Guided tissue regeneration (GTR) strategy is an effective approach to repair periodontal defect by using GTR membranes. However, the commercial GTR membranes still have limitations in periodontal tissue regeneration owing...
This study proposes an effective intelligent predictive model for prediction of cervical hyperextension injury. The prediction model is constructed by combing an improved sine cosine algorithm (SCA) with support vector machines (SVM), which is named COSCA-SVM. The core of the developed model is the COSCA method that combines the opposition-based learning mechanism and covariance mechanism to boost and recover the exploratory competence of SCA. The proposed COSCA approach is utilized to optimize the two critical parameters of the SVM, and it is also employed to catch the optimal feature subset. Based on the optimal parameter combination and feature subset, COSCA-SVM is able to make self-directed prediction of cervical hyperextension injury. The proposed COSCA was compared with other well-known and effective methods using 23 benchmark problems. Simulation results verify that the proposed COSCA is significantly superior to studied methods in dealing with majority of benchmark problems. Meanwhile, the proposed COSCA-SVM is compared with six other machine learning approaches considering a reallife dataset. Results have shown that the proposed COSCA-SVM can achieve better classification routine and higher stability on all four indicators. Therefore, we can expect that COSCA-SVM can be a promising building block for predicting cervical hyperextension injury.
Delayed wound healing in skin is strongly correlated with excessive reactive oxygen species (ROS) generation. Corn peptides (CPs) have robust antioxidant and anti-inflammatory effects. Therefore, the study sought to evaluate the wound healing effect of topical application of CPs embedded in wound dressings fabricated using the coaxial electrospinning technique. A special structure, which was a co-axial structure with a Janus-structured sheath, was displayed on the fiber. The fibers exhibited stable thermal properties, suitable tensile properties, high wettability, excellent biocompatibility, and free radical scavenging capability. Additionally, a first-order release profile of CPs from the fibers showed that approximately 92% of the drug was released within 80 min. In vivo experiments indicated that CPs-loaded fibrous membranes significantly improved the wound healing ratio, thickened the re-epithelialization layer, enhanced fibroblast proliferation, and increased the production of regenerated hair follicles and capillaries. Overall, it is promising that the combination of CPs and fibrous membranes with special structures applies in skin tissue engineering to promote wound repair.
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