Identification of miRNAs Expression Profile in Gastric Cancer Using Self-Organizing Maps (SOM)

In this paper, an unsupervised artificial neural network was implemented to identify the patters of specific signatures. The network was based on the differential expression of miRNAs (under or over expression) found in healthy or cancerous gastric tissues. Among the tissues analyzes, the neural network evaluated 514 miRNAs of gastric tissue that exhibited significant differential expression. The result suggested a specific expression signature nine miRNAs (hsa-mir-21, hsa-mir-29a, hsa-mir-29c, hsa-mir-148a, hsa-mir-141, hsa-let-7b, hsa-mir-31, hsa-mir-451, and hsa-mir-192), all with significant values (p-value < 0.01 and fold change > 5) that clustered the samples into two groups: healthy tissue and gastric cancer tissue. The results obtained “in silico” must be validated in a molecular biology laboratory; if confirmed, this method may be used in the future as a risk marker for gastric cancer development.     

Growth stimulation produced by methylene blue treatment in sweet potato

Methylene blue as an alternative treatment to gamma rays to stimulate growth in sweet potato tissue cultures, was applied in two different ways: – pre-incubation of nodal explants with methylene blue for 1 h using urea as a permeabilizer; – methylene blue directly incorporated into the culture medium. Both treatments stimulated growth, but the better performance being obtained with the second treatment, which had no toxic effect. The activity and electrophoresis pattern of peroxidase after treatment ofIpomoea batatas plantlets with methylene blue or gamma rays did not show similar results for the two treatments. Peroxidase activity was greater in leaves of gamma ray treated plants compared to the non-treated control. The results obtained with the Methylene blue treatment did not significantly change the peroxidase activity relative to the control. This revised version was published online in June 2006 with corrections to the Cover Date.