Heteroarchitectural Gas Diffusion Layer Promotes CO2 Reduction Coupled with Biomass Oxidation at Ampere-Level Current Density.

Achieving high product selectivity at ampere-level current densities is essential for the industrial application of electrochemical CO2 reduction. However, the operational stability of CO2 electrolyzers at large current density has long been hindered by flooding of gas diffusion layer (GDL). Herein, a new heteroarchitectural GDL is designed to overcome flooding.

Large Dipole Moment Enhanced CO Adsorption on Copper Surface: Achieving 68.9% Catalytic Ethylene Faradaic Efficiency at 1.0 A cm.

The electrochemical conversion of carbon dioxide (CO) into hydrocarbon products emerges as a pivotal sustainable strategy for carbon utilization. Cu-based catalysts are currently prioritized as the most effective means for this process, yet it remains a long-term goal to achieve high product selectivity at elevated current densities. This study delved into exploring the influence of a topological poly(2-aminoazulene) with a substantial dipole moment on modulating the Cu surface dipole field to augment the catalytic activity involved in CO reduction.

Aging-induced immune microenvironment remodeling fosters melanoma in male mice via γδ17-Neutrophil-CD8 axis.

Aging is associated with increased tumor metastasis and poor prognosis. However, how an aging immune system contributes to the process is unclear. Here, single-cell RNA sequencing reveals that in male mice, aging shifts the lung immune microenvironment towards a premetastatic niche, characterized by an increased proportion of IL-17-expressing γδT (γδ17) and neutrophils.

Itaconate Ameliorates Experimental Autoimmune Uveitis by Modulating Teff/Treg Cell Imbalance Via the DNAJA1/CDC45 Axis.

Purpose: The aim of this study was to elucidate the effect of itaconate (ITA) on experimental autoimmune uveitis (EAU), to explore its potential mechanism, and to identify potential therapeutic targets.

Methods: We established an animal model of EAU by constructing an immune map of mice treated with ITA and exploring the therapeutic mechanism of ITA by single-cell RNA sequencing and flow cytometry.

Results: ITA mitigated ocular inflammation associated with EAU and reversed the pathogenic differentiation linked to Th17 induction by EAU, along with the reactive oxygen species (ROS) and oxidative stress pathways.

Dietary caloric restriction protects experimental autoimmune uveitis by regulating Teff/Treg balance.

Uveitis, an autoimmune disease, often leads to blindness. CD4 T cells, including regulatory T cells (Tregs) and effector T cells (Th1 and Th17), play a critical role in its pathogenesis. Caloric restriction (CR) has been shown to alleviate autoimmune diseases.

Ketogenic diet modulates immune cell transcriptional landscape and ameliorates experimental autoimmune uveitis in mice.

Background: Uveitis manifests as immune-mediated inflammatory disorders within the eye, posing a serious threat to vision. The ketogenic diet (KD) has emerged as a promising dietary intervention, yet its impact on the immune microenvironments and role in uveitis remains unclear.

Methods: Utilizing single-cell RNA sequencing (scRNA-seq) data from lymph node and retina of mice, we conduct a comprehensive investigation into the effects of KD on immune microenvironments.

An analysis of single-cell data reveals therapeutic effects of AMG487 in experimental autoimmune uveitis.

Uveitis, an ocular autoimmune disease that poses a significant threat to vision, is caused by immune cells erroneously attacking retinal cells and currently lacks specific and effective therapeutic interventions. The CXC chemokine receptor 3 (CXCR3) facilitates the migration of immune cells to sites of inflammation. AMG487, a CXCR3 antagonist, holds potential for treating autoimmune diseases by blocking immunes cells chemotaxis.

Multi-scale multimodal deep learning framework for Alzheimer's disease diagnosis.

Multimodal neuroimaging data, including magnetic resonance imaging (MRI) and positron emission tomography (PET), provides complementary information about the brain that can aid in Alzheimer's disease (AD) diagnosis. However, most existing deep learning methods still rely on patch-based extraction from neuroimaging data, which typically yields suboptimal performance due to its isolation from the subsequent network and does not effectively capture the varying scales of structural changes in the cerebrum. Moreover, these methods often simply concatenate multimodal data, ignoring the interactions between them that can highlight discriminative regions and thereby improve the diagnosis of AD.

Self-supervised Multi-scale Multi-modal Graph Pool Transformer for Sellar Region Tumor Diagnosis.

