Neurodegenerative disorders, including Alzheimer’s disease (AD), Parkinson’s disease (PD), multiple sclerosis (MS), and amyotrophic lateral sclerosis (ALS), are characterized by the progressive and gradual degeneration of neurons. The prevalence and rates of these disorders rise significantly with age. As life spans continue to increase in many countries, the number of cases is expected to grow in the foreseeable future. Early and precise diagnosis, along with appropriate surveillance, continues to pose a challenge. The high heterogeneity of neurodegenerative diseases calls for more accurate and definitive biomarkers to improve clinical therapy. Cell-free DNA (cfDNA), including fragmented DNA released into bodily fluids via apoptosis, necrosis, or active secretion, has emerged as a promising non-invasive diagnostic tool for various disorders including neurodegenerative diseases. cfDNA can serve as an indicator of ongoing cellular damage and mortality, including neuronal loss, and may provide valuable insights into disease processes, progression, and therapeutic responses. This review will first cover the key aspects of cfDNA and then examine recent advances in its potential use as a biomarker for neurodegenerative disorders.

This research investigated the protective properties of Carvacrol (CVC) against Isoproterenol (ISO)-induced oxidative stress, neuroinflammation, and mitochondrial dysfunction in rats. The findings showed that CVC treatment did not significantly modify baseline oxidative stress levels in healthy rats but successfully alleviated ISO-induced oxidative damage by augmenting antioxidant enzyme activity and diminishing lipid peroxidation, as demonstrated by a reduction in MDA levels. These findings indicate that CVC can reinstate antioxidant capability and reduce oxidative damage. Concerning neuroinflammation, ISO therapy markedly increased the expression of pro-inflammatory markers, including TNF-α, IL-1β, c-Fos, BDNF, Nfl, and GFP, signifying a robust inflammatory and damage response. The injection of CVC following ISO exposure markedly decreased the expression of these markers, suggesting that CVC may exert a neuroprotective effect by regulating the inflammatory response and mitigating neuronal and glial damage. CVC demonstrated a notable protective effect on mitochondrial integrity, evidenced by the decreased mRNA expression of mitochondrial damage markers, including NSE, s100B, CALP1, and CALM1 in the CVC-treated groups, showing that CVC mitigates mitochondrial dysfunction. The analysis revealed no significant alterations in the expression levels of Aβ40, pTau181, and tTau across all groups, indicating that these biomarkers were not substantially influenced by CVC treatment under the study’s conditions. However, β-amyloid accumulation varied significantly between groups, highlighting the need for further research to explore CVC’s potential implications in amyloid-related diseases. These findings endorse CVC’s neuroprotective efficacy and therapeutic potential in neurological disorders associated with oxidative stress, inflammation, and mitochondrial impairment. Graphical abstract

Bacterial membrane vesicles (BMVs) are nanoscale, bilayered proteolipid structures secreted by both Gram-negative and Gram-positive bacteria. Initially considered cellular debris, BMVs are now recognized as evolutionarily conserved entities with critical roles in bacterial communication, immune modulation, virulence factor delivery, and horizontal gene transfer. Their structural and functional resemblance to eukaryotic extracellular vesicles has fueled growing interest in their use as versatile vaccine platforms. Licensed meningococcal OMV vaccines established proof-of-concept for their safety and immunogenicity, and ongoing studies are extending applications to enteric pathogens and viral infections. Recent advances in genetic engineering, glycoengineering, and modular antigen display systems have enabled the design of “plug-and-play” BMVs with reduced reactogenicity and enhanced protective efficacy. In parallel, innovations in bioprocessing and formulation technologies are improving scalability, stability, and delivery, including mucosal routes. This review highlights the immunological properties, translational potential, and key challenges of BMV-based vaccines, with an emphasis on strategies to optimize safety, antigen specificity, and manufacturing for next-generation vaccine development.

Early and accurate diagnosis of major production diseases in cattle, many of which may be subclinical, such as mastitis, ketosis, and bovine respiratory disease (BRD), is essential for herd efficiency, animal welfare, and long-term sustainability. Conventional diagnostic approaches based on clinical observation and microbiological assays often lack sensitivity for early-stage detection and show limited predictive value in multifactorial conditions, thereby limiting early risk stratification and timely intervention. Biomarkers derived from accessible matrices such as blood and milk provide valuable insight into inflammatory, metabolic, and reproductive disturbances, especially at subclinical stages. However, their clinical implementation remains limited by pre-analytical and analytical variability (e.g., sample collection, storage, and processing), the absence of standardized thresholds, and the lack of reliable cow-specific decision cut-offs. The integration of multi-omics data, including genomic, transcriptomic, proteomic, and metabolomic layers, with artificial intelligence (AI) enables high-dimensional data integration, automated classification, and predictive modeling in cattle health. While AI-driven approaches show promise in supporting biomarker network interpretation, their translation from predictive modeling frameworks to clinically validated diagnostic systems remains limited. This review critically synthesizes current evidence on the utility and limitations of biomarkers and AI-assisted diagnostics in cattle health management, while identifying key methodological constraints and outlining practical pathways toward standardized and interpretable AI-driven systems.