Genetic heterogeneity of pseudoxanthoma elasticum: the Chinese signature profile of ABCC6 and ENPP1 mutations.

Pseudoxanthoma elasticum (PXE), an autosomal recessive disorder characterized by ectopic mineralization, is caused by mutations in the ABCC6 gene. We examined clinically 29 Chinese PXE patients from unrelated families, so far the largest cohort of Asian PXE patients. In a subset of 22 patients, we sequenced ABCC6 and another candidate gene, ENPP1, and conducted pathogenicity analyses for each variant. We identified a total of 17 distinct mutations in ABCC6, 15 of them being, to our knowledge, previously unreported, including 5 frameshift and 10 missense variants. In addition, a missense mutation in combination with a recurrent nonsense mutation in ENPP1 was discovered in a pediatric PXE case. No cases with p.R1141X or del23-29 mutations, common in Caucasian patient populations, were identified. The 10 missense mutations in ABCC6 were expressed in the mouse liver via hydrodynamic tail-vein injections. One mutant protein showed cytoplasmic accumulation indicating abnormal subcellular trafficking, while the other nine mutants showed correct plasma membrane location. These nine mutations were further investigated for their pathogenicity using a recently developed zebrafish mRNA rescue assay. Minimal rescue of the morpholino-induced phenotype was achieved with eight of the nine mutant human ABCC6 mRNAs tested, implying pathogenicity. This study demonstrates that the Chinese PXE population harbors unique ABCC6 mutations. These genetic data have implications for allele-specific therapy currently being developed for PXE

Recent Progress in Transformer-based Medical Image Analysis

The transformer is primarily used in the field of natural language processing. Recently, it has been adopted and shows promise in the computer vision (CV) field. Medical image analysis (MIA), as a critical branch of CV, also greatly benefits from this state-of-the-art technique. In this review, we first recap the core component of the transformer, the attention mechanism, and the detailed structures of the transformer. After that, we depict the recent progress of the transformer in the field of MIA. We organize the applications in a sequence of different tasks, including classification, segmentation, captioning, registration, detection, enhancement, localization, and synthesis. The mainstream classification and segmentation tasks are further divided into eleven medical image modalities. A large number of experiments studied in this review illustrate that the transformer-based method outperforms existing methods through comparisons with multiple evaluation metrics. Finally, we discuss the open challenges and future opportunities in this field. This task-modality review with the latest contents, detailed information, and comprehensive comparison may greatly benefit the broad MIA community.Comment: Computers in Biology and Medicine Accepte

Lightweight equivariant interaction graph neural network for accurate and efficient interatomic potential and force predictions

In modern computational materials science, deep learning has shown the capability to predict interatomic potentials, thereby supporting and accelerating conventional simulations. However, existing models typically sacrifice either accuracy or efficiency. Moreover, lightweight models are highly demanded for offering simulating systems on a considerably larger scale at reduced computational costs. A century ago, Felix Bloch demonstrated how leveraging the equivariance of the translation operation on a crystal lattice (with geometric symmetry) could significantly reduce the computational cost of determining wavefunctions and accurately calculate material properties. Here, we introduce a lightweight equivariant interaction graph neural network (LEIGNN) that can enable accurate and efficient interatomic potential and force predictions in crystals. Rather than relying on higher-order representations, LEIGNN employs a scalar-vector dual representation to encode equivariant features. By extracting both local and global structures from vector representations and learning geometric symmetry information, our model remains lightweight while ensuring prediction accuracy and robustness through the equivariance. Our results show that LEIGNN consistently outperforms the prediction performance of the representative baselines and achieves significant efficiency across diverse datasets, which include catalysts, molecules, and organic isomers. Finally, to further validate the predicted interatomic potentials from our model, we conduct classical molecular dynamics (MD) and ab initio MD simulation across various systems, including solid, liquid, and gas. It is found that LEIGNN can achieve the accuracy of ab initio MD and retain the computational efficiency of classical MD across all examined systems, demonstrating its accuracy, efficiency, and universality

