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Introduction: The immune system is an intricate network of cellular components that safeguards against pathogens and aberrant cells, with CD4+ T cells playing a central role in this process. Human space travel presents unique health challenges, such as heavy ion ionizing radiation, microgravity, and psychological stress, which can collectively impede immune function. The aim of this research was to examine the consequences of simulated space stressors on CD4+ T cell activation, cytokine production, and gene expression.
Methods: CD4+ T cells were obtained from healthy individuals and subjected to Fe ion particle radiation, Photon irradiation, simulated microgravity, and hydrocortisone, either individually or in different combinations. Cytokine levels for Th1 and Th2 cells were determined using multiplex Luminex assays, and RNA sequencing was used to investigate gene expression patterns and identify essential genes and pathways impacted by these stressors.
Results: Simulated microgravity exposure resulted in an apparent Th1 to Th2 shift, evidenced on the level of cytokine secretion as well as altered gene expression. RNA sequencing analysis showed that several gene pathways were altered, particularly in response to Fe ions irradiation and simulated microgravity exposures. Individually, each space stressor caused differential gene expression, while the combination of stressors revealed complex interactions.
Discussion: The research findings underscore the substantial influence of the space exposome on immune function, particularly in the regulation of T cell responses. Future work should focus expanding the limited knowledge in this field. Comprehending these modifications will be essential for devising effective strategies to safeguard the health of astronauts during extended space missions.
Conclusion: The effects of simulated space stressors on CD4+ T cell function are substantial, implying that space travel poses a potential threat to immune health. Additional research is necessary to investigate the intricate relationship between space stressors and to develop effective countermeasures to mitigate these consequences.
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http://dx.doi.org/10.3389/fimmu.2024.1443936 | DOI Listing |
Sci Prog
September 2025
School of Information Science and Technology, Hangzhou Normal University, Hangzhou, China.
At present, significant progress has been made in the research of image encryption, but there are still some issues that need to be explored in key space, password generation and security verification, encryption schemes, and other aspects. Aiming at this, a digital image encryption algorithm was developed in this paper. This algorithm integrates six-dimensional cellular neural network with generalized chaos to generate pseudo-random numbers to generate the plaintext-related ciphers.
View Article and Find Full Text PDFLangmuir
September 2025
School of Resources and Safety Engineering, Chongqing University, Chongqing 400044, China.
Hydrogen energy is pivotal for driving sustainable development and achieving deep decarbonization; yet, its storage remains a significant challenge. Notably, depleted methane reservoirs can serve as a promising large-scale solution for underground hydrogen storage (UHS). Based on adsorption experiments, Monte Carlo and molecular dynamics methods, the adsorption behavior of H and CH in anthracite and the applicability of five models were discussed.
View Article and Find Full Text PDFPLoS Comput Biol
September 2025
University of Chinese Academy of Sciences, Beijing, China.
The divergence in folding pathways between RNA co-transcriptional folding (CTF) and free folding (FF) is crucial for understanding dynamic functional regulation of RNAs. Here, we developed a simplified all-atom molecular dynamics framework to systematically compare the folding kinetics of an RNA hairpin (PDB:1ZIH) under CTF and FF conditions. By analyzing over 800 microseconds of simulated trajectory, we found that despite convergence to identical native conformations across CTF simulations (with varied transcription rates) and FF simulations, they exhibit distinct preferences for the folding pathways defined by the order of base-pair formation.
View Article and Find Full Text PDFPLoS Comput Biol
September 2025
Program of Computational Sciences, Bard College, Annandale-on-Hudson, New York, United States of America.
Agent-based models (ABMs) have become essential tools for simulating complex biological, ecological, and social systems where emergent behaviors arise from the interactions among individual agents. Quantifying uncertainty through global sensitivity analysis is crucial for assessing the robustness and reliability of ABM predictions. However, most global sensitivity methods demand substantial computational resources, making them impractical for highly complex models.
View Article and Find Full Text PDFIEEE Trans Neural Netw Learn Syst
September 2025
This article proposes a novel model-based planning framework for freeway ramp metering (RM), denoted as Koopman-driven linearized model-based offline planning (KLMOP). This framework integrates the model predictive control (MPC) and offline reinforcement learning (RL) under assumptions of a linear Markov decision process (MDP) with the Koopman operator. KLMOP introduces a fully linearized control framework by learning and modeling the dynamics, reward function, and value function in a latent space through a Koopman-based latent dynamical model (KLDM) and a pessimistic value iteration (PEVI) algorithm.
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