Recently, researchers have challenged to create three-dimensional (3-D) tissues with tissue engineering technology in order to establish in vitro models and new therapy for damaged organ. Most popular approach of tissue engineering is using 3-D biodegradable scaffolds as alternatives of extracellular matrix. By contrast, we have bioengineered pulsatile myocardial tissues by stacking cardiomyocyte sheets, which were harvested from temperature-responsive culture dishes only by lowering temperature. However, the shortage of oxygen and nutrition limits the final tissue thickness. In this study, we tried to fabricate thicker myocardial tissues by promoting oxygen and nutrition permeation using a novel perfusion bioreactor. Triple-layer neonatal rat cardiomyocyte sheets were attached in the culture chamber. Chambers were perfused with 2.4mL/h culture media and 15mmHg pressure gradient was applied to perfuse the culture media through the construct. After 5-days culture, the construct in the bioreactor was thicker and cell-denser than in static condition. These results indicate that the perfusion bioreactor should contribute to myocardial tissue engineering in vitro.

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We have introduced a system which detects the action of getting out of bed using an ultrasonic array sensor to monitor head position. In actuality, the sensor on the ceiling cannot be always located just above the center of a bed. So, we have to evaluate how our system can be robust when the sensor cannot be located on the ceiling just above the center of a bed. Also unwanted ultrasonic derived from the next sensor may disturb our system. Then a new test environment was created to be able to change the location of the sensor compared to the bed by moving the bed back and forth, to the right or to the left 20cm each direction. Also another sensor located 210cm away on the ceiling was added to the system. In the two situations, 7 subjects got out of bed 15 different ways. Two series of test have been done. First, we tested using a PC to execute the program of the getting up from bed. The data was collected every 200ms. Second, we tested using our new prototype, collecting the data every 100ms. We thought the shorter interval of would keep up the higher detection rate. In conclusion, we estimate the correct detection rate as follows in the test environment:
•93.3% with an error rate of 1.9% and a loss rate of 4.8% for sitting up in the bed.
•93.3% with an error rate of 0.5% and a loss rate is 6.2% for getting out of the bed
We could not achieve better detection rates due to the circumstances, which are more critical than the previous report. We think we could better evaluate the robustness of detection algorithm in an actual environment. We think we should enhance our algorithm and especially lower the loss rate in the future.

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Tripping over, which is one of the causes of falling, is caused by lack of recognizing obstacles. The purpose of this study is to investigate obstacle avoidance under the condition of partly interrupted sight. Six young adults stepped over an obstacle of 4.8 cm high under four conditions: (1) without interrupted sight; (2) under partly interrupted sight with 35% of subject's stride; (3) under that with 100% of subject's stride; (4) under that with 200% of subject's stride. The 3D motion of stepping over an obstacle was recorded together with ground reaction force. Step length during approaching and stepping over an obstacle, clearance, toe-obstacle distance, heel-obstacle distance and hip, knee and ankle joint moments were estimated with Vicon system. Additionally, the sensory tests about fear for partly interrupted sight after each trial were made. The results showed that clearance and toe-obstacle distance were extended during stepping over an obstacle with larger partly interrupted sight. It is suggested that the motion changes are caused by fear for partly interrupted sight and the conscious to decrease falling risks.

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Computational analysis has been becoming useful approch to improving design of blood passages in membrane oxgenators. However,most of computationalanalysis methods focused on fluid dynamics in the blood layer to take into account a macro design of a blood passage. We focused on the gas transfer behaviors in a hollow fiber bundle of a commercial oxgenator using computational analysis method. A micro passage model is simulated arectangular part of a hollow fiber bundle. this computational model consists of parallel and staggerd arranged two hollow fiber membranes ^*(225 μm outer diameter and 165 μm inner diameter). Blood flows outside of hollow fobers in a direction of perpendicular to gas flow direction. Our computational analysis method deals coupled mass transfer and fluid dynamics including blood-gas reaction. In initial blood condition, partial oxygen pressure (PO₂), partial carbon dioxide pressure (PCO₂) and hemoglobin concentration (Hb) were set at 37.7 mmHg, 45.0 mmHg and 12.0 g/dL. In Initial gas condition, PO₂ and PCO₂ ere set at 713.0 mmHg and 0.0 mHg. In each results of PO₂ and PCO₂ distributions, maximaum values of PO₂ and minimum values of PCO₂ were demonstrated in the wake of a hollow fiber. Max PO₂ and min PCO₂ were 39.1 mmHg and 44.4 mmHg, 38.9 mmHg and 44.5 mmHg, and 38.7 mmHg and 44.6 mmHg at 1, 3, 5 L/min blood and gas flows, respectively.In conclusion, our computational analysis method is able to display distributions of PO₂ and PCO₂ in a micro passage among hollow fiber membranes.

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In order to realize a novel hand-held monitor of the sympathetic nervous system, we have developed an analytical systenm for salivary amylase activity (sAMY) using a dry-chemistry system named "salivary amylase monitor". The device to quantfy sAMY by the rate method was'completely automated ^*This method was made possible by the fabrication of a disposable test-strip equipped with built-in saliva collecting and reagent papers and an automatic saliva transfer mechanism. A total of one minute was enough to analyse the sAMY. Within a range of sAMY between 12.9 - 150.7 kU/1, the calibration curve for the monitor showed a coefficient with R² = O.972 and coefficient of variation (CV) = 12.6%. The simultaneous repeatability showed within 10%. Moreover, it was demonstrated that (i) the effect of coexisting Protein on the accuracy is less than 5%, (ii) the life time of the test-strip was 1.5years. Thus, this study denmonstrated that the monitor might be used as a better index fbr psychological research.

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