Detection of COVID-19 and its pulmonary stage using Bayesian hyperparameter optimization and deep feature selection methods

Since the first case of COVID-19 was reported in December 2019, many studies have been carried out on artificial intelligence for the rapid diagnosis of the disease to support health services. Therefore, in this study, we present a powerful approach to detect COVID-19 and COVID-19 findings from computed tomography images using pre-trained models using two different datasets. COVID-19, influenza A (H1N1) pneumonia, bacterial pneumonia and healthy lung image classes were used in the first dataset. Consolidation, crazy-paving pattern, ground-glass opacity, ground-glass opacity and consolidation, ground-glass opacity and nodule classes were used in the second dataset. The study consists of four steps. In the first two steps, distinctive features were extracted from the final layers of the pre-trained ShuffleNet, GoogLeNet and MobileNetV2 models trained with the datasets. In the next steps, the most relevant features were selected from the models using the Sine–Cosine optimization algorithm. Then, the hyperparameters of the Support Vector Machines were optimized with the Bayesian optimization algorithm and used to reclassify the feature subset that achieved the highest accuracy in the third step. The overall accuracy obtained for the first and second datasets is 99.46% and 99.82%, respectively. Finally, the performance of the results visualized with Occlusion Sensitivity Maps was compared with Gradient-weighted class activation mapping. The approach proposed in this paper outperformed other methods in detecting COVID-19 from multiclass viral pneumonia. Moreover, detecting the stages of COVID-19 in the lungs was an innovative and successful approach.     

Hemodialysis‐related headache

Headache is one of the most frequently encountered neurological symptoms during hemodialysis. According to International Classification of Headache criteria dialysis‐related headache was defined as the headache occurring during hemodialysis with no specific characteristic. It resolves spontaneously within 72 hours after the hemodialysis session ends. There are few studies in the literature investigating the clinical features of dialysis headache. The pathophysiology of hemodialysis‐related headache is not known, but various triggering factors have been identified, including changes in blood pressure, serum sodium and magnesium levels during hemodialysis sessions, caffeine deprivation and stress. The aim of this article is to evaluate and analyze features of headache in patients undergoing hemodialysis.     

Plasma polymerization‐modified bacterial polyhydroxybutyrate nanofibrillar scaffolds

The design and the development of novel scaffold materials for tissue engineering have attracted much interest in recent years. Especially, the prepared nanofibrillar scaffold materials from biocompatible and biodegradable polymers by electrospinning are promising materials to be used in biomedical applications. In this study, we propose to produce low‐cost and cell‐friendly bacterial electrospun PHB polymeric scaffolds by using Alcaligenes eutrophus DSM 545 strain to PHB production. The produced PHB was characterized by Nuclear Magnetic Resonance (NMR) and Fourier Transform Infrared Spectroscopy (FTIR). Nanofibrous scaffolds were fabricated via electrospinning method that has a fiber diameter approximately 700–800 nm. To investigate cell attachment, cell growth, and antioxidant enzyme activity on positively and negatively charged PHB scaffold, PHB surface was modified by plasma polymerization technique using polyethylene glycol (PEG) and ethylenediamine (EDA). According to the results of superoxide dismutase (SOD) activity study, PEG‐modified nanofibrillar scaffolds indicated more cellular resistance against oxidative stress compared to the EDA modification. As can be seen in cell proliferation results, EDA modification enhanced the cell proliferation more than PEG modification, while PEG modification is better as compared with nonmodified scaffolds. In general, through plasma polymerization technique, surface modified nanofibrillar structures are effective substrates for cell attachment and outgrowth. © 2012 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2013     

Purification and characterisation of a polyphenol oxidase from Boletus erythropus and investigation of its catalytic efficiency in selected organic solvents

Polyphenol oxidase (PPO) was purified from Boletus erythropus using a Sepharose 4B-L-tyrosine-p-amino benzoic acid affinity column. Optimum pH and temperature were found to be 8.0 and 20 °C, respectively, using 4-methylcatechol as a substrate. The enzyme was extremely stable between pH 3.0 and 9.0 after 24 h incubation at 4 °C. B. erythropus PPO was also quite stable between 10 and 30 °C after 4 h incubation. The K_m and V_max values were calculated as 2.8 mM and 1430 U/mg protein by Lineweaver–Burk curve, respectively. The enzyme activity was inhibited by sodium metabisulfite, ascorbic acid, sodium azide and benzoic acid. It was seen that the mushroom PPO was an effective biocatalyst in selected organic solvents, such as dichloromethane, dichloroethane and toluene, when catechin was used as a substrate. All data support that B. erythropus has a highly active PPO, possessing similar biochemical and kinetic characteristics to other plant PPOs.     

A new DEA approach to fully rank DMUs with an application to MBA programs

Data envelopment analysis (DEA) can be used to evaluate the efficiencies of decision‐making units (DMUs) in various areas like education, healthcare, and energy. Several DEA methods are proposed for this purpose; however, some of these methods cannot provide a full ranking and others often overlook some considerations that arise with special characteristics of DMUs. We propose a new DEA‐based approach to achieve a full ranking of DMUs. Our approach takes various issues into account such as the initial efficiency score of the DMU, the DMUs that should be removed from the set for it to become efficient (if any) and its effects on the efficiency scores of other DMUs. We demonstrate the shortcomings of several other DEA methods and discuss how our approach overcomes these. We apply our approach to evaluate 50 MBA programs from Financial Times 2018 rankings and compare the results with the evaluations of other methods. As opposed to some methods, our approach has the advantage of differentiating between all efficient DMUs as well as inefficient ones. In addition, the results demonstrate that we can achieve a consistent ranking that considers different aspects of the problem setting. The generated scores are also used to sort DMUs in classes of preference.