Neurological Sciences最新文献

Background: Cardiovascular autonomic dysfunction was frequently observed in patients with Parkinson's disease (PD), and severe blood pressure (BP) fluctuations posing significant clinical challenges. Although current dopaminergic drugs, (e.g. levodopa/benserazide, selegiline, and piribedil) could improve motor symptoms, the underlying mechanisms of catecholamine storm triggered by concomitant use remained to be fully elucidated.

Case presentation: A 62-year-old female with PD was admitted in May 2025 due to abnormal BP fluctuations accompanied by fatigue lasting 20 days. In 2021, she was diagnosed with PD and received long-term treatment with levodopa/benserazide (125 mg tid), selegiline (2.5 mg tid), and piribedil (50 mg tid). Systolic blood pressure (SBP) measured in the hospital varied from 57 to 217 mmHg. A 24-hour peak urinary dopamine level of 36,733 µg/24 h was documented. Multidisciplinary consultation led to discontinuation of selegiline and piribedil while levodopa/benserazide was maintained. Within 72 h, her urinary dopamine levels decreased by 77% (to 8,424 µg/24 h), and standard deviation of BP was reduced from 29.59 to 9.95 mmHg-a 66% decrease. During 3-month follow-up period, the patient's home monitored SBP remained stable between 110 and 135 mmHg although her motor symptoms have advanced manifesting as ipsilateral limb tremors and increased muscle tone.

Conclusions: This case demonstrated that combined dopaminergic therapy could induce catecholamine storm via synergistic effects, leading to life-threatening fluctuations in BP. Timely identification and adjustment of treatment regimens could effectively reverse cardiovascular risk. However, it was crucial to carefully consider the balance between the progression of motor symptoms and changes in medication treatment.

Magnetoencephalography (MEG), a non-invasive neuroimaging technique with millisecond temporal resolution and millimeter spatial resolution, is an essential tool for investigating neurological disorders. This study conducted a systematic analysis of 4,040 relevant publications from the Web of Science database (2000-2024) using VOSviewer and CiteSpace to identify research hotspots and trends in MEG applications for neurological disorders over the past 24 years. The analysis revealed a steady annual increase in publications, and showed that the research evolved in three distinct phases: the early period (2000-2004) focused primarily on fundamental MEG principles, the intermediate period (2005-2015) shifted toward MEG signal analysis methods including network analysis, and the recent period (2016-2024) emphasized brain network functional connectivity analysis. Emerging research hotspots converged on the clinical application of analytical methods such as brain functional connectivity, encompassing areas such as the early diagnosis of Alzheimer's disease and preoperative evaluation for epilepsy. This study provided the first comprehensive bibliometric analysis of research hotspots and developmental trends in MEG applications for neurological disorders. These findings provided researchers with a clear understanding of the field's evolution and current landscape, thereby facilitating the rapid identification of promising research directions.

Neurological disorders of the brain and spinal cord affect millions of individuals worldwide and continue to rise in prevalence. Conditions such as Alzheimer's disease, Parkinson's disease, epilepsy, spinal cord injury, and neurodevelopmental disorders disrupt cognitive, motor, and autonomic functions, severely impacting quality of life. This review provides an up-to-date and structured examination of neurological disorders and presents novel findings derived from a rigorous search strategy based on Boolean operators and PRISMA-aligned screening. A total of 154 peer-reviewed articles met the inclusion and exclusion criteria and were systematically analyzed. This paper also offers a comprehensive clinical categorization of neurological disorders and outlines their diagnostic and functional challenges. Then, it classifies the architecture of smart assistive technologies across four dimensions-neurological disorders, smart technologies, functional layers, and clinical outcomes-to establish a unified taxonomy for neuro-assistive research. Further, it presents three major smart assistive techniques used for neurological disorders: (i) AI-based techniques, including adaptive neuro-signal decoding algorithms and behavioural anomaly detection using hybrid deep learning; (ii) IoT-based techniques, consisting of context-aware multisensor fusion frameworks and edge-cloud collaborative health networks; and (iii) wearable system techniques that enable continuous, unobtrusive monitoring in real-world contexts. A detailed performance evaluation summarizes key metrics such as Detection Rate (DR%), Precision Rate (PR%), Recall Rate (RR%), and Processing Time (PT), highlighting how parameter variations influence practical deployment. Benchmark datasets are then encapsulated with descriptions of their features, sizes, and access links, enabling dataset-wise comparison and identification of suitable evaluation platforms for future research. This review also identifies current limitations and capabilities of existing smart assistive systems and synthesizes their implications for future directions. By highlighting gaps such as multimodal fusion challenges, data privacy constraints, and the need for adaptive models, this paper proposes a forward-looking framework to make neuro-assistive solutions more clinically accessible. Ultimately, this work advocates for connected, intelligent, and adaptive systems that advance diagnosis, monitoring, and rehabilitation for individuals with neurological disorders.

