Biomedical subjects

Ryo Kosaka

Publications and source records attributed to Ryo Kosaka.

5 recordsLinked to original sources

PubMed2006

Features of early gastric cancer and gastric adenoma by enhanced-magnification endoscopy.

BACKGROUND: Changes to the mucosal surface of early gastric carcinomas and gastric adenomas as viewed by enhanced-magnification endoscopy with acetic acid have not been investigated thoroughly. Using this technology, we investigated the appearance of the gastric surface patterns of neoplastic and surrounding nonneoplastic mucosa. METHODS: Forty-seven consecutive patients with early gastric carcinomas or gastric adenomas underwent enhanced-magnification endoscopy following 1.5% acetic acid instillation. All biopsy specimens were taken from the area at which the enhanced-magnified endoscopic image was obtained. RESULTS: Surface patterns of gastric tumors and the surrounding mucosa were classified into five types: type I, small round pits of uniform size and shape; type II, slit-like pits; type III, gyrus and villous patterns; type IV, irregular arrangements and sizes of pattern types I, II and III; type V, destructive patterns of types I, II and III. The predominant pattern of the surrounding mucosa was type III, and most type III mucosa had characteristics of intestinal metaplasia. Although all elevated adenomas showed type II or type III surface patterns, both depressed adenomas showed type IV. Elevated carcinomas showed type III (42.9%) or type IV (57.1%) surface patterns, while depressed carcinomas showed type IV (70%) or type V (30%). Although differentiated tubular adenocarcinomas showed type III (10.3%), type IV (86.2%), or type V (3.5%) surface patterns, all of the signet-ring cell carcinomas and poorly differentiated tubular adenocarcinomas showed type V. CONCLUSIONS: Enhanced-magnification endoscopy may be useful for identifying gastric tumors and determining the extent of horizontal spread, especially in tumors of the depressed type.

Adenoma↗

PubMed2004

Resonant frequency control for artificial heart using online parameter identification.

To develop effective medical care and therapeutic control using an artificial heart, a new control method has been developed. This new method can control the artificial heart effectively and can adapt to internal physiological behavior using measured physiological data; aortic pressure, aortic flow, and pump flow. This method consists of first, a second-order physiological model, which represents the internal physiological behavior by a mathematical equation; and second, an estimation method, which can identify the physiological parameters; aortic inertia, aortic resistance, aortic compliance, and peripheral resistance by a parameter identification method. It can then calculate the resonant frequency as the control signal for the artificial heart from the identified physiological model. To confirm the effectiveness, the proposed method was evaluated in a computer simulation study. This evaluation showed that the new method could estimate the physiological parameters and the resonant frequency within a 10% error. The impedance of the systemic circulation could also be reduced by this method.

Algorithms↗

PubMed2003

Tsukuba remote monitoring system for continuous-flow artificial heart.

In order to make long-term medical treatment with the use of an artificial heart effective, we developed the Tsukuba remote monitoring system, which enables medical staff to manage the physiological condition of patients and the driving condition of the artificial heart at anytime from a remote place. This remote monitoring system has three functions: first, a remote monitoring function, which enables medical staff to monitor measured data from a remote place anytime by using not only a personal computer but also a cellular phone; second, an analyzing function, which estimates the unmeasured physiological behavior in the body based on a mathematical physiological model; and third, a warning function, which detects physiological problems and malfunction of the artificial heart by applying the if-then rule and sends a warning message to the medical staff. As a result of applying this system to animal experiments, we have confirmed the effectiveness of the proposed system.

Animals↗

PubMed2002

On-line parameter identification of systemic circulation using the delta operator.

To develop effective medical care with the artificial heart, we propose a new method, on-line parameter identification of the systemic circulation using the delta operator which can calculate the time-varying and unmeasured hemodynamics of the internal human body from some measured data: aortic pressure and total flow in real time. This method consists of first, a dynamic physiological model which is configured with the physiological parameters Ca (aortic compliance) and Rp (total peripheral resistance); and second, a system identification method using the delta operator. In the computer simulation study, we could confirm the effectiveness to identify the physiological parameters. In animal experiments with a left ventricular assist system, the physiological parameters, Ca = 1.8 (ml/mm Hg) and Rp = 0.8 (mm Hg s/ml), could be identified on-line.

Adaptation, Physiological↗

PubMed2002

Online parameter identification of second-order systemic circulation model using the delta operator.

To develop effective medical care with the artificial heart, we proposed a new method that can calculate the time varying and unmeasured hemodynamics of the human body from measured physiological data: aortic pressure, aortic flow, and pump flow in real time. This method comprises first, the second order of systemic circulation model, which consists of aortic compliance (Ca), aortic resistance (Ra), aortic inertia (L), and total peripheral resistance (Rp); and second, system identification using the delta operator. In the computer simulation, we confirmed the effectiveness of this method. During the animal experiment with the left ventricular assist system, the physiological parameters could be identified online: mean Ra = 0.04 mm Hg s/ml, mean Ca = 0.65 mm Hg/ml, mean L = 0.004 mm Hg s(2)/ml, and mean Rp = 0.3 mm Hg s/ml. This new method efficiently identified the physiological parameters, which are important not only to support the medical care but also to develop the control method adapted to the physiological behavior.

Animals↗