The molecular mechanisms underlying the selective toxicity of trimethyltin (TMT) remain unclear. Stannin (Snn), a protein preferentially expressed in TMT-sensitive cells, provides a direct link to the molecular basis for TMT toxicity. Recent evidence demonstrated that Snn peptides bind and de-alkylate TMT to dimethyltin (DMT); Snn may mediate both TMT and DMT toxicity. In this study, we demonstrate that Snn co-immunoprecipitates with a scaffolding protein 14-3-3, specifically with 14-3-3zeta isotype. Consistent with this, a detailed amino acid sequence analysis shows that Snn contains a putative 14-3-3 protein-binding site located within its hydrophilic loop. In addition, we present the evidence that Snn overexpression results in reduced extracellular regulated kinase activation and increased p38 activation. In contrast, the activity of c-Jun N-terminal kinase did not change following Snn overexpression. This is the first evidence that demonstrates a direct interaction between Snn and MAPK signaling molecules. Together, these findings indicate a role of Snn in modulation of MAPK signaling pathways through its interactions with 14-3-3zeta.
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http://dx.doi.org/10.1016/j.molbrainres.2005.04.018 | DOI Listing |
Front Neurosci
December 2024
School of Integrated Circuits, Peking University, Beijing, China.
Spiking Neural Networks (SNNs) are typically regards as the third generation of neural networks due to their inherent event-driven computing capabilities and remarkable energy efficiency. However, training an SNN that possesses fast inference speed and comparable accuracy to modern artificial neural networks (ANNs) remains a considerable challenge. In this article, a sophisticated SNN modeling algorithm incorporating a novel dynamic threshold adaptation mechanism is proposed.
View Article and Find Full Text PDFJTO Clin Res Rep
December 2024
Department of Pathology and Medical Biology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
Introduction: Programmed death-ligand 1 (PD-L1) is the main predictive biomarker used to identify patients with NSCLC who are eligible for treatment with immune checkpoint inhibitors. Despite its utility, the predictive capacity of PD-L1 is limited, necessitating the exploration of supplementary predictive biomarkers. In this report, we describe the prognostic value of / mutation status for overall survival (OS) in patients with NSCLC treated with first-line immunotherapy or combined chemoimmunotherapy.
View Article and Find Full Text PDFBMC Neurol
December 2024
Department of Environmental Health, Harvard T H Chan School of Public Health, Boston, MA, 02115, USA.
Parkinson's disease (PD) is a neurodegenerative disease affecting millions of people around the world. Conventional PD detection algorithms are generally based on first and second-generation artificial neural network (ANN) models which consume high energy and have complex architecture. Considering these limitations, a time-varying synaptic efficacy function based leaky-integrate and fire neuron model, called SEFRON is used for the detection of PD.
View Article and Find Full Text PDFNew Phytol
December 2024
State Key Laboratory for Crop Stress Resistance and High-Efficiency Production/Shaanxi Key Laboratory of Apple, College of Horticulture, Northwest A&F University, Yangling, 712100, China.
Sci Rep
December 2024
Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", Università di Bologna, 40126, Bologna, Italy.
Spiking Neural Networks (SNNs) stand as the third generation of Artificial Neural Networks (ANNs), mirroring the functionality of the mammalian brain more closely than their predecessors. Their computational units, spiking neurons, characterized by Ordinary Differential Equations (ODEs), allow for dynamic system representation, with spikes serving as the medium for asynchronous communication among neurons. Due to their inherent ability to capture input dynamics, SNNs hold great promise for deep networks in Reinforcement Learning (RL) tasks.
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