The CNS presents a unique challenge to therapeutic intervention due to its sophisticated organization, the protective blood-brain barrier (BBB), and the low regenerative potential of neural tissue. Over the last few decades, nanotechnology has emerged as a revolutionary technology capable of transforming the diagnosis and treatment of CNS diseases, thereby offering new hope to patients with previously incurable neurodegenerative diseases. This review highlights the therapeutic and diagnostic potential of newer types of nanomaterials-i.e., nanoliposomes (NLs), metallic nanoparticles (MNPs), and carbon nanotubes (CNTs)-which have been found to cross the blood-brain barrier (BBB) and deliver drugs with enhanced specificity and efficacy. Nanoparticle-based therapies have revolutionized drug delivery, gene therapy, in vivo imaging, and molecular profiling for CNS diseases. However, despite such advancements, hurdles remain, particularly in terms of biocompatibility, long-term safety, and site-specific activity within complex biological systems. Herein, we summarize recent advances in the construction of smart nanocarriers and multi-functional platforms for overcoming physiological and pharmacological challenges in CNS therapy. Finally, we emphasize the urgent need for interdisciplinary studies to unlock the full clinical potential of nanotechnology in neurology and answer outstanding questions regarding toxicity, immune responses, and scalability for human application.

Background: Agitation is one of the most distressing neuropsychiatric symptoms in patients with dementia due to Alzheimer's disease (AD), significantly impacting patients' quality of life and increasing caregiver burden. Brexpiprazole, a serotonin-dopamine modulator, shows promise for managing agitation. This meta-analysis evaluates the efficacy and safety in managing agitation associated AD.

Method: A comprehensive literature search was conducted across PubMed, Cochrane, Scopus, Embase and ClinicalTrials.gov from inception until January 2025. We pooled dichotomous outcomes as risk ratios (RR) and continuous outcomes as mean differences (MD) with 95% confidence intervals (CI), using random-effects models. Heterogeneity was assessed using I² and X² statistics. A p-value of < 0.05 was considered statistically significant. All the calculations were performed using RevMan 5.4.

Result: This meta-analysis included 4 studies involving 1440 patients (944 vs. 496) suffering from agitation associated with dementia in AD. Brexpiprazole significantly reduced agitation on CMAI (MD: -3.94 [-6.21 to -1.67], p < 0.001) and NPI-NH (MD: -0.67 [-1.08 to -0.26], p = 0.002) with optimal efficacy at 2-3 mg/day. SAS scores worsened slightly (MD: 0.38 [0.18-0.58], p = 0.0002) while MMSE (p = 0.06) and CGI-S (p = 0.06) remained stable. No significant differences emerged in serious adverse events, mortality, dizziness, or extrapyramidal effects (all p > 0.05).

Conclusion: Brexpiprazole effectively reduces agitation in AD without major safety concerns, though mild motor effects were noted. Study limitations include moderate heterogeneity and short trial durations. Future research should explore long-term outcomes and patient stratification.