A strong association between the HLA-B*1502 allele and SJS and TEN induced by carbamazepine has been shown. This study involving Europeans implicates a different HLA allele, HLA-A*3101, in conferring ...susceptibility to a broad range of carbamazepine-induced reactions.
Carbamazepine is one of the most commonly prescribed drugs for the treatment of epilepsy, as well as trigeminal neuralgia and bipolar disorder. A minority of treated persons have hypersensitivity reactions that vary in prevalence and severity,
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with some forms associated with substantial morbidity and mortality. The mildest form, maculopapular exanthema, occurs in 5 to 10% of treated persons of European ancestry and resolves spontaneously after drug discontinuation. More severe reactions, such as the hypersensitivity syndrome, are associated with mortality of up to 10%
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and include symptoms such as rash, fever, eosinophilia, hepatitis, and nephritis. The most severe reactions, such as . . .
Epilepsy is associated with genetic risk factors and cortico-subcortical network alterations, but associations between neurobiological mechanisms and macroscale connectomics remain unclear. This ...multisite ENIGMA-Epilepsy study examined whole-brain structural covariance networks in patients with epilepsy and related findings to postmortem epilepsy risk gene expression patterns. Brain network analysis included 578 adults with temporal lobe epilepsy (TLE), 288 adults with idiopathic generalized epilepsy (IGE), and 1328 healthy controls from 18 centres worldwide. Graph theoretical analysis of structural covariance networks revealed increased clustering and path length in orbitofrontal and temporal regions in TLE, suggesting a shift towards network regularization. Conversely, people with IGE showed decreased clustering and path length in fronto-temporo-parietal cortices, indicating a random network configuration. Syndrome-specific topological alterations reflected expression patterns of risk genes for hippocampal sclerosis in TLE and for generalized epilepsy in IGE. These imaging-transcriptomic signatures could potentially guide diagnosis or tailor therapeutic approaches to specific epilepsy syndromes.
•Machine learning and artificial intelligence have gained popularity for medical applications.•We applied support vector machine (SV) and deep learning (DL) in termporal lobe epilepsy ...(TLE)•Structural and diffusion-based models showed similar classification accuracies.•Diffusion-based models to diagnose TLE performed better or similar compared to models to lateralize TLE.•Models for patients with hippocampal sclerosis were more accurate than models that stratified non-lesional patients.
Artificial intelligence has recently gained popularity across different medical fields to aid in the detection of diseases based on pathology samples or medical imaging findings. Brain magnetic resonance imaging (MRI) is a key assessment tool for patients with temporal lobe epilepsy (TLE). The role of machine learning and artificial intelligence to increase detection of brain abnormalities in TLE remains inconclusive. We used support vector machine (SV) and deep learning (DL) models based on region of interest (ROI-based) structural (n = 336) and diffusion (n = 863) brain MRI data from patients with TLE with (“lesional”) and without (“non-lesional”) radiographic features suggestive of underlying hippocampal sclerosis from the multinational (multi-center) ENIGMA-Epilepsy consortium. Our data showed that models to identify TLE performed better or similar (68–75%) compared to models to lateralize the side of TLE (56–73%, except structural-based) based on diffusion data with the opposite pattern seen for structural data (67–75% to diagnose vs. 83% to lateralize). In other aspects, structural and diffusion-based models showed similar classification accuracies. Our classification models for patients with hippocampal sclerosis were more accurate (68–76%) than models that stratified non-lesional patients (53–62%). Overall, SV and DL models performed similarly with several instances in which SV mildly outperformed DL. We discuss the relative performance of these models with ROI-level data and the implications for future applications of machine learning and artificial intelligence in epilepsy care.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The 2-year incidence of brain metastases (BrMs) in stage III non-small lung cell cancer (NSCLC) has been estimated to be around 30%. However, recent clinical trials have demonstrated considerably ...lower BrMs rates in this patient population. In this study, we aimed to review the real-world incidence, surveillance, and treatment patterns of BrMs in stage III NSCLC.
Using a retrospective single-center study design, we identified patients with stage III NSCLC who received radiation with curative intent over a 10-year period. Outcome variables included BrMs incidence, overall survival (OS), and survival from date of BrMs. Additionally, we assessed patterns of BrMs surveillance in stage III NSCLC and treatment.
We identified a total of 279 stage III NSCLC patients, of which 160 with adequate records were included in the final analyses adenocarcinoma (n = 96), squamous cell carcinoma (n = 53), other histology subtype (n = 11). The median OS for the entire cohort was 41 months (95% CI, 28-53), while the median time from BrMs to death was 19 months (95% CI, 9-21). Twenty-three patients (14.4%) received planned surveillance brain MRIs at 6, 12, and 24 months after completion of treatment. The remaining 137 patients (85.6%) received brain MRIs at systemic recurrence (restaging) or when neurologically symptomatic. A total of 37 patients (23%) developed BrMs, with a 2-year cumulative BrMs incidence of 17% (95% CI, 11-23). A higher incidence of BrMs was identified in patients with adenocarcinoma relative to those with squamous cell carcinoma (
< 0.01). Similarly, a higher 2-year BrMs incidence was observed in patients who received planned surveillance brain MRI relative to those who did not, although statistical significance was not reached. Stereotactic radiosurgery (SRS) treated 29 of BrMs patients (78.4%) and was preferred over WBRT, which treated only 3 patients (8.1%).
