Sealing Efficacy of a Single-Cone Root Filling After Post Space Preparation

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Impact of pre-angiogenic factors on the treatment effect of bevacizumab in patients with metastatic colorectal cancer

Endothelin-1 (ET-1) and asymmetric dimethylarginine (ADMA) play a major role in tumor growth and metastasis. Our aim was to determine whether there is any association between these endothelial parameters and tumor markers with the clinical outcome of bevacizumab-treated metastatic colorectal cancer (mCRC) patients in terms of response and survival. Pretreatment serum levels of ET-1, ADMA, carcinoembryonic antigen (CEA), and carbohydrate antigen (CA) 19-9 were measured in 36 chemotherapy-naive mCRC patients treated with first-line bevacizumab-based therapy. Additionally, after first cycle of treatment, serum levels of these parameters were reanalyzed. Lower baseline serum ET-1 and ADMA levels were observed in patients responding to bevacizumab-based treatment (respectively, p = 0.037, p = 0.034). Median progression-free survival (PFS) (11 vs. 6 months, p = 0.012) and overall survival (OS) (28 vs 9 months; p = 0.007) were significantly shorter in patients with high pretreatment ET-1 levels. There was a significant decrease in ET-1 and CEA levels after first treatment (p = 0.020, p = 0.012), while ADMA and CA 19-9 levels were not significantly changed. Patients with decreased posttreatment ET-1 levels were shown to have inferior PFS (6 vs 11 months, p = 0.022), but no statistically significant difference was shown with respect to OS (p = 0.141). The effect of bevacizumab on endothelin axis including the biologic basis of decreasing ET-1 levels due to bevacizumab treatment and its association with inferior outcome has to be clarified in prospective trials

ARTIFICIAL INTELLIGENCE APPROACH TO CLASSIFY UNIPOLAR and BIPOLAR DEPRESSIVE DISORDERS

Machine learning (ML) approaches for medical decision making processes are valuable when both high classification accuracy and less feature requirements are satisfied. Artificial neural networks (ANNs) successfully meet the first goal with its adaptive engine while nature inspired algorithms are focusing on the feature selection (FS) process in order to eliminate less informative and less discriminant features. Besides engineering applications of ANN and FS algorithms, medical informatics is another emerging field using similar methods for medical data processing. Classification of psychiatric disorders is one of major focus of medical informatics using artificial intelligence approaches. Being one of the most debilitating psychiatric diseases, bipolar disorder (BD) is frequently misdiagnosed as unipolar disorder (UD), leading to suboptimal treatment and poor outcomes. Thus, discriminating UD and BD at earlier stages of illness could therefore help to facilitate efficient and specific treatment. The use of quantitative electroencephalography (EEG) cordance as a biomarker has greatly enhanced the clinical utility of EEG in psychiatric and neurological subjects. In this context, the paper puts forward a study using two-step hybridized methodology, particle swarm optimization (PSO) algorithm for feature selection process and ANN for training process. The noteworthy performance of ANN-PSO approach stated that it is possible to discriminate 31 bipolar and 58 unipolar subjects using selected features from alpha and theta frequency bands with 89.89% overall classification accurac