Narisorn Kongruttanachok

In this study, we propose a specimen tube prototype and smart specimen transport box using radio frequency identification (RFID) and narrow band–Internet of Things (NB-IoT) technology to use in the Department of Laboratory Medicine, King Chulalongkorn Memorial Hospital. Our proposed method replaces the existing system, based on barcode technology, with shortage usage and low reliability. In addition, tube-tagged barcode has not eliminated the lost or incorrect delivery issues in many laboratories. In this solution, the passive RFID tag is attached to the surface of the specimen tube and stores information such as patient records, required tests, and receiver laboratory location. This information can be written and read multiple times using an RFID device. While delivering the specimen tubes via our proposed smart specimen transport box from one clinical laboratory to another, the NB-IoT attached to the box monitors the temperature and humidity values inside the box and tracks the box’s GPS location to check whether the box arrives at the destination. The environmental condition inside the specimen transport box is sent to the cloud and can be monitored by doctors. The experimental results have proven the innovation of our solution and opened a new dimension for integrating RFID and IoT technologies into the specimen logistic system in the hospital.

Recent advances have allowed the monitoring of several volatile organic compounds (VOCs) in human exhaled breath, and many of them are being utilized as a biomarker to diagnose several diseases, including diabetes. Among several VOCs, isopropanol (IPA) has been reported as a common volatile compound in the exhaled breath of patients with type 1 and type 2 diabetes. In this article, an experimental approach is discussed to develop a highly selective and sensitive IPA vapor sensor system. The fabricated sensor is comprised of a small and portable glass slide coated with molecularly imprinted polymer containing specific binding sites compatible with IPA molecules. The developed sensor is based on the wavelength interrogation technique. The fabricated device is analyzed for the detection of IPA vapor with different concentrations varying from 50% to 100%. The sensor exhibits maximum sensitivities of 0.37, 0.30, and 0.62 nm/%IPA, respectively, for 30, 60, and 90 min, respectively, and an excellent sensitivity of 0.63 nm/%IPA for 120 min exposure along with good selectivity among a similar class of VOCs. The major features of the sensor i.e., small size, portability, cost-effectiveness, high sensitivity, and good selectivity, make it a potential candidate for diabetes monitoring. The promising results of the sensor illustrate its potential in diabetes monitoring applications.