At our center, BrMs incidence in stage III NSCLC patients was lower than historically reported but notably higher than the incidence described in recent clinical trials. Routine BrMs surveillance potentially allows earlier detection of asymptomatic BrMs. However, asymptomatic BrMs were mostly detected on restaging MRI at the time of recurrence.
Over the last decade, the field of
has combined high-throughput genotype data with quantitative magnetic resonance imaging (QMRI) measures to identify genes associated with brain structure, ...cognition, and several brain-related disorders. Despite its successful application in different psychiatric and neurological disorders, the field has yet to be advanced in epilepsy. In this article we examine the relevance of imaging genomics for future genetic studies in epilepsy from three perspectives. First, we discuss prior genome-wide genetic mapping efforts in epilepsy, considering the possibility that some studies may have been constrained by inherent theoretical and methodological limitations of the genome-wide association study (GWAS) method. Second, we offer a brief overview of the imaging genomics paradigm, from its original inception, to its role in the discovery of important risk genes in a number of brain-related disorders, and its successful application in large-scale multinational research networks. Third, we provide a comprehensive review of past studies that have explored the eligibility of brain QMRI traits as
for epilepsy. While the breadth of studies exploring QMRI-derived endophenotypes in epilepsy remains narrow, robust syndrome-specific neuroanatomical QMRI traits have the potential to serve as accessible and relevant intermediate phenotypes for future genetic mapping efforts in epilepsy.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
We aimed to 1) determine if subcortical volume deficits are common to mesial temporal lobe epilepsy (MTLE) patients and their unaffected siblings 2) assess the suitability of subcortical volumetric ...traits as endophenotypes for MTLE.
MRI-based volume measurements of the hippocampus, amygdala, thalamus, caudate, putamen and pallidium were generated using an automated brain reconstruction method (FreeSurfer) for 101 unrelated 'sporadic' MTLE patients 70 with hippocampal sclerosis (MTLE+HS), 31 with MRI-negative TLE, 83 unaffected full siblings of patients and 86 healthy control subjects. Changes in the volume of subcortical structures in patients and their unaffected siblings were determined by comparison with healthy controls. Narrow sense heritability was estimated ipsilateral and contralateral to the side of seizure activity.
MTLE+HS patients displayed significant volume deficits across the hippocampus, amygdala and thalamus ipsilaterally. In addition, volume loss was detected in the putamen bilaterally. These volume deficits were not present in the unaffected siblings of MTLE+HS patients. Ipsilaterally, the heritability estimates were dramatically reduced for the volume of the hippocampus, thalamus and putamen but remained in the expected range for the amygdala. MRI-negative TLE patients and their unaffected siblings showed no significant volume changes across the same structures and heritability estimates were comparable with calculations from a healthy population.
The findings indicate that volume deficits for many subcortical structures in 'sporadic' MTLE+HS are not heritable and likely related to acquired factors. Therefore, they do not represent suitable endophenotypes for MTLE+HS. The findings also support the view that, at a neuroanatomical level, MTLE+HS and MRI-negative TLE represent two distinct forms of MTLE.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Parkinson's disease (PD) progresses at highly variable rates in different individuals but, in general, has a fairly stable rate of progression in each patient. In cases where the decline in cognition ...and behavior suddenly accelerates, we usually think of co-existent Alzheimer pathology, as most demented PD patients also have Alzheimer disease (AD) changes, although not necessarily meeting criteria for a distinct pathological diagnosis of AD.BACKGROUNDParkinson's disease (PD) progresses at highly variable rates in different individuals but, in general, has a fairly stable rate of progression in each patient. In cases where the decline in cognition and behavior suddenly accelerates, we usually think of co-existent Alzheimer pathology, as most demented PD patients also have Alzheimer disease (AD) changes, although not necessarily meeting criteria for a distinct pathological diagnosis of AD.Clinico-pathological case Results: A 75-year-old woman presented with a typical PD course including a good response to L-Dopa. Four years after diagnosis she developed a rapid decline in motor symptoms, severe cognitive fluctuations, and rapidly progressive dementia, dying within one year of the onset of the rapid progression.METHODSClinico-pathological case Results: A 75-year-old woman presented with a typical PD course including a good response to L-Dopa. Four years after diagnosis she developed a rapid decline in motor symptoms, severe cognitive fluctuations, and rapidly progressive dementia, dying within one year of the onset of the rapid progression.While most cases of Parkinson's disease dementia (PDD) show concomitant Alzheimer's pathology, the sudden acceleration of the disease does not necessarily indicate the presence of concomitant Alzheimer's disease.CONCLUSIONSWhile most cases of Parkinson's disease dementia (PDD) show concomitant Alzheimer's pathology, the sudden acceleration of the disease does not necessarily indicate the presence of concomitant Alzheimer's disease.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
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