Pyroptosis is the process of inflammatory cell death. The primary function of pyroptosis is to induce strong inflammatory responses that defend the host against microbe infection. Excessive pyroptosis, however, leads to several inflammatory diseases, including sepsis and autoimmune disorders. Pyroptosis can be canonical or noncanonical. Upon microbe infection, the canonical pathway responds to pathogen-associated molecular patterns (PAMPs) and damage-associated molecular patterns (DAMPs), while the noncanonical pathway responds to intracellular lipopolysaccharides (LPS) of Gram-negative bacteria. The last step of pyroptosis requires the cleavage of gasdermin D (GsdmD) at D275 (numbering after human GSDMD) into N- and C-termini by caspase 1 in the canonical pathway and caspase 4/5/11 (caspase 4/5 in humans, caspase 11 in mice) in the noncanonical pathway. Upon cleavage, the N-terminus of GsdmD (GsdmD-N) forms a transmembrane pore that releases cytokines such as IL-1β and IL-18 and disturbs the regulation of ions and water, eventually resulting in strong inflammation and cell death. Since GsdmD is the effector of pyroptosis, promising inhibitors of GsdmD have been developed for inflammatory diseases. This review will focus on the roles of GsdmD during pyroptosis and in diseases.

The advancements of information technology and related processing techniques have created a fertile base for progress in many scientific fields and industries. In the fields of drug discovery and development, machine learning techniques have been used for the development of novel drug candidates. The methods for designing drug targets and novel drug discovery now routinely combine machine learning and deep learning algorithms to enhance the efficiency, efficacy, and quality of developed outputs. The generation and incorporation of big data, through technologies such as high-throughput screening and high through-put computational analysis of databases used for both lead and target discovery, has increased the reliability of the machine learning and deep learning incorporated techniques. The use of these virtual screening and encompassing online information has also been highlighted in developing lead synthesis pathways. In this review, machine learning and deep learning algorithms utilized in drug discovery and associated techniques will be discussed. The applications that produce promising results and methods will be reviewed.

The International Agency for Research on Cancer (IARC) has recently reported a 66% increase in the global number of cancer deaths since 1960. In the US alone, about one in eight women is expected to develop invasive breast cancer(s) (breast cancer) at some point in their lifetime. Traditionally, a BC diagnosis includes mammography, ultrasound, and some high-end molecular bioimaging. Unfortunately, these techniques detect BC at a later stage. So early and advanced molecular diagnostic tools are still in demand. In the past decade, various histological and immuno-molecular studies have demonstrated that BC is highly heterogeneous in nature. Its growth pattern, cytological features, and expression of key biomarkers in BC cells including hormonal receptor markers can be utilized to develop advanced diagnostic and therapeutic tools. A cancer cell's progression to malignancy exhibits various vital biomarkers, many of which are still underrepresented in BC diagnosis and treatment. Advances in genetics have also enabled the development of multigene assays to detect genetic heterogeneity in BC. However, thus far, the FDA has approved only four such biomarkers—cancer antigens (CA); CA 15-3, CA 27-29, Human epidermal growth factor receptor 2 (HER2), and circulating tumor cells (CTC) in assessing BC in body fluids. An adequately structured portable-biosensor with its non-invasive and inexpensive point-of-care analysis can quickly detect such biomarkers without significantly compromising its specificity and selectivity. Such advanced techniques are likely to discriminate between BC and a healthy patient by accurately measuring the cell shape, structure, depth, intracellular and extracellular environment, and lipid membrane compositions. Presently, BC treatments include surgery and systemic chemo- and targeted radiation therapy. A biopsied sample is then subjected to various multigene assays to predict the heterogeneity and recurrence score, thus guiding a specific treatment by providing complete information on the BC subtype involved. Thus far, we have seven prognostic multigene signature tests for BC providing a risk profile that can avoid unnecessary treatments in low-risk patients. Many comparative studies on multigene analysis projected the importance of integrating clinicopathological information with genomic-imprint analysis. Current cohort studies such as MINDACT, TAILORx, Trans-aTTOM, and many more, are likely to provide positive impact on long-term patient outcome. This review offers consolidated information on currently available BC diagnosis and treatment options. It further describes advanced biomarkers for the development of state-of-the-art early screening and diagnostic technologies.

Significance: The somatic hypermutation of immunoglobulin (Ig) variable (V) regions is required to produce high-affinity protective antibodies. Activation-induced deaminase (AID) initiates the accumulation of mutations in the V regions at frequencies that are a million times higher than the normal mutation rates in non-Ig genes. On the basis of the in vivo pattern of mutations in the highly usedIGHV3-23*01human V region and the manipulation of DNA sequence motifs in that V region in a mutating B-cell line and biochemically, we have concluded that particular DNA sequence motifs focus and influence AID activity on those parts of the V region that affect antigen binding and should be considered in vaccine strategies.