International Journal of Neurology and Neurosurgery

Mobile phone addiction is an emerging behavioural disorder characterized by compulsive and excessive smartphone usage, often linked to dopaminergic dysregulation in the brain’s reward circuitry, particularly involving the mesolimbic pathway. Dysfunctions in dopamine transmission specifically through D1 and D2 receptor signalling have been implicated in the reinforcement of addictive behaviours and impaired impulse control (Kuss & Griffiths, 2015; Montag et al., 2019). In this study, we employed a systems pharmacology approach integrating network pharmacology and molecular docking to decode the therapeutic potential of Musa acuminata (banana), a nutritionally rich plant known for its neuroactive phytochemicals, in modulating dopaminergic targets relevant to mobile addiction. Phytoconstituents of Musa acuminata were sourced from IMPPAT 2.0 and the Human Metabolome Database (HMDB), and their potential targets were predicted using STITCH. Mobile addiction-associated genes, particularly those related to dopamine regulation, were retrieved from GeneCards, yielding 1,872 disease related targets. A protein-protein interaction (PPI) network was constructed via the STRING database, and central hub genes were identified using the CytoHubba plugin in Cytoscape. Key targets such as DRD2, SLC6A3, and COMT were prioritized for molecular docking against top-scoring phytochemicals including dopamine precursors and flavonoids found in Musa acuminata. Molecular docking using DockThor revealed significant binding affinities between active compounds and dopaminergic receptors, suggesting potential modulation of neural pathways implicated in addiction. This integrative computational analysis highlights the prophylactic potential of Musa acuminata in rebalancing dopaminergic function and offers a promising phytotherapeutic direction for the management of mobile phone addiction

Neurodegenerative diseases and nerve injury are among the most formidable medical conditions today due to their complicated molecular basis, multiclonal clinical presentations, and restricted therapeutic options. Computational biology has revolutionized the tools available for studying these diseases by combining information from multiple high-throughput platforms, such as genomics, transcriptomics, epigenomics, proteomics, and metabolomics. Using computational pipelines, scientists have discovered dysregulated pathways, miRNA biomarkers, and epigenetic changes that underlie neuropathic pain and chronic nerve damage. For instance, bioinformatic approaches have identified the regulatory function of certain miRNAs in ion channel function, neuroinflammation, and neuronal excitability, while epigenetic analysis has accounted for the chronicity of pain by way of histone-based gene silencing. Multi-omics integration in neurodegeneration has found there are common molecular signatures across Alzheimer’s, Parkinson’s, and amyotrophic lateral sclerosis with implications for shared pathways of stress response, apoptosis, and synaptic dysfunction. Computational modeling, such as machine learning and network biology, also facilitates the discovery of predictive biomarkers and regulatory hubs as potential therapeutic targets. Notably, these approaches are also useful in uncommon neuropathies, when patient groups are small and for direct experimentation, usually restricted. In the future, the intersection of AI-enabled analytics, single-cell and spatial omics, and systemslevel network modeling will allow for improved precision diagnostics and inform personalized therapy approaches. So, computational biology not only enhances our mechanistic insight into nerve injury but also has the transformative power of personalized medicine in neuropathies and neurodegenerative diseases.

Background: Stroke is a leading cause of adult disability and mortality worldwide. Beyond motor and sensory deficits, cognitive dysfunction after stroke substantially affects independence and rehabilitation. Timely detection of post-stroke cognitive impairment (PSCI) allows early intervention. The present study assessed neurocognitive functions in post-acute stroke patients who underwent endovascular or decompressive surgical intervention and compared them with healthy controls using the Addenbrooke’s Cognitive Examination III (ACE-III). Methods: This hospital-based observational case–control study included 40 consecutive patients (18–80 years) with first-ever acute stroke (< 7 days) admitted to the Neurosciences Department, Santokba Durlabhji Memorial Hospital, Jaipur, and 40 age and sex-matched healthy controls. Cases underwent endovascular thrombectomy, aneurysm coiling, clipping, or decompressive craniectomy. Cognitive performance was evaluated at 3 and 6 months using the Hindi-adapted ACEIII assessing five domains attention/orientation, memory, fluency, language, and visuospatial abilities. Statistical analysis used SPSS v22; p < 0.05 was considered significant. Results: Mean patient age was 58.6 ± 11.2 years; 60%were male. Ischemic stroke comprised 72.5%of cases and haemorrhagic 27.5%. Hypertension (42.5%) and diabetes (27.5%) were the most frequent comorbidities. At 3 months, 72.5%of patients demonstrated cognitive impairment versus 62.5%at 6 months. Total ACE-III scores improved from 60.9 ± 10.9 to 74.5 ± 10.7 (p < 0.001). Memory and language domains were most affected initially. Cognitive outcomes did not differ significantly by stroke subtype.