1
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      STING recognition of viral dsDNA by nociceptors mediates pain in mice

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Related collections

          Most cited references96

          • Record: found
          • Abstract: found
          • Article: not found

          Fiji: an open-source platform for biological-image analysis.

          Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Pathogen recognition and innate immunity.

            Microorganisms that invade a vertebrate host are initially recognized by the innate immune system through germline-encoded pattern-recognition receptors (PRRs). Several classes of PRRs, including Toll-like receptors and cytoplasmic receptors, recognize distinct microbial components and directly activate immune cells. Exposure of immune cells to the ligands of these receptors activates intracellular signaling cascades that rapidly induce the expression of a variety of overlapping and unique genes involved in the inflammatory and immune responses. New insights into innate immunity are changing the way we think about pathogenesis and the treatment of infectious diseases, allergy, and autoimmunity.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found

              Attributes and predictors of long COVID

              Reports of long-lasting coronavirus disease 2019 (COVID-19) symptoms, the so-called 'long COVID', are rising but little is known about prevalence, risk factors or whether it is possible to predict a protracted course early in the disease. We analyzed data from 4,182 incident cases of COVID-19 in which individuals self-reported their symptoms prospectively in the COVID Symptom Study app1. A total of 558 (13.3%) participants reported symptoms lasting ≥28 days, 189 (4.5%) for ≥8 weeks and 95 (2.3%) for ≥12 weeks. Long COVID was characterized by symptoms of fatigue, headache, dyspnea and anosmia and was more likely with increasing age and body mass index and female sex. Experiencing more than five symptoms during the first week of illness was associated with long COVID (odds ratio = 3.53 (2.76-4.50)). A simple model to distinguish between short COVID and long COVID at 7 days (total sample size, n = 2,149) showed an area under the curve of the receiver operating characteristic curve of 76%, with replication in an independent sample of 2,472 individuals who were positive for severe acute respiratory syndrome coronavirus 2. This model could be used to identify individuals at risk of long COVID for trials of prevention or treatment and to plan education and rehabilitation services.
                Bookmark

                Author and article information

                Journal
                Brain, Behavior, and Immunity
                Brain, Behavior, and Immunity
                Elsevier BV
                08891591
                October 2024
                October 2024
                : 121
                : 29-42
                Article
                10.1016/j.bbi.2024.07.013
                f9bf0bd3-fee4-4336-8e22-fd32933987d9
                © 2024

                https://www.elsevier.com/tdm/userlicense/1.0/

                https://www.elsevier.com/legal/tdmrep-license

                http://creativecommons.org/licenses/by/4.0/

                History

                Comments

                Comment on this article