Abstract

The mechanisms of axonal trafficking and membrane targeting are well established for sodium channels, which are the principle targets for perineurally applied local anaesthetics. However, they have not been thoroughly investigated for G protein coupled receptors such as mu-opioid receptors (MOR). Focusing on these axonal mechanisms, we found that axonal MOR functionality is quite distinct in two different pain states, i.e. hindpaw inflammation and nerve injury. We observed axonal membrane MOR binding and functional G protein coupling exclusively at sites of CCI nerve injury. Moreover at these axonal membrane sites, MOR exhibited extensive co-localization with the membrane proteins SNAP and Na/K-ATPase as well as NGF-dependent enhanced lipid rafts and L1CAM anchoring proteins. Silencing endogenous L1CAM with intrathecal L1CAM specific siRNA, disrupting lipid rafts with the perineurial cholesterol-sequestering agent MβCD, as well as suppressing NGF receptor activation with the perineurial NGF receptor inhibitor K252a abrogated MOR axonal membrane integration, functional coupling, and agonist-elicited antinociception at sites of nerve injury. These findings suggest that local conceptual changes resulting from nerve injury are required for the establishment of functional axonal membrane MOR. Axonal integration and subsequent accessibility of functionally coupled MOR are of great relevance particularly for patients suffering from severe pain due to nerve injury or tumour infiltration.

In the medical field, the identification of skin cancer (Malignant Melanoma) in dermoscopy images is still a challenging task for radiologists and researchers. Due to its rapid increase, the need for decision support systems to assist the radiologists to detect it in early stages becomes essential and necessary. Computer Aided Diagnosis (CAD) systems have significant potential to increase the accuracy of its early detection. Typically, CAD systems use various types of features to characterize skin lesions. The features are often concatenated into one vector (early fusion) to represent the image. In this paper, we present a novel method for melanoma detection from images. First the lesions are segmented by combining Particle Swarm Optimization and Markov Random Field methods. Then the K-means is applied on the segmented lesions to separate them into homogeneous clusters, from which important features are extracted. Finally, an Artificial Neural Network with Radial Basis Function is applied for the detection of melanoma. The method was tested on 200 dermoscopy images. The experimental results show that the proposed method achieved higher accuracy in terms of melanoma detection, compared to alternative methods.

Veterinary antibiotics, bacteria carrying antibiotic resistance determinants located on mobile genetic elements and nutrients are spread on agricultural soil using manure as fertilizer. However, systematic quantitative studies linking antibiotic concentrations and antimicrobial resistance genes (ARGs) in manure and the environment are scarce but needed to assess environmental risks. In this microcosm study, a sandy and a loamy soil were mixed with manure spiked with streptomycin or doxycycline at five concentrations. Total-community DNA was extracted on days 28 and 92, and the abundances of ARGs (aadA, strA, tet(A), tet(M), tet(W), tet(Q), sul1, qacE/qacEΔ1) and class 1 and 2 integron integrase genes (intI1 and intI2) were determined by qPCR relative to 16S rRNA genes. Effects on the bacterial community composition were evaluated by denaturing gradient gel electrophoresis of 16S rRNA gene amplicons. Manure application to the soils strongly increased the relative abundance of most tested genes. Antibiotics caused further enrichments which decreased over time and were mostly seen at high concentrations. Strikingly, the effects on relative gene abundances and soil bacterial community composition were more pronounced in sandy soil. The concept of defining antibiotic threshold concentrations for environmental risk assessments remains challenging due to the various influencing factors.