Identification and characterization of Jingmen tick virus from Rhipicephalus microplus in Hunan, China.

Jingmen tick virus (JMTV) is a tick-borne pathogen known to affect human beings, characterized by a segmented genome structure that defies the conventional understanding of the Flaviviridae family. In the present study, we employed metagenomic analysis to screen for tick-borne viruses in Hunan Province, China, and identified five JMTV variants with complete genomes from Rhipicephalus microplus ticks sampled from cattle. These viral strains exhibited the highest sequence similarity to JMTV isolates previously reported in Hubei Province, China. However, evidence of genomic reassortment was detected, particularly with the S2 segment showing greater similarity to the strains from Japan. Phylogenetic analysis demonstrated that JMTV strains cluster predominantly based on their geographic origin. In agreement with the homology data, the S1, S3, and S4 segments of the strains identified in this study grouped with those from Hubei Province, while the S2 segment displayed a distinct topological structure. Moreover, JMTV displayed limited replication in mammal-derived cells, but thrived in tick-derived cell lines. In addition to the commonly used R. microplus-derived BME/CTVM23 cells, we found that JMTV also proliferated robustly in both Ixodes scapularis-derived ISE6 and Ixodes ricinus-derived IRE/CTVM19 cells, offering new avenues for in vitro production of the virus. In summary, this study expands the known geographic distribution and genetic diversity of JMTV, providing valuable insights into its epidemiology and potential for in vitro cultivation.

Learning co-plane attention across MRI sequences for diagnosing twelve types of knee abnormalities.

Multi-sequence magnetic resonance imaging is crucial in accurately identifying knee abnormalities but requires substantial expertise from radiologists to interpret. Here, we introduce a deep learning model incorporating co-plane attention across image sequences to classify knee abnormalities. To assess the effectiveness of our model, we collected the largest multi-sequence knee magnetic resonance imaging dataset involving the most comprehensive range of abnormalities, comprising 1748 subjects and 12 types of abnormalities. Our model achieved an overall area under the receiver operating characteristic curve score of 0.812. It achieved an average accuracy of 0.78, outperforming junior radiologists (accuracy 0.65) and remains competitive with senior radiologists (accuracy 0.80). Notably, with the assistance of model output, the diagnosis accuracy of all radiologists was improved significantly (p < 0.001), elevating from 0.73 to 0.79 on average. The interpretability analysis demonstrated that the model decision-making process is consistent with the clinical knowledge, enhancing its credibility and reliability in clinical practice.

Artificial intelligence improves the diagnosis of human leukocyte antigen (HLA)-B27-negative axial spondyloarthritis based on multi-sequence magnetic resonance imaging and clinical features.

Background: Axial spondyloarthritis (axSpA) is frequently diagnosed late, particularly in human leukocyte antigen (HLA)-B27-negative patients, resulting in a missed opportunity for optimal treatment. This study aimed to develop an artificial intelligence (AI) tool, termed NegSpA-AI, using sacroiliac joint (SIJ) magnetic resonance imaging (MRI) and clinical SpA features to improve the diagnosis of axSpA in HLA-B27-negative patients. Methods: We retrospectively included 454 HLA-B27-negative patients with rheumatologist-diagnosed axSpA or other diseases (non-axSpA) from the Third Affiliated Hospital of Southern Medical University and Nanhai Hospital between January 2010 and August 2021. They were divided into a training set (n=328) for 5-fold cross-validation, an internal test set (n=72), and an independent external test set (n=54). To construct a prospective test set, we further enrolled 87 patients between September 2021 and August 2023 from the Third Affiliated Hospital of Southern Medical University. MRI techniques employed included T1-weighted (T1W), T2-weighted (T2W), and fat-suppressed (FS) sequences. We developed NegSpA-AI using a deep learning (DL) network to differentiate between axSpA and non-axSpA at admission. Furthermore, we conducted a reader study involving 4 radiologists and 2 rheumatologists to evaluate and compare the performance of independent and AI-assisted clinicians. Results: NegSpA-AI demonstrated superior performance compared to the independent junior rheumatologist (≤5 years of experience), achieving areas under the curve (AUCs) of 0.878 [95% confidence interval (CI): 0.786-0.971], 0.870 (95% CI: 0.771-0.970), and 0.815 (95% CI: 0.714-0.915) on the internal, external, and prospective test sets, respectively. The assistance of NegSpA-AI promoted discriminating accuracy, sensitivity, and specificity of independent junior radiologists by 7.4-11.5%, 1.0-13.3%, and 7.4-20.6% across the 3 test sets (all P<0.05). On the prospective test set, AI assistance also improved the diagnostic accuracy, sensitivity, and specificity of independent junior rheumatologists by 7.7%, 7.7%, and 6.9%, respectively (all P<0.01). Conclusions: The proposed NegSpA-AI effectively improves radiologists' interpretations of SIJ MRI and rheumatologists' diagnoses of HLA-B27-negative axSpA.

