Dataset Information

Reverse Engineering Applied to Red Human Hair Pheomelanin Reveals Redox-Buffering as a Pro-Oxidant Mechanism.

ABSTRACT: Pheomelanin has been implicated in the increased susceptibility to UV-induced melanoma for people with light skin and red hair. Recent studies identified a UV-independent pathway to melanoma carcinogenesis and implicated pheomelanin's pro-oxidant properties that act through the generation of reactive oxygen species and/or the depletion of cellular antioxidants. Here, we applied an electrochemically-based reverse engineering methodology to compare the redox properties of human hair pheomelanin with model synthetic pigments and natural eumelanin. This methodology exposes the insoluble melanin samples to complex potential (voltage) inputs and measures output response characteristics to assess redox activities. The results demonstrate that both eumelanin and pheomelanin are redox-active, they can rapidly (sec-min) and repeatedly redox-cycle between oxidized and reduced states, and pheomelanin possesses a more oxidative redox potential. This study suggests that pheomelanin's redox-based pro-oxidant activity may contribute to sustaining a chronic oxidative stress condition through a redox-buffering mechanism.

SUBMITTER: Kim E

PROVIDER: S-EPMC4680885 | biostudies-other | 2015 Dec

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Reverse Engineering Applied to Red Human Hair Pheomelanin Reveals Redox-Buffering as a Pro-Oxidant Mechanism.

Kim Eunkyoung E Panzella Lucia L Micillo Raffaella R Bentley William E WE Napolitano Alessandra A Payne Gregory F GF

Scientific reports 20151216

Pheomelanin has been implicated in the increased susceptibility to UV-induced melanoma for people with light skin and red hair. Recent studies identified a UV-independent pathway to melanoma carcinogenesis and implicated pheomelanin's pro-oxidant properties that act through the generation of reactive oxygen species and/or the depletion of cellular antioxidants. Here, we applied an electrochemically-based reverse engineering methodology to compare the redox properties of human hair pheomelanin wi ...[more]

PMID: 26669666

Similar Datasets

Project description:The human ability to introspect on thoughts, perceptions or actions − metacognitive ability − has become a focal topic of both cognitive basic and clinical research. At the same time it has become increasingly clear that currently available quantitative tools are limited in their ability to make unconfounded inferences about metacognition. As a step forward, the present work introduces a comprehensive modeling framework of metacognition that allows for inferences about metacognitive noise and metacognitive biases during the readout of decision values or at the confidence reporting stage. The model assumes that confidence results from a continuous but noisy and potentially biased transformation of decision values, described by a confidence link function. A canonical set of metacognitive noise distributions is introduced which differ, amongst others, in their predictions about metacognitive sign flips of decision values. Successful recovery of model parameters is demonstrated, and the model is validated on an empirical data set. In particular, it is shown that metacognitive noise and bias parameters correlate with conventional behavioral measures. Crucially, in contrast to these conventional measures, metacognitive noise parameters inferred from the model are shown to be independent of performance. This work is accompanied by a toolbox (ReMeta) that allows researchers to estimate key parameters of metacognition in confidence datasets. eLife digest Metacognition is a person’s ability to think about their own thoughts. For example, imagine you are walking in a dark forest when you see an elongated object. You think it is a stick rather than a snake, but how sure are you? Reflecting on one’s certainty about own thoughts or perceptions – confidence – is a prime example of metacognition. While our ability to think about our own thoughts in this way provides many, perhaps uniquely human, advantages, confidence judgements are prone to biases. Often, humans tend to be overconfident: we think we are right more often than we actually are. Internal noise of neural processes can also affect confidence. Understanding these imperfections in metacognition could shed light on how humans think, but studying this phenomenon is challenging. Current methods are lacking either mechanistic insight about the sources of metacognitive biases and noise or rely on unrealistic assumptions. A better model for how metacognition works could provide a clearer picture. Guggenmos developed a mathematical model and a computer toolbox to help researchers investigate how humans or animals estimate confidence in their own thoughts and resulting decisions . The model splits metacognition apart, allowing scientists to explore biases and sources of noise at different phases in the process. It takes two kinds of data: the decisions study participants make, and how sure they are about their decision being correct. It then recreates metacognition in three phases: the primary decision, the metacognitive readout of the evidence, and the confidence report. This allows investigators to see where and when noise and bias come into play. Guggenmos tested the model using independent data from a visual discrimination task and found that it was able to predict how confident participants reported to be in their decisions. Metacognitive ability can change in people with mental illness. People with schizophrenia have often been found to be overconfident in their decisions, while people with depression can be underconfident. Using this model to separate the various facets of metacognition could help to explain why. It could also shed light on human thinking in general.

Dataset Information

Reverse Engineering Applied to Red Human Hair Pheomelanin Reveals Redox-Buffering as a Pro-Oxidant Mechanism.

Publications

Reverse Engineering Applied to Red Human Hair Pheomelanin Reveals Redox-Buffering as a Pro-Oxidant Mechanism.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets