Application of probabilistic neural network in the clinical diagnosis of cancers based on clinical chemistry data

As a recently developed and powerful classification tool, probabilistic neural network was used to distinguish cancer patients from healthy persons according to the levels of nucleosides in human urine. Two datasets (containing 32 and 50 patterns, respectively) were investigated and the total consistency rate obtained was 100% for dataset 1 and 94% for dataset 2. To evaluate the performance of probabilistic neural network, linear discriminant analysis and learning vector quantization network, were also applied to the classification problem. The results showed that the predictive ability of the probabilistic neural network is stronger than the others in this study. Moreover, the recognition rate for dataset 2 can achieve to 100% if combining, these three methods together, which indicated the promising potential of clinical diagnosis by combining different methods. (C) 2002 Elsevier Science B.V. All rights reserved.

The application of microwave radiation to analytical and environment chemistry

This review presents the latest advances in the application of microwave energy to analytical chemistry. The fundamental principles of microwave field interaction with the matter are presented and their significance for the chemist is discussed, followed by the basic principles of microwave equipment construction and operation. Examples of the techniques that utilized microwave energy for digestion, extraction, chemical reaction, preconcentration, and desorption of the analytical sample are presented. A separate section describes the examples of usage of microwave technology in catalysis, environmental, and nuclear chemistry and engineering.