Background This study aims to compare the clinical results of patients with upper thoracic vertebral fractures treated with pedicle screw and posterior spinal fusion with preoperative surgical planning and 3-dimensional (3D) modeling and patients treated with freehand screws. Methods Fifty patients who underwent pedicle screw placement with a diagnosis of upper thoracic fracture between June 2018 and October 2020 were included in our study. Pedicle screws were used in 25 patients (group 1) after the planning was completed with the help of 3D preoperative printing and modeling. Pedicle screws were applied in 25 patients in the control group (group 2) using the freehand technique. Intraoperative bleeding amount, operation time, and correct screw placement data in both groups were recorded. Results The operation time was 134 ± 22 minutes for group 1 and 152 ± 38 minutes for group 2. The difference in operation times was found to be statistically significant (p < 0.05). Based on axial and sagittal reconstruction images, the accuracy rate of pedicle screw placement (grades 0 and 1) in group I was 96.6% compared to 83.6% in group II. The minor perforation rate (grade 1, <2 mm) was 5.8% in group I compared to 11.8% in group II. The moderate perforation rate (grade 2, 2-4 mm) was 3.4% in group I compared to 14% in group II. The severe perforation rate (grade 3, >4 mm) was 2.3% in group II; however, misplaced screws were not associated with neurological deficits. The difference in overall accuracy rates between the two groups was significant (p < 0.05). Conclusions For 3D models of upper thoracic pedicle screw insertion, guide plates can be produced inexpensively and individually. It provides a new method for the accurate placement of upper thoracic pedicle screws with high accuracy and secure use in screw insertion.

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Introduction Application fields of bone tissue engineering studies continue to enlarge. New biocompatible materials aimed to improve bone repairment and regeneration of implants are being discovered everyday by scientists, engineers, and surgeons. Biocompatible, safe, and efficient biomaterials with a gradually increasing importance and field of application today are materials used to fulfill and support the functions of organs and living tissues in human body [1]. Hydroxyapatite, collagen, hyaluronic acid, poly fumarates, poly caprolactone, polylactic acid (PLA), polyglycolic acid (PGA), and PLA and PGA copolymers are some of the biomaterials used in medical field. Characteristics of these materials such as durability and compatibility were increased by combinations. In addition Background/aim: Application fields of bone tissue engineering studies continue to expand. New biocompatible materials aimed to improve bone repairment and regeneration of implants are being discovered everyday by scientists, engineers, and surgeons. Our objective in this study is to combine polylactic acid which is a polymer with hydroxyapatite in the repairment of bone defects considering the increased need by medical application fields. Materials and methods: After 750 g of PLA with a diameter of 2.85 mm was granulated into minimum particles, these particles were homogenously mixed with hydroxyapatite prepared in laboratory environment. Using this mixture, HA-PLA filament with a diameter of 2.85 mm was prepared in the extrusion device in Kütahya Medical Sciences University Innovative Technology Laboratory. The temperature was 250 °C and the gearmotor speed was 9 rpm during extrusion. X-ray diffraction (XRD) analysis was made for crystal phase analyses of the produced hydroxyapatite powder, to determine the produced main phase and examine whether a minor phase occurred. Vickers microhardness test was applied on both samples to measure the endurance levels of the samples prepared with HA-PLA filament. A loading force of 10 kg was applied on the samples for 10 s. Results: Hydroxyapatite peaks in XRD spectrum of the sample presented in figures are concordant with Joint Committee on Powder Diffraction Standards, JCPDS-File Card No. 01-075-9526 and no significant minor phase was observed. For both samples, hardness value was observed to increase between 3 and 5 mm. Conclusion: Surfacing hydroxyapatite on metallic materials is possible. By similar logic, to increase durability with low cost, characteristics of biomaterials can be improved with combinations such as hydroxyapatite PLA. Thus, we found that while these materials have usage limitations due to present disadvantages when used alone, it is possible to increase their efficiency and availability through different combinations.

Background: In every year, lung cancer is an important cause of deaths in the world. Early detection of lung cancer is important for treatment, and non-invasive rapid methods are needed for diagnosis. Introduction: In this study, we aimed to detect lung cancer using deep learning methods and determine the contribution of deep learning to the classification of lung carcinoma using a convolutional neural network (CNN). Method: A total of 301 patients with diagnosed with lung carcinoma pathologies in our hospital were included in the study. In the thorax computed tomography (CT) performed for diagnostic purposes prior to treatment. After tagging the section images, tumor detection, small-non-small cell lung carcinoma differentiation, adenocarcinoma-squamous cell lung carcinoma differentiation, and adenocarcinoma-squamous cell-small cell lung carcinoma differentiation were sequentially performed using deep CNN methods. Result: : In total, 301 lung carcinoma images were used to detect tumors, and the model obtained with the deep CNN system had 0.93 sensitivity, 0.82 precision, and 0.87 F1 score in detecting lung carcinoma. In the differentiation of small cell-non-small cell lung carcinoma, the sensitivity, precision and F1 score of the CNN model at the test stage were 0.92, 0.65, and 0.76, respectively. In the adenocarcinoma-squamous cancer differentiation, the sensitivity, precision, and F1 score were 0.95, 0.80, and 0.86, respectively. The patients were finally grouped as small cell lung carcinoma, adenocarcinoma, and squamous cell lung carcinoma, and the CNN model was used to determine whether it could differentiate these groups. The sensitivity, specificity, and F1 score of this model were 0.90, 0.44, and 0.59, respectively for this differentiation. Conclusion.: In this study, we successfully detected tumors and differentiated between adenocarcinoma-squamous cell carcinoma groups with the deep learning method using the CNN model. Due to their non-invasive nature and success of the deep learning methods, they should be integrated into radiology to diagnose lung carcinoma.

Studies on cranial gunshot injuries in the Syrian war are present in the literature. However, the effect of surgical timing on the clinical outcomes of patients undergoing surgical treatment has not been discussed extensively. In this study, the time from injury to surgery is called ''time to surgery.'' Kilis, a city close to Aleppo, Afrin, and Azez, where the conflicts in Syria are intense, is one of the cities where the first emergency treatments were administered. This study aimed to evaluate patients who underwent surgery in Kilis State Hospital due to cranial gunshot injury in the Syrian war and to investigate the effect of surgical timing on mortality and Glasgow Outcome Score.Surgical treatment was applied to 42 (32.8%) patients in the first 4 hours, 64 (50%) patients within 4 to 24 hours, and 22 (17.2%) patients between 24 hours and 3 days. As the time to surgery decreased, the good Glasgow Outcome Score (GOS) (4-5) outcome rates increased. The differences in surgical timing and GOS results of patients with Glasgow Coma Score (GCS) <8 and >8 were found to be significant for good GOS results. As the time to surgery decreased for patients with a GCS <8 and >8, mortality rates decreased equally. This result was statistically significant.Our study showed that surgical timing is as important as early intubation, aggressive resuscitation, and admission GCS for both survey and GOS.