Osteopoikilosis is a rare bone dysplasia which is inherited as an autosomal dominant trait with a prevalence of less than 0.1 per million.1 It is characterised by dense ovoid or circular spots in cancellous bone which may appear at birth or during skeletal growth. It is usually found in the metaphyseal and epiphyseal regions of long bones, the carpals and tarsals, the end of large turbular bones and around the acetabula. It is clinically asymptomatic and occasionally associated with hereditary multiple exostosis and dermatofibrosis lenticularis disseminata. It is not associated with spontaneous fractures and treatment is unnecessary. However a case of osteosarcoma developing in a man with osteopoikilosis has been reported. The first case of osteopoikilosis was reported in Malaysia four years ago in a 25 years old lady who is also of Indian descent. It would be interesting to know if these two patients are related. Since the bone lesions could easily be mistaken for metastatic disease, it is important that family physicians be aware of the benign nature of this condition.
Polymer electrolytes and composites have prevailed in the high performance and mobile marketplace during recent years. Polymer-based solid electrolytes possess the benefits of low flammability, excellent flexibility, good thermal stability, as well as higher safety. Several researchers have paid attention to the optical properties of polymer electrolytes and their composites. In the present review paper, first, the characteristics, fundamentals, advantages and principles of various types of polymer electrolytes were discussed. Afterward, the characteristics and performance of various polymer hosts on the basis of specific essential and newly published works were described. New developments in various approaches to investigate the optical properties of polymer electrolytes were emphasized. The last part of the review devoted to the optical band gap study using two methods: Tauc's model and optical dielectric loss parameter. Based on recently published literature sufficient quantum mechanical backgrounds were provided to support the applicability of the optical dielectric loss parameter for the band gap study. In this review paper, it was demonstrated that both Tauc's model and optical dielectric loss should be studied to specify the type of electron transition and estimate the optical band gap accurately. Other parameters such as absorption coefficient, refractive index and optical dielectric constant were also explored.
The most widely used method for detecting Coronavirus Disease 2019 (COVID-19) is real-time polymerase chain reaction. However, this method has several drawbacks, including high cost, lengthy turnaround time for results, and the potential for false-negative results due to limited sensitivity. To address these issues, additional technologies such as computed tomography (CT) or X-rays have been employed for diagnosing the disease. Chest X-rays are more commonly used than CT scans due to the widespread availability of X-ray machines, lower ionizing radiation, and lower cost of equipment. COVID-19 presents certain radiological biomarkers that can be observed through chest X-rays, making it necessary for radiologists to manually search for these biomarkers. However, this process is time-consuming and prone to errors. Therefore, there is a critical need to develop an automated system for evaluating chest X-rays. Deep learning techniques can be employed to expedite this process. In this study, a deep learning-based method called Custom Convolutional Neural Network (Custom-CNN) is proposed for identifying COVID-19 infection in chest X-rays. The Custom-CNN model consists of eight weighted layers and utilizes strategies like dropout and batch normalization to enhance performance and reduce overfitting. The proposed approach achieved a classification accuracy of 98.19% and aims to accurately classify COVID-19, normal, and pneumonia samples.
This study proposes a novel nature-inspired meta-heuristic optimizer based on the Reptile Search Algorithm combed with Salp Swarm Algorithm for image segmentation using gray-scale multi-level thresholding, called RSA-SSA. The proposed method introduces a better search space to find the optimal solution at each iteration. However, we proposed RSA-SSA to avoid the searching problem in the same area and determine the optimal multi-level thresholds. The obtained solutions by the proposed method are represented using the image histogram. The proposed RSA-SSA employed Otsu's variance class function to get the best threshold values at each level. The performance measure for the proposed method is valid by detecting fitness function, structural similarity index, peak signal-to-noise ratio, and Friedman ranking test. Several benchmark images of COVID-19 validate the performance of the proposed RSA-SSA. The results showed that the proposed RSA-SSA outperformed other metaheuristics optimization algorithms published in the literature.
Kidney tumor (KT) is one of the diseases that have affected our society and is the seventh most common tumor in both men and women worldwide. The early detection of KT has significant benefits in reducing death rates, producing preventive measures that reduce effects, and overcoming the tumor. Compared to the tedious and time-consuming traditional diagnosis, automatic detection algorithms of deep learning (DL) can save diagnosis time, improve test accuracy, reduce costs, and reduce the radiologist's workload. In this paper, we present detection models for diagnosing the presence of KTs in computed tomography (CT) scans. Toward detecting and classifying KT, we proposed 2D-CNN models; three models are concerning KT detection such as a 2D convolutional neural network with six layers (CNN-6), a ResNet50 with 50 layers, and a VGG16 with 16 layers. The last model is for KT classification as a 2D convolutional neural network with four layers (CNN-4). In addition, a novel dataset from the King Abdullah University Hospital (KAUH) has been collected that consists of 8,400 images of 120 adult patients who have performed CT scans for suspected kidney masses. The dataset was divided into 80% for the training set and 20% for the testing set. The accuracy results for the detection models of 2D CNN-6 and ResNet50 reached 97%, 96%, and 60%, respectively. At the same time, the accuracy results for the classification model of the 2D CNN-4 reached 92%. Our novel models achieved promising results; they enhance the diagnosis of patient conditions with high accuracy, reducing radiologist's workload and providing them with a tool that can automatically assess the condition of the kidneys, reducing the risk of misdiagnosis. Furthermore, increasing the quality of healthcare service and early detection can change the disease's track and preserve the patient's life.