The sellar region tumor is a brain tumor that only exists in the brain sellar, which affects the central nervous system. The early diagnosis of the sellar region tumor subtypes helps clinicians better understand the best treatment and recovery of pa-tients. Magnetic resonance imaging (MRI) has proven to be an effective tool for the early detection of sellar region tumors.

All-Aqueous Embedded 3D Printing for Freeform Fabrication of Biomimetic 3D Constructs.

All-aqueous embedded 3D printing, which involves extruding inks in an aqueous bath, has emerged as a transformative platform for the freeform fabrication of 3D constructs with precise control. The use of a supporting bath not only enables the printing of arbitrarily designed 3D constructs but also broadens ink selection for various soft matters, advancing the wide application of this technology. This review focuses on recent progress in the freeform preparation of 3D constructs using all-aqueous embedded 3D printing.

FAMF-Net: Feature Alignment Mutual Attention Fusion with Region Awareness for Breast Cancer Diagnosis via Imbalanced Data.

Automatic and accurate classification of breast cancer in multimodal ultrasound images is crucial to improve patients' diagnosis and treatment effect and save medical resources. Methodologically, the fusion of multimodal ultrasound images often encounters challenges such as misalignment, limited utilization of complementary information, poor interpretability in feature fusion, and imbalances in sample categories. To solve these problems, we propose a feature alignment mutual attention fusion method (FAMF-Net), which consists of a region awareness alignment (RAA) block, a mutual attention fusion (MAF) block, and a reinforcement learning-based dynamic optimization strategy(RDO).

Interleukin-6 in non-infectious uveitis: Biology, experimentalevidence and treatment strategies.

Uveitis is the leading cause of visual impairment worldwide. Interleukin-6 (IL-6), which is upregulated in response to inflammation, is one of the most important inflammatory cytokines associated with uveitis. Two major IL-6 receptors (IL-6R) mediate the pro-inflammatory and anti-inflammatory biological effects of IL-6.

Silica nanoparticle design for colorectal cancer treatment: Recent progress and clinical potential.

Colorectal cancer (CRC) is the third most common cancer worldwide and the second most common cause of cancer death. Nanotherapies are able to selectively target the delivery of cancer therapeutics, thus improving overall antitumor efficiency and reducing conventional chemotherapy side effects. Mesoporous silica nanoparticles (MSNs) have attracted the attention of many researchers due to their remarkable advantages and biosafety.

EYA4 reduces chemosensitivity of osteosarcoma to doxorubicin through DNA damage repair.

Previous studies have demonstrated that Eyes Absent 4 (EYA4) influences the proliferation and migration of tumor cells. Notably, studies have established that EYA4 can also limit tumor sensitivity to chemotherapeutic agents. The objective of this study was to investigate the effect of EYA4 in conferring drug resistance in osteosarcoma (OS).

Alzheimer's disease diagnosis from multi-modal data via feature inductive learning and dual multilevel graph neural network.

Multi-modal data can provide complementary information of Alzheimer's disease (AD) and its development from different perspectives. Such information is closely related to the diagnosis, prevention, and treatment of AD, and hence it is necessary and critical to study AD through multi-modal data. Existing learning methods, however, usually ignore the influence of feature heterogeneity and directly fuse features in the last stages.

Federated learning using model projection for multi-center disease diagnosis with non-IID data.

Multi-center disease diagnosis aims to build a global model for all involved medical centers. Due to privacy concerns, it is infeasible to collect data from multiple centers for training (i.e.

Zeolite-Templated Carbons Supported Rh and Ru Electrocatalysts for Highly Active Hydrogen Evolution Reaction.

Here, we report the systematical synthesis of zeolite-templated carbon (ZTC) supported Ru and Rh mono- or bi-metallic electrocatalysts towards hydrogen evolution reaction (HER). The zeolite A or ZSM-5 derived ZTC supports and metal sites were adjusted, and all electrocatalysts outperformed the commercial Pt/C electrocatalyst for HER performance. In particular, the RhRu/(ZTC/ZSM5) sample exhibited superior catalytic performance with the overpotential of 24.

3D Multimodal Fusion Network With Disease-Induced Joint Learning for Early Alzheimer's Disease Diagnosis.

Multimodal neuroimaging provides complementary information critical for accurate early diagnosis of Alzheimer's disease (AD). However, the inherent variability between multimodal neuroimages hinders the effective fusion of multimodal features. Moreover, achieving reliable and interpretable diagnoses in the field of multimodal fusion remains challenging.