Frontiers in Neurology最新文献

[This corrects the article DOI: 10.3389/fneur.2025.1619236.].

Background: Management of chronic inflammatory demyelinating polyneuropathy (CIDP) is challenged by heterogeneity in severity, comorbidities, potential adverse effects, and treatment accessibility. This study aimed to elucidate treatment utilization among patients with CIDP in the United States (US) to identify potential unmet needs.

Methods: Adult patients with CIDP were identified using Komodo Health's Healthcare Map™ (January 2016-December 2020). Descriptive statistics related to utilization of CIDP treatments over 1-year post-index were analyzed. Among patients who used immunoglobulin (Ig), chronic Ig users were defined as patients with ≥8 Ig courses, and intermittent Ig users as patients with <8 Ig courses during 1-year post-index.

Results: Among 3,409 patients with CIDP identified, the majority (81% [n = 2,758]) were treated for CIDP while 19% (n = 651) were untreated for CIDP during 1-year post-index. Steroids (73% [n = 2017]) followed by Ig (65% [n = 1,803]) were most commonly utilized. Of patients who used Ig, 62% (n = 1,113) were chronic and 38% (n = 690) were intermittent users during the 1-year post-index. A large proportion of Ig users received concomitant CIDP treatments, most commonly steroids. Patients who received >60 mg/day oral steroids on average over the 1-year post-index continued to use concomitant CIDP treatment, most commonly Ig.

Conclusion: Steroids and Ig were mainstay treatments among patients with CIDP. A substantial proportion of Ig users were chronic users who also received other CIDP therapies, with steroids being most common. This suggests a potentially pronounced burden among patients treated with frequent Ig and steroids.

Introduction Multiple Sclerosis (MS) is a chronic condition affecting the central nervous system, often leading to urinary incontinence (UI), balance disturbances, and fatigue. This study examines the relationship between UI, core muscle morphology, balance, and fatigue in patients with MS (PwMS) to inform rehabilitation strategies. Methods A cross-sectional observational study was conducted with 27 PwMS (17 with UI and 10 without). Abdominal muscle thickness (transversus abdominis (TA), internal obliques, and external obliques) was assessed via ultrasound. UI-related Quality of Life was evaluated using questionnaires (ICIQ-SF and I-QOL), balance was assessed with the Trunk Impairment Scale (TIS) and Berg Balance Scale (BBS), and fatigue was measured using the Modified Fatigue Impact Scale (MFIS). Results Significant correlations were observed between UI, TA thickness during contraction and balance with the TIS demonstrating greater sensitivity than the BBS. PwMS with UI exhibited reduced TA thickness and poorer scores in balance and fatigue, particularly in the cognitive subscale of the MFIS. Logistic regression revealed that the severity of UI predicts functional balance, with an overall model accuracy of 70.8%. Conclusions Core dysfunction may link UI, balance and fatigue in PwMS. Strengthening the TA and pelvic floor muscles should be a rehabilitation priority to improve UI, postural stability, and daily function.

Background: Mild cognitive impairment (MCI) is a critical early stage of Alzheimer's disease (AD). Elucidating the comorbidity characteristics, influencing factors, and molecular mechanisms between MCI and AD in community-based populations is crucial for early intervention in cognitive impairment.

Methods: 2,234 elderly individuals aged 50 years or older from 14 communities in Pudong New District, Shanghai, were enrolled in this study. The Montreal Cognitive Assessment (MoCA), Mini-Mental State Examination (MMSE), and Clinical Dementia Rating (CDR) were used to divide individuals into a control group (n = 1,160) and an MCI group (n = 1,074). The associations of demographic characteristics, lifestyle, and psychological status with MCI were analyzed. Transcriptome data from GSE140829 (training set) and GSE63060 (validation set) were obtained from the GEO database. Weighted gene co-expression network analysis (WGCNA) was used to identify MCI signature genes. KEGG pathway analysis was combined with the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway to elucidate mechanisms of comorbidity. Targeted intervention agents were screened based on the DSigDB database. Molecular docking (MD) was used to evaluate the binding ability between small molecules and target proteins.

