A cooperative metabolic syndrome estimation with high precision sensing unit.

In this letter, we discuss a sensor-integrated system model for metabolic syndrome prediction with workflow system. This model measures not only a cell temperature variation using invasive method but also controlling simulation for metabolic syndrome prediction. To identify the system realization, we discuss the schemes for predicting metabolic syndrome from measurement of mitochondrial activity by using high precision sensors and integrated simulation model of human energetic under high performance workflow computing environment. To predict metabolic syndrome, we built a sensor-integrated chamber that had network interface to deliver analysis results of human cells, annotation data from public hospital, and metabolic data. Using the proposed system, we showed the possibility to evaluate the functionality of human mitochondria and analyze energy metabolism.

Development of a non-invasive measurement system to the thickness of the subcutaneous adipose tissue layer.

BACKGROUND/PURPOSE: It is important to measure the thickness of hypodermis, including the subcutaneous fat layer in several fields such as global assessment of nutritional status and monitoring of dietary manipulation. It also provides useful information concerning the amount of peripheral adipose tissue and can be used as an index of obesity. METHODS: To measure the thickness of the subcutaneous adipose tissue layer, the optical properties of tissue components, several lipids and animal tissue samples in vitro were measured over the wavelength range from 1000 to 1700 nm, using the transmittance measurement system and the diffuse reflectance measurement system. A wavelength range for the thickness measurements was selected from the experimental result. The corresponding signal according to the changes of adipose tissue thickness was measured. RESULTS: Experimental data measured by the non-invasive measurement system were compared with the actual thickness of animal samples, and the results could be explained by the three-layered tissue model. The result showed high correlation to the thickness changes (R2 = 0.9954). CONCLUSIONS: The experimental result implies positive possibility for the development of a system to measure the thickness of subcutaneous adipose tissue using near infrared in vivo.