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Sruthi Chiluveru

Chiluveru Sruthi

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Genetic networks of Alzheimer's disease, aging, and longevity in humans.

Published on: January 03, 2024

Original author: Timothy Balmorez, et al. (2023) (DOI: 10.3390/ijms24065178)

Human genomic analysis and genome-wide association studies (GWAS) have identified genes that are risk factors for early and late-onset Alzheimer’s disease (AD genes). Prior research has concentrated on a particular group of genes that have been demonstrated to either cause or be a risk factor for AD, even though the genetics of aging and longevity have been thoroughly examined. Therefore, it is unclear how the genes linked to AD, aging, and lifespan are related to one another. Here, the use of Reactome's (bioinformatic database) gene set enrichment analysis, which cross-references over 100 bioinformatic databases to enable interpretation of the biological functions of gene sets through a wide range of methodologies, to identify the genetic interaction networks (also known as pathways) of aging and longevity within the context of AD. Reactome is an open source bioinformatic database with two functions. The database comprises twenty-seven groups of biological pathways, including autophagy, cell cycle, cell-cell communication, cellular responses to external stimuli, chromatin organization, circadian clock, developmental biology, digestion and absorption, and disease. It is a manually curated and peer-reviewed knowledge base of biological pathways. There is a great deal of cross-referencing between the route, reaction, and molecule websites and over 100 bioinformatics resources, such as the UCSC Genome Browser, PubMed, Ensembl and UniProt databases, ChEBI small molecule databases, and NCBI Gene. Second, gene set enrichment analysis (GSEA) can be carried out using it as a bioinformatics tool. GSEA is a computer technique that uses genome-wide profiles to determine and interpret the biological activities of a group of genes. Methodology: 1. Datasets (a) Three sets of genes: human Alzheimer’s disease (AD) genes, aging-related (AR) genes, and longevity genes. (b) Two additional gene sets were created by: (1) identifying the genes shared both by the AD genes and by the AR genes (AD–AR Overlap) and (2) identifying the genes shared both by AD genes and longevity genes (AD–longevity overlap). 2. Gene Ontology: Reactome Analysis (a) Reactome FIViz was used to determine enrichment in the Functional Interaction (FI) network. (b) The team used Cytoscape ver. 3.8.2 (Java version: 11.0.6) to run the Reactome software plugin, Reactome FIViz app. This study identified a diverse range of biochemical pathways, using the Reactome analysis of each individual set of AD, AR, and longevity genes. The hallmarks of each subset and comparing each individual gene set to the pathways involved in the overlapping (AD–AR and AD–longevity) gene sets. Gene set enrichment analysis (GSEA) is a computational method that is useful to interpret the biological functions of a gene set with statistical confidence. Reactome incorporates GSEA embedded with the knowledge database that covers more than 100 bioinformatics resources. The Reactome outputs include statistical confidence levels with p-values and false discovery rate, which raises the confidence level of evidence. This study identified a diverse range of biochemical pathways, using the Reactome analysis of each individual set of AD, AR, and longevity genes. Conclusion: In this study, using human gene sets successfully identified and overviewed the gene–gene networks of aging and longevity and their association with Alzheimer’s disease genes. The Reactome analysis provides the genetic pathways with gene set enrichment and statistical confidence levels. The genetic hallmarks identified will provide unexpectedly broad mechanisms, suggesting a wide variety of implications in the field of aging. Research such as this study on the genetic network is expected to link aging, mid-life common diseases, and Alzheimer’s disease. Impact of the research: 1. The genetic interaction networks among aging, longevity, and AD provide the extraction and translation of the gene information into the hallmarks as well, as are the key to developing effective treatments for AD. 2. The significant genes involved in these pathways, including TP53, FOXO, SUMOylation, IL4, IL6, APOE, and CEPT. 3. These genes suggest that mapping the gene network pathways provides a useful basis for further medical research on AD and healthy aging.

Development of an eHealth tool for capturing and analyzing the immune-related adverse events (irAEs) in cancer treatment.

Published on: October 04, 2023

Original author: Moradian S, et al. (2023) (DOI: 10.1177/11769351231178587)

Immunotherapy is rapidly advancing and can now be considered a powerful new tool in the treatment of cancer. However, it is associated with a myriad of Immune-related adverse events (irAEs). These irAEs can affect numerous body organs and are potentially life-threatening if not promptly recognized and treated. Therefore, early recognition and effective management of these irAEs are critical to reduce the treatment sequelae. Patients are often required to monitor and manage a range of potentially diverse and complicated irAEs without readily available clinical support and report poor quality of self-management support in ambulatory cancer care. This sub-optimal irAEs management has resulted in high rates of cancer symptom severity and avoidable emergency department visits and hospitalization. mHealth technologies are emerging as a solution to this problem. Additionally, adapted the system’s risk-scoring and decision-support algorithms to align with Canadian evidence-based protocols for symptom triage. Building on this foundation, developed V-Care, an eHealth platform comprising an electronic patient-reported outcome (ePRO) for a new follow-up pathway for cancer patients receiving immune checkpoint inhibitors (ICIs). Although immunotherapy is associated with unique irAEs that are more unpredictable than chemotherapy adverse events (AEs), early detection and relevant treatment initiation can manage most of these side effects. Methodology: The team co-developed a digital platform (V-Care) using ePROs to create a new follow-up pathway for cancer patients receiving immunotherapy. To operationalize the first 3 phases of the CeHRes roadmap, they employed multiple methods that were integrated throughout the development process, rather than being performed in a linear fashion. The teams employed an agile approach in a dynamic and iterative manner, engaging key stakeholders throughout the process. Development of the V-Care is reported according to the British Medical Research Council (MRC) guideline for the development of complex interventions that describes the whole process from development to implementation. In the process of developing and evaluating a complex intervention, the MRC guidance includes 4 phases: development, feasibility and piloting, and evaluation and implementation. Additionally, the Center for eHealth Research and Disease Management (CeHRes) roadmap for the development of eHealth technologies was utilized, which comprises distinct development phases ranging from contextual inquiry to operationalization. A multi-disciplinary team played a pivotal role in the successful design and development of the V-Care platform. This diverse team, comprised of researchers, oncology clinicians, digital health experts, computer scientists, and patient advocates, exemplified the power of collaboration in overcoming challenges and creating a robust, user-centric system. In the multidisciplinary collaboration to develop V-Care, clinicians also played a vital role by providing their expertise and perspective on the tool’s development. As key stakeholders, they were consulted throughout the process to offer input on the objectives and strategies necessary to address the identified needs. Their practical experience and understanding of patient care allowed the team to develop a more comprehensive and effective solution tailored to the realities of clinical practice. Results: The development of the application was categorized into 2 phases: “user interface” (UI) and “user experience” (UX) designs. In the first phase, the pages of the application were segmented into general categories, and feedback from all stakeholders was received and used to modify the application. In phase 2, mock-up pages were developed and sent to the Figma website. Moreover, the Android Package Kit (APK) of the application was installed and tested multiple times on a mobile phone to proactively detect and fix any errors. After resolving some technical issues and adjusting errors on the Android version to improve the user experience, the iOS version of the application was developed. The V-Care platform aims to bridge the gap in existing ePRO systems by focusing on the unique challenges faced by cancer patients receiving immunotherapy. The platform’s goal was to improve patient outcomes, reduce healthcare system costs, and promote a more collaborative and personalized approach to cancer care. By providing patients with access to tailored self-care advice and facilitating prompt medical interventions when needed, V-Care has the potential to revolutionize the management of irAEs and improve the quality of life for cancer patients undergoing ICIs treatments. Conclusion: The V-Care platform can be used to investigate the reported symptoms experienced by cancer patients receiving Immune checkpoint inhibitors (ICIs) and to compare them with the results from the clinical trials. Furthermore, the project may utilize ePRO tools to collect symptoms from patients and provide insight into whether the reported symptoms are linked to the treatment. Impact of the Research: 1. V-Care provides a secure, easy-to-use interface for patient-clinician communication and data exchange. 2. New follow-up pathway for cancer patients receiving immunotherapy. 3. Identification of Myriad of Immune-related adverse events (irAEs), which affect numerous body organs and are potentially life-threatening.


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