Frontiers in Neurology最新文献

Background: Charcot-Marie-Tooth disease (CMT), a slowly advancing hereditary nerve disorder, presents a significant challenge in the medical field. Effective drugs for treatment are lacking, and we struggle to find sensitive markers to track the disease's severity and progression. In this study, our objective was to investigate the levels of neurofilament light chain (NfL), glial fibrillary acid protein (GFAP), fibroblast growth factor 21 (FGF-21) and growth differentiation factor 15 (GDF-15) in individuals with CMT and to compare them to a control group. Our primary goal is to determine whether these biomarker levels are related to the severity of the disease.

Methods: Initially, 44 patients with CMT and 44 controls participated in this study. CMT diagnosis was approved by genetic testing. Disease severity was assessed through clinical evaluations using the CMT Neuropathy Score version 2 (CMTNSv2). NfL and GFAP concentrations were measured using Single molecule array, while FGF-21 and GDF-15 concentrations were measured by enzyme-linked immunosorbent assays.

Results: In the group of patients with CMT, the concentrations of GDF15, FGF21, NfL, and GFAP were significantly higher than in the control group (p < 0.05). NfL and GFAP levels were correlated with the CMTNSv2 score (rs = 0.46, p = 0.002; rs = 0.31, p = 0.04).

Conclusion: Our study has provided confirmation that plasma concentrations of NfL, GFAP, GDF15, and FGF21 are significantly elevated in patients with CMT compared to controls. Furthermore, NfL and GFAP levels were correlated with the clinical severity of CMT. These findings suggest that NfL and GFAP can be reliable disease indicators in future research.

Background: Postural Orthostatic Tachycardia Syndrome (POTS) is a complex form of dysautonomia that presents with abnormal autonomic reflexes upon standing, leading to symptoms such as lightheadedness, tachycardia, fatigue, and cognitive impairment. The COVID-19 pandemic has brought renewed attention to POTS due to its overlap with post-acute sequelae of COVID-19 (PASC). Studies have found that a substantial percentage of COVID-19 survivors exhibit symptoms resembling POTS, elevating POTS diagnoses to previously unseen levels. We systematically reviewed the literature for existing high-quality evidence on potential interventions.

Methods: A systematic review of the literature was performed to identify studies of oral medications for the management of POTS. We searched for published manuscripts on the medical management of POTS through 6 April 2024 which met pre-specified inclusion criteria. We conducted quality appraisal and assessed risk of bias before extracting the data and performing synthesis to determine the current state of the evidence; particularly in the context of PASC.

Results: The study search and selection process identified 32 studies that met inclusion criteria, comprising randomized controlled trials, observational studies, and systematic reviews. Most included studies were judged to be of moderate to high quality, with largely low risk of bias. The most frequently studied medications were beta-blockers, ivabradine, and midodrine. Ivabradine and midodrine demonstrated the highest rate of symptomatic improvement, while beta-blockers showed the largest reduction in heart rate variability. Limited evidence was available for PASC-associated POTS, but findings suggest that treatments may have similar efficacy in both PASC and non-PASC cases.

Conclusion: Ivabradine, midodrine, and beta-blockers currently appear to be reasonable front-line choices in pharmacologic management of POTS (PASC associated and otherwise). Further RCTs that evaluate long term outcomes of medications are needed to further establish evidence based pharmacologic treatment approaches for POTS. Particular areas of inquiry include differential efficacy of recommended therapies based on POTS subtypes, and a need for treatments directly targeting the underlying autonomic nervous system dysfunction.

Systematic review registration: PROSPERO, identifier CRD42024505967, https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=505967.

Objective: To develop and validate an explainable machine learning (ML) model predicting the risk of hemorrhagic transformation (HT) after intravenous thrombolysis.

