Multiple Sclerosis (MS) is a chronic immune-mediated neurodegenerative disorder of the Central Nervous System (CNS) that affects more than 2.8 million people worldwide. It is recognized as one of the leading causes of non-traumatic neurological disability among young adults. It is an inflammatory demyelination disease with axonal damage and progressive neurological disability, which creates high personal, social, and economic costs. The development of neuroimaging, immunobiology, and molecular biology has significantly enhanced the accuracy of diagnosis, disease monitoring, and therapeutic development, enabling earlier intervention and better relapse management with disease-modifying therapeutic agents. These innovations have improved clinical training, retention, and optimization of treatment for relapsing forms of the disease. The Pathophysiology of MS is complex, and the factors that lead to the condition are a complicated interplay of genetic risk, environmental exposure (Epstein-Barr virus, vitamin D deficiency, and smoking), and impaired immunological responses. Despite these gains, there are some significant limitations. There are limited therapeutic interventions that are effective against progressive multiple sclerosis; remyelination failure still plays a role in irreversible disability. Predicting treatment response in this disorder is yet to be identified. The existing literature has identified gaps in understanding immune dysregulation, neurodegeneration, and new modulators, including viral exposures, the gut microbiota, and cell signalling pathways. These unaddressed gaps impede the design of individual and neuroprotective therapeutic approaches that can modify the disease course over the long term. This review critically examines the recent developments and current issues in multiple sclerosis, including etiology and Pathophysiology and diagnostic and therapeutic interventions, with a particular focus on highlighting critical areas of knowledge gaps as well as future research directions that may be used in support of multidisciplinary and integrative approaches to enhance the quality of life and multiple sclerosis clinical outcomes in patients.
K. Ravi, K. Arora· Current Neurovascular Resear...· 0 citations
The systematic monitoring of post-marketing drug safety is paramount to ensuring patient
welfare and population-level health, requiring the timely detection and prevention of adverse
drug events. Traditional pharmacovigilance schemes, which largely rely on spontaneous
reporting, have weaknesses, including underreporting, delays in the detection of signals, and a
lack of cohesion in data sources. As heterogeneous health data grows exponentially,
conventional methods are becoming less effective at providing complete, real-time ADR
monitoring.
This study examined the potential of improved computational methods, particularly artificial
intelligence (AI) and deep learning, to enhance pharmacovigilance practices. Neural network
architectures, including Bidirectional Encoder Representations with Transformers (BERT), Long
Short-term Memory (LSTM), and Convolutional Neural Networks (CNNs), are capable of
detecting meaningful patterns in unstructured and complex data. Natural language processing
methods have been identified as capable of interpreting free-text clinical narratives, PROs, and
biomedical literature relevant to drug safety monitoring.
A wide range of data environments has been studied, including electronic health records, global
safety-reporting schemes, the scientific literature, and patient communities on the Internet. The
commonly used performance evaluation metrics were also reviewed in this study to assess the
model's strength and reliability. The feasibility of such technologies in practice can be seen in the
field of operation of these robots, such as automated ADR detection using the FDA Adverse
Event Reporting System (FAERS) and early signal detection via social media mining.
These results indicate the potential for substantial improvements in current pharmacovigilance
systems through the use of AI-based models that can identify latent relationships and deliver
safety alerts in near-real time. Nevertheless, there are still problematic areas, including data
heterogeneity, lack of standardization, algorithmic bias, low interpretability, and ethical and
regulatory issues. This work highlights the need to ensure the integration of AI in
pharmacovigilance by collaborating with other disciplines and revising regulatory guidelines.
Overall, this research offers practical recommendations for the development of data-driven
surveillance of drug safety in recent healthcare systems.
C. R. Darwin, Meruva Sathish Kumar, S. Marakatham et al.· Current Drug Safety· 0 citations
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