Abstract: An acute kidney injury predicting system and a method thereof are proposed. A processor reads the data to be tested, the detection data, the machine learning algorithm and the risk probability comparison table from a main memory. The processor trains the detection data according to the machine learning algorithm to generate an acute kidney injury prediction model, and inputs the data to be tested into the acute kidney injury prediction model to generate an acute kidney injury characteristic risk probability and a data sequence table. The data sequence table lists the data to be tested in sequence according to a proportion of each of the data to be tested in the acute kidney injury characteristics. The processor selects one of the medical treatment data from the risk probability comparison table according to the acute kidney injury characteristic risk probability.

Abstract: A thrust nut for bearing against an axle includes a nut body, a first race member and a row of rolling elements. The nut body has opposing first and second surfaces and defines a threaded hole therein. The first race member is arranged to face the first surface of the nut body. The nut body serves as a second race member and cooperates with the first race member to co-define an annular track therebetween. The row of rolling elements is disposed in the annular track between the first race member and the nut body.

Abstract: A thrust screw for bearing against an axle includes a screw body, a first race member and a row of rolling elements. The screw body has a head and a shank. The head has opposing first and second surfaces, and the shank integrally extends from the first surface of the head. The first race member is disposed around the shank of the screw body. In particular, the head of the screw body serves as a second race member and cooperates with the first race member to co-define an annular track therebetween. And the row of rolling elements is disposed in the track between the first race member and the head of the screw body.