Microbiome-Gut-Brain Axis: AI Insights
Published on: 25th June, 2024
Microbiome-gut-brain axis represents a complex, bidirectional communication network connecting the gastrointestinal tract and its microbial populations with the central nervous system (CNS). This complex system is important for maintaining physiological homeostasis and has significant implications for mental health. The human gut has trillions of microorganisms, collectively termed gut microbiota, which play important roles in digestion, immune function, and production of various metabolites. Some current research shows that these microorganisms strongly influence the brain function and behaviour of individuals, forming the basis of the microbiome-gut-brain axis. The communication between gut microbiota and the brain occurs via multiple pathways: neural pathway (e.g., vagus nerve), endocrine pathway (e.g., hormone production), immune pathway (e.g., inflammation modulation), and metabolic pathway (e.g., production of short-chain fatty acids). Dysbiosis, or imbalance of gut microbiota, has been linked to mental health disorders such as anxiety, depression, multiple sclerosis, autism spectrum disorders, etc, offering new perspectives on their etiology and potential therapeutic interventions. Artificial Intelligence (AI) has emerged as a powerful tool in interpreting the complexities of the microbiome-gut-brain axis. AI techniques, such as machine learning and deep learning, enable the integration and analysis of large, multifaceted datasets, uncovering patterns and correlations that can be avoided by traditional methods. These techniques enable predictive modeling, biomarker discovery, and understanding of underlying biological mechanisms, enhancing research efficiency and covering ways for personalized therapeutic approaches. The application of AI in microbiome research has provided valuable insights into mental health conditions. AI models have identified specific gut bacteria linked to disease, offered predictive models, and discovered distinct microbiome signatures associated with specific diseases. Integrating AI with microbiome research holds promise for revolutionizing mental health care, offering new diagnostic tools and targeted therapies. Challenges remain, but the potential benefits of AI-driven insights into microbiome-gut-brain interactions are immense and offer hope for innovative treatments and preventative measures to improve mental health outcomes.
Exploring Environmental Neurotoxicity Assessment Using Human Stem Cell-Derived Models
Published on: 15th November, 2024
Neurotoxicity is increasingly recognized as a critical factor impacting long-term health, with growing evidence linking it to both neurodevelopmental and neurodegenerative diseases. Pesticides, widely used in agriculture and industry, have emerged as significant contributors to neurotoxic risk, given their capacity to disrupt key neurodevelopmental processes at low exposure levels. As conventional animal models present limitations in interspecies translation, human-derived neuron-based in vitro screening strategies are urgently needed to assess potential toxicants accurately. Human-induced pluripotent stem cells (hiPSCs) offer an innovative and scalable source for human-specific neuronal models that complement traditional animal-based approaches and support the development of predictive assays for neurotoxicity. Recent various stem cell models, including 2D cultures, 3D organoids, and microfluidic systems, are now available, advancing predictive neurotoxicology by simulating key aspects of human neural development and function. With the integration of High-Throughput (HT) and High-Content (HC) screening methodologies, these hiPSC-based systems enable efficient, large-scale evaluation of chemical effects on neural cells, enhancing our ability to detect early biomarkers of neurotoxic effects. Identifying early biomarkers of neurotoxic is essential to developing therapeutic interventions before irreversible damage occurs. This is particularly crucial in the context of developmental neurotoxicity, where early exposure to toxicants can have lifelong consequences. This review specifically presents an in-depth overview of the current progress in hiPSC-derived neural models and their applications in neurotoxicity testing, with a specific focus on their utility in assessing pesticide-induced neurotoxicity. Emphasizing future research priorities, we highlight the potential of these models to transform predictive toxicology, offering more human-relevant assessments and advancing the field toward a more precise evaluation of environmental neurotoxicants.
PTM-Fetuin-A: A Novel Biomarker for Early Detection of Diabetic Kidney Disease
Published on: 24th January, 2025
Chronic Kidney Disease (CKD) is a significant public health issue with a rising prevalence globally. Diabetic kidney disease (DKD), a leading cause of CKD, necessitates improved biomarkers for early detection and effective management. Traditional markers such as serum creatinine, estimated glomerular filtration rate (eGFR), and albuminuria have notable limitations in sensitivity and specificity, especially for early detection. Fetuin-A, specifically its post-translationally modified form (PTM-Fetuin-A), has emerged as a potential novel biomarker for DKD. This study evaluates PTM-Fetuin-A in a cohort of Bulgarian patients with type 1 and type 2 diabetes, assessing its correlation with traditional markers such as albuminuria and eGFR. Significant correlations were observed between PTM-Fetuin-A and these indicators (e.g., Pearson’s r = 0.447, p = 0.025 for albuminuria), highlighting its ability to detect early kidney function decline. Furthermore, PTM-Fetuin-A demonstrated potential as a non-invasive tool for identifying normoalbuminuric DKD, addressing gaps left by conventional biomarkers. By offering additional prognostic value, PTM-Fetuin-A could improve the early diagnosis and clinical management of diabetic patients, reducing the burden of CKD.
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