Assessment of the Role of Renal Organic Anion Transporters in Drug-Induced Nephrotoxicity

In the present review we have attempted to assess the involvement of the organic anion transporters OAT1, OAT2, OAT3, and OAT4, belonging to the SLC22 family of polyspecific carriers, in drug-induced renal damage in humans. We have focused on drugs with widely recognized nephrotoxic potential, which have previously been reported to interact with OAT family members, and whose underlying pathogenic mechanism suggests the participation of tubular transport. Thus, only compounds generally believed to cause kidney injury either by means of direct tubular toxicity or crystal nephropathy have been considered. For each drug, or class of agents, the evidence for actual transport mediated by individual OATs under in vivo conditions is discussed. We have then examined their role in the context of other carriers present in the renal proximal tubule sharing certain substrates with OATs, as these are critical determinants of the overall contribution of OAT-dependent transport to intracellular accumulation and transepithelial drug secretion, and thus the impact it may have in drug-induced nephrotoxicity

Measuring the visual salience of alignments by their non-accidentalness

International audienceQuantitative approaches are part of the understanding of contour integration and the Gestalt law of good continuation. The present study introduces a new quantitative approach based on the a contrario theory, which formalizes the non-accidentalness principle for good continuation. This model yields an ideal observer algorithm, able to detect non-accidental alignments in Gabor patterns. More precisely, this parameterless algorithm associates with each candidate percept a measure, the Number of False Alarms (NFA), quantifying its degree of masking. To evaluate the approach, we compared this ideal observer with the human attentive performance on three experiments of straight contours detection in arrays of Gabor patches. The experiments showed a strong correlation between the detectability of the target stimuli and their degree of non-accidentalness, as measured by our model. What is more, the algorithm's detection curves were very similar to the ones of human subjects. This fact seems to validate our proposed measurement method as a convenient way to predict the visibility of alignments. This framework could be generalized to other Gestalts