KIF4A: A potential biomarker for prediction and prognostic of prostate cancer.

PURPOSE: To investigate the clinical relevance and biological function of the kinesin super-family protein 4A (KIF4A) expression in prostate cancer (PCa). METHODS: We examined 1) the relationship between the expression of KIF4A and clinico-pathological characteristics of PCa patients using a tissue microarray and the Cancer Genome Atlas database, 2) the prognostic value of KIF4A expression in patients using Kaplan-Meier plots and 3) the functions of KIF4A in LNCaP and DU145 cells, such as cell proliferation, cell cycle and cell apoptosis. RESULTS: Compared with normal prostate, the mRNA and protein expressions of KIF4A were up-regulated in PCa. The up-regulation expression rates of KIF4A in PCa were significantly related to the Gleason score (P.

The study of multiple diagnosis models of human prostate cancer based on Taylor database by artificial neural networks.

BACKGROUND: Prostate cancer (PCa) is the most common malignancy seen in men and the second leading cause of cancer-related death in males. The incidence and mortality associated with PCa has been rapidly increasing in China recently. METHODS: Multiple diagnostic models of human PCa were developed based on Taylor database by combining the artificial neural networks (ANNs) to enhance the ability of PCa diagnosis. Genetic algorithm (GA) is used to select feature genes as numerical encoded parameters that reflect cancer, metastatic, or normal samples. Back propagation (BP) neural network and learning vector quantization (LVQ) neural network were used to build different Cancer/Normal, Primary/Metastatic, and Gleason Grade diagnostic models. RESULTS: The performance of these modeling approaches was evaluated by predictive accuracy (ACC) and area under the receiver operating characteristic curve (AUC). By observing the statistically significant parameters of the three training sets, our Cancer/Normal, Primary/Metastatic, and Gleason Grade models' with ACC and AUC can be drawn (97.33%, 0.9832), (99.17%, 0.9952), and (90.48%, 0.8742), respectively. CONCLUSION: These results indicated that our diagnostic models of human PCa based on Taylor database combining the feature gene expression profiling data and artificial intelligence algorithms might act as a powerful tool for diagnosing PCa. Gleason Grade diagnostic models were used as novel prognostic diagnosis models for biochemical recurrence-free survival and overall survival, which might be helpful in the prognostic diagnosis of PCa in patients.

To Approach or to Avoid? Motivation Differentially Mediates the Effect of Hardiness on Depressive Symptoms in Chinese Military Personnel.

OBJECTIVE: To investigate the mediation effect of approach/avoidance motivation between hardiness and depressive symptoms. METHODS: Cross-sectional design was utilized. Two independent samples of military servicemen (G1: military personnel in the Armed Forces; G2: Chinese army military cadets) (n1 = 98, n2 =140) were sampled and investigated. The assessment tools of hardiness scale (DRS), behavioral activation and inhibition scales (BAS/BIS), and Center for Epidemiological Survey-Depression Scale (CES-D)/Beck Depression Inventory (BDI) were used. General linear model was conducted to examine the predictive role of hardiness (DRS) and motivation (BAS/BIS) on depressive symptoms (CES-D or BDI). The mediating role of BAS/BIS between hardiness and depressive symptoms was examined. RESULTS: (1) Across army soldiers and military medical university cadets, hardiness (ß=-0.394, P<0.001) and behavioral inhibition (ß=0.297, P<0.001) significantly predicted depressive symptoms. (2) For soldiers only, behavioral inhibition mediated the significant association between hardiness and depressive symptoms (ß=-0.043, SE=0.027, 95%CI=-0.130~-0.008). (3) For cadets only, behavioral activation-Drive significantly predicted depressive symptoms (ß=-0.237, P=0.012), and hardiness operates through behavioral activation-Drive to influence depressive symptoms (ß=-0.057, SE=0.036, 95%CI=-0.151~-0.078). CONCLUSION: Individuals who are low in hardiness and behavioral activation-Drive and who are high in behavioral inhibition showed more severe depressive symptoms. The relationship between hardiness and depressive symptoms was mediated by behavioral activation-Drive in cadets and behavioral inhibition in soldiers. The proposed model offers a useful approach for the development of hardiness training programs to alter approach/avoidance motivation in the military context. Future training program of hardiness could lay more emphasis on promotion of perseverance in pursuing goals in hardy individuals, which may in turn improve active coping.

Development and validation of the critical thinking disposition inventory for Chinese medical college students (CTDI-M).

BACKGROUND: This study aimed to develop and conduct psychometric testing of the Critical Thinking Disposition Inventory to measure the critical thinking disposition of Chinese medical college students. METHODS: The study was conducted in two stages: (a) item generation, reliability analysis and exploratory factor analysis (EFA) and (b) confirmatory factor analysis (CFA) and testing of psychometric properties (Cronbach' s alpha, test-retest reliability and convergent validity). The subjects included 1035 Chinese medical college students. The test-retest reliability of the instrument was determined at a two-week interval (n = 61). A general linear regression model was developed to examine the predictive effects of gender, age and major on CT disposition. The data were analysed with SPSS 22.0 and Amos 21.0 during item development and the reliability and validity analyses. Vista was utilized for parallel analysis during the principal axis analysis. RESULTS: Eighteen final items were sorted into 3 factors, which were identified as "Open-mindedness", "Systematicity/Analyticity" and "Truth-seeking", with cumulative variance of 41.37, 46.00 and 49.59%, respectively. The Cronbach's alpha was 0.924, and the factors' alphas ranged from 0.824 to 0.862. The correlational analysis indicated significant correlations between the subscales of the CTDI-CM and the total scores of the CTDI-CV, indicating modest evidence for the convergent validity of the CTDI-CM. Gender, age and education significantly predicted the CT disposition of Chinese medical students. Open-mindedness and Systematicity/Analyticity were higher for medical students than for nursing students. CONCLUSIONS: This study presents a reliable and valid instrument for clinical thinking disposition. Future studies should explore other predictive factors of CT dispositions (e.g., cognitive/motivational) and criterion validity.