Despite advancements in treatment protocols, cancer is one of the leading cause of deaths worldwide. Therefore, there is a need to identify newer and personalized therapeutic targets along with screening technologies to combat cancer. With the advent of pan-omics technologies, such as genomics, transcriptomics, proteomics, metabolomics, and lipidomics, the scientific community has witnessed an improved molecular and metabolomic understanding of various diseases, including cancer. In addition, three-dimensional (3-D) disease models have been efficiently utilized for understanding disease pathophysiology and as screening tools in drug discovery. An integrated approach utilizing pan-omics technologies and 3-D in vitro tumor models has led to improved understanding of the intricate network encompassing various signalling pathways and molecular cross-talk in solid tumors. In the present review, we underscore the current trends in omics technologies and highlight their role in understanding genotypic-phenotypic co-relation in cancer with respect to 3-D in vitro tumor models. We further discuss the challenges associated with omics technologies and provide our outlook on the future applications of these technologies in drug discovery and precision medicine for improved management of cancer. Graphical Abstract

India experienced a tragic second wave after the end of March 2021, which was far more massive than the first wave and was driven by the emergence of the novel delta variant (B.1.617.2) of the SARS-CoV-2 virus. In this study, we explored the local and national landscape of the viral variants in the period immediately preceding the second wave to gain insight into the mechanism of emergence of the delta variant and thus improve our understanding of the causation of the second wave. We randomly selected 20 SARS-CoV-2 positive samples diagnosed in our lab between 3 February and 8 March 2021 and subjected them to whole genome sequencing. Nine of the 20 sequenced genomes were classified as kappa variant (B.1.617.1). The phylogenetic analysis of pan-India SARS-CoV-2 genome sequences also suggested the gradual replacement of the α variant with the kappa variant during this period. This relative consolidation of the kappa variant was significant, since it shared 3 of the 4 signature mutations (L452R, E484Q and P681R) observed in the spike protein of delta variant and thus was likely to be the precursor in its evolution. This study demonstrates the predominance of the kappa variant in the period immediately prior to the second wave and underscores its role as the “bridging variant” between the α and delta variants that drove the first and second waves of COVID-19 in India, respectively.

Precise reasons for severe manifestation of SARS-CoV-2 remain unanswered, and efforts have been focused on respiratory system management. Demonstration of unequivocal presence of SARS-CoV-2 in vital body organs by cadaver autopsy was the only way to prove multi-organ involvement. Hence, the primary objective of the study was to determine presence of the SARS-CoV-2 in various organs of patients succumbing to SARS-CoV-2 infection. A total of 246 samples from different organs of 21 patients who died due to severe COVID-19 illness were investigated by qRT-PCR, and SARS-CoV-2 was detected in 181 (73.57%) samples and highest positivity of SARS-CoV-2 being (expectedly) found in nasopharynx (90.4%) followed by bilateral lungs (87.30%), peritoneal fluid (80%), pancreas (72.72%), bilateral kidneys (68.42%), liver (65%) and even in brain (47.2%). The deceased patients were categorized to three subgroups based upon the extent of organs in which SARS-CoV-2 was detected by qRT-PCR (high intensity ≥80%, intermediate intensity = 65–80% and low intensity ≤65% organs involvement). It was conclusively established that SARS-CoV-2 has the property of invasion beyond lungs and even crosses the blood–brain barrier, resulting in multi-system disease; this is probably the reason behind cytokine storm, though it is not clear whether organ damage is due to direct injury caused by the virus or result of inflammatory assault. Significant inverse correlation was found between the Ct value of lung samples and number of organs involved, implying that higher viral load in lungs is directly proportionate to involvement of extrapulmonary organs and patients with higher viral load in respiratory secretions should be monitored more closely for any warning signs and the treatment strategies should also address involvement of other organs for better outcome, because lungs, though the primary site of infection, are not the only organ system responsible for pathogenesis of systemic illness.

Dengue fever remains a significant public health concern in tropical regions, including Central India, where outbreaks are frequent and associated with high morbidity and mortality. This study investigated the dynamics of dengue virus transmission and evolution in Central India from 2019 to 2023, focusing on the emergence of new strains and their impact on outbreak patterns. For this, 40 mosquito pools and 300 patient samples were recruited for the study. Phylogenetic and Bayesian evolutionary analyses performed on CPrM region and whole genome sequences generated by Sanger and Illumina sequencing, respectively, revealed the emergence and predominance of a novel DENV-2 genotype IV lineage III strain in the 2019 and 2023 outbreaks, which displaced the previously circulating DENV-1 genotype responsible for the 2016–2017 outbreak. Despite pre-existing DENV-1 neutralizing antibodies in the community (67 healthy volunteers), the novel DENV-2 strain exhibited higher viral loads and a greater reproduction number (R0), contributing to rapid disease spread. Molecular clock and Shannon entropy analyses suggest that DENV evolution occurred within the mosquito vector, driven by natural selection. Our findings highlight the importance of continuous DENV surveillance, including genetic characterization in both vectors and hosts, to understand viral evolution and predict future outbreaks. Rapid urbanization and inadequate sanitation in densely populated regions like India create ideal breeding grounds for mosquitoes, facilitating the introduction and establishment of novel DENV strains. Interrupting the vector–DENV–host cycle through targeted interventions is crucial for effective dengue control.

Vitiligo is a well - known skin disorder with complex etiology. Vitiligo pathogenesis is multifaceted with many ramifications. A computational systemic path was designed to first propose candidate disease proteins by merging properties from protein interaction networks and gene ontology terms. All in all 109 proteins were identified and suggested to be involved in the onset of disease or its progression. Later, a composite approach was employed to prioritize vitiligo disease proteins by comparing and benchmarking the properties against standard target identification criteria. This includes sequence-based, structural, functional, essentiality, protein-protein interaction, vulnerability, secretability, assayability and druggability information. The existing information was seamlessly integrated into efficient pipelines to propose a novel protocol for assessment of targetability of disease proteins. Using the online data resources and the scripting, an illustrative list of 68 potential drug targets was generated for vitiligo. While this list is broadly consistent with the research community's current interest in certain specific proteins, and suggests novel target candidates that may merit further study, it can still be modified to correspond to a user - specific environment, either by adjusting the weights for chosen criteria (i.e. a quantitative approach) or by changing the considered criteria (i.e. a qualitative approach).