Frontiers in Neurology最新文献

Autoimmune autonomic ganglionopathy (AAG) is a rare and acquired immune-mediated disease that leads to wide autonomic failure, mainly characterized by orthostatic hypotension, gastrointestinal dysfunction, anhidrosis and poorly reactive pupils. This disorder is usually associated with autoantibodies to the ganglionic nicotinic acetylcholine receptor (gAChR-Ab). In this study, we describe a case of a gAChR-Ab-positive AAG patient with two therapeutic stages. The patient responded well after the first stage of methylprednisolone pulse therapy and subsequent low-dose prednisone. However, AAG relapsed after stopping oral prednisone. In the second stage, repeated methylprednisolone pulse therapy was less effective than before. Fortunately, multiple plasma exchange treatments improved the patient's symptoms. In the end, low-dose oral prednisone and mycophenolate mofetil provided significant improvement in this patient during long-term follow-up. AAG is a relatively rare neuroimmunological disease with insidious onset and confused clinical features, while it responds well to the conventional immunotherapy, and some patients may require a long-term immunotherapy. Emphasizing the importance of early detection and treatment in clinical practice. Moreover, it should be noted that the reduction and withdrawal of immunosuppressants should be slow and cautious.

Retinal ganglion cells (RGCs) generally fail to regenerate axons, resulting in irreversible vision loss after optic nerve injury. While many studies have shown that modulating specific genes can enhance RGCs survival and promote optic nerve regeneration, inducing long-distance axon regeneration in vivo through single-gene manipulation remains challenging. Nevertheless, combined multi-gene therapies have proven effective in significantly enhancing axonal regeneration. At present, research on promoting optic nerve regeneration remains slow, with most studies unable to achieve axonal growth beyond the optic chiasm or reestablish connections with the brain. Future research priorities include directing axonal growth along correct pathways, facilitating synapse formation and myelination, and modifying the inhibitory microenvironment. These strategies are crucial not only for optic nerve regeneration but also for broader applications in central nervous system repair. In this review, we discuss multifactors therapeutic strategies for optic nerve regeneration, offering insights into advancing nerve regeneration research.

Introduction: Glaucoma is a leading cause of blindness, often progressing asymptomatically until significant vision loss occurs. Early detection is crucial for preventing irreversible damage. The pupillary light reflex (PLR) has proven useful in glaucoma diagnosis, and mobile technologies like the AI-based smartphone pupillometer (AI Pupillometer) offer a promising solution for accessible screening. This study assesses the reliability of the AI Pupillometer in detecting glaucoma.

Methods: In Experiment 1, 20 healthy participants were assessed using both the AI Pupillometer and the NPi-200 device to evaluate equivalence in measuring PLR. Each eye underwent three trials. Experiment 2 included 46 participants, 24 with primary open-angle glaucoma (POAG) and 22 healthy controls. PLR measurements from the AI Pupillometer were correlated with structural and functional ocular parameters. An additional study expanded the sample to 387 participants (103 glaucoma patients, 284 controls), focusing on differential pupillometry parameters to minimize ambient light interference.

Results: In Experiment 1, the AI Pupillometer demonstrated strong correlations with the NPi-200 in key parameters like initial pupil size (r = 0.700), constricted pupil size (r = 0.755), and constriction velocity (r = 0.541), confirming its reliability. In Experiment 2, although no statistically significant differences in light-corrected PLR parameters were found between groups, glaucoma patients had a marginally higher constricted pupil size (p = 0.1632). Significant correlations were observed between pupillometry and advanced ocular imaging results, notably between constriction amplitude and visual field loss. The additional study revealed significant differences in constriction amplitude (p = 0.014) and relative pupil size change (p = 0.0072) between glaucoma patients and controls, reinforcing the AI Pupillometer's diagnostic potential.

Conclusion: This study confirms the AI Pupillometer as a reliable, accessible tool for glaucoma screening. Mobile diagnostics could enhance early detection, improving outcomes for glaucoma patients.

