SARS-CoV-2 N protein interacts with SLC7A11 to cause ferroptosis in acute lung injury

Allergologia et Immunopathologia · 2025-05-01 · 2 citations

Background: The nucleocapsid protein (N protein) in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is elevated in bodily fluids at the onset of infection and has recently been found to have a direct role in lung damage. However, the exact mode of action of the N protein in acute lung injury is still unknown. Method: Recombinant N protein was used to treat mice and A549 cells in vivo and in vitro. Enzyme-linked immunosorbent assay and hematoxylin and eosin staining were used to detect the levels of inflammatory factors and lung damage in lung tissue. The total iron and Fe2+ contents and the expression of ferroptosis markers in mouse lung tissues and cells were detected. Co-immunoprecipitation detects the binding of N protein and solute carrier family 7 member 11 (SLC7A11). Replenishment experiments were conducted by activating SLC7A11 to study the effect of SLC7A11 on N protein–induced lung injury. Result: Recombinant N protein caused acute lung injury and lung inflammation, increased total iron and Fe2+ contents in vivo and in vitro, promoted the expression of ACSL4, inhibited the expression of GPX4 and FTH1, and triggered ferroptosis. Recombinant N protein can interact with SLC7A11, and activating SLC7A11 can reverse N protein–induced ferroptosis and acute lung injury. Conclusion: SARS-CoV-2 N protein can directly interact with SLC7A11 to cause ferroptosis, which produces a lot of inflammatory factors and results in lung injury in mice.

Hierarchically Aligned Aramid Nanofiber Aerogel Framework Enhances Ionic Transport and Interfacial Stability of Solid‐State Lithium‐Metal Batteries

Advanced Functional Materials · 2025-09-12 · 4 citations

Abstract Solid polymer electrolytes (SPEs) have attracted significant attention for enabling high‐energy density and high‐safety lithium metal batteries due to their low interfacial impedance, superior electrode compatibility, and mechanical flexibility. However, challenges such as low room‐temperature ionic conductivity, limited Li⁺ transference number, and insufficient mechanical robustness still impede their practical applications. Herein, a novel SPE (denoted as PMVAL) is designed and supported by an aramid nanofiber (ANF) aerogel framework featuring vertically aligned ion transport channels. The ANF aerogel, fabricated via a non‐solvent induced phase separation strategy, forms hierarchical multilayered pore arrays that promote directional Li⁺ migration within PMVAL, achieving an impressive high ionic conductivity of 0.82 × 10 −4 S cm −1 at 30 °C. This engineered framework also facilitates the formation of a functional organic‐inorganic composite solid electrolyte interphase at the PMVAL/Li interface, enabling homogeneous lithium deposition and effective dendrite suppression. Consequently, Li|PMVAL|LiFePO 4 cells exhibit remarkable cycle stability, delivering over 5000 cycles at 1 C (60 °C) and 1000 cycles at 0.5 C (30 °C) with a Coulombic efficiency exceeding 99.8%. Moreover, flexible pouch cells demonstrate excellent safety and stability under mechanical abuse (bending, piercing, and cutting), indicating the great promise of this strategy for next‐generation solid‐state energy storage systems.

Harnessing Photocatalytic Hydrogen Atom Transfer for Deconjugative Isomerization of α,β-Dehydro Amino Acids

CCS Chemistry · 2025-11-18

Regulating Lithium Metal Nucleation and Growth for Dendrite Suppression: from Liquid-Electrolyte to Solid-State Batteries

Dian hua xue/Dian huaxue · 2025-11-28 · 1 citations

Lithium metal anodes, with a theoretical capacity of up to 3860 mAh·g−1, are regarded as the cornerstone for developing next-generation high-energy-density batteries. However, several key challenges hinder their practical applications, including dendrite formation, unstable solid electrolyte interphase (SEI), side reactions with electrolytes, and associated safety risks. This review systematically explores the mechanisms of lithium nucleation, growth, and stripping in both liquid and solid-state battery systems, analyzing critical theoretical concepts like heterogeneous nucleation thermodynamics, surface diffusion kinetics, space charge effects, and SEI-induced nucleation, which are crucial for understanding the genesis of dendrite growth. Additionally, the review discusses the electrochemical-mechanical coupling failures that lead to SEI degradation and the formation of dead lithium. For liquid systems, the review proposes strategies to mitigate dendrite formation and SEI instability, which include electrolyte optimization, artificial SEI design, and electrode framework design. In solidstate batteries, the review offers a granular analysis of the interface challenges associated with polymer, sulfide, and halide electrolytes and summarizes different solutions for different solid-state electrolytes. Meanwhile, the review emphasizes the importance of advanced characterization techniques and computational modeling in understanding and regulating the interface between lithium metal and electrolytes. Looking ahead, the review highlights future research directions that emphasize the integration of cross-disciplinary approaches to tackle these interconnected challenges. By addressing these issues, the path will be clear for the rapid commercialization and widespread application of lithium metal batteries, bringing us closer to realizing stable, high-energy-density batteries that can satisfy the escalating demands of modern energy storage applications across various industries.

From mold to Ah level pouch cell design: bipolar all-solid-state Li battery as an emerging configuration with very high energy density

EES batteries. · 2025-01-01 · 5 citations

Bipolar all-solid-state batteries (ASSBs) represent an innovative battery architecture and have attracted significant attention due to their high energy density, enhanced safety, and simplified packaging design.

