Cognitive dysfunction is common in multiple sclerosis (MS) and validated batteries are limited in languages other than English. We aimed to translate, cross-culturally adapt, validate, and assess reliability of Minimal Assessment of Cognitive Function in MS (MACFIMS) in Persian. The MACFIMS is a well-constructed battery in the MS literature. The battery was adapted to Persian in accordance with available guidelines. A total of 158 MS patients and 90 controls underwent neuropsychological assessment. For reliability assessment the battery was re-administered in a subset of 41 patients after a short interval using alternate forms to mitigate practice effects (approximately 10 days). Patients performed significantly worse than controls in all cognitive tests, supporting discriminant validity of our adapted battery. Approximately half of patients (46.2%) showed cognitive impairment as defined by the impairment in two or more tests. The Symbol Digit Modalities Test was the most robust test by ROC analysis. All tests showed acceptable to good level of reliability. This is the first validation of gold-standard cognitive testing in Persian. The Persian MACFIMS shows nearly the same psychometrics as its English counterpart.

Neuromyelitis optica (NMO) exhibits substantial similarities to multiple sclerosis (MS) in clinical manifestations and imaging results and has long been considered a variant of MS. With the advent of a specific biomarker in NMO, known as anti-aquaporin 4, this assumption has changed; however, the differential diagnosis remains challenging and it is still not clear whether a combination of neuroimaging and clinical data could be used to aid clinical decision-making. Computer-aided diagnosis is a rapidly evolving process that holds great promise to facilitate objective differential diagnoses of disorders that show similar presentations. In this study, we aimed to use a powerful method for multi-modal data fusion, known as a multi-kernel learning and performed automatic diagnosis of subjects. We included 30 patients with NMO, 25 patients with MS and 35 healthy volunteers and performed multi-modal imaging with T1-weighted high resolution scans, diffusion tensor imaging (DTI) and resting-state functional MRI (fMRI). In addition, subjects underwent clinical examinations and cognitive assessments. We included 18 a priori predictors from neuroimaging, clinical and cognitive measures in the initial model. We used 10-fold cross-validation to learn the importance of each modality, train and finally test the model performance. The mean accuracy in differentiating between MS and NMO was 88%, where visible white matter lesion load, normal appearing white matter (DTI) and functional connectivity had the most important contributions to the final classification. In a multi-class classification problem we distinguished between all of 3 groups (MS, NMO and healthy controls) with an average accuracy of 84%. In this classification, visible white matter lesion load, functional connectivity, and cognitive scores were the 3 most important modalities. Our work provides preliminary evidence that computational tools can be used to help make an objective differential diagnosis of NMO and MS.

The implemented divergence/curl regularization was successfully tested, leading to promising results in comparison with competitive regularization methods. Future work is required to establish parameter tuning and reduce the computational cost.

Background:Adverse effects of excessive body mass reduction among wrestlers dictate minimum weight determination through body composition. Although skinfold equations are essential to estimate body composition in the field setting, they are mostly derived from Western societies and may lack generalizability to other populations.Purpose:Previously published skinfold equations lacked external validity in predicting body density of Iranian wrestlers. We aimed to derive a new anthropometric model specific to young Iranian male wrestlers.Study design:Cross-sectional cohort study.Level of evidence:Level 3.Methods:One hundred twenty-six Iranian male wrestlers with at least 1 year of experience and a mean age of 19 ± 4.0 years underwent underwater weight analysis for body density estimation and anthropometric measurements. The previously published equations were validated, followed by new regression modeling, using multivariable fractional polynomials, with body density as the criterion predicted by common anthropometric variables. The final model was validated throughout the modeling procedure using 1000 bootstrap replications.Results:The mean body fat percentage (%BF) was 12.6% (95% CI, 11.9%-13.4%), lower than that of previous studies. Six previously published equations each had significant deviations from the line of identity (all P < 0.001). The new prediction equation combined subscapular, tricipital, and midaxillary skinfolds and body mass index cubed to predict body density.Conclusion:The development of ethnicity-specific equations, using statistically unbiased and comprehensive validation methods, is imperative for body composition estimation to determine the minimum weight for regulation of health in athletes.Clinical Relevance:Using equations without external validation can bias the prediction of minimum weight, leading to unsafe weight reduction by athletes. Compared with a previous study, much lower mean %BF was found using an ethnicity-specific equation (12.6% vs 15.9%). This difference observed in %BF prediction could affect safe fat reduction in athletes.

