筋萎縮性側索硬化症

OBJECTIVE: Amyotrophic lateral sclerosis (ALS) has a markedly distinctive clinical and neuroradiological signature, with the preferential involvement of specific brain networks and the apparent sparing of others. The molecular underpinnings of the strikingly selective anatomical vulnerability have not been fully elucidated to date despite the potential therapeutic relevance of characterizing neurotransmitter receptor-defined susceptibility to degeneration. METHODS: A large neuroimaging study was undertaken with 258 participants to systematically evaluate topological associations between neurodegeneration and neurotransmitter expression distributions. Patients were stratified based on their genetic profile into sporadic and C90rf72 hexanucleotide repeat expansion carriers. Anatomical associations were evaluated between patterns of atrophy and topological neurotransmitter receptor distributions. Cross-sectional and longitudinal trends were comprehensively evaluated over 4 consecutive timepoints. RESULTS: Our analyses reveal topological associations between neurodegeneration in sporadic ALS and GABA-A receptor α5-selective component (GABAa5), serotonin 1a receptor, and kappa opioid receptor expression maps. In addition to these networks, neuronal loss in patients with GGGGCC hexanucleotide repeat expansions exhibit predilection to glutamatergic, endocannabinoid, and microglial systems. Our multi-timepoint longitudinal analyses reveal dynamic temporal associations between focal volume loss and neurotransmitter expression with increasing spatial associations with noradrenaline transporter and GABAa5, but high attrition rates preclude definite longitudinal inferences. INTERPRETATION: Our data suggest the preferential vulnerability of GABAergic, serotonergic, kappa opioid mediated networks in sporadic ALS. In C9orf72-assocaited ALS, glutamatergic, endocannabinoid circuits are also susceptible and microglia-mediated neuroinflammation is also implicated. The comprehensive evaluation of neurotransmitter-receptor associations not only offer academic insights regarding pathophysiological processes in ALS, but may inform targeted therapy development strategies. ANN NEUROL 2026.

OBJECTIVE: Wrist function is essential in performing activities of daily living (ADLs). However, there is limited experimental evidence on the functional impact of wrist Abduction-Adduction (Ab-Ad) joint assistance in upper limb exoskeletons (ULEs) during ADLs. This study provides the first implementation and demonstration of a clock spring-based wrist Ab-Ad joint into a five degree of freedom (DoF) ULE, EXOTIC2 exoskeleton and evaluates its effect, to support individuals with severe motor impairments. METHODS: A compact, lightweight wrist module with tendon-driven abduction and spring-driven adduction was integrated into the EXOTIC exoskeleton. Eight adults with no motor disabilities completed drinking and scratching tasks under randomized wrist-enabled and wrist-locked conditions along with a preliminary feasibility test in one individual with Amyotrophic lateral sclerosis (ALS). Kinematic and task performance metrics including wrist range of motion, task completion time, spillage and leveling metrics were assessed. RESULTS: Implementing the wrist Ab-Ad DoF improved task success metrics. Spill incidence during the drinking task decreased from 56% to 3%, and leveling success for scratching task improved from 28% to 75%. CONCLUSION: Integrating wrist Ab-Ad assistance improved key functional task outcomes without increasing execution time. SIGNIFICANCE: The study provides the experimental evidence that active wrist Ab-Ad control enhances task-level performance in exoskeleton-assisted ADLs and supports the inclusion of wrist deviation in future assistive exoskeletons.

BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease. Lipid metabolism is closely related to neuronal function and energy homeostasis, but the genetic association between specific lipid species and ALS risk remains unclear. OBJECTIVE: This study aimed to investigate the potential causal associations between genetically predicted lipid species and ALS risk using a two-sample Mendelian randomization (MR) approach. METHODS: Summary-level GWAS data for 179 lipid species were obtained from 7,174 Finnish participants in the GeneRISK cohort. ALS GWAS data included 29,612 ALS cases and 122,656 controls. The inverse variance weighted (IVW) method was used as the primary MR approach, supplemented by MR-Egger, weighted median, weighted mode, and simple mode analyses. False discovery rate (FDR) correction was applied across all lipid traits based on IVW P values. Sensitivity analyses were conducted to assess heterogeneity, horizontal pleiotropy, and robustness. RESULTS: After FDR correction, genetically predicted higher levels of diacylglycerol (DAG) (18:1_18:1), phosphatidylcholine (PC) (16:1_18:1), PC (18:0_18:1), phosphatidylethanolamine (PE) (O-16:1_18:2), and several triacylglycerol (TAG) species were associated with increased ALS risk. Phosphatidylinositol (PI) (16:0_18:1) showed only a nominal protective association and did not remain significant after FDR correction. Sensitivity analyses did not indicate substantial heterogeneity or horizontal pleiotropy. CONCLUSION: This MR study provides genetic evidence supporting potential associations between specific lipid species and ALS risk. These findings highlight lipid metabolism as a relevant pathway in ALS susceptibility.

BACKGROUND: Consumer AI platforms are increasingly used by patients to interpret medical reports, including ENMG results for ALS. While AI shows promise in controlled clinical settings (e.g., stroke imaging, melanoma detection), consumer-facing tools often provide overconfident, context-free diagnostic assertions (e.g., 'definitive evidence of ALS'), leading to premature and potentially harmful life-altering decisions. OBJECTIVE: To highlight the clinical, ethical, and regulatory risks of unregulated AI in ALS diagnosis and propose actionable solutions. DISCUSSION: We present a case of AI-mediated misdiagnosis, analyze the limitations of consumer-facing AI (lack of clinical context, longitudinal data, and specialist oversight), and discuss the "authority paradox" (patients trusting AI outputs over clinicians' nuanced assessments). We propose a structured 4-step clinical approach for managing AI-mediated self-diagnoses and urge regulators to classify such tools as high-risk under the EU AI Act. CONCLUSION: The uncritical adoption of consumer AI in ALS diagnosis represents a public health risk. Clinicians, regulators, and developers must collaborate to ensure AI serves patients safely and ethically.

Neurodegenerative diseases are progressive disorders that involve the loss and dysfunction of neurons. Alzheimer's disease, Parkinson's disease, Amyotrophic lateral sclerosis, Huntington's disease, Frontotemporal dementia are examples of diseases. While different clinically, these disorders have a common genetic, molecular and cellular basis. This review examines the common genetic pathways, along with the interactions between genes of major neurodegenerative diseases, with a focus on the key genes, such as APOE, SNCA, MAPT, TARDBP, LRRK2 and HTT. The common pathogenic mechanisms considered to play a major role in disease progression include protein misfolding and aggregation, mitochondrial dysfunction, oxidative stress, neuroinflammation, diminished autophagy, and impaired lysosomal function, as well as synaptic degeneration. The review also emphasizes the role of systems biology strategies, such as genome-wide association studies, transcriptomics, proteomics, metabolomics, interactome analysis, and multi-omics integration, to unveiling complex molecular networks in neurodegeneration. Furthermore, the emerging biomarker strategies and therapeutic strategies targeting convergence signaling pathways including NF-κB, PI3K-Akt-mTOR, MAPK and Wnt/β-catenin are summarized. The common genetic basis and the cross-connecting molecular mechanisms of the various neurodegenerative diseases could help in the discovery of new biomarkers and pan-therapeutic targets. Further advances in molecular genetics, computational biology and precision medicine are needed to enhance early detection and the creation of effective disease-modifying treatments.