Search

Background: Our previous research highlighted the potential role of immunoglobulin G4 (IgG4) in mediating immunosuppression within the tumor microenvironment (TME). Tertiary lymphoid structures (TLS) in the TME have important immune-related functions. This study aims to analyze the distribution characteristics of IgG4-expressing cells, regulatory T cells (Tregs), and M2-type macrophages as well as to elucidate the relationship between IgG4 and the polarization of M2 macrophages within TLS in esophageal cancer. Object: To elucidate the distribution of IgG4, Treg cells, and M2 macrophages in TLS and to assess the impact of IgG4 on macrophage polarization. Methods: Esophageal cancer tissue were analyzed with multiplex immunofluorescence to determine the spatial distribution and density of B cells, T cells, and their subtypes. The relationship between IgG4 and CD8+ T cells in TLS, along with interleukin-10 (IL-10) expression and Treg presence, was studied. Serum IgG4 and IL-10 levels were compared between patients and healthy controls. In vitro , the impact of IgG4 on monocyte differentiation into M2 macrophages was observed. Results: IgG4 density was inversely related with CD8+ T cells in mature TLS indicating a potential immunosuppressive role (P<0.05,*). Serum analysis revealed that both IgG4 (P<0.01, **) and IL-10 (P<0.0001, ****) were significantly elevated and positively correlated in tumor patients compared to controls (P<0.01, **). In vitro experiments confirmed that IgG4 monocyte differentiation into M2 macrophages, potentially enhancing the immunosuppressive phenotype in TLS. Conclusion: IgG4 and IL-10 may contribute to immunosuppression in esophageal cancer by promoting the polarization of M2 macrophages within TLS, which could be a therapeutic target. [ABSTRACT FROM AUTHOR]

Fabric defect detection is crucial for quality control in fabric manufacturing but remains a challenge due to the multi-scale characteristics of defects and their integration with the fabric background. To address this, we propose an efficient fabric defect detection model, DGHR-YOLO. First, the deformable convolution block (DCB) is introduced into the backbone network, leveraging its dynamic receptive field to effectively capture defect morphology and enhance feature extraction. Secondly, we propose the GS-SPPF module, designed to mitigate semantic information loss, optimize feature fusion, and improve both accuracy and inference speed. Thirdly, a lightweight High-level Screening Feature Pyramid Network (HS-FPN) is introduced, enabling effective multi-scale feature fusion while maintaining low complexity. Finally, a one-shot aggregation module based on channel shuffle and re-parameterized convolution is introduced, enhancing feature interaction across scales. Experimental results on the Tianchi textile dataset demonstrate that DGHR-YOLO achieves mAP0.5 and mAP0.5:0.95 scores of 85.8% and 72.7%, with respective improvements of 2.7% and 3.8% over YOLOv8m, while maintaining low parameter count and computational complexity. [ABSTRACT FROM AUTHOR]

Charged channels are considered an effective design for achieving efficient monovalent cation transport; however, it remains challenging to establish a direct relationship between charge microenvironments and ionic conductivity within the pores. Herein, we report a series of crystalline covalent organic frameworks (COFs) with identical skeletons but different charge microenvironments and explore their intra-pore charge-driven ion transport performance and mechanism differences. We found that the charged nature determines ion-pair action sites, modes, host-guest interaction, thereby influencing the dissociation efficiency of ion pairs, the hopping ability of cations, and the effective carrier concentration. The order of transport efficiency for Li+, Na+, and H+ follows anion > zwitterion > cation > neutrality. Ionic COFs exhibit up to 11-fold higher ionic conductivity than neutral COFs. Notably, the ionic conductivity of anionic COF achieves 2.0×10-4 S cm-1 for Li+ at 30°C and 3.8×10-2 S cm-1 for H+ at 160°C, surpassing most COF-based ionic conductors. This COF platform for efficient ion migration and stable battery cycling in lithium-metal quasi-solid-state batteries has also been verified as proof of concept. This work offers new insights into the development and structure-activity relationship studies of the next generation of solid-state ionic conductors., (© 2025 Wiley‐VCH GmbH.)

Femtosecond laser has been widely applied for manufacturing micro-/nanostructures in micro-optics, microelectronics, and biomedical engineering. However, tight focusing remains restricted by efficiency, and large-field scanning with a scanning mirror sacrifices precision. In this paper, we proposed a medium-assisted femtosecond laser projection manufacturing (MA-FPM) approach, which enables the fabrication of controllable patterns that incorporate 115 nm periodic laser-induced periodic surface structures (LIPSS). We demonstrated the capability of this technology in fabricating different patterns and pattern arrays. Additionally, we illustrated the application of the micro-nanostructures in plasmon enhancement through Raman spectroscopy. The MA-FPM technique holds promise for metasurface, biosensing, label-free microscopy, and tissue engineering.

Trap-assisted non-radiative recombination losses and moisture-induced degradation significantly impede the development of highly efficient and stable inverted (p-i-n) perovskite solar cells (PSCs), which require high-quality perovskite bulk. In this research, we mitigate these challenges by integrating thermally stable perovskite layers with Lewis base covalent organic frameworks (COFs). The ordered pore structure and surface binding groups of COFs facilitate cyclic, multi-site chelation with undercoordinated lead ions, enhancing the perovskite quality across both its bulk and grain boundaries. This process not only reduces defects but also promotes improved energy alignment through n-type doping at the surface. The inclusion of COF dopants in p-i-n devices achieves power conversion efficiencies (PCEs) of 25.64 % (certified 24.94 %) for a 0.0748-cm 2 device and 23.49 % for a 1-cm 2 device. Remarkably, these devices retain 81 % of their initial PCE after 978 hours of accelerated aging at 85°C, demonstrating remarkable durability. Additionally, COF-doped devices demonstrate excellent stability under illumination and in moist conditions, even without encapsulation., (© 2024 Wiley-VCH GmbH.)

Hardware system customized toward the demands of graph neural network learning would promote efficiency and strong temporal processing for graph-structured data. However, most amorphous/polycrystalline oxides-based memristors commonly have unstable conductance regulation due to random growth of conductive filaments. And graph neural networks based on robust and epitaxial film memristors can especially improve energy efficiency due to their high endurance and ultra-low power consumption. Here, robust and epitaxial Gd: HfO2-based film memristors are reported and construct a weighted echo state graph neural network (WESGNN). Benefiting from the optimized epitaxial films, the high switching speed (20 ns), low energy consumption (2.07 fJ), multi-value storage (4 bits), and high endurance (10 9 ) outperform most memristors. Notably, thanks to the appropriately dispersed conductance distribution (standard deviation = 7.68 nS), the WESGNN finely regulates the relative weights of input nodes and recursive matrix to realize state-of-the-art performance using the MUTAG and COLLAB datasets for graph classification tasks. Overall, robust and epitaxial film memristors offer nanoscale scalability, high reliability, and low energy consumption, making them energy-efficient hardware solutions for graph learning applications., (© 2025 The Author(s). Advanced Science published by Wiley‐VCH GmbH.)