Interaction between 3’,5’-cyclic monophosphate, volume-regulated anion channels and the Na+-HCO3- cotransporter NBCe1 in the regulation of nutrient- and hypotonicity-induced insulin release from rat pancreatic islets and tumoral insulin-producing BRIN-BD11 cells

info:eu-repo/semantics/published

Effect of cytochalasin B on 3-O-[(14)C]-methyl-D-glucose or D-[U-(14)C]glucose handling by BRIN-BD11 cells.

The present study aimed to investigate the effects of cytochalasin B (20 μM) on the uptake of 3-O-[(14)C]-methyl-D-glucose or D-[U-(14)C]glucose (8.3 mM each) by BRIN-BD11 cells. Taking into account the distribution space of tritiated water ((3)HOH), which was unexpectedly increased shortly after exposure of the cells to cytochalasin B and then progressively returned to its control values, and that of L-[1-(14)C]glucose, used as an extracellular marker, it was demonstrated that cytochalasin B caused a modest, but significant inhibition of the uptake of D-glucose and its non-metabolized analog by the BRIN-BD11 cells. These findings resemble those observed in acinar or ductal cells of the rat submaxillary gland and displayed a relative magnitude comparable to that found for the inhibition of D-glucose metabolism by cytochalasin B in purified pancreatic islet B cells. These findings reinforce the view that the primary site of action of cytochalasin B is located at the level of the plasma membrane.

Assessing segmentation processes by click detection: online measure of statistical learning, or simple interference?

Statistical learning can be used to extract the words from continuous speech. Gómez, Bion, and Mehler (Language and Cognitive Processes, 26, 212–223, 2011) proposed an online measure of statistical learning: They superimposed auditory clicks on a continuous artificial speech stream made up of a random succession of trisyllabic nonwords. Participants were instructed to detect these clicks, which could be located either within or between words. The results showed that, over the length of exposure, reaction times (RTs) increased more for within-word than for between-word clicks. This result has been accounted for by means of statistical learning of the between-word boundaries. However, even though statistical learning occurs without an intention to learn, it nevertheless requires attentional resources. Therefore, this process could be affected by a concurrent task such as click detection. In the present study, we evaluated the extent to which the click detection task indeed reflects successful statistical learning. Our results suggest that the emergence of RT differences between within- and between-word click detection is neither systematic nor related to the successful segmentation of the artificial language. Therefore, instead of being an online measure of learning, the click detection task seems to interfere with the extraction of statistical regularities.