ImmunoTargets and Therapy最新文献

Sepsis is a systemic inflammatory response triggered by infection, which can result in multiple organ dysfunctions, including disseminated intravascular coagulation (DIC) and acute lung injury (ALI), ultimately leading to patient mortality. The pathophysiology of sepsis is intricate, involving excessive immune activation, cytokine storms, endothelial damage, and microcirculatory dysfunction. Dysregulated host responses frequently give rise to severe complications, markedly elevating mortality rates. Neutrophil extracellular traps (NETs) are web-like structures consisting of DNA, histones, and granular proteins, released by neutrophils upon activation. Ongoing research into NETs has uncovered their significant pathophysiological roles in clinical conditions, including sepsis. This review outlines the mechanisms of NET formation, release, classification, detection methods, and relevant biomarkers. Additionally, it delves into the signaling pathways involved in NET generation, their pathophysiological implications in sepsis and its complications, and evaluates their potential utility in clinical laboratory diagnostics.

Background: The intricate interplay between the intestinal microbiota and the immune system has emerged as a central theme in understanding autoimmune disease pathogenesis. This review comprehensively explores the role of gut microbiota in shaping immune development, establishing immune tolerance, and contributing to both local and systemic immune regulation.

Methods: This review synthesizes the modulatory effects of microbial metabolites (eg, short-chain fatty acids and indole derivatives) on regulatory T cells (Tregs) and inflammatory pathways. The concept of "dysbiosis" is examined from functional and compositional perspectives, linking microbial imbalances to autoimmune disorders (IBD, MS, RA, and T1D). Microbiota-targeted therapeutic interventions (probiotics, prebiotics, FMT) are also evaluated.

Key findings: The synthesis of the literature confirms that microbial metabolites have a direct impact on Treg differentiation and inflammatory pathways. Dysbiosis, through functional and compositional disruptions, is strongly associated with the pathogenesis of various autoimmune disorders, including Inflammatory Bowel Disease, Multiple Sclerosis, Rheumatoid Arthritis, and Type 1 Diabetes. Therapeutic interventions such as probiotics, prebiotics, and Fecal Microbiota Transplantation show promising potential in restoring microbial and immune homeostasis.

Conclusion: This review highlights the role of the gut-immune axis in autoimmune diseases. Despite current challenges, such as individual variability and determining causality, future directions toward precision microbiota and immune modulation are promising. This study provides a robust foundation for researchers and clinicians seeking to understand and therapeutically target the gut-immune axis.

Purpose: To establish an interpretable deep learning framework for automated classification of rheumatoid arthritis (RA) in hand radiographs, with emphasis on elucidating model decision-making patterns and enabling clinical translation through web-based deployment.

Patients and methods: A retrospective multicenter study analyzed 1,655 hand radiographs (809 RA patients, including early RA cases, and 846 healthy controls). Enhanced data (random rotation, brightness/contrast adjustment) was applied to the collected X-ray images to improve the model's generalization ability and performance. Subsequently, A lightweight Visual Geometry Group (VGG)-8 convolutional neural network was trained and validated using processed hand X-ray images. This model has the ability to distinguish RA patients from healthy controls. The interpretability of the model was systematically evaluated using both Gradient-weighted Class Activation Mapping (Grad-CAM) and Shapley Additive Explanations (SHAP). Finally, a web application was developed using Streamlit that supports JPEG input, helps to address the clinical practicality of the model.

Results: For distinguishing RA patients from healthy individuals, the classifier achieved excellent training performance (AUC=0.99, accuracy=0.94) and generalizable testing metrics (AUC=0.81, accuracy=0.74). Specifically, the model was successfully constructed and demonstrated good performance in external validation. Interpretability analysis revealed areas of pathological significance, with Grad CAM heatmaps highlighting structural abnormalities (joint space stenosis, bone erosion, trabecular structural changes), and SHAP values analysis identifying metacarpophalangeal and wrist joints as key predictive features. A web application developed using Python and Streamlit framework can assist in the diagnosis of RA hand X-ray images in clinical practice.

Conclusion: This work advances clinical diagnosis, including early RA patients, by integrating deep learning with interpretable decision paths in hand radiographic analysis, while helping clinicians to use the model more proficiently. The framework provides both diagnostic assistance and educational insights into RA radiographic markers.

Background: Sepsis is a complex and heterogeneous syndrome characterized by dysregulated immune responses and multiple forms of programmed cell death (PCD). Comprehensive understanding of the PCD landscape may provide insights into prognosis and therapeutic targets, whereas its role in sepsis is not well-explored.

Methods: Using the microarray dataset for sepsis (GSE65682), we systematically profiled 14 PCD patterns in sepsis and stratified patients into molecular subtypes with distinct immune landscapes and clinical outcomes. PCD-related prognostic signature was developed and validated across multiple cohorts. Single-cell and multi-organ transcriptomic analyses were conducted to elucidate cellular heterogeneity and temporal dynamics. Molecular docking was used to explore interactions between active compounds of Simiao Yongan Decoction (SMYAD) and key PCD-related proteins.

