Epithelial ovarian cancer is the leading cause of cancer deaths in women. To date, an effective screening tool for ovarian cancer has not been identified Several clinical and biological factors including serum cancer antigen 125 (CA- 125) have been assessed for prognostic and predictive relevance CA-125 is an epithelial marker derived from coelomic epithelium. It is elevated in 90% of advanced ovarian cancers and in 50% of early ovarian cancers while 20% of ovarian cancers have low or no expression of CA- 125 CA-125 concentrations were measured by Mini Vidas test (VIDAS CA125 II / BIOMERIEUX / France). The median CA-125 levels were significantly higher in the sera of ovarian cancer patients than in those with benign tumors an

Verbal Antonyms: A research in the relationship in meaning Between the words in Arabic language

This article investigates the decline of language loyalty in the age of audiovisual nearness. It is a socio-linguistic review of previous literature related to language disloyalty. It reviews the current theoretical efforts on the impact of audiovisual nearness created by social media and language loyalty. The descriptive design is used. The argument behind this review is that the audiovisual nearness provided by social media negatively affects language loyalty. This article concludes that the current theoretical efforts have paid much attention to the relationship between the audiovisual nearness and language loyalty. Such efforts have highlighted the fact that the social media platforms have provided unprecedented nearness that provoke in

In this paper, we investigate the automatic recognition of emotion in text. We perform experiments with a new method of classification based on the PPM character-based text compression scheme. These experiments involve both coarse-grained classification (whether a text is emotional or not) and also fine-grained classification such as recognising Ekman’s six basic emotions (Anger, Disgust, Fear, Happiness, Sadness, Surprise). Experimental results with three datasets show that the new method significantly outperforms the traditional word-based text classification methods. The results show that the PPM compression based classification method is able to distinguish between emotional and nonemotional text with high accuracy, between texts invo