Molecularly imprinted polymers based biomimetic sensors for mosapride citrate detection in biological fluids.

Computational modeling was applied to study the intermolecular interactions in the pre-polymerization mixture and find a suitable functional monomer to use in the design of a new molecularly imprinted polymer (MIP) for mosapride citrate which is considerably a large molecule (as the citrate ion is also included in calculations as it has centers that can take part in interaction with monomer via hydrogen bonding). Based on these calculations, methacyrlic acid (MAA) was selected as a suitable functional monomer. Mosapride citrate selective MIP and a non-imprinted polymer (NIP) were synthesized and characterized using FTIR, TGA and SEM and then incorporated in carbon paste electrodes (CPEs). The designed modified sensor revealed linear responses in the ranges of 1×10-4-8×10-7 and 8×10-7-8×10-8molL-1 with a limit of detection (LOD) of 2.6×10-8molL-1. The results of the sensor exhibited high selectivity over interfering species and could be applied for the determination of mosapride citrate in pure solutions, pharmaceutical preparations, urine and human serum samples.

Identification of patients with preeclampsia from normal subjects using wavelet-based spectral analysis of heart rate variability.

BACKGROUND: The spectral analysis of the heart rate variability (HRV) shows a decrease in the power of the high frequency (HF) component in preeclamptic pregnancy compared with normal pregnancy; such a decrease is associated with an increase in the low frequency (LF) and the very low frequency (VLF) power. The physiological interpretation is that preeclamptic pregnancy is associated with a facilitation of sympathetic regulation and an attenuation of parasympathetic influence of HR compared with non-pregnancy and normal pregnancy. OBJECTIVE: To use an efficient nased on spectral analysis non-invasive technique to identify preeclamptic pregnant subjects from normal pregnant in Oman. METHODS: The soft-decision wavelet-based technique is implemented to find the power of the HRV bands in high resolution manner compared to the classical fast Fourier Transform method. Data was obtained from 20 preeclamptic pregnant subjects and 20 normal pregnant controls of the same pregnancy duration, obtained from Nizwa and Sultan Qaboos University hospitals in Oman. RESULTS: The soft-decision wavelet method succeeds to identify patients from normal pregnant with specificity, sensitivity and accuracy of 90%, 80% and 85%, respectively, compared to the FFT which results in 75% specificity, sensitivity and accuracy. CONCLUSION: The LF power obtained by Soft-decision wavelet decomposition is shown to be a successful feature for identification of preeclampsia.

PIC microcontroller-based RF wireless ECG monitoring system.

This paper presents a radio-telemetry system that provides the possibility of ECG signal transmission from a patient detection circuit via an RF data link. A PC then receives the signal through the National Instrument data acquisition card (NIDAQ). The PC is equipped with software allowing the received ECG signals to be saved, analysed, and sent by email to another part of the world. The proposed telemetry system consists of a patient unit and a PC unit. The amplified and filtered ECG signal is sampled 360 times per second, and the A/D conversion is performed by a PIC16f877 microcontroller. The major contribution of the final proposed system is that it detects, processes and sends patients ECG data over a wireless RF link to a maximum distance of 200 m. Transmitted ECG data with different numbers of samples were received, decoded by means of another PIC microcontroller, and displayed using MATLAB program. The designed software is presented in a graphical user interface utility.