MOBODY: Model Based Off-Dynamics Offline Reinforcement Learning

ArXiv.org · 2025-06-10

We study off-dynamics offline reinforcement learning, where the goal is to learn a policy from offline source and limited target datasets with mismatched dynamics. Existing methods either penalize the reward or discard source transitions occurring in parts of the transition space with high dynamics shift. As a result, they optimize the policy using data from low-shift regions, limiting exploration of high-reward states in the target domain that do not fall within these regions. Consequently, such methods often fail when the dynamics shift is significant or the optimal trajectories lie outside the low-shift regions. To overcome this limitation, we propose MOBODY, a Model-Based Off-Dynamics Offline RL algorithm that optimizes a policy using learned target dynamics transitions to explore the target domain, rather than only being trained with the low dynamics-shift transitions. For the dynamics learning, built on the observation that achieving the same next state requires taking different actions in different domains, MOBODY employs separate action encoders for each domain to encode different actions to the shared latent space while sharing a unified representation of states and a common transition function. We further introduce a target Q-weighted behavior cloning loss in policy optimization to avoid out-of-distribution actions, which push the policy toward actions with high target-domain Q-values, rather than high source domain Q-values or uniformly imitating all actions in the offline dataset. We evaluate MOBODY on a wide range of MuJoCo and Adroit benchmarks, demonstrating that it outperforms state-of-the-art off-dynamics RL baselines as well as policy learning methods based on different dynamics learning baselines, with especially pronounced improvements in challenging scenarios where existing methods struggle.

Structural analysis of assembly steel frames considering rotational deformation of external extended end-plate connection components

Advances in Structural Engineering · 2025-06-28

The end-plate bolted connection represents a critical technology in prefabricated steel structures, presenting challenges in accurately capturing force transmission and joint deformation within engineering design and structural analysis. This study performed loading tests on end-plate bolted connections, examining the shear deformation in the end-plate region, the relative deformation between the end-plate and column flange, and the yield deformation of the end-plate stiffeners and their collective impact on rotational behavior. Based on these findings, an equivalent rotational spring model was developed to integrate these three deformation components. The model’s moment-rotation relationship was then employed to establish equivalent rotational springs within rigid, semi-rigid, and hinged prefabricated steel frame models, followed by static pushover and dynamic time-history analyses of the overall structural system. Results demonstrate that the equivalent rotational spring model effectively simulates a range of connection behaviors from hinged to rigid. By adjusting the rotational characteristics of the equivalent spring model and the floor locations, the sequence of plastic hinge formation can be modified, enhancing structural deformation and energy dissipation capacity, thus enabling efficient overall structural analysis. These findings offer significant insights for designing prefabricated steel frame structures based on joint performance.

Viral metagenomics reveals diverse viruses in the fecal samples of children with acute respiratory infection

Frontiers in Microbiology · 2025-04-07 · 1 citations

Introduction Changes in the gut microbiome have been associated with the development of acute respiratory infection (ARI). However, due to methodological limitations, our knowledge of the gut virome in patients with ARIs remains limited. Methods In this study, fecal samples from children with ARI were investigated using viral metagenomics. Results The fecal virome was analyzed, and several suspected disease-causing viruses were identified. The five viral families with the highest abundance of sequence reads were Podoviridae , Virgaviridae , Siphoviridae , Microviridae , and Myoviridae . Additionally, human adenovirus, human bocavirus, human astrovirus, norovirus, and human rhinovirus were detected. The genome sequences of these viruses were respectively described, and phylogenetic trees were constructed using the gene sequences of the viruses. Discussion We characterized the composition of gut virome in children with acute respiratory infections. However, further research is required to elucidate the relationship between acute respiratory infection and gut viruses.

Reducing External Pressure Demands in Solid‐State Lithium Metal Batteries: Multi‐Scale Strategies and Future Pathways

Advanced Energy Materials · 2025-10-10 · 8 citations

Abstract Solid‐state lithium metal batteries (SSLMBs) are poised to revolutionize energy storage technologies by combining exceptional energy density with inherent safety. Yet, their commercialization faces fundamental challenges: poor solid–solid interfacial contacts, lithium dendrite proliferation, and electro‐chemo‐mechanical failure. This perspective presents a comprehensive analysis of external pressure as a multi‐scale engineering lever for SSLMBs, bridging atomic‐level ion transport, interfacial stabilization, and industrial‐scale device integration with particular emphasis on its dynamic interplay with internal stress. At the atomic scale, applied pressure densifies electrode/electrolyte architectures, optimizes ion‐transport pathways, and mitigates lattice distortion‐induced stresses. Microscopically, it enables intimate interfacial contacts, homogenizes Li deposition stresses to suppress dendrites, and stabilizes interphases. Macro‐scale strategies demonstrate how dynamic pressure coupling through in(ex) situ monitoring and roll‐to‐roll compaction can sustain interfacial integrity in large‐area cells by counterbalancing internal stress evolution. External pressure is positioned as a tunable design parameter that synergizes materials innovation with process engineering to simultaneously enhance electrochemical performance and mechanical resilience. Looking ahead, intelligent pressure‐management systems integrating machine learning‐driven adaptive control, stress‐responsive materials, and operando characterization tools is proposed. These advancements will be pivotal for realizing pressure‐optimized SSLMBs that meet the energy density (>500 Wh kg −1 ) and cycling stability demands of electric aviation and grid storage, which will accelerate the global transition to sustainable energy.

Promoting the Photothermocatalytic Performance of Co3O4 by C-doping for Toluene Oxidation

Catalysis Letters · 2025-04-14 · 3 citations