Search Results (143)

Background: Charcot–Marie–Tooth disease (CMT) is a genetically and phenotypically heterogeneous hereditary neuropathy. Axonal CMT type 2 (CMT2) subtypes often exhibit overlapping clinical features, which makes molecular genetic analysis essential for accurate diagnosis and subtype differentiation. Methods: This retrospective study included five pediatric patients who presented with gait disturbance, muscle weakness, and foot deformities and were subsequently diagnosed with axonal forms of CMT. Clinical data, electrophysiological studies, neuroimaging, and genetic analyses were evaluated. Whole exome sequencing (WES) was performed in three sporadic cases, while targeted CMT gene panel testing was used for two siblings. Variants were interpreted using ACMG guidelines, supported by public databases (ClinVar, HGMD, and VarSome), and confirmed by Sanger sequencing when available. Results: All had absent deep tendon reflexes and distal muscle weakness; three had intellectual disability. One patient was found to carry a novel homozygous frameshift variant (c.2568_2569del) in the IGHMBP2 gene, consistent with CMT2S. Other variants were identified in the NEFH (CMT2CC), DYNC1H1 (CMT2O), and MPV17 (CMT2EE) genes. Notably, a previously unreported co-occurrence of MPV17 mutation and congenital heart disease was observed in one case. Conclusions: This study expands the clinical and genetic spectrum of pediatric axonal CMT and highlights the role of early physical examination and molecular diagnostics in detecting rare variants. Identification of a novel IGHMBP2 variant and unique phenotypic associations provides new insights for future genotype–phenotype correlation studies. Full article

Introduction: Burnout syndrome, long described as an “occupational phenomenon”, now affects 15–20% of the general workforce and more than 50% of clinicians, teachers, social-care staff and first responders. Its precise nosological standing remains disputed. We conducted a mechanistic review of electroencephalography (EEG) studies to determine whether burnout is accompanied by reproducible brain-function alterations that justify disease-level classification. Methods: Following PRISMA-adapted guidelines, two independent reviewers searched PubMed/MEDLINE, Scopus, Google Scholar, Cochrane Library and reference lists (January 1980–May 2025) using combinations of “burnout,” “EEG”, “electroencephalography” and “event-related potential.” Only English-language clinical investigations were eligible. Eighteen studies (n = 2194 participants) met the inclusion criteria. Data were synthesised across three domains: resting-state spectra/connectivity, event-related potentials (ERPs) and longitudinal change. Results: Resting EEG consistently showed (i) a 0.4–0.6 Hz slowing of individual-alpha frequency, (ii) 20–35% global alpha-power reduction and (iii) fragmentation of high-alpha (11–13 Hz) fronto-parietal coherence, with stage- and sex-dependent modulation. ERP paradigms revealed a distinctive “alarm-heavy/evaluation-poor” profile; enlarged N2 and ERN components signalled hyper-reactive conflict and error detection, whereas P3b, Pe, reward-P3 and late CNV amplitudes were attenuated by 25–50%, indicating depleted evaluative and preparatory resources. Feedback processing showed intact or heightened FRN but blunted FRP, and affective tasks demonstrated threat-biassed P3a latency shifts alongside dampened VPP/EPN to positive cues. These alterations persisted in longitudinal cohorts yet normalised after recovery, supporting trait-plus-state dynamics. The electrophysiological fingerprint differed from major depression (no frontal-alpha asymmetry, opposite connectivity pattern). Conclusions: Across paradigms, burnout exhibits a coherent neurophysiological signature comparable in magnitude to established psychiatric disorders, refuting its current classification as a non-disease. Objective EEG markers can complement symptom scales for earlier diagnosis, treatment monitoring and public-health surveillance. Recognising burnout as a clinical disorder—and funding prevention and care accordingly—is medically justified and economically imperative. Full article

