Background The aim of this study is to histologically and biomechanically investigate the effects of local PRP and ozone therapy (O2O3) on tendon-to-bone healing in a rabbit model of the supraspinatus tendon tear. Methods Four groups were formed to have seven rabbits in each group: repair, R; repair + PRP, RP; repair + ozone, RO; and repair + PRP + ozone, RPO. The supraspinatus tendon was detached by sharp dissection from the footprint and an acute tear pattern was created. Thereafter, tendon repair was performed with the transosseous technique. In the RP group, PRP, and in the RPO group, PRP + O2O3 mixture was injected to the tendon repair site. In the RO group, O2O3 gas mixture was injected into subacromial space three times a week for a total of 4 weeks. The study was ended at postoperative 6th week. Results When compared with the R group, a statistically significant increase was observed in the biomechanical strength of the RP and RPO groups. The highest increase in biomechanical strength was detected in the RPO group. The histology of the RO and RPO groups showed better collagen fiber continuity and orientation than the R and RP groups. Conclusions The results obtained from this study show that the ozonized PRP can be used as biological support to increase tendon-to-bone healing. However, these results need to be supported by clinical studies.

No abstract

No abstract

Recently, deep models have been very popular because they achieve excellent performance with many classification problems. Deep networks have high computational complexities and require specific hardware. To overcome this problem (without decreasing classification ability), a hand-modeled feature selection method is proposed in this paper. A new shape-based local feature extractor is presented which uses the geometric shape of the frustum. By using a frustum pattern, textural features are generated. Moreover, statistical features have been extracted in this model. Textures and statistics features are fused, and a hybrid feature extraction phase is obtained; these features are low-level. To generate high level features, tunable Q factor wavelet transform (TQWT) is used. The presented hybrid feature generator creates 154 feature vectors; hence, it is named Frustum154. In the multilevel feature creation phase, this model can select the appropriate feature vectors automatically and create the final feature vector by merging the appropriate feature vectors. Iterative neighborhood component analysis (INCA) chooses the best feature vector, and shallow classifiers are then used. Frustum154 has been tested on three basic hand-movement sEMG datasets. Hand-movement sEMG datasets are commonly used in biomedical engineering, but there are some problems in this area. The presented models generally required one dataset to achieve high classification ability. In this work, three sEMG datasets have been used to test the performance of Frustum154. The presented model is self-organized and selects the most informative subbands and features automatically. It achieved 98.89%, 94.94%, and 95.30% classification accuracies using shallow classifiers, indicating that Frustum154 can improve classification accuracy.