A Single Microorganism Epitope Attenuates the Development of Murine Autoimmune Arthritis: Regulation of Dendritic Cells via the Mannose Receptor

A single epitope of Leishmania analog of the receptors for activated C kinase (LACK) from Leishmania major, the polypeptide LACK156–173, is recognized by Vβ4+/Vα8+ T cells, and activate these cells that drives the subsequent T helper (Th)2 response. This study was undertaken to investigate the therapeutic potential of the LACK156–173 epitope in murine autoimmune arthritis models. To explore the influence of the LACK156–173 epitope on murine collagen antibody-induced arthritis, as well as its immunological mechanism, we vaccinated or treated mice with a LACK156–173 epitope expression plasmid or polypeptide. The effect of LACK156–173 epitope was then evaluated by clinical scores, histopathology, and quantitative real-time polymerase chain reaction (qRT-PCR) analysis. Using flow cytometry, we measured the subsets and maturity of CD11c+ dendritic cells (DCs), as well as T cell polarization, in co-culture experiments. We also measured cytokine gene expression and production. The murine macrophage-like cell line RAW264.7 was used to identify the receptor for the epitope. Vaccination or treatment of the mice with the LACK156–173 epitope expression plasmid or polypeptide ameliorated the severity of arthritis. qRT-PCR analysis revealed that the LACK156–173 epitope improved the balance of effector T cells in synovial tissue compared to that in untreated arthritis controls. Toll-like receptor (TLR) 4 expression was diminished by LACK156–173. The epitope also influenced T cell polarization by regulating the differentiation, maturation, and functions of CD11c+ DCs and upregulating Jagged1 ligand expression. Blocking the mannose receptor (MR) significantly attenuated LACK156–173 epitope-induced macrophage activation. Our data indicate that vaccination or treatment with a single microorganism epitope, LACK156–173, is a highly efficient therapy for murine autoimmune arthritis. The therapeutic effects are mediated by the regulation of the differentiation, maturation, and functions of DCs via MR, resulting in the upregulation of Jagged1 expression and Th2 cell polarization. Our results demonstrate the therapeutic potential of the LACK156–173 epitope in rheumatoid arthritis

Intra-Articular Injection of Human Synovial Membrane-Derived Mesenchymal Stem Cells in Murine Collagen-Induced Arthritis: Assessment of Immunomodulatory Capacity In Vivo

The aim of this study was to evaluate the efficacy of human synovial membrane-derived MSCs (SM-MSCs) in murine collagen-induced arthritis (CIA). Male mice (age 7–9 weeks) were injected intra-articularly with SM-MSCs obtained from patients with osteoarthritis, on days 28, 32, and 38 after bovine type II collagen immunization. The efficacy of SM-MSCs in CIA was evaluated clinically and histologically. Cytokine profile analyses were performed by real-time polymerase chain reaction and multiplex analyses. Splenic helper T (Th) cell and regulatory B cell subsets were analyzed by flow cytometry. Intra-articular SM-MSC injection ameliorated the clinical and histological severity of arthritis. Decrease in tumor necrosis factor-α, interferon-γ, and interleukin- (IL-) 17A and increase in IL-10 production were observed after SM-MSC treatment. Flow cytometry showed that Th1 and Th17 cells decreased, whereas Th2, regulatory T (Treg), and PD-1+CXCR5+FoxP3+ follicular Treg cells increased in the spleens of SM-MSC-treated mice. Regulatory B cell analysis showed that CD21hiCD23hi transitional 2 cells, CD23lowCD21hi marginal zone cells, and CD19+CD5+CD1d+IL-10+ regulatory B cells increased following SM-MSC treatment. Our results demonstrated that SM-MSCs injected in inflamed joints in CIA had a therapeutic effect and could prevent arthritis development and suppress immune responses via immunoregulatory cell expansion