Methods: We retrospectively enrolled patients who received intravenous tissue plasminogen activator (IV-tPA) thrombolysis within 4.5 h after symptom onset to form the original modeling cohort. HT was defined as any hemorrhage on head CT scan completed within 48 h after IV-tPA administration. We utilized the Random Forest (RF), Multilayer Perceptron (MLP), Adaptive Boosting (AdaBoost), and Gaussian Naive Bayes (GauNB) algorithms to develop ML-HT models. The models' predictive performance was evaluated using confusion matrix (including accuracy, precision, recall, and F1 score), and discriminative analysis (area under the receiver-operating-characteristic curve, ROC-AUC) in the original cohort, followed by validation in an independent external cohort. The models' explainability was assessed using SHapley Additive exPlanations (SHAP) global feature plot, SHAP Summary Plot, and Partial Dependence Plot.

Results: A total of 1,007 patients were included in the original modeling cohort, with an HT incidence of 8.94%. The RF-based ML-HT model showed metrics of 0.874 (accuracy), 0.972 (precision), 0.890 (recall), 0.929 (F1 score); with ROC-AUC of 0.7847 in the original cohort and 0.7119 in the external validation cohort. The MLP model showed 0.878, 0.967, 0.989, 0.978, 0.7710, and 0.6768, respectively. The AdaBoost model showed 0.907, 0.967, 0.989, 0.978, 0.7798, and 0.6606, respectively. The GauNB model showed 0.848, 0.983, 0.598, 0.716, 0.6953, and 0.6289, respectively. The explainable analysis of the RF-based ML model indicated that the National Institute of Health Stroke Scale (NIHSS) score, age, platelet count, and atrial fibrillation were the primary determinants for HT following IV-tPA thrombolysis.

Conclusion: The RF-based explainable ML model demonstrated promising predictive ability for estimating the risk of HT after IV-tPA thrombolysis and may have the potential to assist the clinical decision-making in emergency settings.

Background: Alzheimer's disease (AD) is a common neurodegenerative disorder worldwide and the using of magnetic resonance imaging (MRI) in the management of AD is increasing. The present study aims to summarize MRI in AD researches via bibliometric analysis and predict future research hotspots.

Methods: We searched for records related to MRI studies in AD patients from 2004 to 2023 in the Web of Science Core Collection (WoSCC) database. CiteSpace was applied to analyze institutions, references and keywords. VOSviewer was used for the analysis of countries, authors and journals.

Results: A total of 13,659 articles were obtained in this study. The number of published articles showed overall exponential growth from 2004 to 2023. The top country and institution were the United States and the University of California System, accounting for 40.30% and 9.88% of the total studies, respectively. Jack CR from the United States was the most productive author. The most productive journal was the Journal of Alzheimers Disease. Keyword burst analysis revealed that "machine learning" and "deep learning" were the keywords that frequently appeared in the past 6 years. Timeline views of the references revealed that "#0 tau pathology" and "#1 deep learning" are currently the latest research focuses.

Conclusion: This study provides an in-depth overview of publications on MRI studies in AD. The United States is the leading country in this field with a concentration of highly productive researchers and high-level institutions. The current research hotspot is deep learning, which is being applied to develop noninvasive diagnosis and safer treatment of AD.

This review aims to summarize current knowledge and highlight recent findings on the association between cryptogenic stroke (CS) and patent foramen ovale (PFO). By presenting sometimes conflicting data, the review underscores the necessity for further research to clarify the complex mechanisms behind PFO-related CS and optimize its management. Results from research identifies specific conditions and scores, such as the risk of paradoxical embolism (RoPE) score, that help assess the likelihood of PFO-related cryptogenic stroke and guide treatment decisions. PFO closure has demonstrated substantial benefits in select cases, especially those with high-risk PFO features, though complications such as atrial fibrillation were frequently documented. Biomarker measurements, such as reduced total homocysteine (tHcy) level after PFO closure or high D-dimer levels indicating a higher risk of stroke recurrence, represent newer areas of study with a promising future in medical practice. Cryptogenic stroke (CS) remains a diagnostic challenge. This article reviews the current understanding of PFO-related CS, focusing on the interplay of concomitant pathological conditions, PFO closure, stroke recurrence, and some of the related biomarkers.

