Biomedical subjects

Dariush K Nassiri

Publications and source records attributed to Dariush K Nassiri.

3 recordsLinked to original sources

PubMed2003

Chronic inflammation and increased arterial stiffness in patients with cardiac syndrome X.

AIMS: Endothelial dysfunction and subangiographic atheroma have been reported in patients with cardiac syndrome X (CSX) but little is known regarding chronic inflammation and reduced arterial distensibility as pathogenic mechanisms. We assessed whether markers of inflammation and arterial distensibility differ in CSX patients compared to control subjects. METHODS AND RESULTS: We studied 30 consecutive CSX patients (mean age 57+/-6 years, 25 women) and 30 healthy controls (mean age 54+/-8 years, 25 women). High sensitivity C-reactive protein (hs-CRP) levels were significantly higher in patients with CSX compared to controls (2.6 [1.7-4.5] vs 1.5[0.7-2.7] mg/l, P=0.02). Hs-CRP levels correlated with carotid intima-media thickness (IMT) (Spearman's rho=0.51; P=0.013). CSX patients also had significantly increased mean IMT values than controls (P<0.0001). Arterial stiffness and elastic modulus were also significantly increased in CSX patients compared to control subjects (P=0.04 and P=0.04, respectively). Distensibility tended to be lower in CSX patients than controls although this difference did not reach statistical significance. CONCLUSIONS: This study showed for the first time that compared to control subjects, patients with CSX have higher hs-CRP serum levels, increased mean common carotid artery IMT and increased arterial stiffness. The role of these abnormalities in the pathogenesis of CSX deserves investigation.

Adult↗

PubMed2002

New approach for modelling ultrasound blood backscatter signal.

The ultrasound (US) scattered signal from blood has been treated as a random signal by many investigators. However, the degree of randomness of a medium is a relative term that can change considerably with the resolution of the sensor. In this study, the backscattered signal from blood has been looked at as a chaotic signal. By this treatment, according to Taken's theorem, a single variable (e.g., amplitude of the blood-backscattered signal) can be used to reconstruct the nonlinear dynamics of the blood-scattered signal. Multilayer perceptron neural network architecture, with error back-propagation, has been formulated and used as a basis for building and testing the chaotic model of the backscattered signal. This chaotic model is used successfully as a short-term predictor of the backscattered signal from blood-mimicking fluid (BMF) flowing in a vascular flow phantom under pulsatile flow. This modelling approach can be useful, for example, in detecting blood-borne emboli.

Blood↗

PubMedDate not supplied

Microemboli detection using ultrasound backscatter.

Microemboli detection and characterisation have recently received great attention due to its clinical importance in the management of cerebrovascular disease. The new method presented in this paper is directly based on the idea that the ultrasound (US) backscattered signal from flowing blood is chaotic (El-Brawany and Nassiri 2002). The detection technique involves building a nonlinear model of the deterministic characteristics of the chaotic backscatter signal from blood, and the use of this model to look at the prediction error as a primary decision-making criterion for the microemboli detector. A complementary feature to the prediction error, namely, the degree of coherence between the US excitation pulse and the prediction error signal is used to enhance the detection process. The detector has been built using a feed-forward neural network with error back-propagation. The detection technique is tested successfully using a vascular flow phantom with solid spheres and bubbles of known sizes introduced in the flow circuit to mimic solid and gaseous emboli. Receiver operating characteristic (ROC) curve is used to assess the performance of the detection process. The total classification rate ranges from 88% to 96%.

Blood Flow Velocity↗