Self-adaptive moth flame optimizer combined with crossover operator and Fibonacci search strategy for COVID-19 CT image segmentation.

The COVID-19 is one of the most significant obstacles that humanity is now facing. The use of computed tomography (CT) images is one method that can be utilized to recognize COVID-19 in early stage. In this study, an upgraded variant of Moth flame optimization algorithm (Es-MFO) is presented by considering a nonlinear self-adaptive parameter and a mathematical principle based on the Fibonacci approach method to achieve a higher level of accuracy in the classification of COVID-19 CT images. The proposed Es-MFO algorithm is evaluated using nineteen different basic benchmark functions, thirty and fifty dimensional IEEE CEC'2017 test functions, and compared the proficiency with a variety of other fundamental optimization techniques as well as MFO variants. Moreover, the suggested Es-MFO algorithm's robustness and durability has been evaluated with tests including the Friedman rank test and the Wilcoxon rank test, as well as a convergence analysis and a diversity analysis. Furthermore, the proposed Es-MFO algorithm resolves three CEC2020 engineering design problems to examine the problem-solving ability of the proposed method. The proposed Es-MFO algorithm is then used to solve the COVID-19 CT image segmentation problem using multi-level thresholding with the help of Otsu's method. Comparison results of the suggested Es-MFO with basic and MFO variants proved the superiority of the newly developed algorithm.

Hesperidin hexosomal loaded nanodispersion: insights of its antimycobacterial, cytotoxic and anti-HCoV effects.

Fruits of Citrus sinensis L. Osbeck var. Valencia contain hesperidin as a major flavanone glycoside. Hesperidin (H) was isolated from the peels of Valencia orange and formulated as hexosomal nanodispersions (F1) adopting the hot emulsification method. The antimycobacterial activity(anti-TB) was evaluated through a microplate Alamar blue (MABA) assay where F1 showed significant activity with MIC = 0.19 µM. To unravel the potential mechanism of the anti-TB, a molecular docking study of H using the Mycobacterial Dihydrofolate reductase (Mtb. DHFR) enzyme was performed. Hesperidin exhibited significant interactions with Mtb. DHFR active site. Sulforhodamine B assay was applied to evaluate cytotoxic activity against the lung cancer cell line (A-549). F1 showed a cytotoxic effect at IC50= 33 µM. It also has potent antiviral activity against Human Coronavirus 229E with IC50= 258.8 µM utilising crystal violet assay. Peels of Valencia orange could be a source of bioactive metabolites to control significant diseases.

Improved manta ray foraging optimization for multi-level thresholding using COVID-19 CT images.

Coronavirus disease 2019 (COVID-19) is pervasive worldwide, posing a high risk to people's safety and health. Many algorithms were developed to identify COVID-19. One way of identifying COVID-19 is by computed tomography (CT) images. Some segmentation methods are proposed to extract regions of interest from COVID-19 CT images to improve the classification. In this paper, an efficient version of the recent manta ray foraging optimization (MRFO) algorithm is proposed based on the oppositionbased learning called the MRFO-OBL algorithm. The original MRFO algorithm can stagnate in local optima and requires further exploration with adequate exploitation. Thus, to improve the population variety in the search space, we applied Opposition-based learning (OBL) in the MRFO's initialization step. MRFO-OBL algorithm can solve the image segmentation problem using multilevel thresholding. The proposed MRFO-OBL is evaluated using Otsu's method over the COVID-19 CT images and compared with six meta-heuristic algorithms: sine-cosine algorithm, moth flame optimization, equilibrium optimization, whale optimization algorithm, slap swarm algorithm, and original MRFO algorithm. MRFO-OBL obtained useful and accurate results in quality, consistency, and evaluation matrices, such as peak signal-to-noise ratio and structural similarity index. Eventually, MRFO-OBL obtained more robustness for the segmentation than all other algorithms compared. The experimental results demonstrate that the proposed method outperforms the original MRFO and the other compared algorithms under Otsu's method for all the used metrics.