Results: Two clusters with differential transcriptional programs and immune infiltration patterns were identified, in which Cluster 1 showed poorer prognosis. We then developed a seven-gene signature (ELANE, CTSG, MPO, CAMP, TFRC, IL1B, CASP5) that effectively stratified patients by survival outcomes, with robust predictive performance across independent datasets. Neutrophils, monocytes, plasma, and dendritic cells were major mediators of PCD-associated immune dysregulation, in which neutrophils showing the strongest response. Temporal transcriptomics revealed peak expression of prognostic genes in bone marrow and peripheral blood within three days post-onset, suggesting an early therapeutic window. Finally, molecular docking indicated that SMYAD compounds may target PCD proteins (MPO, ELANE, IL1B) and modulate immune responses.

Conclusion: This study delineates the multi-dimensional role of PCD in sepsis, establishes a reliable prognostic model with strong predictive value, and highlights SMYAD as a potential multi-target therapy. These findings provide new avenues for risk stratification and suggest the promise of integrating PCD biology with adjunctive immunomodulatory strategies.

Background: Hepatitis B virus (HBV) infection (surface antigen positive, HBsAg+) has been related to the increased risk in follicular lymphoma (FL). The further understanding of features in HBV-associated FL remains lacking.

Methods: We explored clinical risk factors in HBsAg-positive patients from multicentric clinical investigation retrospectively (n = 276) and integrated HBV-related factors into Follicular Lymphoma International Prognostic Index (FLIPI) scoring system for risk prediction. The methylation profiles in pre- and paired HBsAg+FL occurring progression of disease within 2 years (POD24) were determined using the Human Methylation 850K BeadChip platform. Bulk RNA sequencing was performed for gene expression in samples from the same patient and confirmed using Myc^Cd19Cre C57BL/6J chimera mice.

Results: We found that HBsAg+ FL with a higher incidence of POD24. The high HBV-DNA load (>10⁵ copies/mL) was identified as a pivotal risk factor. HBsAg+ FL with the rapidly decreasing viral load showed lower incidence of POD24 than those without viral control (P = 0.026). Integrated risk stratification incorporating HBV-related clinical parameters based on FLIPI scoring systems had potential predictive value for high-risk patients (AUC = 0.616, P = 0.002). The methylation profiles in pre-POD24-HBsAg+FL and paired POD24-HBsAg+FL showed distinguished signatures of methylated KMT2A, EP300-AS1, ARID1B, MHC I class molecular genes related to tumor cells, and TNFRSF1A, LTA, IQCE genes related to immune cells. Of note, we confirmed that the crucial CXCR5 mRNA expression with specific methylated regions was inversely correlated to featured MYC mRNA expression as "trans" regulation in both POD24-HBsAg+FL and Myc^Cd19Cre lymphoma model.

Conclusion: Integrated clinicopathological features into prediction system may provide precise risk stratification for HBV-positive FL. Modifiable DNA methylation acts as the potential targets for the combined treatment strategy to delay POD24 occurrence.

Myeloid-derived suppressor cells (MDSCs) are a heterogeneous population of immature myeloid cells that accumulate under pathological conditions such as cancer, where they contribute to immune evasion, tumor progression, and resistance to therapy. These cells exert potent immunosuppressive effects by inhibiting T cell activation, inducing regulatory T cells, modulating antigen-presenting cells, and shaping an immunosuppressive tumor microenvironment (TME). Their suppressive functions involve multiple mechanisms, including amino acid depletion, production of reactive oxygen and nitrogen species, expression of immune checkpoint ligands, and secretion of immunoregulatory cytokines such as IL-10 and TGF-β. Besides these immune-related roles, MDSCs also promote tumor growth through non-immunological mechanisms, including the stimulation of angiogenesis and undergoing metabolic reprogramming. These adaptations support their survival and function in the hostile TME. Given their multifaceted role in cancer, MDSCs represent a promising target for therapeutic intervention. Furthermore, their abundance and dynamic modulation during treatment confer them tremendous potential as biomarkers to monitor therapy efficacy and stratify patients. This review provides a comprehensive overview of MDSC biology, their mechanisms of action, and their emerging clinical relevance as biomarkers and therapeutic targets. We also explore current strategies aimed at targeting MDSCs, including their depletion, inhibition of recruitment, functional blockade, and promotion of their differentiation into mature myeloid cells. Integrating these approaches with existing cancer therapies holds great promise for enhancing antitumor immunity and overcoming resistance in both solid tumors and hematologic malignancies.

Hyper-IgE syndromes represent an increasingly recognized and heterogeneous group of disorders characterized phenotypically by eczema, recurrent infections, and markedly elevated serum IgE levels. The identification of novel molecular defects in recent years has complicated definitive diagnosis, underscoring the genetic and clinical diversity of this group. In addition to immunological abnormalities, non-immunological manifestations-particularly those affecting connective tissue-contribute to significant comorbidities. The primary objectives of management are to control infections, prevent long-term complications, and improve quality of life. In this review, we summarize the clinical and laboratory features of disorders currently classified under hyper-IgE syndromes according to the most recent International Union of Immunological Societies framework and provide perspectives on their management.