Mechanism of Lian Hua Qing Wen capsules regulates the inflammatory response caused by M1 macrophage based on cellular experiments and computer simulations.

PURPOSE: The polarization of macrophages with the resulting inflammatory response play a crucial part in tissue and organ damage due to inflammatory. Study has proved Lian Hua Qing Wen capsules (LHQW) can reduce activation of inflammatory response and damage of tissue derived from the inflammatory reactions. However, the mechanism of LHQW regulates the macrophage-induced inflammatory response is unclear. Therefore, we investigated the mechanism of LHQW regulated the inflammatory response of M1 macrophages by cellular experiments and computer simulations. METHODS: This study has analysed the targets and mechanisms of macrophage regulating inflammatory response at gene and protein levels through bioinformatics. The monomeric components of LHQW were analyzed by High Performance Liquid Chromatography (HPLC). We established the in vitro cell model by M1 macrophages (Induction of THP-1 cells into M1 macrophages). RT-qPCR and immunofluorescence were used to detect changes in gene and protein levels of key targets after LHQW treatment. Computer simulations were utilized to verify the binding stability of monomeric components and protein targets. RESULTS: Macrophages had 140,690 gene targets, inflammatory response had 12,192 gene targets, intersection gene targets were 11,772. Key monomeric components (including: Pinocembrin, Fargesone-A, Nodakenin and Bowdichione) of LHQW were screened by HPLC. The results of cellular experiments indicated that LHQW could significantly reduce the mRNA expression of CCR5, CSF2, IFNG and TNF, thereby alleviating the inflammatory response caused by M1 macrophage. The computer simulations further validated the binding stability and conformation of key monomeric components and key protein targets, and IFNG/Nodakenin was able to form the most stable binding conformation for its action. CONCLUSION: In this study, the mechanism of LHQW inhibits the polarization of macrophages and the resulting inflammatory response was investigated by computer simulations and cellular experiments. We found that LHQW may not only reduce cell damage and death by acting on TNF and CCR5, but also inhibit the immune recognition process and inflammatory response by regulating CSF2 and IFNG to prevent polarization of macrophages. Therefore, these results suggested that LHQW may act through multiple targets to inhibit the polarization of macrophages and the resulting inflammatory response.

Cross-species single-cell analysis uncovers the immunopathological mechanisms associated with IgA nephropathy progression.

IgA nephropathy (IgAN) represents the main cause of renal failure, while the precise pathogenetic mechanisms have not been fully determined. Herein, we conducted a cross-species single-cell survey on human IgAN and mouse and rat IgAN models to explore the pathogenic programs. Cross-species single-cell RNA sequencing (scRNA-Seq) revealed that the IgAN mesangial cells (MCs) expressed high levels of inflammatory signatures CXCL12, CCL2, CSF1, and IL-34 and specifically interacted with IgAN macrophages via the CXCL12/CXCR4, CSF1/IL-34/CSF1 receptor, and integrin subunit alpha X/integrin subunit alpha M/complement C3 (C3) axes. IgAN macrophages expressed high levels of CXCR4, PDGFB, triggering receptor expressed on myeloid cells 2, TNF, and C3, and the trajectory analysis suggested that these cells derived from the differentiation of infiltrating blood monocytes. Additionally, protein profiling of 21 progression and 28 nonprogression IgAN samples revealed that proteins CXCL12, C3, mannose receptor C-type 1, and CD163 were negatively correlated with estimated glomerular filtration rate (eGFR) value and poor prognosis (30% eGFR as composite end point). Last, a functional experiment revealed that specific blockade of the Cxcl12/Cxcr4 pathway substantially attenuated the glomerulus and tubule inflammatory injury, fibrosis, and renal function decline in the mouse IgAN model. This study provides insights into IgAN progression and may aid in the refinement of IgAN diagnosis and the optimization of treatment strategies.

A radiograph-based deep learning model improves radiologists' performance for classification of histological types of primary bone tumors: A multicenter study.