Objective: To investigate the altered characteristics of cortical morphology and individual-based morphological brain networks in type 2 diabetes mellitus (T2DM), as well as the neural network mechanisms underlying cognitive impairment in T2DM.

Methods: A total of 150 T2DM patients and 130 healthy controls (HCs) were recruited in this study. The study used voxel- and surface-based morphometric analyses to investigate morphological alterations (including gray matter volume, cortical thickness, cortical surface area, and localized gyrus index) in the brains of T2DM patients. Then two methods, Jensen-Shannon divergence-based similarities (JSDs) and Kullback-Leibler divergence-based similarities (KLDs), were used to construct individual morphometric brain networks based on gray matter volume, to discover altered features of the topological network and extract abnormal key brain regions. Subsequently, partial correlation analyses were performed to explore the relationship between clinical biochemical indices, neuropsychological test scores, and altered cortical morphology and network indices.

Results: Brain regions with reduced gray matter volume and cortical thickness in T2DM patients were mainly concentrated in the frontal lobe, temporal lobe, parietal lobe, anterior cingulate gyrus, insula, lingual gyrus, and cerebellar hemispheres. The global attributes of the Individual-based morphological brain network were significantly reduced (Cp, Eloc, σ), with an increase in the nodal efficiency of the hippocampus and the nodal local efficiency of the anterior cingulate gyrus, and the nodal local efficiency of the parahippocampal gyrus and transverse temporal gyrus were reduced. There was a correlation between these node attributes and cognitive scale scores.

Conclusion: This study demonstrated that patients with T2DM exhibit generalized cortical atrophy and damage to individual morphologic brain networks. It also identified overlapping and cognitively relevant key brain regions, primarily within the limbic/paralimbic network (especially the hippocampus and cingulate gyrus), which may serve as imaging markers for identifying cognitive deficits in T2DM. These findings offer new insights into the neural network mechanisms underlying T2DM-associated brain damage and cognitive impairment.

Background: Autonomic dysfunction plays an essential role in dementia, however, it is not known whether electrocardiogram autonomic dysfunction-related indicators are associated with the severity of dementia. In this study, we attempted to investigate whether these indicators are correlated in patients with vascular dementia and Alzheimer's disease compared with normal health individuals. For this purpose, we measured and analyzed the predictive value of heart rate deceleration capacity (DC), heart rate deceleration runs (DRs), heart rate acceleration capacity (AC) along with the plasma levels of lipoprotein-associated phospholipase A2 (Lp-PLA2).

Methods: We compared 83 dementia cases including 41 vascular dementia (VD), 42 Alzheimer's disease (AD) patients with 42 elderly health controls. The Mini-Mental State Examination (MMSE) scores, DC, DRs, AC, and Lp-PLA2 levels were comprehensively evaluated.

Results: Our studies showed that DC and DRs in VD and AD groups were significantly lower than those in controls, while AC values were significantly higher. Furthermore, the risk stratification (high- and moderate-) of DC, DRs, and AC in VD and AD groups was increased, while the low-risk was simultaneously decreased. In addition, DC and DRs were positively while AC and Lp-PLA2 were negatively correlated with MMSE scores. Logistic regression analysis indicated that DC, DRs, AC, and Lp-PLA2 were associated with dementia. Moreover, the areas under the ROC curves showed that the combination of five variables and AC + Lp-PLA2 were 0.970 (95% CI, 0.923-0.992) and 0.940 (95% CI, 0.882-0.974) were larger than each single indicator alone.

Conclusion: Distinctive alterations in dynamic electrocardiogram-related indicators reveal a decline in autonomic nervous functions among individuals with dementia. By incorporating comprehensive analyses of DC, DRs, AC, and Lp-PLA2 values, the specificity and sensitivity of dementia diagnosis can be significantly enhanced.