In recent decades, flexible electronics have witnessed remarkable advancements in multiple fields, encompassing wearable electronics, human–machine interfaces (HMI), clinical diagnosis, and treatment, etc. Nevertheless, conventional rigid electronic devices are fundamentally constrained by their inherent non-stretchability and poor conformability, limitations that substantially impede their practical applications. In contrast, conductive hydrogels (CHs) for electronic skin (E-skin) and healthcare monitoring have attracted substantial interest owing to outstanding features, including adjustable mechanical properties, intrinsic flexibility, stretchability, transparency, and diverse functional and structural designs. Considerable efforts focus on developing CHs incorporating various conductive materials to enable multifunctional wearable sensors and flexible electrodes, such as metals, carbon, ionic liquids (ILs), MXene, etc. This review presents a comprehensive summary of the recent advancements in CHs, focusing on their classifications and practical applications. Firstly, CHs are categorized into five groups based on the nature of the conductive materials employed. These categories include polymer-based, carbon-based, metal-based, MXene-based, and ionic CHs. Secondly, the promising applications of CHs for electrophysiological signals and healthcare monitoring are discussed in detail, including electroencephalogram (EEG), electrocardiogram (ECG), electromyogram (EMG), respiratory monitoring, and motion monitoring. Finally, this review concludes with a comprehensive summary of current research progress and prospects regarding CHs in the fields of electronic skin and health monitoring applications. Full article

Brugada syndrome is a rare inherited heart disease characterized by ventricular arrhythmias and characteristic ST segment elevation, which increases the risk of sudden death. Studies have shown that the pathogenesis of this disease involves a variety of gene mutations, including abnormal functions of sodium, calcium, and potassium ion channels, resulting in cardiac electrophysiological disorders. These variants affect excitability and conduction of cardiomyocytes, thereby increasing the susceptibility to ventricular arrhythmias and sudden death. However, many genetic variants remain of uncertain significance or are insufficiently characterized, necessitating further investigation. This review summarizes the genetic variants associated with Brugada syndrome and discusses their potential implications for improving diagnosis and therapeutic approaches. Full article

As critical interfaces bridging biological systems and electronic devices, the performance of bioelectrodes directly determines the sensitivity, selectivity, and reliability of biosensors. Recent advancements in functional nanomaterials (e.g., carbon nanomaterials, metallic nanoparticles, 2D materials) have substantially enhanced the application potential of bioelectrodes in disease detection, metabolic monitoring, and early diagnosis through strategic material selection, structural engineering, interface modification, and antifouling treatment. This review systematically examines the latest progress in nanomaterial-enabled interface design of bioelectrodes, with particular emphasis on performance enhancements in electrophysiological/electrochemical signal acquisition and multimodal sensing technologies. We comprehensively analyze cutting-edge developments in dynamic metabolic parameter monitoring for chronic disease management, as well as emerging research on flexible, high-sensitivity electrode interfaces for early disease diagnosis. Furthermore, this work focused on persistent technical challenges regarding nanomaterial biocompatibility and long-term operational stability while providing forward-looking perspectives on their translational applications in wearable medical devices and personalized health management systems. The proposed framework offers actionable guidance for researchers in this interdisciplinary field. Full article

Attention-deficit/hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder with chronic inattention, hyperactivity, and impulsivity and is linked with significant functional impairment. Despite being highly prevalent, diagnosis of ADHD continues to rely on subjective assessment reports of behavior and is often delayed or inaccurate. This review summarizes current advances in biomarkers and neuropsychological tests for the improvement of ADHD diagnosis and treatment. Key biomarkers are neuroimaging methods (e.g., structural and functional MRI), electrophysiological measures (e.g., EEG, ERP), and biochemical measures (e.g., cortisol, vitamin D). Additionally, novel experimental measures, e.g., eye-tracking, pupillometry, and microbiome analysis, hold the promise to be objective and dynamic measures of ADHD symptoms. The review also comments on the impact of the burden of ADHD on quality of life, e.g., emotional well-being, academic achievement, and social functioning. Additionally, differences between individuals, such as age, sex, comorbidities, and the impact of social and family support, are also addressed in relation to ADHD outcomes. In summary, we highlight the potential of these emerging biomarkers and tools to revolutionize ADHD diagnosis and guide personalized treatment strategies. These insights have significant implications for improving patient outcomes. Full article

