CD24 is a glycosylphosphatidylinositol-anchored surface protein frequently overexpressed in solid tumors and increasingly recognized as an innate immune checkpoint that suppresses macrophage-mediated phagocytosis through engagement of Siglec-10 in humans (Siglec-G in mice). Beyond its associations with tumor aggressiveness and stem-like phenotypes, CD24 functions at a critical interface between tumor-intrinsic plasticity and myeloid-driven immune suppression within the tumor microenvironment (TME). Despite growing therapeutic interest, clinical translation of CD24 targeting has been limited by tumor heterogeneity, redundancy among innate immune checkpoints, safety concerns related to physiological CD24 expression, and the absence of functional biomarker frameworks. In this review, we synthesize recent advances in CD24 biology, biomarker-guided stratification strategies, and emerging CD24-directed therapeutic modalities. We highlight unresolved controversies, define key translational challenges, and propose future directions for integrating CD24 targeting into precision immunotherapy strategies tailored to dominant immune resistance mechanisms in solid tumors.
İlkim Deniz Toprak, MD, Sule Celik Kamaci, MD, Merve Hormet Igde, MD
et al.
Aim: The study aimed to analyse the characteristics of subtypes of chronic spontaneous urticaria (CSU) and determine the factors that may influence treatment response. Methods: Clinical and laboratory characteristics of CSU patients were compared between 3 groups: (1) those with isolated urticaria (CSUwoAE, n = 50, 27.93%), (2) those with both urticaria and angioedema (CSUwAE, n = 86, 48.04%), and (3) those with isolated angioedema, also referred to as chronic histaminergic angioedema (CHA, n = 43, 24.02%). The Visual Analogue Scale for the worst attack (VAS-WA), Visual Analogue Scale for angioedema control (VAS-AEC), angioedema control test (AECT), Urticaria Control Test (UCT) and Angioedema Quality of Life (AEQoL) were the assessment tools. Results: A weak correlation was observed between disease onset age and BMI (r = 0.295, p < 0.001). The age of onset was higher in CHA than in CSUwAE (p < 0.001). Oropharyngeal angioedema was more frequent in CHA (p = 0.022), whereas eyelid angioedema occurred more often in CSUwAE (p = 0.001). CHA patients had more impaired AEQoL (p = 0.043) and poorer disease control per AECT and VAS-AEC (p < 0.001, p = 0.001, respectively). The rate of high-dose antihistamine response among patients with angioedema was lower in the CHA group (n = 1; 12.5%) compared with the CSUwAE group (n = 10; 66.7%; p = 0.027). Irrespective of CSU subtypes, omalizumab response varied by BMI in patients with angioedema (n = 51; p = 0.002), with the least response in obese patients (p = 0.033). Conclusion: Our study observed that obesity appeared to be associated with a later age of CSU onset and with a lower likelihood of omalizumab responsiveness in patients with angioedema; however, these findings should be interpreted as associations rather than suggesting any causal relationship. Additionally, CHA patients seem to have more severely impaired quality of life and a less controlled disease compared to CSUwAE.
Osteopontin (OPN) is a multifunctional glycoprotein with various structural domains that enable it to perform diverse functions in both physiological and pathological states. This review comprehensively examines OPN from multiple perspectives, including its protein structure, interactions with receptors, interactions with immune cells, and roles in kidney diseases and transplantation. This review explores the immunological duality of OPN and its significance and value as a biomarker and therapeutic target in kidney transplantation. In cancer, OPN typically promotes tumor evasion by suppressing the immune system. Conversely, in immune-related kidney diseases, particularly kidney transplantation, OPN activates the immune system by enhancing the migration and activation of immune cells, thereby exacerbating kidney damage. This immunological duality may stem from different OPN splice variants and the exposure, after cleavage, of different structural domains, which play distinct biological roles in cellular interactions. Additionally, OPN has a significant biological impact posttransplantation and on chronic kidney disease and, highlighting its importance as a biomarker and potential therapeutic target. Future research should further explore the specific mechanisms of OPN in kidney transplantation to improve treatment strategies and enhance patient quality of life.
