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, O₂O₃gas 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.

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.

Rotator cuff tear (RCT) and biceps tendinosis (BT) are the two most common shoulder disorders worldwide. These disorders can be diagnosed using magnetic resonance imaging (MRI), but the expert interpretation is manual, time-consuming, and subjected to human errors. Therefore, a fixed-size feature extraction model was created to objectively and accurately perform automated binary classification of RCT vs. normal and BT vs. normal on MRI images. We have developed an exemplar deep feature extraction model to diagnose RCT and BT disorders. The model was tested on a new MR image dataset comprising transverse, sagittal, and coronal MRI images of the shoulder that had been organized into three cases. BT was studied on transverse MRI images (Case 1), while RCT was studied on sagittal (Case 2) and coronal MRI images (Case 3). Our model comprised deep feature generation using a pre-trained VGG19, feature selection using iterative neighborhood component analysis (INCA), and classification using shallow standard classifiers k-nearest neighbors (KNN), support vector machine (SVM), and artificial neural network (ANN). In the feature extraction phase, two fully connected layers were used to extract deep features from the original image, and sixteen fixed-size patches obtained by the division of the original image. This model was named Vision VGG19 (ViVGG), analogous to vision transformers (ViT). The feature vector is extracted from the raw image dataset, and 16 feature vectors are extracted from each fixed-size patch. Seventeen feature vectors obtained from each image are obtained from fc6 and fc7 layers of the pre-trained VGG19, are merged to obtain final feature vector. INCA was used to choose the top features from the created features, and the chosen features were classified using shallow classifiers. We defined three cases to evaluate the proposed ViVGG19 to diagnose RT and BCT disorders. Our proposed ViVGG19 model achieved more than 99% accuracy using the KNN classifier. ViVGG19 is a very effective model for detecting RCT and BT disorders on shoulder MRI images. The developed automated system is ready to be tested with a bigger diverse database obtained from different medical centers.

Background and Objectives: The aim of this study was to evaluate retrospectively the radiological and functional outcomes of closed reduction and internal fixation for intertrochanteric femoral fractures (IFF) using three different proximal femoral nails (PFN). Materials and Methods: In total, 309 individuals (143 males and 166 females) who underwent surgery for IFF using a PFN between January 2018 and January 2021 were included in the study. Our surgical team conducted osteosynthesis using the A-PFN^® (TST, Istanbul, Turkey) nail, the PROFIN^® (TST, Istanbul, Turkey), and the Trigen InterTAN (Smith & Nephew, Memphis, TN, USA) nail. The PFNs were compared based on age, gender, body mass index (BMI), length of stay (LOS) in intensive care, whether to be admitted to intensive care, mortality in the first year, amount of transfusion, preoperative time to surgery, hospitalisation time, duration of surgery and fluoroscopy, fracture type and reduction quality, complication ratio, and clinical and radiological outcomes. The patients’ function was measured with the Harris Hip Score (HHS) and the Katz Index of Independence in Activities of Daily Living (ADL). Results: Pain in the hip and thigh is the most common complication, followed by the V-effect. The Z-effect was seen in 5.7% of PROFIN patients. A-PFN was shown to have longer surgical and fluoroscopy durations, lower HHS values, and much lower Katz ADL Index values compared to the other two PFNs. The V-effect occurrence was significantly higher in the A-PFN group (36.7%) than in the InterTAN group. The V-effect was seen in 33.1% of 31A2-type fractures but in none of the 31A3-type fractures. Conclusions: InterTAN nails are the best choice for IFFs because they have high clinical scores after surgery, there is no chance of Z-effect, and the rate of V-effect is low.

Objective: To assess the predictive value of systemic inflammatory markers for postoperative complications in older adults undergoing posterior spinal instrumentation for either lumbar spinal stenosis (LSS) or osteoporotic vertebral fractures (OVFs). This study design as a retrospective observational study. Methods: Fifty-four patients aged ≥ 55 years who underwent posterior spinal instrumentation between 2020 and 2023 were retrospectively analyzed. Patients were grouped into LSS (n = 27) and OVF (n = 27) cohorts. Preoperative, early postoperative, and 6-month follow-up systemic inflammatory marker levels, including the Systemic Inflammatory Response Index (SIRI), Systemic Immune-Inflammation Index (SII), Neutrophil-to-Lymphocyte Ratio (NLR), Platelet-to-Lymphocyte Ratio (PLR), and Monocyte-to-Lymphocyte Ratio (MLR), were recorded. The primary outcome was the occurrence of postoperative infectious complications. ROC curve analysis was conducted to evaluate the discriminatory power of each marker. Results: SIRI values were significantly higher in the OVF group than in the LSS group at all time points (p < 0.05). Postoperative complications occurred in 7.4% of patients, equally distributed between groups. ROC analysis showed that preoperative SIRI had the highest predictive value (AUC = 0.743), with a cutoff value of 2.69 yielding 100% sensitivity and 65.3% specificity. Other indices showed poor predictive accuracy (AUC < 0.70). Conclusions: Preoperative SIRI is a promising, easily obtainable biomarker for identifying older patients at higher risk of postoperative complications following posterior spinal instrumentation. Its implementation may improve preoperative risk stratification in spine surgery.