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The Coronavirus Disease 2019 (COVID-19) is widespread throughout the world and poses a serious threat to public health and safety. A COVID-19 infection can be recognized using computed tomography (CT) scans. To enhance the categorization, some image segmentation techniques are presented to extract regions of interest from COVID-19 CT images. Multi-level thresholding (MLT) is one of the simplest and most effective image segmentation approaches, especially for grayscale images like CT scan images. Traditional image segmentation methods use histogram approaches; however, these approaches encounter some limitations. Now, swarm intelligence inspired meta-heuristic algorithms have been applied to resolve MLT, deemed an NPhard optimization task. Despite the advantages of using meta-heuristics to solve global optimization tasks, each approach has its own drawbacks. However, the common flaw for most meta-heuristic algorithms is that they are unable to maintain the diversity of their population during the search, which means they might not always converge to the global optimum. This study proposes a cooperative swarm intelligence-based MLT image segmentation approach that hybridizes the advantages of parallel meta-heuristics and MLT for developing an efficient image segmentation method for COVID-19 CT images. An efficient cooperative model-based meta-heuristic called the CPGH is developed based on three practical algorithms: particle swarm optimization (PSO), grey wolf optimizer (GWO), and Harris hawks optimization (HHO). In the cooperative model, the applied algorithms are executed concurrently, and a number of potential solutions are moved across their populations through a procedure called migration after a set number of generations. The CPGH model can solve the image segmentation problem using MLT image segmentation. The proposed CPGH is evaluated using three objective functions, cross-entropy, Otsu's, and Tsallis, over the COVID-19 CT images selected from open-sourced datasets. Various evaluation metrics covering peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and universal quality image index (UQI) were employed to quantify the segmentation quality. The overall ranking results of the segmentation quality metrics indicate that the performance of the proposed CPGH is better than conventional PSO, GWO, and HHO algorithms and other state-of-the-art methods for MLT image segmentation. On the tested COVID-19 CT images, the CPGH offered an average PSNR of 24.8062, SSIM of 0.8818, and UQI of 0.9097 using 20 thresholds. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Peptic ulcer associated with H. pylori infection and antibiotics resistance warranted the execution of the foregoing study to produce natural and safe alternatives. GC-MS analysis of the essential oil derived from the ripe and unripe peel of Citrus sinensis var. Valencia grown in Egypt was investigated besides its anti-Helicobacter pylori and COX-2 inhibition actions. Ripe mature peels displayed a higher oil yield than unripe ones (0.8 and 0.6%, respectively). Twenty-nine compounds were identified in all samples with volatile monoterpenes as the predominant class, (88.90% and 90.62% in fresh ripe peel (P1) and dried ripe peel (P2), respectively, however unripe fresh orange peel (P3) showed a lower quantity (83.95%). D-Limonene was the major compound in P1 and P2 (85% and 88 %, respectively), while 73% were found in P3. In comparison to standard Clarithromycin (MIC90 = 1.95 ± 0.002 μg/ml), the nano-hexosomal formula of P1 reduced H. pylori action more than P1 alone with MIC90= 1.95 ± 0.0029 μg/ml and MIC90= 3.90 ± 0.006 μg/ml for formulated and unformulated P1, respectively. An anti-inflammatory assay via COX-2 inhibition revealed that the formulated P1 is more potent (IC50= 0.85 μg/ml) than P1 alone (IC50= 2.04 μg/ml) in comparison with Celecoxib® (IC50= 0.28 μg/ml). To unravel the mechanism of the anti-Helicobacter pylori activity, an in silico study on D-Limonene has been performed, using H. pylori urease enzyme, which revealed that D-Limonene showed promising binding scores. Thus, Valencia orange peel essential oil offers a natural, cheap, and relatively safe candidate to combat Helicobacter pylori infection. [ABSTRACT FROM AUTHOR]