVECs) tube formation assays, and simple cells were used as control group. Finally, a bilateral abdominal wall defect model (1.5 cm×1.0 cm) was established in 18 Sprague Dawley rats aged 8-10 weeks, with the composite used on one side (experimental group) and PP mesh on the other side (control group). The effects on promoting wound healing, preventing adhesion, angiogenesis, and anti-inflammation were investigated through macroscopic observation, histological staining (HE and Masson staining), and immunohistochemical staining (CD31, CD68). Results The composite appeared as a pale yellow, transparent solid with a thickness of 2-3 mm, with the PP mesh securely encapsulated within the hydrogel. Scanning electron microscopy revealed that the hydrogel contained interconnected pores measuring 100-300 μm, forming a porous structure. Contact angle measurements indicated that CS hydrogel exhibited good hydrophilicity, while PP mesh was highly hydrophobic. In vitro cell culture experiments showed that DAPI staining indicated fewer positive cells in the experimental group after 1 day of culture, while the cells in control group covered the entire well plate. After 3 days of culture, the cells in experimental group were spherical and displayed uneven fluorescence, suggesting that the material could reduce cell adhesion while supporting cell infiltration. HUVECs tube formation experiments demonstrated an increase in cell numbers in experimental group with a trend towards tube formation, while cells in control group were sparsely distributed and showed no migration. In the rat abdominal wall defect repair experiment, results showed that after 1 week post-surgery, the experimental group had tissue and blood vessels infiltrating, and by 4 weeks, the integrity was well restored with significant regeneration of muscle and blood vessels, while the control group exhibited adhesions and incomplete healing. HE staining results indicated weaker cell infiltration in the experimental group, with cell density significantly higher than that of the control group at 2 and 4 weeks post-surgery (P<0.05). Masson staining revealed that collagen fibers in the experimental group were arranged neatly, with significantly increased collagen content at 2 weeks post-surgery (P<0.05), while collagen content was similar in both groups at 4 weeks (P>0.05). Immunohistochemical staining showed that CD31-positive cells were evenly distributed between muscle layers in the experimental group, whereas the control group exhibited notable defects. At 2 weeks after operation, the CD31-positive cell ratio was significantly higher than that in the control group (P<0.05); at 2 and 4 weeks after operation, the CD68-positive cell ratio in the experimental group was significantly lower than that in the control group (P<0.05). Conclusion CS hydrogel has a positive effect on preventing adhesions and promoting wound healing, exhibiting anti-inflammatory and pro-angiogenic properties during the healing process. This provides a promising strategy to address challenges related to abdominal adhesions and reconstruction.
Temporomandibular joint disorder (TMD) is a common oral and maxillofacial disease, which is difficult to detect due to its subtle early symptoms. In this study, a TMD intelligent diagnostic system implemented on edge computing devices was proposed, which can achieve rapid detection of TMD in clinical diagnosis and facilitate its early-stage clinical intervention. The proposed system first automatically segments the important components of the temporomandibular joint, followed by quantitative measurement of the joint gap area, and finally predicts the existence of TMD according to the measurements. In terms of segmentation, this study employs semi-supervised learning to achieve the accurate segmentation of temporomandibular joint, with an average Dice coefficient (DC) of 0.846. A 3D region extraction algorithm for the temporomandibular joint gap area is also developed, based on which an automatic TMD diagnosis model is proposed, with an accuracy of 83.87%. In summary, the intelligent TMD diagnosis system developed in this paper can be deployed at edge computing devices within a local area network, which is able to achieve rapid detecting and intelligent diagnosis of TMD with privacy guarantee.