Long-term outdoor reliability assessment of a wireless unit for air-quality monitoring based on nanostructured films integrated on micromachined platforms.

We have fabricated and tested in long-term field operating conditions a wireless unit for outdoor air quality monitoring. The unit is equipped with two multiparametric sensors, one miniaturized thermo-hygrometer, front-end analogical and digital electronics, and an IEEE 802.15.4 based module for wireless data transmission. Micromachined platforms were functionalized with nanoporous metal-oxides to obtain multiparametric sensors, hosting gas-sensitive, anemometric and temperature transducers. Nanoporous metal-oxide layer was directly deposited on gas sensing regions of micromachined platform batches by hard-mask patterned supersonic cluster beam deposition. An outdoor, roadside experiment was arranged in downtown Milan (Italy), where one wireless sensing unit was continuously operated side by side with standard gas chromatographic instrumentation for air quality measurements. By means of a router PC, data from sensing unit and other instrumentation were collected, merged, and sent to a remote data storage server, through an UMTS device. The whole-system robustness as well as sensor dataset characteristics were continuously characterized over a run-time period of 18 months.

Developing a genomic-based point-of-care diagnostic system for rheumatoid arthritis and multiple sclerosis.

In this paper the methodology of designing a genomic-based point-of-care diagnostic system composed of a microfluidic Lab-On-Chip, algorithms for microarray image information extraction and knowledge modeling of clinico-genomic patient data is presented. The data are processed by genome wide association studies for two complex diseases: rheumatoid arthritis and multiple sclerosis. Respecting current technological limitations of autonomous molecular-based Lab-On-Chip systems the approach proposed in this work aims to enhance the diagnostic accuracy of the miniaturized LOC system. By providing a decision support system based on the data mining technologies, a robust portable integrated point-of-care diagnostic assay will be implemented. Initially, the gene discovery process is described followed by the detection of the most informative SNPs associated with the diseases. The clinical data and the selected associated SNPs are modeled using data mining techniques to allow the knowledge modeling framework to provide the diagnosis for new patients performing the point-of-care examination. The microfluidic LOC device supplies the diagnostic component of the platform with a set of SNPs associated with the diseases and the ruled-based decision support system combines this genomic information with the clinical data of the patient to outcome the final diagnostic result.