The recent development of a rat model of amyotrophic lateral sclerosis (ALS) in which the rats harbor a mutated human SOD1 (G93A) gene has greatly expanded the range of potential experiments, because the rats' large size permits biochemical analyses and therapeutic trials, such as the intrathecal injection of new drugs and stem cell transplantation. The precise nature of this disease model remains unclear. We described three disease phenotypes: the forelimb-, hindlimb-, and general-types. We also established a simple, non-invasive, and objective evaluation system using the body weight, inclined plane test, cage activity, automated motion analysis system (SCANET), and righting reflex. Moreover, we created a novel scale, the Motor score, which can be used with any phenotype and does not require special apparatuses. With these methods, we uniformly and quantitatively assessed the onset, progression, and disease duration, and clearly presented the variable clinical course of this model; disease progression after the onset was more aggressive in the forelimb-type than in the hindlimb-type. More importantly, the disease stages defined by our evaluation system correlated well with the loss of spinal motor neurons. In particular, the onset of muscle weakness coincided with the loss of approximately 50% of spinal motor neurons. This study should provide a valuable tool for future experiments to test potential ALS therapies.

Pulmonary characteristics differ in patients, and the suitable setting of ventilation condition is needed for every patient in the artificial respiration. The pulmonary elastance is one of the important features of lung, and it is a basis for deciding the air way pressure limit value. To get the pulmonary elastance of the of the patient from measurement data of the artificial respiration, the fuzzy logic technique has been proposed for estimating the pulmonary elastance and the static P -V curve in our previous works. In this paper, a new technique of fuzzy modeling based on data combination of two respiration phases is proposed to improve the estimation precision, and some estimation examples using real patient data are given to illustrate the superiority of the proposed method over the previous algorithm in the precision.

Artificial respirators are widely used for patients with little or no autonomous breathing ability. Doctors are required to pay scrupulous attention for the use of the artificial respirators. And doctors must set the artificial respirator in consideration of each patient's pulmonary characteristic. However, we do not understand the pulmonary characteristic of the patient by the measurement of data. Therefore, the setting of the artificial respirator is decided by the experience and the intuition of the doctor now. Purpose of this study are to develop a method to estimate the static P − V curve and the pulmonary elastance of the patient and to set a ventilation condition of the artificial respirator. The static P − V curve and the pulmonary elastance expresses the important feature of the lung, and the static P − V curve is a basis for deciding the air-way pressure limit value.In our previous work, we have presented an estimation technique of the pulmonary elastance by fuzzy logic. Parameters of the pulmonary elastance (f E (V )) are different in each fuzzy rules. Then, it is said that other parameters do not change in a short time(one cycle breath). Nevertheless, in the previous study, these parameters were estimated to be different values in parameters estimation of each fuzzy rule. It is considered that the estimated precision of the static P − V curve is influenced by these values. We solve this problem using new estimation procedure. In addition, a ventilation condition of the artificial respirator is set using estimated static P − V curve.

The ventilation condition of arti¿cial respiration should be set carefully, and the setting is expected to match individual pulmonary characteristic of each patient. To this purpose, the pulmonary elastance estimation method based on fuzzy logic has been proposed by the authors. In this paper, a recursive estimation method of estimating respiratory system is addressed to improve the accuracy of estimated model. Some examples by using practical clinic data are given to show the effectiveness of addressed method.

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