Cancer is one of the leading causes of death worldwide. Early detection of cancer can play a decisive role in cancer treatment and improving survival rates. Conventional cancer detection methods, such as biopsy, imaging and blood tests are generally invasive and time-consuming, and their results have accuracy issues. Biosensors with artificial intelligence integration play a significant and evolving role in cancer diagnostics, offering non-invasive, rapid, and highly sensitive methods for early detection, monitoring, and treatment of cancer. Biosensors detect specific biomarkers associated with cancerous cells or tumours, such as nucleic acid (DNA, RNA), small molecules, peptides, proteins and metabolites. In recent years, many predictive artificial intelligence models and bioinformatics tools have been developed to integrate biosensors, emerging as powerful tools for cancer diagnostics. This review explores the role of biosensors in cancer detection, the development and application of predictive AI models and bioinformatics tools in cancer detection through biosensor technologies, and the challenges associated with their clinical adoption.

Bacteriophages are viruses that infect bacteria, which are essential for controlling bacterial diversity. Among the novel aspects, phage display-based strategies are used for epitope mapping and the development of immunotherapy. A recent classification system has been developed based on the recent sequencing methods and bioinformatic tools. The unique specificity of phages is of increasing use in biocontrol, where bacteriophages are applied to target and reduce harmful bacterial populations in agriculture, food preservation and safety, offering a sustainable alternative to chemical exposure and a plausible solution to excessive misuse of antibiotics. Phage therapy has emerged as a complement to antibiotics for difficult-to-treat infectious diseases such as multi-drug resistant bacteria where other alternatives are lacking. The ability of bacteriophages to specifically target pathogenic bacteria while sparing the normal flora makes them attractive treatment options. Among the challenges are the slow uptake of phage therapy in the clinical setting, a lack of standardisation and regulatory issues. Nevertheless, phage-based strategies are likely to become a future cornerstone for biocontrol and treatment of infectious diseases.

Probiotics are live microorganisms that have gained significant attention due to their potential to improve human health. Nowadays, probiotics are widely used to prevent and treat gastrointestinal disorders, such as irritable bowel syndrome, diarrhea, and inflammatory bowel disease. In the past few years, probiotics have been explored for their role in immune modulation, mental health, and skin conditions. Probiotic strains from the bacterial genus Lactobacillus, Bifidobacterium, and yeast Saccharomyces boulardii have demonstrated positive effects on gut microbiota composition as well as overall health. Advances in machine learning models based on next-generation genomic sequencing information and microbiome research are unveiling new probiotic strains and supporting further development of personalized probiotic therapies tailored to individual microbiomes. In spite of their promising health benefits, many challenges still remain, including strain-specific variability, regulatory hurdles, and long-term safety and efficacy concerns. This review article covers the overall current market scenario, probiotic research and development, and new bioinformatics approaches in the discovery of new probiotic strain identification for health benefits.

Visceral leishmaniasis is a neglected tropical disease. Drug resistance and toxicity are the critical issues with the currently available antileishmanial drugs. Therefore, research efforts are underway to identify and validate new drug targets specific to Leishmania parasite. The enzyme homoserine dehydrogenase (HSD) functions in the third step of aspartate pathway. The present study focuses on the biophysical and biochemical characterization of HSD enzyme from Leishmania donovani (LdHSD) which is unique to the parasite with no homologous enzyme in the host. LdHSD gene was cloned in pET28c(+) vector and transformed in E. coli BL21 (DE3) strain. LdHSD recombinant enzyme of molecular weight 46.6 kDa with 6X-His tag at the C-terminal end was expressed, purified by nickel affinity chromatography and confirmed by western blot analysis using anti-His antibody. Effect of pH, temperature, salts, metal ions and amino acids on the recombinant enzyme were evaluated. Kinetic parameters of LdHSD were evaluated for substrates L-homoserine and NADP+. Biophysical analysis revealed that the enzyme is rich in β-sheets. Thermal denaturation study revealed that the protein is stable up to 45 °C. Furthermore, comprehensive comparative sequence analysis and structural modeling revealed the structural and functionally important residues, which are involved in the catalytic mechanisms. The putative binding mode of the natural substrate L-homoserine into the active site of LdHSD was also elucidated. These findings provide a foundation for the development of selective, target-based inhibitors against the HSD enzyme of the parasite.

Lipolytic enzymes include triacylglycerol lipases (EC 3.1.1.3) and esterases (EC 3.1.1.1) that catalyze the cleavage and formation of ester bonds. They are potential industrial biocatalysts because of their broad range of activities on natural and synthetic substrates, high stability in organic solvents, thermal stability, stability in highly acidic and alkaline pH conditions and enantio-, regio-and chemo-selectivity. They also have varied ap-plications in different sectors, among which industrial biotechnology, the production of cleaning agents, and pharmaceuticals are the most important ones. Identifying extremophilic lipolytic enzymes is of paramount in-terest and is a growing field in academic and industrial research. This review is focused on the current knowledge and future avenues of investigation on lipolytic enzymes sourced from the underexploited archaeal domain. Archaea is a potential source for novel extremophilic enzymes, which have high demand in the industries. The archaeal lipases and esterases are clustered into different families based on their similarity/dissimilarity at the genetic level and protein structures. The updated information on characterized and putative lipase sequences has also been presented in this paper. Common structural scaffolds of archaeal lipases have been deduced and dis-cussed in this review. However, huge diversity at the level of their genetic sequences has yet to be correlated with the structure-function relationship. Based on their biochemical properties, possible applications and future prospective of archaeal lipolytic enzymes have also been proposed.