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Anterior cruciate ligament (ACL) tear is a common sports-related injury, and cartilage injury always emerges as a serious complication following ACL tear, significantly impacting the physical and psychological well-being of affected individuals. Over the years, efforts have been directed toward finding strategies to repair cartilage injury after ACL tear. In recent times, procyanidins, known for their anti-inflammatory and antioxidant properties, have emerged as potential key players in addressing this concern. This article focuses on summarizing the research progress of procyanidins in repairing cartilage injury after ACL tear. It covers the roles, mechanisms, and clinical significance of procyanidins in repairing cartilage injury following ACL tear and explores the future prospects of procyanidins in this domain. This review provides novel insights and hope for the repair of cartilage injury following ACL tear.
RESUMO
Airport pavement concrete often suffers from freeze-thawing damage in high latitude and cold areas. In addition, the use of aircraft deicer makes the airport pavement concrete suffer from salt-freezing damage. To improve the durability of airport pavement concrete, modified polyester synthetic fiber (FC), cellulose fiber (CF), and basalt fiber (BF) reinforced concrete were prepared in this paper. The mechanical strength, pore structure, and frost resistance (freeze-thawing and salt freezing) of fiber-reinforced concrete were investigated. The effects of the combined action of fiber (fiber type and content) and surface treatment methods (spraying silane and impregnating silane) on the frost resistance of concrete were investigated. The results show that the flexural strength of concrete is positively correlated with the elastic modulus of fiber, but has little effect on the compressive strength. Fiber can reduce mass loss and dynamic modulus loss of concrete subjected to frost damage. FC more effectively improved the frost resistance of concrete than CF. After 30 cycles of salt freezing, the spalling amount of concrete sprayed or soaked with silane was decreased by 65.5% and 55.5%, respectively. Adding fiber and impregnating silane reduced the spalled concrete by up to 70.5%. Spraying silane treatment is better than impregnating silane treatment in enhancing the frost resistance of concrete because a better silane condensation reaction is achieved with spraying silane.
RESUMO
Some neural models achieve outstanding results in image recognition, semantic segmentation and natural language processing. However, their classification performance on structured and small-scale datasets that do not involve feature extraction is worse than that of traditional algorithms, although they require more time to train. In this paper, we propose a brain-like neural model with interactive stimulation (NMIS) that focuses on data classification. It consists of a primary neural field and a senior neural field that play different cognitive roles. The former is used to correspond to real instances in the feature space, and the latter stores the category pattern. Neurons in the primary field exchange information through interactive stimulation and their activation is transmitted to the senior field via inter-field interaction, simulating the mechanisms of neuronal interaction and synaptic plasticity, respectively. The proposed NMIS is biologically plausible and does not involve complex optimization processes. Therefore, it exhibits better learning ability on small-scale and structured datasets than traditional BP neural networks. For large-scale data classification, a nearest neighbor NMIS (NN_NMIS), an optimized version of NMIS, is proposed to improve computational efficiency. Numerical experiments performed on some UCI datasets show that the proposed NMIS and NN_NMIS are significantly superior to some classification algorithms that are widely used in machine learning.
