The idea that seven transmembrane receptors (7TMRs; also designated G-protein coupled receptors, GPCRs) might form dimers or higher order oligomeric complexes was formulated more than 20 years ago and has been intensively studied since then. In the last decade, bioluminescence resonance energy transfer (BRET) has been one of the most frequently used biophysical methods for studying 7TMRs oligomerization. This technique enables monitoring physical interactions between protein partners in living cells fused to donor and acceptor moieties. It relies on non-radiative transfer of energy between donor and acceptor, depending on their intermolecular distance (1-10 nm) and relative orientation. Results derived from BRET-based techniques are very persuasive; however, they need appropriate controls and critical interpretation. To overcome concerns about the specificity of BRET-derived results, a set of experiments has been proposed, including negative control with a non-interacting receptor or protein, BRET dilution, saturation, and competition assays. This article presents the theoretical background behind BRET assays, then outlines mathematical models for quantitative interpretation of BRET saturation and competition assay results, gives examples of their utilization and discusses the possibilities of quantitative analysis of data generated with other RET-based techniques.
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http://dx.doi.org/10.3389/fendo.2012.00104 | DOI Listing |
PLoS One
January 2025
Intensive Care Unit, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, PR China.
Background: Shenfu injection (SFI), derived from a traditional Chinese medicine (TCM) prescription, is an effective drug for the treatment of sepsis-induced myocardial injury (SIMI) with good efficacy, but its exact therapeutic mechanism remains unclear.
Methods: SwissTargetPrediction and GeneCards database were used to obtain relevant targets for SFI and SIMI. STRING 11.
PLoS One
January 2025
National Institute of Public Health, University of Southern Denmark, Copenhagen K, Denmark.
Latent transition analysis (LTA) is a useful statistical modelling approach for describe transitions between latent classes over time. LTA may be characterized in terms of prevalence at each time point and through transition probabilities over time. Investigating predictors of these transitions is often of key interest.
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January 2025
School of Statistics and Mathematics, Yunnan University of Finance and Economics, Kunming, Yunnan, China.
With the popularity of circular economy around the world, transactions in the second-hand sailboat market are extremely active. Determining pricing strategies and exploring their regional effects is a blank area of existing research and has important practical and statistical significance. Therefore, this article uses the random forest model and XGBoost algorithm to identify core price indicators, and uses an innovative rolling NAR dynamic neural network model to simulate and predict second-hand sailboat price data.
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January 2025
Department of Laboratory, The Second Hospital of Shanxi Medical University, Taiyuan, Shanxi, P.R. China.
Background: Systemic lupus erythematosus (SLE) is a complex and incurable autoimmune disease, so several drug remission for SLE symptoms have been developed and used at present. However, treatment varies by patient and disease activity, and existing medications for SLE were far from satisfactory. Novel drug targets to be found for SLE therapy are still needed.
View Article and Find Full Text PDFPLoS Genet
January 2025
Department of Pediatric and Adolescent Medicine, Mayo Clinic, 200 1st St. SW, Rochester, Minnesota 55905, United States of America.
Motor neuron diseases, such as amyotrophic lateral sclerosis (ALS) and progressive bulbar palsy, involve loss of muscle control resulting from death of motor neurons. Although the exact pathogenesis of these syndromes remains elusive, many are caused by genetically inherited mutations. Thus, it is valuable to identify additional genes that can impact motor neuron survival and function.
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