Development and Validation of a Skinfold Model for Estimation of Body Density for a Safe Weight Reduction in Young Iranian Wrestlers.

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.

Size reproducibility of gadolinium oxide based nanomagnetic particles for cellular magnetic resonance imaging: effects of functionalization, chemisorption and reaction conditions.

We developed biofunctionalized nanoparticles with magnetic properties by immobilizing diethyleneglycol (DEG) on Gd2O3, and PEGilation of small particulate gadolinium oxide (SPGO) with two methoxy-polyethyleneglycol-silane (mPEG-Silane 550 and 2000 Da) using a new supervised polyol route, described recently. In conjunction to the previous study to achieve a high quality synthesis and increase in the product yield of nanoparticles; assessment of the effects of functionalization, chemisorption and altered reaction conditions, such as NaOH concentration, temperature, reaction time and their solubility, on size reproducibility were determined as the goals of this study. Moreover, the effects of centrifugation, filtration and dialysis of the solution on the nono magnetic particle size values and their stability against aggregation have been evaluated. Optimization of reaction parameters led to strong coating of magnetic nanoparticles with the ligands which increases the reproducibility of particle size measurements. Furthermore, the ligand-coated nanoparticles showed enhanced colloidal stability as a result of the steric stabilization function of the ligands grafted on the surface of particles. The experiments showed that DEG and mPEG-silane (550 and 2000 Dalton) are chemisorbed on the particle surfaces of Gd2O3 and SPGO which led to particle sizes of 5.9 ± 0.13 nm, 51.3 ± 1.46 nm and 194.2 ± 22.1 nm, respectively. The small size of DEG-Gd2O3 is acceptably below the cutoff of 6nm, enabling easy diffusion through lymphatics and filtration from kidney, and thus provides a great deal of potential for further in-vivo and in-vitro application.

Classification algorithms with multi-modal data fusion could accurately distinguish neuromyelitis optica from multiple sclerosis.

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.

Validity and reliability of a Persian translation of the Minimal Assessment of Cognitive Function in Multiple Sclerosis (MACFIMS).

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.