In the face of recent advances in Cervical cancer (CC) treatment, therapeutic and surgical procedures for CC management are still inadequate. In the current study for the first time Andrographolide (Andro) has been explored for its multitarget therapeutic efficacy on NF-kB, COX-2, and PI3K/AKT expressions together in CC. The expression levels of NF-kB, COX-2, PI3K and PTEN in the CC patient samples, both at mRNA and protein levels have shown significant association with poor survival and increased tumor aggressiveness. The binding efficacy of Andro was investigated using molecular docking and molecular dynamic simulations, and the protein and ligand complex for NF-kB and COX-2 has shown high binding energy. Andro displayed cytotoxicity by impeding the in-vitro proliferation of CC cells. Andro significantly supressed the NF-kB, COX-2, and PI3K expression and enhanced the expression levels of PTEN at protein levels in-vitro. Andro induced apoptosis in a dose dependent manner and significantly inhibited the migration and invasion of CC cells. Andro exhibited similar activity in-vivo and suppressed the CC tumor growth in xenograft C57BL/6 mice model. The anti-tumor activity of Andro, both in-vitro and in-vivo has shown considerable downregulation of NF-kB and COX-2 and induced apoptosis through impeding the PI3K/AKT signalling pathway. These findings from the above study projects, administration of Andro as an effective alternate safe compound to curtail and impede cervical cancer progression.

Objectives: Parkinson’s disease (PD) is the second most common neurodegenerative disorder which resists around 10 million people worldwide. It develops when nerve cells in a region of the brain that regulates movement become damaged; the symptoms usually begin gradually and become critical over time. In this study, we proposed to investigate the topological properties of functional brain networks within healthy controls (HCs) and PD patients. Also, we evaluated the gender difference among PD patients through graph theoretical approach. Materials and Methods: The rs-fMRI (resting-state functional magnetic resonance imaging) data of fifty-one PD patients and healthy controls was applied to generate the brain functional connectome. The functional whole-brain connectome was constructed by thresholding partial correlation matrices of 160 regions from Dosenbach brain atlas. From the graph theory approach, global and nodal metrics were analysed, and we observed considerable changes in PD patients in comparison with healthy controls. Results: Findings suggest that there is a significant difference in the topological characteristics of PD patients, and this was found to be evident in the default mode network (DMN) and occipital regions. Conclusion: This study provides essential insights from network changes to the clinically relevant information for the PD progression.

Network biology currently focuses primarily on metabolic pathways, gene regulatory, and protein-protein interaction networks. While these approaches have yielded critical information, alternative methods to network analysis will offer new perspectives on biological information. A little explored area is the interactions between domains that can be captured using domain co-occurrence networks (DCN). A DCN can be used to study the function and interaction of proteins by representing protein domains and their co-existence in genes and by mapping cancer mutations to the individual protein domains to identify signals.

Cancer is a complex disease that is caused by multiple genetic factors. Researchers have been studying protein domain mutations to understand how they affect the progression and treatment of cancer. These mutations can significantly impact the development and spread of cancer by changing the protein structure, function, and signalling pathways. As a result, there is a growing interest in how these mutations can be used as prognostic indicators for cancer prognosis. Recent studies have shown that protein domain mutations can provide valuable information about the severity of the disease and the patient's response to treatment. They may also be used to predict the response and resistance to targeted therapy in cancer treatment. The clinical implications of protein domain mutations in cancer are significant, and they are regarded as essential biomarkers in oncology. However, additional techniques and approaches are required to characterize changes in protein domains and predict their functional effects. Machine learning and other computational tools offer promising solutions to this challenge, enabling the prediction of the impact of mutations on protein structure and function. Such predictions can aid in the clinical interpretation of genetic information. Furthermore, the development of genome editing tools like CRISPR/Cas9 has made it possible to validate the functional significance of mutants more efficiently and accurately. In conclusion, protein domain mutations hold great promise as prognostic and predictive biomarkers in cancer. Overall, considerable research is still needed to better define genetic and molecular heterogeneity and to resolve the challenges that remain, so that their full potential can be realized.