Coenzyme Q10 in atherosclerosis.

Atherosclerotic disease is a chronic disease that predominantly affects the elderly and is the most common cause of cardiovascular death worldwide. Atherosclerosis is closely related to processes such as abnormal lipid transport and metabolism, impaired endothelial function, inflammation, and oxidative stress. Coenzyme Q10 (CoQ10) is a key component of complex Ⅰ in the electron transport chain and an important endogenous antioxidant that may play a role in decelerating the progression of atherosclerosis. Here, the different forms of CoQ10 presence in the electron transport chain are reviewed, as well as its physiological role in regulating processes such as oxidative stress, inflammatory response, lipid metabolism and cellular autophagy. It was also found that CoQ10 plays beneficial effects in atherosclerosis by mitigating lipid transportation, endothelial inflammation, metabolic abnormalities, and thrombotic processes from the perspectives of molecular mechanisms, animal experiments, and clinical evidence. Besides, the combined use of CoQ10 with other drugs has better synergistic therapeutic effects. It seems reasonable to suggest that CoQ10 could be used in the treatment of atherosclerotic cardiovascular diseases while more basic and clinical studies are needed.

An Empirical Study of Training Data Selection Methods for Ranking-Oriented Cross-Project Defect Prediction.

Ranking-oriented cross-project defect prediction (ROCPDP), which ranks software modules of a new target industrial project based on the predicted defect number or density, has been suggested in the literature. A major concern of ROCPDP is the distribution difference between the source project (aka. within-project) data and target project (aka. cross-project) data, which evidently degrades prediction performance. To investigate the impacts of training data selection methods on the performances of ROCPDP models, we examined the practical effects of nine training data selection methods, including a global filter, which does not filter out any cross-project data. Additionally, the prediction performances of ROCPDP models trained on the filtered cross-project data using the training data selection methods were compared with those of ranking-oriented within-project defect prediction (ROWPDP) models trained on sufficient and limited within-project data. Eleven available defect datasets from the industrial projects were considered and evaluated using two ranking performance measures, i.e., FPA and Norm(Popt). The results showed no statistically significant differences among these nine training data selection methods in terms of FPA and Norm(Popt). The performances of ROCPDP models trained on filtered cross-project data were not comparable with those of ROWPDP models trained on sufficient historical within-project data. However, ROCPDP models trained on filtered cross-project data achieved better performance values than ROWPDP models trained on limited historical within-project data. Therefore, we recommended that software quality teams exploit other project datasets to perform ROCPDP when there is no or limited within-project data.

[Expression and prognostic value of transcriptional factor sp1 in breast cancer].

BACKGROUND & OBJECTIVE: Transcriptional factor Sp1 is involved in many biological processes, such as cell proliferation, apoptosis, differentiation and transformation, and plays an important role in invasion and metastasis of tumors. However, the expression patterns of Sp1 in various tumors are different. This study was to investigate the correlations of Sp1 expression to metastasis, invasion, and prognosis of breast cancer. METHODS: The expression of Sp1 in 60 specimens of breast cancer and 12 specimens of adjacent breast tissue was detected by EnVision immunohistochemistry. Its correlation to clinicopathologic features of breast cancer was analyzed by Cox regression analysis. RESULTS: The positive rate of Sp1 was 71.67% in breast cancer tissues, and 33.33% in adjacent tissues. Sp1 staining in cancer tissue was positively correlated to TNM stage (r=0.349, P<0.05), tumor invasion (r=0.407, P<0.01), and lymph node metastasis (r=0.314, P<0.05). Univariate analysis indicated that the overall survival rate was significantly lower in Sp1-positive patients than in Sp1-negative patients (41.86% vs. 82.35%, P<0.05). Cox multivariate analysis showed that Sp1 expression, TNM stage, invasion and lymph node metastasis were independent prognostic factors of breast cancer. CONCLUSIONS: Sp1 maybe participate in the invasion and metastasis of breast cancer, and is one of the valuable markers indicating poor prognosis of breast cancer. Sp1 detection, with consideration of tumor invasion and clinical stage, may increase the accuracy of predicting prognosis of patients with breast cancer.