Since the discovery of Toll, in the fruit fly Drosophila melanogaster, as the first described pattern recognition receptor (PRR) in 1996, many families of these receptors have been discovered and characterized. PRRs play critically important roles in pathogen recognition to initiate innate immune responses that ultimately link to the generation of adaptive immunity. Activation of PRRs leads to the induction of immune and inflammatory genes, including proinflammatory cytokines and chemokines. It is increasingly clear that many PRRs are linked to a range of inflammatory, infectious, immune, and chronic degenerative diseases. Several drugs to modulate PRR activity are already in clinical trials and many more are likely to appear in the near future. Here, we review the different families of mammalian PRRs, the ligands they recognize, the mechanisms of activation, their role in disease, and the potential of targeting these proteins to develop the anti-inflammatory therapeutics of the future.
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http://dx.doi.org/10.1124/pr.114.009928 | DOI Listing |
J Hazard Mater
December 2024
The Key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters, Ministry of Education, School of Environment and Energy, South China University of Technology, Guangzhou, Guangdong 510006, PR China.
Microbe-mediated remediation becomes a desire method for removal of persistent organic pollutants (POPs) due to its eco-friendly and sustainable nature. The improvement of practical feasibility requires constructing comprehensive species pool, while it is still limited by the rapid recognition of potential bacterial resources from environment. Here, based on the relative abundances of bacterial OTUs and pollutant concentrations, we established indexes to assess their tolerance to organochlorine pesticides (OCPs) and flame retardants (FRs) that are atmospheric transported and naturally accumulated in forest soil via forest filter effect.
View Article and Find Full Text PDFJ Vis
January 2025
Department of Psychology, New York University, New York, NY, USA.
Active object recognition, fundamental to tasks like reading and driving, relies on the ability to make time-sensitive decisions. People exhibit a flexible tradeoff between speed and accuracy, a crucial human skill. However, current computational models struggle to incorporate time.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom.
Background: Patients with behavioural variant frontotemporal dementia (bvFTD) and right temporal variant frontotemporal dementia (rtvFTD) commonly exhibit abnormal hedonic and other behavioural responses to sounds, however hearing dysfunction in this disorder is poorly characterised. Here we addressed this issue using the Queen Square Tests of Auditory Cognition (QSTAC) - a neuropsychological battery for the systematic assessment of central auditory functions (including pitch pattern perception, environmental sound recognition, sound localisation and emotion processing) in cognitively impaired people.
Method: The QSTAC was administered to 12 patients with bvFTD, 7 patients with rtvFTD and 24 patients with comparator dementia syndromes (primary progressive aphasia and typical Alzheimer's disease) and 15 healthy age-matched individuals.
Nat Commun
January 2025
Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, China.
Ultraviolet (UV) detection is extensively used in a variety of applications. However, the storage and processing of information after detection require multiple components, resulting in increased energy consumption and data transmission latency. In this paper, a reconfigurable UV photodetector based on CeO/SrTiO heterostructures is demonstrated with in-sensor computing capabilities achieved through interface engineering.
View Article and Find Full Text PDFSci Rep
January 2025
Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen, 518060, China.
Graph neural networks (GNNs) have emerged as a prominent approach for capturing graph topology and modeling vertex-to-vertex relationships. They have been widely used in pattern recognition tasks including node and graph label prediction. However, when dealing with graphs from non-Euclidean domains, the relationships, and interdependencies between objects become more complex.
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