Blaire Okunsai

This review critically examines current evidence regarding the ability of microplastics and nanoplastics (MNPs) to interact with and traverse the blood–brain barrier (BBB), their neurotoxic effects, and their potential relevance to pathways associated with neurodegenerative diseases. Experimental evidence indicates that MNPs can reach neural tissues and disrupt BBB integrity, with transport and biological effects influenced by particle size, shape, surface properties, and chemical composition. In vitro and animal studies demonstrate that MNP exposure can induce oxidative stress, mitochondrial dysfunction, neuroinflammation, BBB impairment, and altered proteostasis. These responses overlap with mechanisms implicated in Alzheimer’s disease (AD), Parkinson’s disease (PD), and amyotrophic lateral sclerosis (ALS). Emerging human studies have also detected plastic particles in postmortem brain tissue and cerebrospinal fluid, raising concerns regarding potential CNS exposure. However, human evidence remains limited, and the observed molecular and cellular responses are not specific to MNP exposure or individual neurodegenerative diseases. Current evidence supports the biological plausibility of MNP-induced neurotoxicity but does not establish a causal relationship between MNP exposure and human neurodegenerative disease. Oxidative stress, inflammation, mitochondrial dysfunction and impaired proteostasis are shared responses across numerous environmental toxicants and pathological conditions. Therefore, associations between MNP exposure and AD, PD or ALS should be interpreted cautiously. Standardized and contamination-controlled analytical methods, environmentally relevant exposure models, and longitudinal epidemiological studies are required to determine whether chronic MNP exposure represents an independent neurological risk factor or modifies existing vulnerability to neurodegenerative disease.

Background: Alzheimer’s disease (AD) is a multifactorial neurodegenerative disorder characterized by amyloid-β aggregation, tau hyperphosphorylation, oxidative stress, and mitochondrial dysfunction. Emerging evidence indicates that the gut microbiota plays a critical role in modulating neuroinflammatory, and metabolic pathways involved in AD pathogenesis through the microbiota-gut-brain axis. Objective: This systematic review aims to comprehensively evaluate the role of the microbiota-gut-brain axis in Alzheimer’s disease, with a particular focus on its mechanistic links to oxidative stress, mitochondrial dysfunction, and amyloid pathology, as well as its therapeutic potential. Methodology: A comprehensive literature search was conducted using PubMed, Scopus, and Web of Science databases, focusing on studies evaluating gut microbiota composition, metabolomic changes, oxidative stress markers, mitochondrial activity, and therapeutic interventions in AD models and patients. Results: Altered gut microbial composition in AD is associated with increased pro-inflammatory taxa (Escherichia-Shigella, Bacteroides) and depletion of short-chain fatty acid (SCFA) producing bacteria (Faecalibacterium, Roseburia). Dysbiosis contributes to systemic inflammation, disrupted intestinal permeability, and microglial activation, leading to oxidative damage and mitochondrial impairment in neurons. Preclinical and clinical studies indicate that probiotics, prebiotics, and fecal microbiota transplantation can restore redox balance, reduce neuroinflammation, and improve cognitive outcomes. Multi-omics and AI-based models are emerging as tools for identifying microbiome-derived biomarkers for early AD detection. Conclusion: The gut microbiota-mitochondria-oxidative stress axis represents a promising therapeutic target in Alzheimer’s disease. Future research should focus on longitudinal human studies, standardized microbial profiling, and personalized microbiome-based interventions to translate these mechanistic insights into clinical benefit.

Sciatic nerve injury (SNI) is neurological condition that affecting the motor and sensory functions of muscles, characterized by the Wallerian degeneration and regeneration myelin axon. Motor neuron injuries result in muscle weakness and atrophy, sensory neuron damage impairs sensation, and autonomic injuries lead to organ dysfunction. This study investigates the potential effects of Clitoria ternatea nanoemulsion with different dosage (250 mg/kg, 500 mg/kg and 1,000 mg/kg) on Sprague Dawley (SD) rats that induced with sciatic nerve crush injury. Sciatic nerve crush injury was induced on SD rats using modified Watchmaker’s forceps for 15 seconds. On day 14 and 28, behaviour analysis of motor and sensory test was evaluated to assess the functional recovery. On day 28, the muscles were weight and stained with Haematoxylin & Eosin, while sciatic nerve were stained using Cresyl violet, Toluidine blue and TEM. The outcome of the treatment shown an improvement in functional recovery in the motor and sensory test. Additionally, the histomorphometry examination shows the same pattern as the behaviour analysis, increased in the degree maturity of the regenerated nerve fibres which resulting more axonal growth and thicker myelin. In conclusion, CT nanoemulsion treatment in high dose treatment shows promising potential treatment in treating sciatic nerve injury by improving functional recovery and myelin integrity.

Artificial intelligence (AI) and machine learning (ML), especially deep learning, have profoundly transformed biology by enabling precise interpretation of complex genomic and proteomic data. This review presents a comprehensive overview of cutting-edge AI methodologies spanning from foundational neural networks to advanced transformer architectures and large language models (LLMs). These tools have revolutionized our ability to predict gene function, identify genetic variants, and accurately determine protein structures and interactions, exemplified by landmark milestones such as AlphaFold and DeepBind. We elaborate on the synergistic integration of genomics and protein structure prediction through AI, highlighting recent breakthroughs in generative models capable of designing novel proteins and genomic sequences at unprecedented scale and accuracy. Furthermore, the fusion of multi-omics data using graph neural networks and hybrid AI frameworks has provided nuanced insights into cellular heterogeneity and disease mechanisms, propelling personalized medicine and drug discovery. This review also discusses ongoing challenges including data quality, model interpretability, ethical concerns, and computational demands. By synthesizing current progress and emerging frontiers, we provide insights to guide researchers in harnessing AI’s transformative power across the biological spectrum from genes to functional proteins.

Artificial Intelligence (AI) and Machine Learning (ML), especially deep learning, have revolutionized genomics and protein structure prediction, advancing precision medicine and drug discovery. This review focuses on the most widely used AI and ML algorithms including deep learning models, from early neural networks to advanced transformer architectures and Large Language Models (LLMs), are transforming our ability to interpret genomic data, predict gene function, and accurately determine protein structures and interactions. We highlight key breakthroughs such as AlphaFold and DeepBind and discuss their impact on understanding complex biological systems. Furthermore, we address the inherent connections between genomics and protein structure prediction, emphasizing how insights from one field often inform and accelerate progress in the other. We also discuss recent advancements, such as single-cell analysis using graph neural networks (e.g., scGNN). The review classifies deep learning methods (CNNs, RNNs, transformers), evaluating their strengths, limitations, and suitable applications. We also delve into the challenges, including data quality, model interpretability, and computational demands, and explore future directions, such as the integration of multi-omics data and the development of hybrid models. Future directions, such as integrating multi-omics data and developing hybrid models, aim to enhance scalability and clinical utility. This review provides insights for researchers applying AI and ML in these fields, outlining current progress and emerging opportunities.