Graft-versus-host disease (GVHD) after allogeneic stem cell transplantation (alloSCT) is characterized by interleukin-6 (IL-6) dysregulation. IL-6 can mediate effects via various pathways, including classical, trans, and cluster signaling. Given the recent availability of agents that differentially inhibit these discrete signaling cascades, understanding the source and signaling and cellular targets of this cytokine is paramount to inform the design of clinical studies. Here we demonstrate that IL-6 secretion from recipient dendritic cells (DCs) initiates the systemic dysregulation of this cytokine. Inhibition of DC-driven classical signaling after targeted IL-6 receptor (IL-6R) deletion in T cells eliminated pathogenic donor Th17/Th22 cell differentiation and resulted in long-term survival. After engraftment, donor DCs assume the same role, maintaining classical IL-6 signaling-dependent GVHD responses. Surprisingly, cluster signaling was not active after transplantation, whereas inhibition of trans signaling with soluble gp130Fc promoted severe, chronic cutaneous GVHD. The latter was a result of exaggerated polyfunctional Th22-cell expansion that was reversed by IL-22 deletion or IL-6R inhibition. Importantly, inhibition of IL-6 classical signaling did not impair the graft-versus-leukemia effect. Together, these data highlight IL-6 classical signaling and downstream Th17/Th22 differentiation as important therapeutic targets after alloSCT.
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http://dx.doi.org/10.1182/blood.2019000396 | DOI Listing |
PLoS One
January 2025
Department of Computer Science, Khalifa University, Abu Dhabi, UAE.
A methodology is proposed, which addresses the caveat that line-of-sight emission spectroscopy presents in that it cannot provide spatially resolved temperature measurements in non-homogeneous temperature fields. The aim of this research is to explore the use of data-driven models in measuring temperature distributions in a spatially resolved manner using emission spectroscopy data. Two categories of data-driven methods are analyzed: (i) Feature engineering and classical machine learning algorithms, and (ii) end-to-end convolutional neural networks (CNN).
View Article and Find Full Text PDFEntropy (Basel)
January 2025
Advanced Network Research Laboratories, NEC Corporation, Kawasaki 211-8666, Kanagawa, Japan.
We demonstrated the coexistence of an S-band CV-QKD signal with fully loaded C+L-band classical signals for the first time. The secret key rate of the S-band QKD system was 986 kbps with the C+L-band WDM signals transmitted through a 20 km G.654.
View Article and Find Full Text PDFEntropy (Basel)
December 2024
The School of Electric Engineering and Intelligentization, Dongguan University of Technology, Dongguan 523808, China.
In this paper, we propose a random frequency division multiplexing (RFDM) method for multicarrier modulation in mobile time-varying channels. Inspired by compressed sensing (CS) technology which use a sensing matrix (with far fewer rows than columns) to sample and compress the original sparse signal simultaneously, while there are many reconstruction algorithms that can recover the original high-dimensional signal from a small number of measurements at the receiver. The approach choose the classic sensing matrix of CS-Gaussian random matrix to compress the signal.
View Article and Find Full Text PDFBrain Sci
January 2025
School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Backgrounds: Virtual reality (VR) has become a transformative technology with applications in gaming, education, healthcare, and psychotherapy. The subjective experiences in VR vary based on the virtual environment's characteristics, and electroencephalography (EEG) is instrumental in assessing these differences. By analyzing EEG signals, researchers can explore the neural mechanisms underlying cognitive and emotional responses to VR stimuli.
View Article and Find Full Text PDFJCEM Case Rep
February 2025
University of Utah Health, Division of Endocrinology, Salt Lake City, UT 84108, USA.
Glucocorticoid resistance syndrome (GRS) is caused by inactivating pathogenic variants in the glucocorticoid receptor gene . Reduced glucocorticoid receptor signaling leads to decreased tissue sensitivity to cortisol and resultant biochemical hypercortisolism without the classic clinical features of Cushing syndrome. Patients variably present with signs and symptoms of mineralocorticoid and androgen excess from ACTH overstimulation of the adrenal cortex.
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