Isreal Ayobami Onifade

Alzheimer’s disease (AD) is a leading form of dementia, marked by complex neuropathological features such as amyloid-β (Aβ) plaques and tau tangles. Recent research highlights the significant role of unmethylated cytosine–phosphate–guanine (CpG) motifs, short DNA sequences where a cytosine nucleotide is directly followed by a guanine nucleotide, linked by a phosphate group, in the disease’s development. CpG motifs, predominantly found in bacterial and mitochondrial DNA, can provoke immune responses when they appear in unexpected contexts. This review explores the intricate relationship between unmethylated CpG DNA and AD pathogenesis, investigating the mechanisms by which bacterial DNA enters the brain, activates immune pathways, and contributes to the progression of Alzheimer’s disease. Understanding these processes may offer new avenues for research and therapeutic intervention in AD.

Skeletal muscle is essential in orchestrating voluntary movement and contributes to approximately half of the body's overall mass. Dysregulation in proper skeletal muscle development can lead to muscle degenerative disorders, such as severe Duchenne Muscular Dystrophy (DMD). Understanding the process of muscle development has been the central focus of numerous biological studies, focusing on myogenic transcription factors and signaling molecules like Pax3, Pax7, Myf5, MyoD, WNT, TGF-β, and BMP, IGF1, AKT/mTOR pathway. Also, studies have suggested that m6A (N6-methyladenosine) plays a crucial role in various biological processes, including skeletal muscle regeneration and development. m6A modification is the prevalent form of RNA methylation in eukaryotes, playing a pivotal role in the post-transcriptional control of gene expression. The goal of my library thesis is to review the significant role of m6A RNA methylation in processes encompassing skeletal muscle regeneration.

Background The persistnt resurgence of influence and influenza-like illness despite concerted vaccination interventions is a global health burden, thus necessitating accurate tools for early intervention and preparedness. This scoping review aims to map the currently available literature on artificial intelligence (AI)-based forecasting models for seasonal influenza and to identify trends in those published models, approaches, and research gaps. Methods A detailed search was conducted in PubMed, Scopus, and IEEE Xplore to find relevant studies published between 2014 and 2025. The AI techniques (such as machine learning and deep learning) applied in predicting seasonal influenza activity are considered eligible studies. Model types, data inputs, performance metrics, and validation approaches were summarized on data that were extracted and charted. Results Nine studies met the inclusion criteria and were included. Owing to their effectiveness in solving temporal sequence models, many deep learning models have been applied, including the long short-term memory (LSTM) model and the CNN LSTM hybrid model. The data sources are epidemiological records, meteorological variables and social media signals. Most of the models achieved excellent predictive accuracy, but shortcomings in model interpretability, external validation or consistency across performance reporting became issues. Conclusions Although AI-based models show promising capabilities for predicting influenza, there are still issues related to standardization and deployment in the real world. Future work should focus on real-time data integration, external validation and interpretable transferable models appropriate for a wide variety of health settings. This graphical framework encapsulates AI-based forecasting models for seasonal influenza, depicted as a navigational chart through the research terrain. A central magnifying glass over a globe anchors the global health challenge, guiding the viewer through a flowchart-like journey. A funnel filters literature from PubMed, Scopus, and IEEE Xplore (2014–2025), yielding 9 pivotal studies. Layered icons delineate machine learning and deep learning models, with LSTM and CNN-LSTM hybrids highlighted. Interconnected circles symbolize diverse data inputs— epidemiological, meteorological, and social media—converging into a data integration hub. The bar chart connotes high predictive accuracy, tempered by a warning sign flagging interpretability, validation, and reporting challenges. A roadmap at the journey’s end points to future horizons: real-time data integration, external validation, and interpretable models, charting the course for advancing global influenza preparedness.

RNA-binding proteins (RBPs) are integral components of cellular machinery, playing crucial roles in the regulation of gene expression and maintaining genetic stability. Their interactions with RNA molecules govern critical processes such as mRNA splicing, stability, localization, and translation, which are essential for proper cellular function. These proteins interact with RNA molecules and other proteins to form ribonucleoprotein complexes (RNPs), hence controlling the fate of target RNAs. The interaction occurs via RNA recognition motif, the zinc finger domain, the KH domain and the double stranded RNA binding motif (all known as RNA-binding domains (RBDs). These domains are found within the coding sequences (intron and exon domains), 5’ untranslated regions (5’UTR) and 3’ untranslated regions (3’UTR). Dysregulation of RBPs can lead to genomic instability, contributing to various pathologies, including cancer neurodegenerative diseases, and metabolic disorders. This study comprehensively explores the multifaceted roles of RBPs in genetic stability, highlighting their involvement in maintaining genomic integrity through modulation of RNA processing and their implications in cellular signalling pathways. Furthermore, it discusses how aberrant RBP function can precipitate genetic instability and disease progression, emphasizing the therapeutic potential of targeting RBPs in restoring cellular homeostasis. Through an analysis of current literature, this study aims to delineate the critical role of RBPs in ensuring genetic stability and their promise as targets for innovative therapeutic strategies.

Background: Alzheimer's disease (AD) has become common as the number of aged people increases making it as a socioeconomic problem lately. To date, no success is recorded for disease-modifying therapies for AD but only drugs for symptomatic relief exist. Research has been centered on the role of amyloid-β on the pathogenesis of AD, which has led to the development of drugs that target Aβ (β- and γ-secretase inhibitors) to reduce the amount of Aβ formed. However, the existing β and γ-secretase inhibitors were associated with harmful side effects, low efficacy, and inability to cross the blood-brain barrier. Objective: This study therefore used in silico approach to predict the inhibitory properties of alkaloids as potential drug targets against AD. Methods: Thus, in this current study, 54 alkaloids from the PhytoHub server (phytohub.eu), and two approved drugs were docked against β-secretases. Additionally, galantamine and 5 alkaloids with the utmost binding potential with β-secretase were subjected to pharmacokinetics evaluation and docked against γ-secretase. Results: From the result, 5 compounds displayed for both docking periods, with demissidine, solasodine, tomatidine, and solanidine having better BE than the control drugs. Based on the pharmacokinetics evaluation, 4 compounds possessed good pharmacokinetic evaluation and biological activities than galantamine. Conclusions: This study suggests that demissidine, solasodine, tomatidine, and solanidine are promising dual inhibitors against β- and γ-secretase proteins in silico . However, there is an urgent need to carry out in vitro and in vivo experiments on these new leads to validate the findings of this study.