PURPOSE: To develop a deep learning (DL) model for classifying histological types of primary bone tumors (PBTs) using radiographs and evaluate its clinical utility in assisting radiologists. METHODS: This retrospective study included 878 patients with pathologically confirmed PBTs from two centers (638, 77, 80, and 83 for the training, validation, internal test, and external test sets, respectively). We classified PBTs into five categories by histological types: chondrogenic tumors, osteogenic tumors, osteoclastic giant cell-rich tumors, other mesenchymal tumors of bone, or other histological types of PBTs. A DL model combining radiographs and clinical features based on the EfficientNet-B3 was developed for five-category classification. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were calculated to evaluate model performance. The clinical utility of the model was evaluated in an observer study with four radiologists. RESULTS: The combined model achieved a macro average AUC of 0.904/0.873, with an accuracy of 67.5 %/68.7 %, a macro average sensitivity of 66.9 %/57.2 %, and a macro average specificity of 92.1 %/91.6 % on the internal/external test set, respectively. Model-assisted analysis improved accuracy, interpretation time, and confidence for junior (50.6 % vs. 72.3 %, 53.07[s] vs. 18.55[s] and 3.10 vs. 3.73 on a 5-point Likert scale [P < 0.05 for each], respectively) and senior radiologists (68.7 % vs. 75.3 %, 32.50[s] vs. 21.42[s] and 4.19 vs. 4.37 [P < 0.05 for each], respectively). CONCLUSION: The combined DL model effectively classified histological types of PBTs and assisted radiologists in achieving better classification results than their independent visual assessment.

Automatic segmentation of fat metaplasia on sacroiliac joint MRI using deep learning.

OBJECTIVE: To develop a deep learning (DL) model for segmenting fat metaplasia (FM) on sacroiliac joint (SIJ) MRI and further develop a DL model for classifying axial spondyloarthritis (axSpA) and non-axSpA. MATERIALS AND METHODS: This study retrospectively collected 706 patients with FM who underwent SIJ MRI from center 1 (462 axSpA and 186 non-axSpA) and center 2 (37 axSpA and 21 non-axSpA). Patients from center 1 were divided into the training, validation, and internal test sets (n = 455, 64, and 129). Patients from center 2 were used as the external test set. We developed a UNet-based model to segment FM. Based on segmentation results, a classification model was built to distinguish axSpA and non-axSpA. Dice Similarity Coefficients (DSC) and area under the curve (AUC) were used for model evaluation. Radiologists' performance without and with model assistance was compared to assess the clinical utility of the models. RESULTS: Our segmentation model achieved satisfactory DSC of 81.86% ± 1.55% and 85.44% ± 6.09% on the internal cross-validation and external test sets. The classification model yielded AUCs of 0.876 (95% CI: 0.811-0.942) and 0.799 (95% CI: 0.696-0.902) on the internal and external test sets, respectively. With model assistance, segmentation performance was improved for the radiological resident (DSC, 75.70% vs. 82.87%, p < 0.05) and expert radiologist (DSC, 85.03% vs. 85.74%, p > 0.05). CONCLUSIONS: DL is a novel method for automatic and accurate segmentation of FM on SIJ MRI and can effectively increase radiologist's performance, which might assist in improving diagnosis and progression of axSpA. CRITICAL RELEVANCE STATEMENT: DL models allowed automatic and accurate segmentation of FM on sacroiliac joint MRI, which might facilitate quantitative analysis of FM and have the potential to improve diagnosis and prognosis of axSpA. KEY POINTS: • Deep learning was used for automatic segmentation of fat metaplasia on MRI. • UNet-based models achieved automatic and accurate segmentation of fat metaplasia. • Automatic segmentation facilitates quantitative analysis of fat metaplasia to improve diagnosis and prognosis of axial spondyloarthritis.

Characterization of antigen presentation capability for neoantigen-based products using targeted LC-MS/MS method.