Mitochondria is the cell's powerhouse. Mitochondrial disease refers to a group of clinically heterogeneous disorders caused by dysfunction in the mitochondrial respiratory chain, often due to mutations in mitochondrial DNA (mtDNA) or nuclear DNA (nDNA) that encodes mitochondrial proteins. This dysfunction can lead to a variety of clinical phenotypes, particularly affecting organs with high energy demands, such as the brain and muscles. Epilepsy is a prevalent neurological disorder in children and is also a frequent manifestation of mitochondrial disease. The exact mechanisms underlying epilepsy in mitochondrial disease remain unclear and are thought to involve multiple contributing factors. This review explores common mitochondrial diseases associated with epilepsy, focusing on their prevalence, seizure types, EEG features, therapeutic strategies, and outcomes. It also summarizes the relationship between the molecular genetics of mitochondrial respiratory chain components and the development of epilepsy.

Objective: To evaluate the clinical utility of improved machine learning models in predicting poor prognosis following endovascular intervention for intracranial aneurysms and to develop a corresponding visualization system.

Methods: A total of 303 patients with intracranial aneurysms treated with endovascular intervention at four hospitals (FuShun County Zigong City People's Hospital, Nanchong Central Hospital, The Third People's Hospital of Yibin, The Sixth People's Hospital of Yibin) from January 2022 to September 2023 were selected. These patients were divided into a good prognosis group (n = 207) and a poor prognosis group (n = 96). An improved machine learning model was employed to analyze patient clinical data, aiding in the construction of a prediction model for poor prognosis in intracranial aneurysm endovascular intervention. This model simultaneously performed feature selection and weight determination. Logistic multivariate analysis was used to validate the selected features. Additionally, a visualization system was developed to automatically calculate the risk level of poor prognosis.

Results: In the training set, the improved machine learning model achieved a maximum F1 score of 0.8633 and an area under the curve (AUC) of 0.9118. In the test set, the maximum F1 score was 0.7500, and the AUC was 0.8684. The model identified 10 key variables: age, hypertension, preoperative aneurysm rupture, Hunt-Hess grading, Fisher score, ASA grading, number of aneurysms, intraoperative use of etomidate, intubation upon leaving the operating room, and surgical time. These variables were consistent with the results of logistic multivariate analysis.

Conclusions: The application of improved machine learning models for the analysis of patient clinical data can effectively predict the risk of poor prognosis following endovascular intervention for intracranial aneurysms at an early stage. This approach can assist in formulating intervention plans and ultimately improve patient outcomes.

Introduction: Prompt treatment with IV thrombolytics (IVT) in acute ischemic stroke (AIS) patients is critical for improved recovery and survival. Recently, hospital systems have switched to the IVT tenecteplase (TNK) instead of the FDA-approved alteplase (tPA) for treatment. Multiple studies and meta-analyses evaluating the efficacy and safety of TNK demonstrate similar or superior outcomes when compared to tPA. TNK is not FDA-approved for treatment, which has led to hesitation in its use and increased attention on its complication profile, including the risk of intracranial hemorrhage (ICH).

Methods: Data from AIS consults conducted in the emergency departments of 220 facilities across 26 states, between 1 January 2022 and 31 May 2023, were extracted from the TeleCare by TeleSpecialists™ database. The encounters were reviewed for IVT candidates, door-to-needle (DTN) time, type of IVT administered, use of advanced imaging, presence of LVO, occurrence and type of complications, complication type, symptomatic ICH, and the ECASS II ICH score.

Results: A total of 2,305 TNK patients and 3,337 tPA patients were extracted. DTN times were faster (37 min vs. 42 min, p < 0.0001), and more total complications were observed in the TNK group (87 vs. 80, p = 0.0035). In non-LVO IVT patients, the TNK group had more complications (57 vs. 47, p = 0.0078), specifically ICH (48 vs. 35, p = 0.0036). No statistically significant difference in the incidence of ICH was observed between the TNK group and the tPA group (21 vs. 18, p = 0.07). In IVT patients not accepted for NIR, the TNK group had more complications (77 vs. 69, p = 0.005), specifically ICH (63 vs. 51, p = 0.0026). In IVT patients accepted for NIR, no significant differences were observed. There were no statistically significant differences in symptomatic ICH between the groups.

