40 research outputs found
Drosophila Nociceptors Mediate Larval Aversion to Dry Surface Environments Utilizing Both the Painless TRP Channel and the DEG/ENaC Subunit, PPK1
A subset of sensory neurons embedded within the Drosophila larval body wall have been characterized as high-threshold polymodal nociceptors capable of responding to noxious heat and noxious mechanical stimulation. They are also sensitized by UV-induced tissue damage leading to both thermal hyperalgesia and allodynia very similar to that observed in vertebrate nociceptors. We show that the class IV multiple-dendritic(mdIV) nociceptors are also required for a normal larval aversion to locomotion on to a dry surface environment. Drosophila melanogaster larvae are acutely susceptible to desiccation displaying a strong aversion to locomotion on dry surfaces severely limiting the distance of movement away from a moist food source. Transgenic inactivation of mdIV nociceptor neurons resulted in larvae moving inappropriately into regions of low humidity at the top of the vial reflected as an increased overall pupation height and larval desiccation. This larval lethal desiccation phenotype was not observed in wild-type controls and was completely suppressed by growth in conditions of high humidity. Transgenic hyperactivation of mdIV nociceptors caused a reciprocal hypersensitivity to dry surfaces resulting in drastically decreased pupation height but did not induce the writhing nocifensive response previously associated with mdIV nociceptor activation by noxious heat or harsh mechanical stimuli. Larvae carrying mutations in either the Drosophila TRP channel, Painless, or the degenerin/epithelial sodium channel subunit Pickpocket1(PPK1), both expressed in mdIV nociceptors, showed the same inappropriate increased pupation height and lethal desiccation observed with mdIV nociceptor inactivation. Larval aversion to dry surfaces appears to utilize the same or overlapping sensory transduction pathways activated by noxious heat and harsh mechanical stimulation but with strikingly different sensitivities and disparate physiological responses
Contrasting clinical evidence for market authorisation of cardio-vascular devices in Europe and the USA: a systematic analysis of 10 devices based on Austrian pre-reimbursement assessments
Drug-microbiota interactions and treatment response: Relevance to rheumatoid arthritis
Knowledge about associations between changes in the structure and/or function of intestinal microbes (the microbiota) and the pathogenesis of various diseases is expanding. However, interactions between the intestinal microbiota and different pharmaceuticals and the impact of these on responses to treatment are less well studied. Several mechanisms are known by which drug-microbiota interactions can influence drug bioavailability, efficacy, and/or toxicity. This includes direct activation or inactivation of drugs by microbial enzymes which can enhance or reduce drug effectiveness. The extensive metabolic capabilities of the intestinal microbiota make it a hotspot for drug modification. However, drugs can also influence the microbiota profoundly and change the outcome of interactions with the host. Additionally, individual microbiota signatures are unique, leading to substantial variation in host responses to particular drugs. In this review, we describe several known and emerging examples of how drug-microbiota interactions influence the responses of patients to treatment for various diseases, including inflammatory bowel disease, type 2 diabetes and cancer. Focussing on rheumatoid arthritis (RA), a chronic inflammatory disease of the joints which has been linked with microbial dysbiosis, we propose mechanisms by which the intestinal microbiota may affect responses to treatment with methotrexate which are highly variable. Furthering our knowledge of this subject will eventually lead to the adoption of new treatment strategies incorporating microbiota signatures to predict or improve treatment outcomes
Positive evaluation and learning: Looking for ‘success’ in Netherlands road safety policy
Separating a mixture of two normals with proportional covariances
Clustering analysis, Linear classification, Multivariate outlier detection, Normal mixture, Proportional covariance matrices,