Background: Every year, lung cancer contributes to a high percentage deaths in the world. Early detection of lung cancer is important for its effective treatment, and non-invasive rapid methods are usually used 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). Methods: A total of 301 patients diagnosed with lung carcinoma pathologies in our hospital were included in the study. In the thorax, Computed Tomography (CT) was performed for diagnostic purposes prior to the treatment. After tagging the section images, tumor detection, small and 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 exhibited 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, in 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 the success of the deep learning methods, they should be integrated into radiology to diagnose lung carcinoma.

The primary scope of this study is to determine the gamma radiation absorption characteristics of a commercial LD glass-ceramic biomaterial through GAMOS simulation and Phy-X/PSD software, and then compare these results with experimental data to provide information about a radiation absorption property of this material. Elemental analyses of the LD glass-ceramic biomaterial were conducted in this research using X-ray diffraction (XRD) techniques. In the experimental study, gamma rays with energies from 81 keV to 1408 keV, emitted by sources such as ¹³³Ba, ¹⁵²Eu, ²²Na, ¹³⁷Cs, ⁵⁷Co, and ⁶⁰Co, were detected utilizing a gamma spectrometer with a 2″×2″ NaI(Tl) detector and associated electronic systems. The experimental radiation absorption data were analyzed alongside the results generated by the GAMOS simulation and the Phy-X/PSD program for comparison. The linear attenuation coefficients were 0.418, 0.361, 0.266, 0.289, 0.250, 0.208, 0.184, 0.175, 0.142, 0.177, 0.123, 0.123, 0.135, and 0.122 cm-1 at gamma energies of 81, 121.8, 276.4, 302.9, 356, 383.9, 511, 661.7, 779, 964.1, 1173.2, 1274.5, 1332.5, and 1408 keV, respectively. The mass attenuation coefficient values began to decrease rapidly as the gamma photon was increased to 1 MeV. Half value layer ranged from 1.315 cm at 81 keV to 6.243 cm at 1408 keV. Similarly, tenth value layer ​​also increased when photon energy increased. The mean free path values ​​vary between 2.394 cm^− 1 and 8.184 cm^− 1. In conclusion, it has been observed that the radiation absorption parameters of lithium disilicate dental biomaterial vary depending on the energy level.

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 Kutahya Medical Sciences University Innovative Technology Laboratory. The temperature was 250 degrees 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.

Brain arteriovenous malformations (AVMs) are congenital anomalies that present as intracranial hemorrhage or epilepsy. AVMs often remain clinically silent for extended periods. Although AVM treatme...

Against the detrimental effects of ionizing radiation, time, distance, and shielding are the three most significant protective methods. Lead is the material of choice for shielding, particularly for personal protective equipment. However, lead's density, rigidity, and toxicity are significant disadvantages. In recent years, tungsten-containing rubber (TCR) and other lightweight, flexible, and non-toxic shielding materials have emerged as viable alternatives to lead. The purpose of this study is to examine the X-ray absorption capacity of TCR material, which can serve as an alternative to lead-based personal protective equipment. Using GAMOS simulation, radiation absorption characteristics for 11 different X-ray energies ranging from 30 to 1000 keV were obtained and compared with Phy-X/PSD data. While the MFP value produced from the GAMOS code for the TCR with 100 keV energy X rays was 0.0204 cm, the Phy-X/PSD value was 0.0296 cm. The HVL value of the TCR material for X-rays with an energy of 100 keV was 0.021 cm for Phy-X/PSD and 0.014 cm for GAMOS. It has been observed to provide excellent radiation protection against X-rays in the diagnostic imaging field. By providing greater flexibility than lead shielding materials, TCR can play a crucial role in decreasing radiation exposure.