Purpose: Soluble programmed cell death-1 (sPD-1) level can predict hepatitis B surface antigen (HBsAg) loss in adult chronic hepatitis B (CHB) patients. However, whether sPD-1 level can serve as a potential seromarker for predicting HBsAg loss in pediatric patients remained to determine.

Patients and methods: Ninety-two pediatric HBeAg-positive CHB patients who received peginterferon (PegIFN) therapy with available serum samples were studied retrospectively. The average follow-up time was 45.0 months. Virological biomarkers and sPD-1 were serially measured.

Results: A total of 45 (48.9%) children achieved HBsAg loss at the end of treatment (EOT), and 84.4% (38/45) of them remained HBsAg-negative at the end of follow-up. At baseline, sPD-1 levels were comparable between patients who subsequently achieved HBsAg loss and those who did not (P = 0.217). However, a significantly more pronounced increase in sPD-1 levels was observed during PegIFN treatment in the HBsAg loss group (P_trend < 0.001). Consequently, at weeks 12, 24, and EOT, sPD-1 levels were significantly higher in children with HBsAg loss compared to those without (P < 0.001 at all time-points). In ROC curve analysis, sPD-1 had strong discriminatory ability for HBsAg loss at weeks 12 and 24, with area under ROC scores of 0.842 (95% CI, 0.744-0.946) and 0.863 (95% CI, 0.758-0.969), respectively, slightly lower than HBsAg but higher than HBV DNA.

Conclusion: Early on-treatment serum sPD-1 level has a potential predictive value for HBsAg loss in pediatric patients with HBeAg-positive CHB, which might provide a clue to optimize the management of PegIFN therapy. However, a prospective, multi-center study is warranted for further validation.

Purpose: This study investigated shared molecular pathways linking systemic lupus erythematosus (SLE) and coronary artery disease (CAD) to uncover mechanisms of coronary injury in SLE.

Patients and methods: Bulk transcriptomic datasets (GSE45291 for SLE, GSE61145 for CAD) were analyzed to identify differentially expressed genes (DEGs), immune cell infiltration patterns, and co-expression networks. A diagnostic model was constructed and validated using external cohorts (GSE49454 for SLE, GSE179789 for CAD). Machine learning prioritized core genes, validated in both external cohorts and in SLE patients with coronary injury (GSE264125). Cellular localization and intercellular communication were explored by analyzing single-cell RNA-seq data (GSE135779). qPCR was used to validate the gene expression in peripheral blood mononuclear cells (PBMCs) from patients.

Results: We identified 146 common DEGs enriched in immune pathways related to cell toxicity, and found shared dysregulation in cytotoxic lymphocytes such as natural killer (NK) cells and CD8⁺ T cells. Through co-expression analysis and DEG intersection, we pinpointed 11 hub genes (eg, GZMK, KLRK1, GNLY). A diagnostic model based on these genes showed strong performance (SLE: AUC 0.881 training, 0.666 validation; CAD: AUC 0.897 training, 0.781 validation). Machine learning highlighted GZMK and KLRK1 as core genes, which were further validated for their combined diagnostic utility (AUC: 0.782-1.000) in SLE-related coronary injury. Single-cell analysis revealed that these genes are primarily active in cytotoxic CD8⁺ T cells and NK cells, with GZMK linked to CLEC-mediated signaling and KLRK1 to HLA activation. Finally, we confirmed higher expression of these genes in blood cells from SLE patients with coronary artery disease using qPCR.

Conclusion: SLE and CAD share a cytotoxic lymphocyte-driven molecular axis, with GZMK/KLRK1-mediated immune dysregulation as a key contributor to coronary injury in SLE. GZMK and KLRK1 may represent promising biomarkers for early detection and risk stratification of SLE-associated coronary complications. Notably, the discrimination (AUC=1.000) was observed in a limited subgroup of SLE patients with coronary microvascular dysfunction (n=4), warranting further validation in expanded cohorts.

Tuberculosis (TB) is a global infectious disease caused by Mycobacterium tuberculosis (Mtb). Serving as the primary effector cells, macrophages play a crucial role in host immune responses against Mtb. During Mtb infection, macrophages undergo extensive metabolic reprogramming, notably glycolysis, the pentose phosphate pathway (PPP) and the tricarboxylic acid (TCA) cycle, to adapt to the challenges posed by the pathogen, with glucose metabolic rewiring being particularly critical. This review focuses on the dynamic reprogramming of glucose metabolism in macrophages during Mtb infection, highlighting how metabolic adjustments influence the activation state, polarization, and functional capacity of macrophages. Furthermore, we explore the role of glucose metabolic reprogramming in shaping the immune responses against Mtb, particularly its contribution to granuloma formation and maintenance. By understanding the intricate interplay between metabolic rewiring and immune function, we discuss the therapeutic potential of targeting glucose metabolic pathways in macrophages as a novel strategy for TB treatment. Overall, this review emphasizes the need for a deeper understanding of the relationship between glucose metabolism reprogramming and the biological function of Mtb-infected macrophages and the development of novel immunometabolic therapies-such as metformin (AMPK activator) or PKM2 modulators already used in oncology- to improve the outcomes of TB patients.