Results: The prevalence of AD in the MCI group (30.17%) was significantly higher than that in the control group (p < 0.001), and MCI and AD were significantly positively correlated. Age, gender, smoking, living arrangements, mobile phone use, pet ownership, stress, anxiety, and depression were key influencing factors for MCI (p < 0.05). The proportion of individuals living with children and grandchildren (57.45%) in the MCI group was significantly higher than that in the control group (16.29%) (p < 0.001). WGCNA identified 273 MCI signature genes. KEGG pathway analysis showed that these genes were significantly enriched in neurodegenerative disease pathways, including AD pathways (with the AD pathway ranking first in the "Human Diseases" category). Targeted intervention screening identified the natural compounds boldine (comprehensive score 961.58) and piceatannol (comprehensive score 358.46) as potential drug candidates (p < 0.05), both of which have strong binding ability to target proteins.

Conclusion: MCI patients in the community are at high risk of AD, and their comorbidity characteristics are affected by multidimensional lifestyle and psychological factors. Boldine and piceatannol may be potential natural compounds for the intervention of cognitive impairment. The results of this study can provide a theoretical basis for the early prevention and precise intervention of cognitive impairment in the community.

The global burden of neurological disease is rising amid an aging population and accelerating climate change, yet environmental determinants of brain health remain underrecognized within neurology. Climate-related factors-including air pollution, extreme heat, environmental contaminants, and ecological disruptions-can contribute to neuroinflammation, cerebrovascular disease, neurodegenerative disorders, and mental health conditions. Development of climate-relevant short- and long-term goals within the field of neurology is in line with the discipline's increasing interest in improving population brain health. This commentary categorizes "green" brain health priorities into five domains: (1) clinical practice, (2) public communication, (3) education and training, (4) research, and (5) policy. The recommendations put forth constitute an agenda that is relevant to many stakeholders, including professional societies, like the AAN. A dedicated commitment to environmental determinants of brain health is imperative to safeguarding global neurologic well-being in the face of an escalating climate crisis.

Background/objectives: The variability in acute ischemic stroke (AIS) outcomes is closely associated with collateral circulation status. While fluid-attenuated inversion recovery vascular hyperintensity (FVH) and multimodal CT parameters (e.g., rLMC score, rCBV) were associated with 90-day functional outcomes in AIS patients, their combined predictive value and clinical utility warrant further investigation. This study investigates the combined predictive value of FVH and multimodal CT parameters for collateral assessment and prognosis in AIS.

Methods: We retrospectively and consecutively enrolled AIS patients with internal carotid artery or middle cerebral artery stenosis/occlusion who did not receive intravenous thrombolysis or mechanical thrombectomy. All patients underwent one-stop CT angiography-CT perfusion and multimodal MRI within 72 h of symptom onset. Evaluations included FVH scores (based on modified ASPECTS regions), rLMC scores, Maas scores, and ASITN/SIR collateral grading. Spearman analysis assessed correlations between FVH and CTA collateral scores. Univariate and multivariate logistic regression indicated the independent predictors of a 90-day functional outcome [favorable (mRS 0-2) vs. poor (mRS 3-6)], with receiver operating characteristic (ROC) curves evaluating predictive performance.

Results: The cohort comprised 112 patients (70 favorable outcomes, 42 poor outcomes). FVH scores showed a negative correlation with ASITN/SIR collateral grades (r = -0.432, p < 0.001). Compared to the favorable outcome group, the poor outcome group exhibited higher baseline National Institute of Health Stroke Scale (NIHSS) scores, elevated FVH scores, reduced rLMC scores, and lower rCBV values (all p < 0.05). Multivariate analysis indicated that NIHSS score, FVH score, rLMC score, and rCBV were independent predictors of poor outcomes. ROC analysis demonstrated strong predictive performance for rLMC score (AUC = 0.848, 95%CI 0.778-0.919), FVH score (AUC = 0.662, 95%CI 0.550-0.774), and rCBV (AUC = 0.727, 95%CI 0.631-0.822).

Conclusion: Multimodal CT combined with MRI facilitates early AIS diagnosis and collateral assessment. The integration of FVH with CT parameters (rLMC score and rCBV) was associated with the prediction of functional outcomes in AIS patients.

Background: Abdominal migraine (AM) is an episodic syndrome characterized by recurrent, self-limiting episodes of abdominal pain with autonomic features, now recognized to affect both children and adults according to ICHD-3 criteria. Its diagnosis is clinical and requires the exclusion of organic gastrointestinal or renal diseases, yet no standardized treatment exists, leading to therapeutic approaches often adapted from migraine management. Challenges in diagnosis, due to difficulties in symptom description by children and cognitive biases in adults, frequently result in underdiagnosis, repeated consultations, and diminished quality of life. This study aims to analyze the clinical characteristics, diagnostic and therapeutic approaches, and outcomes of AM in pediatric and adult patients based on a large case series.

