Gene-gene interactions likely contribute to the etiology of multifactorial diseases such as cerebral venous thrombosis (CVT) and could be one of the main sources of known missing heritability. We explored Factor XI (F11) and ABO gene interactions among patients with CVT. Patients with CVT of European ancestry from the large Bio-Repository to Establish the Aetiology of Sinovenous Thrombosis (BEAST) international collaboration were recruited. Codominant modelling was used to determine interactions between genome-wide identified F11 and ABO genes with CVT status. We studied 882 patients with CVT and 1,205 ethnically matched control participants (age: 42 ± 15 vs 43 ± 12 years, p = 0.08: sex: 71% male vs 68% female, p = 0.09, respectively). Individuals heterozygous (AT) for the risk allele (T) at both loci (rs56810541/F11 and rs8176645/ABO) had a 3.9 (95% CI 2.74-5.71, p = 2.75e-13) increase in risk of CVT. Individuals homozygous (TT) for the risk allele at both loci had a 13.9 (95% CI 7.64-26.17, p = 2.0e-15) increase in risk of CVT. The presence of a non-O blood group (A, B, AB) combined with TT/rs56810541/F11 increased CVT risk by OR = 6.8 (95% CI 4.54-10.33, p = 2.00e15), compared with blood group-O combined with AA. Interactions between factor XI and ABO genes increase risk of CVT by 4- to 14-fold.

To validate a recently published study in which skin biopsy was reported as a valuable alternative to brain biopsy in diagnosing CSF1R-related disorder (CSF1R-RD). Blinded evaluation of skin samples was performed by independent reviewers using light and electron microscopy collected from a group of CSF1R variant carriers (n = 10) with various genotypes (mono and biallelic), different stages of the disease (asymptomatic and symptomatic), and exposed to different therapies (glucocorticoids, hematopoietic stem cell transplantation, and TREM2 agonist), and from a group of healthy controls (n = 5). Biopsies from patients with CSF1R-RD at various disease stages were indistinguishable from controls determined using light microscopy and electron microscopy. We found no distinctive axonal pathology in skin biopsies collected from CSF1R variant carriers at all stages of the disease. Our results are consistent with clinical and neurophysiologic features of the CSF1R-RD, in that peripheral nervous system involvement has not been reported. Studies aiming to discover new biomarkers are important, but the results must be validated with larger numbers of patients and healthy controls. Based on blinded light and electron microscopic studies of skin biopsies, there is no evidence that CSF1R-RD is associated with distinctive changes in cutaneous peripheral nerves. This suggests that skin biopsy is not useful in diagnosis of CSF1R-RD. This study provides Class III evidence that skin biopsy does not distinguish those with CSF1R-RD, or carriers, from normal controls.

A reliable method of predicting large vessel occlusion (LVO) stroke in data sets without neuroimaging could be retrospectively applied to expand research efforts. We conducted a retrospective, cross-sectional cohort analysis of the Get With The Guidelines (GWTG)-Stroke registry. We included adult patients with a final diagnosis of ischemic stroke from 2016 to 2021 who had brain and vascular imaging and excluded those with missing data or posterior circulation stroke. We included 416,022 patients of which 125,381 (30.1%) had LVO. The mean age was 71 years, and 48.2% were female. The area under the receiver operating curve (AUC) for the final model, including age, sex, hypertension, dyslipidemia, atrial fibrillation, diabetes, TOAST stroke mechanism, and NIH Stroke Scale (NIHSS), was 0.79 (95% CI 0.79-0.80). Without TOAST mechanism, the AUC was 0.74. The specificity did not exceed 0.5 using different cut points for the NIHSS. We found that 30% of adult acute ischemic stroke patients in GWTG-Stroke have LVO and that the combination of clinical covariates and NIHSS is only moderately predictive of LVO status. These results are consistent with previous studies and suggest it may not be possible to retrospectively predict LVO with high accuracy in data sets without vascular neuroimaging.