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Abstract-The role of calcification inside fibroatheroma during carotid artery stenting operation is controversial. Cardiologists face a major problem of "plaque vulnerability" during the placement of both balloon and stent, with stiff plaques containing advanced calcifications that break the arterial wall or give rise to unstable thromboembolic stroke. The aim of this work is to evaluate the role of calcification (in terms of material and mechanical properties) in plaque vulnerability and wall rupture and to find plaque maximum resistance before breaking. Image-based models of carotid artery stenting were used and Finite Element Analysis (FEA) was performed to simulate the impact of balloon and stent expansion in the presence of calcified plaques. In detail, a nonlinear static structural analysis was performed on 20 patients acquired using in vivo MDCT angiography. The Agatston Calcium score was obtained for each patient and subject-specific local Elastic Modulus was calculated. The in silico results showed that by imposing maximum ultimate external load of 1.2MPa and 4.2MPa on balloon and stent respectively, average ultimate stress of 55.

There is still debate on whether the relationship between blood pressure (BP) and risk of cardiovascular diseases (CVD) in patients with type 2 diabetes (T2D) is linear or not. Since these cardio-metabolic disturbances share interrelated complex pathogenic mechanisms, we aimed to assess the relationship of baseline systolic (SBP)/diastolic pressures with CVD and coronary heart disease (CHD) events in a cohort of Iranians with T2D during a median follow-up of 8.8 years. Of all 1009 eligible participants with T2D with a mean (s.d.) age of 54.4 (11.6) years and free of CVD at baseline, we primarily focused on 260 participants undergoing anti-hypertensive treatment. Multivariate Cox proportional hazard models were used to evaluate hazard ratios (HR) of BP categories for CVD and CHD events. Also, multivariable regression modelling was applied to obtain their risk curve. We detected a J-shaped phenomenon between the continuous baseline BP and risk of CVD events. Considering 130⩽SBP<140 mm Hg as reference, a SBP ⩾140 mm Hg was associated with increased CVD (HR [95% confidence interval (CI)]: 2.43 [1.23-4.82]) and CHD (HR [95% CI]: 2.05 [1.02-4.13]) risk. However, a SBP⩽120 mm Hg in those with drug-treated hypertension also showed higher risk for CVD (HR [95% CI]: 2.80 [1.24-6.34]) and CHD events (HR [95% CI]: 2.39 [1.03-5.57]). Our findings revealed that the risk for macrovascular morbidity events was inversely associated with BP reduction below the level of 120/80 mm Hg for those with diabetes. People with diabetes might benefit from a BP management to a moderate range of 120/80-140/90 mm Hg for CVD risk reduction.

Automated motion reduction in dynamic infrared imaging is on demand in clinical applications, since movement disarranges time−temperature series of each pixel, thus originating thermal artifacts that might bias the clinical decision. All previously proposed registration methods are feature based algorithms requiring manual intervention. The aim of this work is to optimize the registration strategy specifically for Breast Dynamic Infrared Imaging and to make it user−independent. We implemented and evaluated 3 different 3D time−series registration methods: 1. Linear affine, 2. Non−linear Bspline, 3. Demons applied to 12 datasets of healthy breast thermal images. The results are evaluated through normalized mutual information with average values of 0.70 ±0.03, 0.74 ±0.03 and 0.81 ±0.09 (out of 1) for Affine, Bspline and Demons registration, respectively, as well as breast boundary overlap and Jacobian determinant of the deformation field. The statistical analysis of the results showed that symmetric diffeomorphic Demons’ registration method outperforms also with the best breast alignment and non−negative Jacobian values which guarantee image similarity and anatomical consistency of the transformation, due to homologous forces enforcing the pixel geometric disparities to be shortened on all the frames. We propose Demons’ registration as an effective technique for time−series dynamic infrared registration, to stabilize the local temperature oscillation.