Conclusion: The TNK group was found to have significantly more complications, including ICH, than the tPA group driven by non-LVO patients. A closer analysis of the potential for increased risk to non-LVO patients is warranted based on this large, multistate, and multi-hospital system study.

Objective: This study aims to investigate the effects of preoperative intracerebral hematoma volume (HVpre), hematoma volume 6-8 days post-surgery (HVpost), and the rate of hematoma volume change (HVpre-HVpost)/HVpre on the prognosis of patients with aneurysmal subarachnoid hemorrhage (aSAH).

Materials and methods: CT imaging data from 62 aSAH patients admitted to our hospital's Neurosurgery Department between January 2022 and December 2023 were obtained, both preoperatively and 6-8 days postoperatively. The hematoma volumes were measured using 3D-Slicer. Patients' recovery at 3 months post-discharge was assessed using the Modified Rankin Scale (mRS), categorizing the patients into a good prognosis group (mRS score 1-2) and a poor prognosis group (mRS score 3-5). Multivariate logistic regression analysis was conducted to identify independent risk factors for poor prognosis. Statistical methods were employed to compare preoperative and postoperative hematoma volumes with commonly used clinical scores. The predictive value of HVpre and HVpost for poor prognosis was evaluated using ROC curves. The rate of volume change was stratified by interquartile ranges, and the impact of different change rates on prognosis was compared.

Results: Significant differences were found between good and poor prognosis groups in age, GCS score, Hunt-Hess grade, mFisher grade, BVpre, BVpost, and (HVpre-HVpost)/HVpre (p < 0.05). Logistic regression identified gender, age, BVpre, BVpost, and volume change rate as independent risk factors (p < 0.01). Increased GCS scores and higher Hunt-Hess and mFisher grades correlated with increased HVpre and HVpost. Higher hemorrhage reduction rates were linked to better outcomes. ROC curves showed HVpre and HVpost AUC values (0.831 and 0.857, respectively) were significantly higher than clinical scales. An HVpre volume over 22.25 mL and HVpost over 15.67 mL indicated a higher risk of poor prognosis, with sensitivities of 79.3 and 80.7%, and specificities of 67.1 and 69.3%.

Conclusion: HVpre, HVpost, and (HVpre-HVpost)/HVpre can serve as neuroimaging biomarkers for assessing patients after aSAH and can effectively predict clinical prognosis.

Introduction: In the past decade, flow diverters (FDs) have increasingly been used to treat cerebral aneurysms with unfavorable morphology in which other endovascular techniques fall short of being as effective. In-stent stenosis (ISS) is one of the most puzzling and frequent risks of flow diversion therapy observed on follow-ups. This complication, although mostly placid in its clinical course, can have dire consequences if patients become symptomatic. ISS is associated with many factors, none of which have been demonstrated to date to be solely responsible for the phenomenon.

Methods: This study was aimed at evaluating ISS incidence in patients in our clinic who were treated with flow-diverters for aneurysms, located on the supraclinoid segments of the internal carotid artery between September 2022 and May 2023. A retrospective analysis was conducted, which included 137 patients with a total of 142 aneurysms being treated. The main hypothesis was that oversizing of the implant might play a role in ISS development. The performed statistical analysis, aimed at finding a correlation between it and vessel lumen narrowing on the follow-ups. The effects of other known risk factors, such as sex, age, smoking, and hypertension, were also analyzed.

Results: Stent oversizing with respect to the parent artery was positively correlated with subsequent ISS occurrence and severity. Older age was a protective factor against ISS. Patients who actively smoked had diminished risk of developing severe ISS.

Discussion: Stent oversizing can lead to ISS development, which might be more pronounced with larger implant-to-vessel sizing discrepancies. To achieve optimal results, the choice of implant diameter should consider all segments of the vessel in which it will be implanted. In cases of severe symptomatic ISS, continuation of dual anti-platelet therapy is a reasonable and effective option to address this complication.