Background: Syndromic inherited retinal diseases (IRDs) are a clinically and genetically heterogeneous group of disorders, involving the retina and additional organs. Over 80 forms of syndromic IRD have been described. Methods: We aimed to phenotypically and genotypically characterize a cohort of 171 individuals from 140 Israeli families with syndromic IRD. Ophthalmic examination included best corrected visual acuity, fundus examination, visual field testing, retinal imaging and electrophysiological evaluation. Most participants were also evaluated by specialists in fields relevant to their extra-retinal symptoms. Genetic analyses included haplotype analysis, homozygosity mapping, Sanger sequencing and next-generation sequencing. Results: In total, 51% of the families in the cohort were consanguineous. The largest ethnic group was Muslim Arabs. The most common phenotype was Usher syndrome (USH). The most common causative gene was USH2A. In 29% of the families, genetic analysis led to a revised or modified clinical diagnosis. This included confirmation of an atypical USH diagnosis for individuals with late-onset retinitis pigmentosa (RP) and/or hearing loss (HL); diagnosis of Heimler syndrome in individuals with biallelic pathogenic variants in PEX6 and an original diagnosis of USH or nonsyndromic RP; and diagnosis of a mild form of Leber congenital amaurosis with early-onset deafness (LCAEOD) in an individual with a heterozygous pathogenic variant in TUBB4B and an original diagnosis of USH. Novel genotype–phenotype correlations included biallelic pathogenic variants in KATNIP, previously associated with Joubert syndrome (JBTS), in an individual who presented with kidney disease and IRD, but no other features of JBTS. Conclusions: Syndromic IRDs are a highly heterogeneous group of disorders. The rarity of some of these syndromes on one hand, and the co-occurrence of several syndromic and nonsyndromic conditions in some individuals, on the other hand, complicates the diagnostic process. Genetic analysis is the ultimate way to obtain an accurate clinical diagnosis in these individuals. Full article

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by the progressive loss of upper and lower motor neurons, leading to muscle weakness, paralysis, and ultimately respiratory failure. Despite advances in understanding its genetic basis, particularly mutations in Chromosome 9 Open Reading Frame 72 (C9orf72), superoxide dismutase 1 (SOD1), TAR DNA-binding protein (TARDBP), and Fused in Sarcoma (FUS) gene, current diagnostic methods result in delayed intervention, and available treatments offer only modest benefits. This review examines innovative approaches transforming ALS research and clinical management. We explore emerging biomarkers, including the fluid-based markers such as neurofilament light chain, exosomes, and microRNAs in biological fluids, alongside the non-fluid-based biomarkers, including neuroimaging and electrophysiological markers, for early diagnosis and patient stratification. The integration of multi-omics data reveals complex molecular mechanisms underlying ALS heterogeneity, potentially identifying novel therapeutic targets. We highlight current gene therapy strategies, including antisense oligonucleotides (ASOs), RNA interference (RNAi), and CRISPR/Cas9 gene editing systems, alongside advanced delivery methods for crossing the blood–brain barrier. By bridging molecular neuroscience with bioengineering, these technologies promise to revolutionize ALS diagnosis and treatment, advancing toward truly disease-modifying interventions for this previously intractable condition. Full article

Background: Visual evoked potentials (VEPs) are used to detect chiasmal misrouting associated with albinism. However, VEPs are only performed in specialist centres and typically have long waiting lists. The portable electrophysiology device RETeval^® shows promise as a clinical screening tool across a range of ophthalmic conditions. Here, we explore its utility in detecting chiasmal abnormalities associated with albinism. Methods: Flash VEPs were recorded on the RETeval^® and by standard ISCEV techniques for 27 patients with suspected albinism and 40 control patients as part of routine appointments. We retrospectively investigated the agreeability between the two methods. The amplitude/latency of the main component was measured for standard VEPs whilst a correlation value of interhemispheric difference was calculated for the RETeval^® data. Results: We demonstrate a significant difference between albinism patients and controls (p < 0.001) with respect to the interhemispheric difference identified by the RETeval^®. By applying a threshold of 0.001865 to the correlation value, the RETeval^® detected chiasmal misrouting in all 27 patients with albinism and had 97% agreeability to standard testing. Conclusions: This study shows the potential of using the RETeval^® as a clinical tool for the diagnosis of chiasmal anomalies in albinism. The RETeval^® has significant time/cost savings which could hasten diagnoses. Full article