Background and aims: To investigate the feasibility, safety and effectiveness of the ketogenic diet (KD) for super-refractory status epilepticus (SRSE) in the intensive care unit (ICU).

Methods: We conducted a prospective investigation on patients with SRSE treated with the KD. The primary outcome measures were ketosis development as a biomarker of feasibility and resolution of SRSE as effectiveness. KD-related side effects were also investigated.

Results: Twelve patients (9 females and 3 males) with SRSE, with a median age of 34 years [range 16-69, interquartile range (IQR) 18-52], were treated with a KD. The median duration of SRSE prior to KD treatment was 21 days (range 4-46). SRSE resolved in 75% (9/12) of patients at a median of 3 days (range 1-18) after KD initiation. Among the nine KD responders, all were successfully weaned off anesthetic agents at a median of 16 days (range 4-32) after KD initiation, and all were also successfully weaned off ventilator. Side effects varied, and included gastrointestinal intolerances, malnutrition and metabolic abnormalities, electrolyte disturbance, and acute weight loss, although most of them could be corrected. No patient died due to KD, and neurofunctions continued to improve under KD therapy.

Conclusion: The KD may be feasible and effective for the treatment of SRSE in the ICU. Moreover, it is relatively safe. However, there are numerous adverse events that can be corrected under close monitoring.

Objective: To develop a deep learning (DL) model for carotid plaque detection based on CTA images and evaluate the clinical application feasibility and value of the model.

Methods: We retrospectively collected data from patients with carotid atherosclerotic plaques who underwent continuous CTA examinations of the head and neck at a tertiary hospital from October 2020 to October 2022. The model combined ResUNet with the Pyramid Scene Parsing Network (PSPNet) to enhance plaque segmentation. Patient plaques were divided into training, validation, and testing sets in a ratio of 7:1.5:1.5. We analyzed recall (lesion-level sensitivity), sensitivity (patient-level), and precision to evaluate the model's diagnostic performance for carotid plaques. The two stepwise early-stage clinical validation study (Comparison study and Model-human study) was used to simulate real clinical plaque diagnostic scenarios.

Results: In total, 647 patients were included in the dataset, including 475 for training, 86 for validation, and 86 for testing. The DL model based on CTA images showed good precision in plaque diagnosis (validation set: precision = 80.49%, sensitivity = 90.70%, recall = 84.62%; test set: precision = 78.37%, sensitivity = 91.86%, recall = 84.58%). In addition, subgroup analysis of the plaque was carried out in the test set. The model had high accuracy in identifying plaques at different locations (Recall: 83.72, 76.32, 89.25, and 83.02%) and with different morphologies (Recall: 86.03, 79.17%). This model also analyzed the results of different types of plaques and showed good to moderate plaque diagnostic accuracy for different plaque types (Recall: 70.00, 86.87, 84.29%). Especially, in the clinical application scenario analysis, the model's diagnostic results for plaques were found to be higher than those of 4 out of 6 radiologists (p < 0.001). Furthermore, in Model-human Real Clinical Scenarios study, we found that the model improved the radiologists' sensitivity in diagnosing plaques. Additionally, the model's diagnostic time for plaques (6 s) was found to be significantly shorter than that all of radiologists (p < 0.001).

Conclusion: This AI model demonstrated strong clinical potential for carotid plaque detection with improved clinician diagnostic performance, shortening time, and practical implementation in real-world clinical cases.

Background/aims: Oro-pharyngeal dysfunction has been reported in Amyotrophic Lateral Sclerosis (ALS). We aimed to assess ALS patients upper gastrointestinal (GI) motor activity and GI symptoms according to bulbar and spinal onset and severity of ALS.

Methods: ALS bulbar (B) and spinal (S) patients with ALS Functional Rating Scale (ALSFRS-r) ≥35, bulbar sub-score ≥10, and Forced Vital Capacity (FVC) >50%, underwent to: Fiberoptic Endoscopic Evaluation of Swallowing (FEES); esophageal manometry; gastric emptying; Rome symptom questionnaire. Medical Research Council Scale for Muscle Strength (MRC) was performed for the upper and lower limbs. Mann-Whitney's U, Fisher's ranks test, Pearson's test was used.

