Real-Time Telemetry System for Amperometric and Potentiometric Electrochemical Sensors

A real-time telemetry system, which consists of readout circuits, an analog-to-digital converter (ADC), a microcontroller unit (MCU), a graphical user interface (GUI), and a radio frequency (RF) transceiver, is proposed for amperometric and potentiometric electrochemical sensors. By integrating the proposed system with the electrochemical sensors, analyte detection can be conveniently performed. The data is displayed in real-time on a GUI and optionally uploaded to a database via the Internet, allowing it to be accessed remotely. An MCU was implemented using a field programmable gate array (FPGA) to filter noise, transmit data, and provide control over peripheral devices to reduce power consumption, which in sleep mode is 70 mW lower than in operating mode. The readout circuits, which were implemented in the TSMC 0.18-μm CMOS process, include a potentiostat and an instrumentation amplifier (IA). The measurement results show that the proposed potentiostat has a detectable current range of 1 nA to 100 μA, and linearity with an R2 value of 0.99998 in each measured current range. The proposed IA has a common-mode rejection ratio (CMRR) greater than 90 dB. The proposed system was integrated with a potentiometric pH sensor and an amperometric nitrite sensor for in vitro experiments. The proposed system has high linearity (an R2 value greater than 0.99 was obtained in each experiment), a small size of 5.6 cm × 8.7 cm, high portability, and high integration

Development of an In-house Computer Code for the Thermal Analysis of Satellites Using Thermal Network Method and Method of Lines

This paper discusses the development of an in-house computer code for the thermal analysis of satellites. The code uses Thermal Network Method (TNM) in conjunction with the Method of Lines (MOL) for the solution of the energy equation in the presence of radiative heat transfer. The predictive accuracy of the code is demonstrated on a test problem involving a cubic satellite with two solar panels and a single representative payload at its center to account for all other possible equipments in the satellite. The results produced by the present code were compared with the predictions of commercial thermal analysis software on the same test problem and a good agreement between the two sets of results was obtained. The code is a promising tool for the thermal analysis of satellites

Predicting Hospitalization in Children with Acute Asthma

Background: Acute asthma is one of the most common medical emergencies in children. Appropriate assessment/treatment and early identification of factors that predict hospitalization are critical for the effective utilization of emergency services. Objective: To identify risk factors that predict hospitalization and to compare the concordance of the Modified Pulmonary Index Score (MPIS) with the Global Initiative for Asthma (GINA) guideline criteria in terms of attack severity. Methods: The study population was composed of children aged 5-18 years who presented to the Emergency Departments (ED) of the tertiary reference centers of the country within a period of 3 months. Patients were evaluated at the initial presentation and the 1st and 4th hours. Results: Of the 304 patients (median age: 8.0 years [interquartile range: 6.5-9.7]), 51.3% and 19.4% required oral corticosteroids (OCS) and hospitalization, respectively. Attack severity and MPIS were found as predicting factors for hospitalization, but none of the demographic characteristics collected predicted OCS use or hospitalization. Hospitalization status at the 1st hour with moderate/severe attack severity showed a sensitivity of 44.1%, specificity of 82.9%, positive predictive value of 38.2%, and negative predictive value of 86.0%; for MPIS >= 5, these values were 42.4%, 85.3%, 41.0%, and 86.0%, respectively. Concordance in prediction of hospitalization between the MPIS and the GINA guideline was found to be moderate at the 1st hour (kappa = 0.577). Conclusion: Attack severity is a predictive factor for hospitalization in children with acute asthma. Determining attack severity with MPIS and a cut-off value >= 5 at the 1st hour may help physicians in EDs. Having fewer variables and the ability to calculate a numeric value with MPIS makes it an easy and useful tool in clinical practice. (C) 2013 Elsevier Inc

PREDICTING HOSPITALIZATION IN CHILDREN WITH ACUTE ASTHMA

Background: Acute asthma is one of the most common medical emergencies in children. Appropriate assessment/treatment and early identification of factors that predict hospitalization are critical for the effective utilization of emergency services. Objective: To identify risk factors that predict hospitalization and to compare the concordance of the Modified Pulmonary Index Score (MPIS) with the Global Initiative for Asthma (GINA) guideline criteria in terms of attack severity. Methods: The study population was composed of children aged 5-18 years who presented to the Emergency Departments (ED) of the tertiary reference centers of the country within a period of 3 months. Patients were evaluated at the initial presentation and the 1st and 4th hours. Results: Of the 304 patients (median age: 8.0 years [interquartile range: 6.5-9.7]), 51.3% and 19.4% required oral corticosteroids (OCS) and hospitalization, respectively. Attack severity and MPIS were found as predicting factors for hospitalization, but none of the demographic characteristics collected predicted OCS use or hospitalization. Hospitalization status at the 1st hour with moderate/severe attack severity showed a sensitivity of 44.1%, specificity of 82.9%, positive predictive value of 38.2%, and negative predictive value of 86.0%; for MPIS >= 5, these values were 42.4%, 85.3%, 41.0%, and 86.0%, respectively. Concordance in prediction of hospitalization between the MPIS and the GINA guideline was found to be moderate at the 1st hour (kappa = 0.577). Conclusion: Attack severity is a predictive factor for hospitalization in children with acute asthma. Determining attack severity with MPIS and a cut-off value >= 5 at the 1st hour may help physicians in EDs. Having fewer variables and the ability to calculate a numeric value with MPIS makes it an easy and useful tool in clinical practice. (C) 2013 Elsevier Inc