Evaluation of 'partnership care model' in the control of hypertension

One of the shared common goals of World Hypertension League (WHL) and World Health Organization (WHO) is the control of hypertension. Despite many local and international interventions, the goal has not been achieved. This study evaluated an intervention based on the partnership care model to control hypertension in a rural population in the north of Iran. The results showed that the intervention was effective in decreasing systolic and diastolic blood pressure and in increasing the rate of controlled hypertensives (based on criteria of WHO/WHL). The intervention also had positive effects on health-related quality of life, body mass index, anxiety, high density lipoprotein level and compliance score. Based on these results, the partnership care model is effective in hypertension control and is recommended as a model to replace previous approaches in hypertension control.     

Performance enhancement for audio-visual speaker identification using dynamic facial muscle model

Science of human identification using physiological characteristics or biometry has been of great concern in security systems. However, robust multimodal identification systems based on audio-visual information has not been thoroughly investigated yet. Therefore, the aim of this work to propose a model-based feature extraction method which employs physiological characteristics of facial muscles producing lip movements. This approach adopts the intrinsic properties of muscles such as viscosity, elasticity, and mass which are extracted from the dynamic lip model. These parameters are exclusively dependent on the neuro-muscular properties of speaker; consequently, imitation of valid speakers could be reduced to a large extent. These parameters are applied to a hidden Markov model (HMM) audio-visual identification system. In this work, a combination of audio and video features has been employed by adopting a multistream pseudo-synchronized HMM training method. Noise robust audio features such as Mel-frequency cepstral coefficients (MFCC), spectral subtraction (SS), and relative spectra perceptual linear prediction (J-RASTA-PLP) have been used to evaluate the performance of the multimodal system once efficient audio feature extraction methods have been utilized. The superior performance of the proposed system is demonstrated on a large multispeaker database of continuously spoken digits, along with a sentence that is phonetically rich. To evaluate the robustness of algorithms, some experiments were performed on genetically identical twins. Furthermore, changes in speaker voice were simulated with drug inhalation tests. In 3 dB signal to noise ratio (SNR), the dynamic muscle model improved the identification rate of the audio-visual system from 91 to 98%. Results on identical twins revealed that there was an apparent improvement on the performance for the dynamic muscle model-based system, in which the identification rate of the audio-visual system was enhanced from 87 to 96%.     

Fully automatic segmentation of multiple sclerosis lesions in brain MR FLAIR images using adaptive mixtures method and Markov random field model

In this paper, an approach is proposed for fully automatic segmentation of MS lesions in fluid attenuated inversion recovery (FLAIR) Magnetic Resonance (MR) images. The proposed approach, based on a Bayesian classifier, utilizes the adaptive mixtures method (AMM) and Markov random field (MRF) model to obtain and upgrade the class conditional probability density function (CCPDF) and the a priori probability of each class. To compare the performance of the proposed approach with those of previous approaches including manual segmentation, the similarity criteria of different slices related to 20 MS patients were calculated. Also, volumetric comparison of lesions volume between the fully automated segmentation and the gold standard was performed using correlation coefficient (CC). The results showed a better performance for the proposed approach, compared to those of previous works.     

Modeling the gait of normal and Parkinsonian persons for improving the diagnosis

In this study, we present a model for the gait of normal and Parkinson's disease (PD) persons. Gait is semi-periodic and has fractal properties. Sine circle map (SCM) relation has a sinusoidal term and can show chaotic behaviour. Therefore, we used SCM as a basis for our model structure. Moreover, some similarities exist between the parameters of this relation and basal ganglia (BG) structure. This relation can explain the complex behaviours and the complex structure of BG. The presented model can simulate the BG behaviour globally. A model parameter, Ω, has a key role in the model response. We showed that when Ω is between 0.6 and 0.8, the model simulates the behaviour of normal persons; the amounts greater or less than this range correspond to PD persons. Our statistical tests show that there is a significant difference between the Ω of normal and PD patients. We conclude that Ω can be introduced as a parameter to distinguish normal and PD persons. Additionally, our results showed that Spearman correlation between the Ω and the severity of PD is 0.586. This parameter may be a good index of PD severity.     

Design and testing of statistical methods to classify the severity of steppage gait based on center of pressure data

In this research kinematics parameters derived from ground reaction forces were evaluated to limit differential diagnoses and measure the degree of disabilities during walking among neuropathic subjects. 25 neuropathic subjects affected by drop foot and 20 normal subjects were enrolled in the study. Each subject was tested in average 10±2 times for calculating kinetics parameters derived from ground reaction forces. The results revealed that the center of pressure displacement pattern in sole of foot can be a good index for differential diagnoses and measuring the degree of disabilities. This research can extend the clinical applications of ground reaction force plate and introduce suitable criteria to measure the degree of disability among neuropathic patients.     

NOD2 exonic variations in Iranian Crohn's disease patients

Purpose

The NOD2 gene is known to have a strong association with Crohn??s disease, but different trends were reported in occurrence of NOD2 variants in distinct ethnicities. The aim of this study was to assess all exonic sequences of the NOD2 gene in Iranian Crohn's disease patients and healthy controls to identify any existing variation and evaluate their association with Crohn's disease.

Methods

A total of 90 non-related Crohn's disease patients and 120 sex- and age-matched healthy controls of Iranian origin were enrolled in this study. The participants were referred to a tertiary center in a 2-year period (2006?C2008). The exonic regions of the NOD2 gene were amplified by polymerase chain reaction and evaluated by direct sequencing.

Results

A total of 21 sequence variations were identified among all exonic regions of the NOD2 gene, of which eight had an allele frequency of more than 5%. Eight new mutations (one in exon 2 and seven in exon 4) were observed. The three main variants (R702W, G908R, and 1007fs) showed allele frequencies of 13.3%, 2.2%, and 1.7%, respectively. Three new variations (P371T, A794P, and Q908H) and R702W mutation were significantly more frequent in Crohn's disease patients compared to controls.

Conclusions

Eight novel mutations were identified in the NOD2 exons, but the pathophysiological importance of these variants remains unclear. Iranian patients with their different genetic reservoirs may demonstrate some novel characteristics for disease susceptibility.     

Association of educational status with cardiovascular disease: Teheran Lipid and Glucose Study

Objective  

The aim of this study was to evaluate the associations between educational level and cardiovascular disease (CVD) in an older Iranian population.