The generation of an immune response in neoantigen-based products relies on antigen presentation, which is closely analyzed by bioassays for T-cell functions such as tetramer or cytokine release. Mass spectrometry (MS) has the potential to directly assess the antigen-presenting capability of antigen-presenting cells (APCs), offering advantages such as speed, multi-target analysis, robustness, and ease of transferability. However, it has not been used for quality control of these products due to challenges in sensitivity, including the number of cells and peptide diversity. In this study, we describe the development and validation of an improved targeted LC-MS/MS method with high sensitivity for characterizing antigen presentation, which could be applied in the quality control of neoantigen-based products. The parameters for the extraction were carefully optimized by different short peptides. Highly sensitive targeted triple quadrupole mass spectrometry combined with ultra-high performance liquid chromatography (UHPLC) was employed using a selective ion monitoring mode (Multiple Reaction Monitoring, MRM). Besides, we successfully implemented robust quality control peptides to ensure the reliability and consistency of this method, which proved invaluable for different APCs. With reference to the guidelines from ICH Q2 (R2), M10, as well as considering the specific attributes of the product itself, we validated the method for selectivity, specificity, sensitivity, limit of detection (LOD), recovery rate, matrix effect, repeatability, and application in dendritic cells (DCs) associated with neoantigen-based products. The validation process yields satisfactory results. Combining this approach with T cell assays will comprehensively assess cell product quality attributes from physicochemical and biological perspectives.

Hantaan virus inhibits type I interferon response by targeting RLR signaling pathways through TRIM25.

Hantaan virus (HTNV) is responsible for hemorrhagic fever with renal syndrome (HFRS), primarily due to its ability to inhibit host innate immune responses, such as type I interferon (IFN-I). In this study, we conducted a transcriptome analysis to identify host factors regulated by HTNV nucleocapsid protein (NP) and glycoprotein. Our findings demonstrate that NP and Gc proteins inhibit host IFN-I production by manipulating the retinoic acid-induced gene I (RIG-I)-like receptor (RLR) pathways. Further analysis reveals that HTNV NP and Gc proteins target upstream molecules of MAVS, such as RIG-I and MDA-5, with Gc exhibiting stronger inhibition of IFN-I responses than NP. Mechanistically, NP and Gc proteins interact with tripartite motif protein 25 (TRIM25) to competitively inhibit its interaction with RIG-I/MDA5, suppressing RLR signaling pathways. Our study unveils a cross-talk between HTNV NP/Gc proteins and host immune response, providing valuable insights into the pathogenic mechanism of HTNV.

Automatic detection, segmentation, and classification of primary bone tumors and bone infections using an ensemble multi-task deep learning framework on multi-parametric MRIs: a multi-center study.

OBJECTIVES: To develop an ensemble multi-task deep learning (DL) framework for automatic and simultaneous detection, segmentation, and classification of primary bone tumors (PBTs) and bone infections based on multi-parametric MRI from multi-center. METHODS: This retrospective study divided 749 patients with PBTs or bone infections from two hospitals into a training set (N = 557), an internal validation set (N = 139), and an external validation set (N = 53). The ensemble framework was constructed using T1-weighted image (T1WI), T2-weighted image (T2WI), and clinical characteristics for binary (PBTs/bone infections) and three-category (benign/intermediate/malignant PBTs) classification. The detection and segmentation performances were evaluated using Intersection over Union (IoU) and Dice score. The classification performance was evaluated using the receiver operating characteristic (ROC) curve and compared with radiologist interpretations. RESULT: On the external validation set, the single T1WI-based and T2WI-based multi-task models obtained IoUs of 0.71 ± 0.25/0.65 ± 0.30 for detection and Dice scores of 0.75 ± 0.26/0.70 ± 0.33 for segmentation. The framework achieved AUCs of 0.959 (95%CI, 0.955-1.000)/0.900 (95%CI, 0.773-0.100) and accuracies of 90.6% (95%CI, 79.7-95.9%)/78.3% (95%CI, 58.1-90.3%) for the binary/three-category classification. Meanwhile, for the three-category classification, the performance of the framework was superior to that of three junior radiologists (accuracy: 65.2%, 69.6%, and 69.6%, respectively) and comparable to that of two senior radiologists (accuracy: 78.3% and 78.3%). CONCLUSION: The MRI-based ensemble multi-task framework shows promising performance in automatically and simultaneously detecting, segmenting, and classifying PBTs and bone infections, which was preferable to junior radiologists. CLINICAL RELEVANCE STATEMENT: Compared with junior radiologists, the ensemble multi-task deep learning framework effectively improves differential diagnosis for patients with primary bone tumors or bone infections. This finding may help physicians make treatment decisions and enable timely treatment of patients. KEY POINTS: • The ensemble framework fusing multi-parametric MRI and clinical characteristics effectively improves the classification ability of single-modality models. • The ensemble multi-task deep learning framework performed well in detecting, segmenting, and classifying primary bone tumors and bone infections. • The ensemble framework achieves an optimal classification performance superior to junior radiologists' interpretations, assisting the clinical differential diagnosis of primary bone tumors and bone infections.