中学生焦虑与其影响因素的关系模型

Anxiety, which is one of common factors influencing mental health of secondary school students, whose occurrence, development, diagnosis, treatment and intervention have been studied in neurophysiology, behavior and cognition process. in the present study, the relationship between anxieties including trait anxiety and test anxiety and their influencing factors in secondary school students had been explored by exploratory analysis, confirmatory analysis and structure equation modeling. The following conclusions were drawn: (1) There were grade-characteristics in development of trait anxiety in secondary school students. Trait anxiety degree increased with grade. On the other hand, test anxiety showed no grade-characteristics. However, gender differences showed test anxiety in female was higher than in male. In a test, test anxiety increased with test coming and reached the maximum height before test and dropped to the minimum that night after test. (2) Influencing factor inventory on trait anxiety whose reliability and validity were good, has been developed, which was composed of 44 items including 8 factors were study pressure, bad influence of society, self-abased, dysadaptation, introversion, bad body, interrelationship, bad parents raising style etc, of which study pressure was the first reason for trait anxiety. (3) Influencing factor inventory on test anxiety, whose reliability and validity were good, has been developed, which was composed of 30 items including 5 factors. 5 factors were worry about bad school achievement, bad objective environment, intervention of emergency, self-abased, worry about consequence etc, of which study pressure played the most important role on test anxiety. (4) Personality and school pressure affected trait anxiety, while personality and school pressure were affected by environment. Furthermore, test anxiety was affected by worry, personality and, while worry and personality was influenced by spot circumstances. At last, both trait anxiety and test anxiety related with bad personality and bad school achievement.

Artificial neural network classification based on high-performance liquid chromatography of urinary and serum nucleosides for the clinical diagnosis of cancer

Nucleosides in human urine and serum have frequently been studied as a possible biomedical marker for cancer, acquired immune deficiency syndrome (AIDS) and the whole-body turnover of RNAs. Fifteen normal and modified nucleosides were determined in 69 urine and 42 serum samples using high-performance liquid chromatography (HPLC). Artificial neural networks have been used as a powerful pattern recognition tool to distinguish cancer patients from healthy persons. The recognition rate for the training set reached 100%. In the validating set, 95.8 and 92.9% of people were correctly classified into cancer patients and healthy persons when urine and serum were used as the sample for measuring the nucleosides. The results show that the artificial neural network technique is better than principal component analysis for the classification of healthy persons and cancer patients based on nucleoside data. (C) 2002 Elsevier Science B.V. All rights reserved.