Large language models (LLMs) are advanced artificial intelligence (AI) systems that excel in recognizing and generating human-like language, possibly serving as valuable tools for neurology-related information tasks. Although LLMs have shown remarkable potential in various areas, their performance in the dynamic environment of daily clinical practice remains uncertain. This article outlines multiple limitations and challenges of using LLMs in clinical settings that need to be addressed, including limited clinical reasoning, variable reliability and accuracy, reproducibility bias, self-serving bias, sponsorship bias, and potential for exacerbating health care disparities. These challenges are further compounded by practical business considerations and infrastructure requirements, including associated costs. To overcome these hurdles and harness the potential of LLMs effectively, this article includes considerations for health care organizations, researchers, and neurologists contemplating the use of LLMs in clinical practice. It is essential for health care organizations to cultivate a culture that welcomes AI solutions and aligns them seamlessly with health care operations. Clear objectives and business plans should guide the selection of AI solutions, ensuring they meet organizational needs and budget considerations. Engaging both clinical and nonclinical stakeholders can help secure necessary resources, foster trust, and ensure the long-term sustainability of AI implementations. Testing, validation, training, and ongoing monitoring are pivotal for successful integration. For neurologists, safeguarding patient data privacy is paramount. Seeking guidance from institutional information technology resources for informed, compliant decisions, and remaining vigilant against biases in LLM outputs are essential practices in responsible and unbiased utilization of AI tools. In research, obtaining institutional review board approval is crucial when dealing with patient data, even if deidentified, to ensure ethical use. Compliance with established guidelines like SPIRIT-AI, MI-CLAIM, and CONSORT-AI is necessary to maintain consistency and mitigate biases in AI research. In summary, the integration of LLMs into clinical neurology offers immense promise while presenting formidable challenges. Awareness of these considerations is vital for harnessing the potential of AI in neurologic care effectively and enhancing patient care quality and safety. The article serves as a guide for health care organizations, researchers, and neurologists navigating this transformative landscape.

Poststroke epilepsy (PSE) is associated with higher mortality and poor functional and cognitive outcomes in patients with stroke. With the remarkable development of acute stroke treatment, there is a growing number of survivors with PSE. Although approximately 10% of patients with stroke develop PSE, given the significant burden of stroke worldwide, PSE is a significant problem in stroke survivors. Therefore, the attention of health policymakers and significant funding are required to promote PSE prevention research. The current PSE definition includes unprovoked seizures occurring more than 7 days after stroke onset, given the high recurrence risks of seizures. However, the pathologic cascade of stroke is not uniform, indicating the need for a tissue-based approach rather than a time-based one to distinguish early seizures from late seizures. EEG is a commonly used tool in the diagnostic work-up of PSE. EEG findings during the acute phase of stroke can potentially stratify the risk of subsequent seizures and predict the development of poststroke epileptogenesis. Recent reports suggest that cortical superficial siderosis, which may be involved in epileptogenesis, is a promising marker for PSE. By incorporating such markers, future risk-scoring models could guide treatment strategies, particularly for the primary prophylaxis of PSE. To date, drugs that prevent poststroke epileptogenesis are lacking. The primary challenge involves the substantial cost burden due to the difficulty of reliably enrolling patients who develop PSE. There is, therefore, a critical need to determine reliable biomarkers for PSE. The goal is to be able to use them for trial enrichment and as a surrogate outcome measure for epileptogenesis. Moreover, seizure prophylaxis is essential to prevent functional and cognitive decline in stroke survivors. Further elucidation of factors that contribute to poststroke epileptogenesis is eagerly awaited. Meanwhile, the regimen of antiseizure medications should be based on individual cardiovascular risk, psychosomatic comorbidities, and concomitant medications. This review summarizes the current understanding of poststroke epileptogenesis, its risks, prognostic models, prophylaxis, and strategies for secondary prevention of seizures and suggests strategies to advance research on PSE.