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Winkler, L.

Publications and source records attributed to Winkler, L..

2 recordsLinked to original sources

Claudin-12 deficiency causes nerve barrier breakdown, mechanical hypersensitivity and painful neuropathy

Peripheral nerves and their axons are shielded by the blood-nerve and the myelin barrier, but understanding of how these barriers impact nociception is limited. Here, we identified a regulatory axis of the tight junction protein claudin-12, sex-dependently controlling perineurial and myelin barrier integrity. In nerve biopsies, claudin-12 in Schwann cells was lost in male and postmenopausal female patients with painful but not painless polyneuropathy. Global Cldn12 gene-knockout selectively increased perineurial/myelin barrier leakage, damaged tight junction protein expression and morphology, increased proinflammatory cytokines and induced mechanical hypersensitivity in naive and neuropathic male mice, respectively. Other barriers and neurological function remained intact. In vitro transfection studies documented claudin-12 plasma membrane localisation without interaction with other tight junction proteins or intrinsic sealing properties. Rather, claudin-12 had a regulatory tight junction protein function on the myelin barrier via the morphogen SHH in vivo in Cldn12-KO and after local siRNA knockdown. Fertile female mice were completely protected. Collectively, these studies reveal the critical role of claudin-12 maintaining the myelin barrier and highlight restoration of the claudin-12/SHH pathway as a potential target for painful neuropathy.

neuroscience

Revisiting Feature Selection with Data Complexity for Biomedicine

The identification of biomarkers or predictive features that are indicative of a specific biological or disease state is a major research topic in biomedical applications. Several feature selection(FS) methods ranging from simple univariate methods to recent deep-learning methods have been proposed to select a minimal set of the most predictive features. However, there still lacks the answer to the question of "which method to use when". In this paper, we study the performance of feature selection methods with respect to the underlying datasets statistics and their data complexity measures. We perform a comparative study of 11 feature selection methods over 27 publicly available datasets evaluated over a range of number of selected features using classification as the downstream task. We take the first step towards understanding the FS methods performance from the viewpoint of data complexity. Specifically, we (empirically) show that as regard to classification, the performance of all studied feature selection methods is highly correlated with the error rate of a nearest neighbor based classifier. We also argue about the non-suitability of studied complexity measures to determine the optimal number of relevant features. While looking closely at several other aspects, we also provide recommendations for choosing a particular FS method for a given dataset.

bioinformatics