Integrated clustering signature of genomic heterogeneity, stemness and tumor microenvironment predicts glioma prognosis and immunotherapy response

Innovative Therapeutic Strategies in Alzheimer's Disease: A Synergistic Approach to Neurodegenerative Disorders

Alzheimer's disease (AD) remains a significant challenge in the field of neurodegenerative disorders, even nearly a century after its discovery, due to the elusive nature of its causes. The development of drugs that target multiple aspects of the disease has emerged as a promising strategy to address the complexities of AD and related conditions. The immune system's role, particularly in AD, has gained considerable interest, with nanobodies representing a new frontier in biomedical research. Advances in targeting antibodies against amyloid-β (Aβ) and using messenger RNA for genetic translation have revolutionized the production of antibodies and drug development, opening new possibilities for treatment. Despite these advancements, conventional therapies for AD, such as Cognex, Exelon, Razadyne, and Aricept, often have limited long-term effectiveness, underscoring the need for innovative solutions. This necessity has led to the incorporation advanced technologies like artificial intelligence and machine learning into the drug discovery process for neurodegenerative diseases. These technologies help identify therapeutic targets and optimize lead compounds, offering a more effective approach to addressing the challenges of AD and similar conditions.

Genetic Discrimination of Grade 3 and Grade 4 Gliomas by Artificial Neural Network

Gliomas, including anaplastic gliomas (AG; grade 3) and glioblastomas (GBM; grade 4), are malignant brain tumors associated with poor prognosis and low survival rates. Current classification systems based on histopathology have limitations due to intratumoral heterogeneity. The treatment and prognosis are distinctly different between grade 3 and grade 4 gliomas patients. Therefore, there is a need for molecular markers to differentiate these tumors accurately. In this study, we aimed to identify a gene expression signature using an artificial neural network (ANN) in application to microarray and serial analysis of gene expression (SAGE) data for grade 3 (AG) and grade 4 (GBM) gliomas discrimination. We acquired gene expression data from publicly available datasets on glial tumors of grades 3 and 4-a total of 93 grade 3 gliomas and 224 grade 4 gliomas. To select genes for classification, we implemented an artificial neural network-based method using a combination of self-organized maps (SOM) and perceptron. In general, we implemented a multi-stage procedure that involved multiple runs of a genetic algorithm to identify genes that provided optimal clusterization on the SOM. We performed this procedure multiple times, resulting in different sets of genes each time. Eventually, we selected several genes that appeared most frequently in the reduced sets and performed classification using them. Our analysis identified a set of seven genes (BCAS4, GLUD2, KCNJ10, KCND2, AKR7A2, FOLR1, and KIAA0319). The classification accuracy using this gene set was 87.5%. These findings suggest the potential of this gene set as a molecular marker for distinguishing grade 3 (AG) from grade 4 (GBM) gliomas.

Anti-Idiotypic mRNA Vaccine to Treat Autoimmune Disorders

The 80+ existing autoimmune disorders (ADs) affect billions with little prevention or treatment options, except for temporary symptomatic management, leading to enormous human suffering and a monumental financial burden. The autoantibodies formed in most ADs have been identified, allowing the development of novel anti-idiotypic antibodies to mute the autoantibodies using vaccines. Nucleoside vaccines have been successfully tested as antigen-specific immunotherapies (ASI), with mRNA technology offering multi-epitope targeting to mute multiple autoantibodies. This paper proposes using mRNA technology to produce anti-idiotypic antibodies with broad effectiveness in preventing and treating them. This paper delves into the state-of-the-art mRNA design strategies used to develop novel ASIs by selecting appropriate T cell and B cell epitopes to generate anti-idiotypic antibodies. The low cost and fast development of mRNA vaccines make this technology the most affordable for the global control of ADs.