Plasma vascular endothelial growth factors A and C in patients undergoing prostatic biopsy and TURP for suspected prostatic neoplasia.

BACKGROUND: Formation of new blood vessels is necessary for the development and spread of neoplasms more than 1 mm3 in volume, angiogenesis being responsible for formation of new from pre-existing blood vessels. Vascular endothelial growth factor (VEGF) is pivotal and the best studied angiogenic factor in all human cancers. Therefore we designed this study to investigate the role of VEGF-A and VEGF-C in prostate cancer in comparison with BPH controls in a north Indian population. METHODS: In this case-control study a total of 100 subjects were included on the basis of confirmed histopathological reports, out of which 50 were prostate cancer patients and the other 50 were BPH patients with PSA levels >2 ng/ml and abnormal digital rectal examination (DRE) findings during September 2009 to August 2011 from the Department of Urology, KGMU, Lucknow, India. Plasma levels of VEGF were determined using quantitative immunoassay (ELISA- enzyme linked immunosorbent assay). Statistical analysis was carried out using SPSS 15.0 version. RESULTS: The mean age of prostate cancer (67.6±5.72) patients was significantly higher (p=0.005) than BPH (63.6±7.92) patients. Expression of VEGF-A was not significantly higher in disease stage C1 than D1 or D2 and A or B (p=0.13) while the level of VEGF-A was significantly higher (p=0.04) in prostate cancer as compared to BPH subjects (PCa=13.0 pg/ml, BPH=6.8 pg/ml). Levels of VEGF-C were similar in both groups (PCa=832.6 pg/ml, BPH=823.7 pg/ml). In ROC curve, the area under curve (AUC) was 0.70 (95%CI: 0.60-0.80) and the cut-off value for which a higher proportion of patients was correctly classified (20%) was 26.0 pg/mL. CONCLUSION: Although VEGF-A is increased in cancer prostate patients a statistically significant correlation could not be established in this study. VEGF-C was not found to be a useful biomarker.

[A new assessment method for the quality of ecological monitoring data: taking CERN's tree growth dataset as a case].

This paper presented a new and simple assessment method for the quality of ecological monitoring data. This method theorized the associations between the data reliability as an ordinal variable with different number of classes and the data sources such as natural main ecological processes, secondary ecological processes, and extraneous and exotic processes, and offered a new data quality index to estimate the quality of the whole dataset by using the reasonableness ratio of observations. The assessment results provided the reliability class of each dataset, good explanations for outlier (or error data) flagging decisions, and quality value of the whole dataset. The method was applied to assess two tree growth datasets from Chinese Ecosystem Research Network (CERN), and the results demonstrated that the new data quality index could quantitatively evaluate the quality of the tree growth datasets. The new method would facilitate the development of corresponding software.

[Application of indicator species in predicting forest management effect on forest species diversity and community composition].

By using indicator species analysis (ISA) method, this paper studied the feasibility of using indicator species to reflect the responses of species diversity and community composition of subtropical forests in Huitong of China to forest management. Ninety-four significant indicator species from 357 understory species were identified, and a new indicator species dataset (community level) was constructed to examine the association between indicator species dataset and original community dataset, and to evaluate the predictive potential of indicator species in reflecting forest management effect. There existed a strong association between the two datasets (Mantel r = 0.898). The indicator species dataset could well predict the management effect on species diversity (regression analysis, R2 > 0.74) and community composition (ANOVA, F >16.79). When the two datasets were applied to Nonmetric Multi-Dimensional Scaling (NMDS) ordination and K-mean cluster analysis, the indicator species dataset could well identify the forest types with different management treatments, as the original community dataset did. Also, the indicator species dataset nearly played the same role as the original community dataset in identifying the species diversity, community composition, and forest type. It was suggested that for saving costs in overall investigation of forest ecosystem, indicator species could be used as a surrogate of full community to predict forest management effect.