PurposeAlthough smoking is known to cause various symptoms in multiple sclerosis (MS) patients, there have been no reports regarding the relationship between smoking and cognitive impairment in MS. Studying the effects of cigarette smoking in MS patients is imperative as there is a high prevalence of cognitive impairment in MS patients. In this study we examined the potentially deleterious effects of heavy smoking on mentation of patients with MS.Patients and methodsMS patients receiving care at the Neurology Clinic at Bezmialem Vakıf University, between the ages of 18–65 years who have at least graduated elementary school were included in the study. The Brief Repeatable Battery of Neuropsychological Tests (BRB-N) is a commonly used method to assess cognitive function in MS patients and was utilized in our study. Patients that smoked for at least 10 pack-years were considered heavy smokers.ResultsAll the patients were stratified into two groups: heavy smokers (n=20) and nonsmokers (n=24). For heavy smokers, their cognitive functioning was more impaired than that of nonsmokers (P=0.04, χ2=4.227). For patients with cognitive impairment, 78.9% of the Paced Auditory Serial Addition Test and 63.2% of the Symbol Digit Modalities Test scores were found to be lower.ConclusionPrevious reports have suggested that smoking increases the frequency of relapse among individuals with relapsing-remitting MS and accelerates disease progression in patients with progressive MS. According to the results of our study, heavy smokers had increased cognitive impairment when compared to nonsmokers. Extensive studies are necessary to further elucidate the relationship between smoking and cognitive impairment in MS patients.

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Background Fluid attenuated inversion recovery (FLAIR) vascular hyperintensity (FVH) is a novel radiographic marker detected in acute ischemic stroke (AIS) patients, which is linked to slow blood flow and potentially salvageable brain tissue. Poor leptomeningeal collateral status in AIS patients with proximal artery occlusion (PAO) is associated with larger final infarct and worse clinical outcomes, which are also affected by severity of white matter hyperintensity (WMH). We sought to evaluate FVH utility as a marker of acute collateral vessel status and its association with WMH burden in AIS patients. Methods Consecutive AIS patients with PAO on baseline CT angiography (CTA) were retrospectively selected from a prospectively-derived database. FVH was graded by its location, degree, and score on admission MRI obtained immediately after intravenous tissue plasminogen activator (IV tPA) administration. Leptomeningeal collateral flow grade was ranked on admission CTA. WMH volume (WMHV) was assessed using a validated volumetric protocol. Relationship between FVH, collateral flow grade, and WMHV were analyzed. Results Among 39 patients (mean age 70.5±12.7 years; 56% women, mean National Institutes of Health Stroke Scale (NIHSS) score 17.2 (±4.4)), median WMHV was 6.0 cm3. FVH score and collateral flow grade were significantly correlated (Spearman's ρ=0.41, p= 0.009). In a univariate regression model, FVH degree was inversely associated with WMHV (β=−0.33 p=0.04). Conclusions FVH score detected on acute MRI can be used as a surrogate of collateral flow grade in AIS patients. FVH degree is inversely associated with WMHV, possibly signifying diffuse disease of cerebral vasculature in patients with severe leukoaraiosis.

Introduction: Magnetic resonance imaging (MRI) is the most important tool for diagnosis and follow-up in multiple sclerosis (MS). The discrimination of relapsing-remitting MS (RRMS) from secondary progressive MS (SPMS) is clinically difficult, and developing the proposal presented in this study would contribute to the process. Objective: This study aimed to ensure the automatic classification of healthy controls, RRMS, and SPMS by using MR spectroscopy and machine learning methods. Methods: MR spectroscopy (MRS) was performed on a total of 91 participants, distributed into healthy controls (n=30), RRMS (n=36), and SPMS (n=25). Firstly, MRS metabolites were identified using signal processing techniques. Secondly, feature extraction was performed based on MRS Spectra. N-acetylaspartate (NAA) was the most significant metabolite in differentiating MS types. Lastly, binary classifications (healthy controls-RRMS and RRMS-SPMS) were carried out according to features obtained by the Support Vector Machine algorithm. Results: RRMS cases were differentiated from healthy controls with 85% accuracy, 90.91% sensitivity, and 77.78% specificity. RRMS and SPMS were classified with 83.33% accuracy, 81.81% sensitivity, and 85.71% specificity. Conclusions: A combined analysis of MRS and computer-aided diagnosis may be useful as a complementary imaging technique to determine MS types.

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