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Introduction: Amyotrophic lateral sclerosis (ALS), a neurodegenerative disease that causes muscle weakness, paralysis, and death, develops when motor neurons begin to die. There are few proven treatments for ALS, and because the disease is incurable, the exact cause is unknown, making it a devastating condition. According to recent research, marine algae may contain bioactive substances that can be used to treat ALS. Methods: The comprehensive review of recent publications focused on bioactive compounds extracted from various species of marine algae, including their mechanisms of action against oxidative stress, neuroinflammation, and apoptosis in ALS. The publications were reviewed in scientific journals (ScienceDirect, Springer, Taylor & Francis, and MDPI) and indexed in several databases (Scopus, Web of Science, PubMed, Google Scholar, and so on). Discussion: Compounds derived from marine algae, including polyunsaturated fatty acids, fucoxanthin, and polysaccharides, exhibit potential neuroprotective effects by modulating neuroinflammation and oxidative stress levels. Fucoxanthin, fucosterol, and alginate demonstrated potential in mitigating oxidative damage and inflammation, which are critical factors in the pathogenesis of ALS. Conclusion: Bioactive compounds obtained from marine algae demonstrate considerable potential as therapeutic agents for ALS, owing to their capacity to influence multiple pathways linked to oxidative stress and neuroinflammation. Further investigation is required to comprehend their mechanisms and medicinal value, as well as develop novel alternative treatments for ALS.

Introduction: The beginning of exosome biosynthesis is marked by the emergence of the initial endosomes through the inward splitting of the plasma cell membrane. This process is facilitated through the endosomal categorization complex essential for transport, which is also involved in the production of different extracellular vesicles. Exosomes are naturally occurring nanosized vesicles found in all bodily fluids and can be successfully extracted from preserved biological materials, while maintaining their structural integrity. Methods: The articles published recently in high-quality journals (Science Direct, Springer, Institute of Electrical and Electronics Engineers (IEEE), and Taylor & Francis) indexed in various indexing sources such as Scopus, Web of Science, PubMed, Google Scholar, and so on were collected using keywords such as fluid biopsy (FB) exosomes, endosomes, and noncommunicable diseases (NCDs) treatment, new biomarkers, and treatment. Discussion: In recent years, exosomes have emerged as an exciting option for “FB” that has demonstrated significant potential in the areas of noninvasive medical testing, predictions, as well as tracking responses to therapy for noncommunicable diseases. Nevertheless, specific constraints need to be addressed to expand the application of exosome-based FB as a widely accepted and reliable testing method in typical medical facilities. Conclusion: This review provides a comprehensive overview of our present understanding of exosomes to be an FB method for diagnosing, predicting outcomes, and tracking treatment responses in NCDs. It also discusses the main constraints, innovations in technology, as well as future possibilities of using this application in medical treatment.

In the literature, a considerable number of image processing and evaluation procedures are proposed and implemented in various domains due to their practical importance. Thresholding is one of the pre-processing techniques, widely implemented to enhance the information in a class of gray/RGB class pictures. The thresholding helps to enhance the image by grouping the similar pixels based on the chosen thresholds. In this research, an entropy assisted threshold is implemented for the benchmark RGB images. The aim of this work is to examine the thresholding performance of well-known entropy functions, such as Kapur’s and Tsallis for a chosen image threshold. This work employs a Moth-Flame-Optimization (MFO) algorithm to support the automatic identification of the finest threshold (Th) on the benchmark RGB image for a chosen threshold value (Th=2,3,4,5). After getting the threshold image, a comparison is performed against its original picture and the necessary Picture-Quality-Values (PQV) is computed to confirm the merit of the proposed work. The experimental investigation is demonstrated using benchmark images with various dimensions and the outcome of this study confirms that the MFO helps to get a satisfactory result compared to the other heuristic algorithms considered in this study.

Histogram based multilevel thresholding approach is proposed using Brownian distribution (BD) guided firefly algorithm (FA). A bounded search technique is also presented to improve the optimization accuracy with lesser search iterations. Otsu’s between-class variance function is maximized to obtain optimal threshold level for gray scale images. The performances of the proposed algorithm are demonstrated by considering twelve benchmark images and are compared with the existing FA algorithms such as Lévy flight (LF) guided FA and random operator guided FA. The performance assessment comparison between the proposed and existing firefly algorithms is carried using prevailing parameters such as objective function, standard deviation, peak-to-signal ratio (PSNR), structural similarity (SSIM) index, and search time of CPU. The results show that BD guided FA provides better objective function, PSNR, and SSIM, whereas LF based FA provides faster convergence with relatively lower CPU time.

This paper proposes a novel method to tune the I-PD controller structure for the time-delayed unstable process (TDUP) using Bacterial Foraging Optimization (BFO) algorithm. The tuning process is focussed to search the optimal controller parameters (𝐾𝑝, 𝐾𝑖, 𝐾𝑑) by minimising the multiple objective performance criterion. A comparative study on various cost functions like Integral of Squared Error (ISE), Integral of Absolute Error (IAE), Integral of Time-weighted Squared Error (ITSE), and Integral of Time weighted Absolute Error (ITAE) have been attempted for a class of TDUP. A simulation study for BFO-based I-PD tuning has been done to validate the performance of the proposed method. The results show that the tuning approach is a model independent approach and provides enhanced performance for the setpoint tracking with improved time domain specifications.

An enhanced bacteria foraging optimization (EBFO) algorithm-based Proportional + integral + derivative (PID) controller tuning is proposed for a class of nonlinear process models. The EBFO algorithm is a modified form of standard BFO algorithm. A multiobjective performance index is considered to guide the EBFO algorithm for discovering the best possible value of controller parameters. The efficiency of the proposed scheme has been validated through a comparative study with classical BFO, adaptive BFO, PSO, and GA based controller tuning methods proposed in the literature. The proposed algorithm is tested in real time on a nonlinear spherical tank system. The real-time results show that, EBFO tuned PID controller gives a smooth response for setpoint tracking performance.