Kleefstra syndrome (KS) is a rare neurodevelopmental disorder associated with disruptions in the EHMT1 gene, often leading to intellectual disability, autism spectrum behaviors and epilepsy. The electroencephalogram (EEG) serves as a non-invasive tool to explore brain function in KS; yet, systematic characterizations of EEG features remain extremely limited. This review synthesizes current evidence on EEG findings in KS, highlighting the high prevalence of nonspecific abnormalities and seizures, but the absence of a consistent electrophysiological biomarker. Given the growing role of machine learning (ML) in extracting patterns from EEG data in related disorders—such as Angelman, Rett and Fragile X syndromes—this review explores how similar approaches could be adapted for KS. Despite promising perspectives, a lack of large-scale, publicly available EEG datasets hinders the application of ML methodologies in KS research. Future directions are proposed to address these gaps, including standardized EEG data collection, adoption of quantitative EEG analyses and integration of ML techniques adapted for small datasets. This multidisciplinary strategy holds potential for improving early diagnosis, monitoring and personalized interventions in Kleefstra syndrome. Full article

Background: Artificial intelligence (AI) is poised to significantly impact the future of cardiology and electrophysiology, offering new tools to interpret complex datasets, improve diagnosis, optimize clinical workflows, and personalize therapy. ChatGPT-4o, a leading AI-based language model, exemplifies the transformative potential of AI in clinical research, medical education, and patient care. Aim and Methods: In this paper, we present an exploratory dialogue with ChatGPT to assess the role of AI in shaping the future of cardiology, with a particular focus on arrhythmia management and cardiac electrophysiology. Topics discussed include AI applications in ECG interpretation, arrhythmia detection, procedural guidance during ablation, and risk stratification for sudden cardiac death. We also examine the risks associated with AI use, including overreliance, interpretability challenges, data bias, and generalizability. Conclusions: The integration of AI into cardiovascular care offers the potential to enhance diagnostic accuracy, tailor interventions, and support decision-making. However, the adoption of AI must be carefully balanced with clinical expertise and ethical considerations. By fostering collaboration between clinicians and AI developers, it is possible to guide the development of reliable, transparent, and effective tools that will shape the future of personalized cardiology and electrophysiology. Full article

The strong correlation between evoked potentials (EPs) and American Spinal Injury Association (ASIA) scores in individuals with spinal cord injury (SCI) suggests that EPs may serve as reliable predictive markers for rehabilitation progress. Numerous studies have confirmed a relationship between variations in somatosensory evoked potentials (SSEPs) and ASIA scores, especially in the early stages of SCI. Machine learning’s (ML’s) increasing importance in medicine is driven by the growing availability of health data and improved algorithms. It enables the creation of predictive models for disease diagnosis, progression prediction, personalized treatment, and improved healthcare efficiency. Data-driven approaches can significantly improve patient care, reduce costs, and facilitate personalized medicine. The meticulous analysis of medical data is crucial for timely disease identification, leading to effective symptom management and appropriate treatment. This study applies artificial intelligence to identify predictors of SCI progression, as measured by the disability index, ASIA impairment scale (AIS), and final motor recovery. We aim to clarify the prognostic role of electrophysiological testing (SSEPs, MEPs, and nerve conduction studies (NCSs)) in SCI. We analyzed data from a medical database of 123 records. We developed an ML-based intelligent system, utilizing ensemble algorithms combining decision trees and neural network approaches, to predict SCI recovery. Our evaluation showed SEP accuracies of 90% for motor recovery prediction and 80% for AIS scale determination, comparable to full electrophysiology evaluation accuracies of 93% and 89%, respectively, and generally superior results compared to MEP and NCS results. EPs emerged as the best predictors, comparable to a comprehensive electrophysiology assessment, significantly improving accuracy compared to clinical findings alone. An electrophysiological assessment, when available, increased overall accuracy for final motor recovery prediction to 93% (from a maximum of 75%) and, for ASIA score determination, to 89% (from a maximum of 66%). Further validation is needed with a larger dataset. Future research should validate that sensory electrophysiology assessment is a less expensive, portable, and simpler alternative to other prognostic tests and more effective than clinical assessments, like the AIS, biomarker for SCI, and personalized rehabilitation planning. Full article