Methods: A systematic literature review was conducted per PRISMA guidelines. Major databases were searched from inception to June 2025 for case reports and clinical studies on AM. Data on demographics, clinical presentation, treatment, and outcomes were extracted and analyzed.

Results: We included 662 patients (629 children, 33 adults) from 63 studies. The female-to-male ratio was 1.6:1. The median age at onset was 4.2 years in children and 31.0 years in adults, with diagnostic delays of 3.1 and 4.0 years, respectively. Among cases with specific data, periumbilical pain was reported in 43.3% (of 223), while nausea (66.1%), vomiting (53.6%), and headache (47.1%) were common in a cohort of 448 cases. Photophobia, pallor, and anorexia were also frequently observed. Triptans showed the highest acute efficacy (98.04%, 50/51), versus 62.5% (5/8) for NSAIDs. Prophylactics were highly effective: anticonvulsants (95.0%, 19/20), beta-blockers (100%, 12/12), and antihistamines (92.8%, 64/69). These exceptional rates likely reflect reporting bias and require prospective validation.

Conclusion: AM presents with significant clinical heterogeneity but shares core features with migraine disorders. Early diagnosis and management, potentially incorporating agents used in migraine (such as triptans and prophylactics) based on preliminary evidence, may improve outcomes, though this requires confirmation in controlled studies. Increased awareness of non-gastrointestinal symptoms and migraine history is essential for accurate diagnosis.

Background: This study aimed to develop and validate a deep learning model based on preoperative MRI to non-invasively predict Telomerase Reverse Transcriptase (TERT) promoter mutation status in glioma patients.

Methods: A retrospective cohort of 100 patients with histologically confirmed high-grade glioma was included. Regions of interest (VOIs) were manually annotated on contrast-enhanced T1-weighted MRI sequences by senior radiologists. Five deep learning models (RegNet, GhostNet, MobileNet, ResNeXt50, ShuffleNet) were trained and evaluated using accuracy, precision, recall, and F1-score. The dataset was split into training (80%) and internal validation (20%) sets.

Results: RegNet achieved the highest performance with an accuracy of 0.7742, recall of 0.8704, precision of 0.7163, and F1-score of 0.7023. It demonstrated superior ability to capture imaging features associated with TERT mutations compared to other models. The area under the ROC curve (AUC) for RegNet was 0.7182, indicating moderate discriminative power.

Conclusion: The RegNet model effectively predicts TERT promoter mutation status from routine MRI, offering a non-invasive tool for preoperative molecular subtyping of glioma. This approach may facilitate personalized treatment planning and address limitations of invasive tissue-based diagnostics. Further validation with multi-center data is warranted to enhance clinical applicability.

Introduction: This study aims to rigorously evaluate the consistency and reliability of a pluripotent stem cell (PSC) differentiation system and explore how the KCNB1 mutation disrupts the temporal regulation of gene expression during neuronal differentiation and modulates neuron function-related pathways.

Methods: Induced pluripotent stem cells (iPSCs) derived from a patient carrying a KCNB1 variant (c.990G > T, p.Glu330Asp) and from a healthy donor were differentiated into neurons. Differentiation and RNA expression were assessed at multiple time points. Immunofluorescence, RNA sequencing, fuzzy c-means clustering, and pathway analyses were performed.

Results: The differentiation system was successfully established, with cells exhibiting stage-appropriate morphology and maturing into neurons. RNA sequencing revealed consistent gene expression patterns at the neural progenitor cell (NPC) stage but significant differences at the neuron stage between the KCNB1 mutant patient and the healthy donor. Notably, KCNB1 expression was lower in the patient's neurons. Genes specifically clustered in healthy neurons were enriched in synapse-related pathways, while genes clustered in patient neurons were associated primarily with basic cellular metabolism pathways and abolished neuron-specific pathways.

Conclusion: Low expression of KCNB1 disrupts the temporal pattern of gene expression and related neuron-specific pathways during neuronal differentiation and impairs neuronal differentiation and maturity.