Results: Thirteen ALS patients were included (6 F; mean age 61.2 ± 13.7 years, range: 37-87), 5 with B and 8 with S onset (ALSFRS-R score 39.5 ± 4.9, MRC score 128.6 ± 23.3, disease duration 22.8 ± 17.9 months). FEES detected a high dysphagia score in 5 patients with no difference between S and B phenotype. Lower esophageal sphincter pressure was normal in all patients. Esophageal dysmotility was observed in three S and two B onset patients. Upper esophageal sphincter (UES) pressure was higher in all ALS patients. UES spasms and delayed gastric emptying were detected in two B and one S and in two B and four S patients, respectively. There was no correlation between esophagogastric motor abnormalities and clinical characteristics of ALS, nor GI symptoms.

Conclusions: The presence of UES spasm and the delayed gastric emptying in a subgroup of ALS patients may suggest the role of ANS dysfunction in ALS.

Introduction: Early prognosis prediction of acute ischemic stroke (AIS) can support clinicians in choosing personalized treatment plans. The aim of this study is to develop a machine learning (ML) model that uses multiple post-labeling delay times (multi-PLD) arterial spin labeling (ASL) radiomics features to achieve early and precise prediction of AIS prognosis.

Methods: This study enrolled 102 AIS patients admitted between December 2020 and September 2024. Clinical data, such as age and baseline National Institutes of Health Stroke Scale (NIHSS) score, were collected. Radiomics features were extracted from cerebral blood flow (CBF) images acquired through multi-PLD ASL. Features were selected using least absolute shrinkage and selection operator regression, and three models were developed: a clinical model, a CBF radiomics model, and a combined model, employing eight ML algorithms. Model performance was assessed using receiver operating characteristic curves and decision curve analysis (DCA). Shapley Additive exPlanations was applied to interpret feature contributions.

Results: The combined model of extreme gradient boosting demonstrated superior predictive performance, achieving an area under the curve (AUC) of 0.876. Statistical analysis using the DeLong test revealed its significant outperformance compared to both the clinical model (AUC = 0.658, p < 0.001) and the CBF radiomics model (AUC = 0.755, p = 0.002). The robustness of all models was confirmed through permutation testing. Furthermore, DCA underscored the clinical utility of the combined model. The prognostic prediction of AIS was notably influenced by the baseline NIHSS score, age, as well as texture and shape features of CBF.

Conclusion: The integration of clinical data and multi-PLD ASL radiomics features in a model offers a secure and dependable approach for predicting the prognosis of AIS, particularly beneficial for patients with contraindications to contrast agents. This model aids clinicians in devising individualized treatment plans, ultimately enhancing patient prognosis.

Restoration of independent walking ability is the primary objective of stroke rehabilitation; however, not all patients achieve this goal due to diverse impairments in the paretic lower limb and compensatory mechanisms that lead to an asymmetrical and mechanically inefficient gait. This investigation aimed to examine alterations in cortical activation in post-stroke patients while walking with a wearable two-channel functional electrical stimulation (FES) in comparison to walking without FES. This observational study was conducted to discern distinct activation patterns in 19 stroke patients during sessions with and without FES, while using functional near-infrared spectroscopy (fNIRS) to monitor changes in blood oxygen levels. Our findings revealed only a significant reduction in ΔOxy-Hb in the contralesional pre-motor cortex (z = -2.803, p = 0.005) during the FES-on walking sessions compared to the FES-off sessions. Furthermore, all regions in the FES-on session exhibited lower ΔOxy-Hb. Conversely, no significant differences were observed in ΔDeoxy-Hb. Moreover, a significant correlation was found between decrease in cPMC and the reduced cost time of walking under FES-on condition. The fNIRS analysis revealed diminished activation in the contralesional pre-motor cortex when walking with FES, implying that FES may facilitate a more automatic gait pattern while reducing a patient's reliance on contralesional cortical resources. The findings of this study lay the groundwork for long-term neural rehabilitation.