Congenital long QT syndrome (LQTS) is a group of heritable conditions that are associated with cardiac repolarization abnormalities characterized by QT prolongation on electrocardiogram and the risk of life-threatening arrhythmias. The prenatal detection of LQTS presents significant challenges for clinicians, and a multidisciplinary approach is required for optimal prenatal and postnatal management. In this comprehensive literature review, we describe strategies for the fetal diagnosis of LQTS with variable initial presentation, genetic testing in suspected fetal LQTS, the utility of fetal magnetocardiography as an additional diagnostic tool, prenatal management, and postnatal treatment. We focus on a multidisciplinary team approach including fetal cardiology, adult and pediatric electrophysiology, neonatology, maternal–fetal medicine, and genetic counselors, all playing vital roles in the comprehensive prenatal management and orchestration of postnatal treatment to optimize neonatal outcomes. Full article

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is characterized by structural abnormalities, arrhythmias, and a spectrum of genetic and clinical manifestations. Clinically, ARVC is structurally distinguished by right ventricular dilation due to increased adiposity and fibrosis in the ventricular walls, and it manifests as cardiac arrhythmias ranging from non-sustained ventricular tachycardia to sudden cardiac death. Its prevalence has been estimated to range from 1 in every 1000 to 5000 people, with its large range being attributed to the variability in genetic penetrance from asymptomatic to significant burden. It is even suggested that the prevalence is underestimated, as the presence of genotypic mutations does not always lead to clinical manifestations that would facilitate diagnosis. Additionally, while set criteria have been in place since the 1990s, newer understanding of this condition and advancements in cardiac technology have prompted multiple revisions in the diagnostic criteria for ARVC. Novel discoveries of gene variants predisposing patients to ARVC have led to established screening techniques while providing insight into genetic counseling and management. This review aims to provide an overview of the genetics, pathophysiology, and clinical approach to ARVC. It will also focus on clinical presentation, ARVC diagnostic criteria, electrophysiological findings, including electrocardiogram characteristics, and imaging findings from cardiac MRI, 2D, and 3D echocardiogram. Current management options—including anti-arrhythmic medications, device indications, and ablation techniques—and the effectiveness of treatment will also be reviewed. Full article

Background: Early and accurate diagnosis is crucial for effective prevention and treatment of severe mental illnesses, such as schizophrenia and bipolar disorder. However, identifying these conditions in their early stages remains a significant challenge. Our goal was to develop a method capable of detecting latent disease liability in healthy volunteers. Methods: Using questionnaires examining affective temperament and schizotypal traits among voluntary, healthy university students (N = 710), we created three groups. These were a group characterized by an emphasis on positive schizotypal traits (N = 20), a group showing cyclothymic temperament traits (N = 17), and a control group showing no susceptibility in either direction (N = 21). We performed a resting-state EEG examination as part of a complex psychological, electrophysiological, psychophysiological, and laboratory battery, and we developed feature-selection machine-learning methods to differentiate the low-risk groups. Results: Both low-risk groups could be reliably (with 90% accuracy) separated from the control group. Conclusions: Models applied to the data allowed us to differentiate between healthy university students with latent schizotypal or bipolar tendencies. Our research may improve the sensitivity and specificity of risk-state identification, leading to more effective and safer secondary prevention strategies for individuals in the prodromal phases of these disorders. Full article