Epilepsy is a heterogeneous neurological disorder characterized by recurrent unprovoked seizures and complex molecular alterations underlying epileptogenesis. Emerging evidence identifies N6-methyladenosine (m6A) RNA methylation as a key epitranscriptomic regulator linking neuronal activity to gene expression reprogramming in epilepsy. m6A modification is reversibly controlled by a coordinated network of writer (METTL3/METTL14 complex), eraser (FTO, ALKBH5), and reader (YTH domain-containing proteins) proteins. Dysregulation of this machinery has been involved in neuronal hyperexcitability, mitochondrial dysfunction, oxidative stress, and neuroinflammation, processes driving seizure initiation and progression. Activity-dependent remodeling of m6A landscapes alters transcripts encoding ion channels, synaptic proteins, metabolic enzymes, and inflammatory mediators, thereby disrupting the excitatory-inhibitory balance and promoting maladaptive network plasticity. Besides messenger RNAs, promoter antisense RNAs (paRNAs) have emerged as important transcriptional regulators that can also undergo m6A modification, adding a layer of epitranscriptomic control in epileptic circuits. Altered m6A profiles and dysregulated expression of regulatory enzymes in seizure-prone brain regions and resected human epileptic tissues underscore the translational relevance of this pathway. Despite technical and clinical challenges, m6A signatures and m6A‑modified paRNA show promise as dynamic biomarkers of diagnosis, prognosis, and prediction of therapeutic response. This review discusses the molecular mechanisms linking m6A methylation and paRNA regulation to epileptogenesis and highlights emerging diagnostic and therapeutic opportunities, including advances in single-cell and spatial epitranscriptomics to support precision medicine strategies in epilepsy. Future priorities include establishing causality, validating biomarkers in large patient cohorts, and developing targeted m6A‑modulating therapies with appropriate safety and delivery considerations.
Traumatic brain injury (TBI) remains a leading cause of disability and death worldwide, imposing a substantial burden on patients and healthcare systems alike. Among candidate neuroprotective agents, argon (Ar)—a chemically inert noble gas—has attracted growing interest based on promising experimental data. This study systematically evaluated the neuroprotective potential of inhaled argon-oxygen mixture in a rat TBI model, focusing on behavioral outcomes, oxidative stress biomarkers, and structural brain changes. Diffuse TBI was induced in adult male Wistar rats using the Marmarou weight-drop model. Animals were allocated to three groups (n = 10): Sham controls, untreated TBI, and argon-oxygen mixture—treated TBI (FiO2 = 30
Objective: Elucidate the regulatory mechanisms by which cannabinoid reward affects tryptophan metabolism. Methods: Cannabinoid reward behavior was modeled using a conditioned place preference (CPP) paradigm in male C57BL/6J mice that were given CP-55940. After establishing CPP responses to CP-55940, hippocampal tissue was harvested for targeted quantitative analysis of selected tryptophan-derived metabolites. Key metabolites in the serotonin (5-HT) and kynurenine (KYN) pathways were quantified using ELISA. Behavioral and molecular data were analyzed using unpaired two-tailed Student’s t‑tests. Results: Cannabinoid reward was associated with alterations in hippocampal serotonin metabolism, accompanied by altered serotonin turnover, as indicated by changes in the 5‑HT/TRP ratio. Concurrently, the kynurenine pathway exhibited a metabolic shift characterized by increased kynurenic acid (KYNA) and decreased quinolinic acid (QUIN) levels. These findings suggest a metabolic shift in tryptophan utilization, favoring serotonin production and neuroprotection under addiction conditions. Conclusion: This study provided further insight into the neurobiological mechanisms associated with cannabinoid reward and identified alterations in selected metabolite ratios, including 5‑HT/TRP and KYNA/QUIN, that may serve as potential biomarkers for further investigation. These findings also highlight potential therapeutic targets, such as TPH2 (serotonin synthesis), KAT enzymes (KYNA production), and KMO (QUIN formation). These findings provide mechanistic insights that could inform future investigations into targeted interventions for cannabinoid-associated neuropsychiatric dysfunction.
This narrative methodological review aims to critically evaluate statistical practices in skeletal muscle physiology, emphasizing the necessary transition from classical models to mixed-effects approaches. We provide a comprehensive analysis of methods for evaluating the mechanokinetics of muscle contraction, ranging from the historical foundations of classical electrophysiology to modern in situ tensometry protocols. Particular emphasis is placed on the clinical relevance of muscle trauma, an issue that has acquired unprecedented significance due to the military actions in Ukraine, which are accompanied by an increasing incidence of mine-explosive injuries, ischemia-reperfusion trauma, and amputations. The primary methodological challenge addressed is the statistical analysis of hierarchical tensometric data. The article presents evidence that traditional approaches, such as the t-test and classical analysis of variance (ANOVA), frequently lead to pseudoreplication and erroneous conclusions due to the oversight of intra-individual correlations among repeated contractions. We substantiate the necessity of implementing Linear Mixed Models (LMM) as a highly robust framework for analyzing tensometric data, as this approach accurately accounts for random effects and effectively handles unbalanced datasets.
