A cytokine storm is an intense inflammatory response characterized by the overproduction of proinflammatory cytokines, resulting in tissue damage, and organ dysfunction. Cytokines play a crucial role in various conditions, such as coronavirus disease, in which the immune system becomes overactive and releases excessive levels of cytokines, including interleukins, tumor necrosis factor-alpha (TNF-α), and interferon-gamma (IFN-γ). This anomalous response often leads to acute respiratory distress syndrome (ARDS), disseminated intravascular coagulation (DIC), and multiple organ injury (MOI). Glucosinolates are plant secondary metabolites predominantly found in Brassica vegetables, but are also present in other species, such as Moringa Adens and Carica papaya L. When catalyzed by the enzyme myrosinase, glucosinolates produce valuable products, including sulforaphane, phenethyl isothiocyanate, 6-(methylsulfinyl) hexyl isothiocyanate, erucin, goitrin, and moringin. These hydrolyzed products regulate proinflammatory cytokine production by inhibiting the nuclear factor kappa-light-chain-enhancer of activated B-cell (NF-κB) signaling pathway and stimulating the nuclear factor erythroid 2-related factor 2 (Nrf2) signaling pathway. This action can alleviate hyperinflammation in infected cells and modulate cytokine storms. In this review, we aimed to examine the potential role of glucosinolates in modulating cytokine storms and reducing inflammation in various conditions, such as coronavirus disease. Overall, we found that glucosinolates and their hydrolysis products can potentially attenuate cytokine production and the onset of cytokine storms in diseased cells. In summary, glucosinolates could be beneficial in regulating cytokine production and preventing complications related to cytokine storms.

DNA barcoding is a technique for identifying specimens using brief, standardised DNA segments. In a variety of fields, including phylogeny, ecology, population genetics and biodiversity, DNA barcoding has become a successful method for precisely distinguishing species. The method is straightforward, efficient in both time and money and accurate. The key to successful DNA barcoding is choosing the right DNA marker. Since the idea of a quick approach for species identification was first put up in 2003, the scientific community has been keen to realise the potential of DNA barcodes. Cytochrome c oxidase, I (COI) region of the mitochondrial genome is mostly recognised as a standard barcoding region in animals. Later, rbcL + matK pairing, with a 70 % discriminating efficiency, was suggested by the Plant Working Group (PWG) of the Consortium for the Barcoding of Life (CBOL) as the standard barcode in plants. Three conditions must be met for a gene region to be an efficient DNA barcode: it must have sufficient species-level genetic divergence and variability, it must have conserved flanking regions for the widest taxonomic use and for generating universal PCR primers and it should be long enough to facilitate current capability for sequencing and extracting DNA. Different combinations of plastid coding, non-coding and nuclear markers are utilised as supplemental markers to boost the degree of plant species differentiation. The reliability of different barcodes in distinguishing species varies among different groups of plants. As DNA barcoding approaches its twentieth anniversary, technologies are still being developed that make use of this resource, which is constantly expanding in a variety of biological disciplines. Plant DNA barcoding, which became a scientific advance during the last ten years, is frequently employed as a taxonomical aid in identifying species. It is a way of choosing genetic loci that identifies and distinguishes an organism's membership from specific species, variations or even intervarieties. It varies from molecular phylogeny, which identifies an unknown sample from an existing classification rather than identifying patterns of association.

DNA barcoding is a significant and valuable method for identifying species and it is one of the key fields of biodiversity and evolutionary research. It changed biodiversity studies with computational techniques and next-generation sequencing. Post-sequencing data analysis is an essential stage encompassing many critical procedures to ensure precise identification of organisms and their classification. Processing generated mass-sequenced information is a significant problem in any barcoding studies. Various analysis methods are employed for inferring organismal taxonomy, such as tree-based, similarity-based, composition-based and hybrid methods. This study will review the diversity of these computational methods for post-sequence data analysis in DNA barcoding studies. Tree-based techniques (e.g.: MrBayes and RAxML) illustrate evolutionary relationships among species, similarity-based techniques (e.g.: SOrt-ITEMS and BLAST) assist in identifying species by sequence similarities and composition-based techniques (e.g.: Phymm and NBC) categorize species according to their nucleotide composition. These methods are combined in hybrid approaches (e.g.: PhyScimm and RITA) to provide an in-depth investigation. Computational tools for post-sequence analysis use graphical user, command line or web-based interfaces with supervised, unsupervised, or semi-supervised machine learning approaches. Operating systems such as Linux, UNIX, Windows and macOS are used to analyze DNA barcoding data, while Java, R, Python, C/C++ and Perl are the most widely used programming languages. This review emphasizes how crucial it is to incorporate such bioinformatics and computational techniques to improve the robustness and consistency of DNA barcoding studies and provide an adequate set of tools for advanced biodiversity research.