Cydney N. Johnson, Matthew W. Frank, Chrispin Chaguza
et al.
ABSTRACT Profiles of human nasal colonization consistently demonstrate that Staphylococcus aureus and Streptococcus pneumoniae can co-exist in the nasopharynx. Several studies have demonstrated the antagonist relationship between the two organisms via several molecular mechanisms, including competition for nutrients as well as via direct killing by hydrogen peroxide. During nasal colonization, the pneumococcus is in direct contact with the fatty acid h 18:0, which is released into the extracellular environment by S. aureus . We report that h 18:0 is specifically toxic to the pneumococcus among the pathogenic streptococci, providing a unique mechanism for interspecies competition during colonization. Exposure of cells to h 18:0 revealed that S. pneumoniae could rapidly adapt to and overcome the observed toxicity. Whole-genome analysis revealed the mechanism underlying this resistance being linked to a truncation of a glycosyltransferase in the capsule biosynthesis locus and a genomic inversion in the phase variation locus, leading to altered cell surface charge and membrane lipid composition. These physiological differences in the resistant isolates may aid in repelling toxic, charged fatty acids such as h 18:0 from the cell membrane. IMPORTANCE The pneumococcus and S. aureus are two of the most well-characterized residents of the human nasopharynx; yet much remains unknown regarding how the two bacteria interact. Here, we describe the potential of S. aureus -produced h 18:0, whose function and biological impact are still being described, to act as an interspecies competition molecule against S. pneumoniae , and how the pneumococcus can adapt to overcome its toxicity.
Pierrick Coupé, Boris Mansencal, José V. Manjón
et al.
The differential diagnosis of neurodegenerative diseases, characterized by overlapping symptoms, may be challenging. Brain imaging coupled with artificial intelligence has been previously proposed for diagnostic support, but most of these methods have been trained to discriminate only isolated diseases from controls. Here, we develop a novel machine learning framework, named lifespan tree of brain anatomy, dedicated to the differential diagnosis between multiple diseases simultaneously. It integrates the modeling of volume changes for 124 brain structures during the lifespan with non-linear dimensionality reduction and synthetic sampling techniques to create easily interpretable representations of brain anatomy over the course of disease progression. As clinically relevant proof-of-concept applications, we constructed a cognitive lifespan tree of brain anatomy for the differential diagnosis of six causes of neurodegenerative dementia and a motor lifespan tree of brain anatomy for the differential diagnosis of four causes of parkinsonism using 37594 MRI as a training dataset. This original approach enhanced significantly the efficiency of differential diagnosis in the external validation cohort of 1754 cases, outperforming existing state-of-the art machine learning techniques. Lifespan tree holds promise as a valuable tool for differential diagnostic in relevant clinical conditions, especially for diseases still lacking effective biological markers.
Gustavo A. Sousa, Diogo L. M. Souza, Enrique C. Gabrick
et al.
The study of infectious disease propagation is essential for understanding and controlling epidemics. One of the most useful tools for gaining insights into the spread of infectious diseases is mathematical modelling. In terms of mathematical epidemiology, the main models are based on compartments, such as SI, SIR, and SEIR. These models offer mathematical frameworks for representing the proliferation dynamics of various diseases, for instance flu and smallpox. In this work, we explore these models using two distinct mathematical approaches, Cellular Automata (CA) and ODEs. They are able to reproduce the spread dynamics of diseases with their own individuality. CA models incorporate the local interaction among individuals with discrete time and space, while ODEs provide a continuous and simplified view of a disease propagation in large and homogeneous populations. By comparing these two approaches, we find that the shape of the curves of all models is similar for both representations. Although, the growth rates differ between CA and ODE. One of our results is to show that the CA yields a power-law growth, while the ODE growth rate is well-represented by an exponential function. Furthermore, a substantial contribution of our work is using a hyperbolic tangent to fit the initial growth of infected individuals for all the considered models. Our results display a strong correlation between simulated data and adjusted function. We mainly address this successful result by the fact that the hyperbolic function captures both growing: the power-law (when considered the first terms of infinite sums) and combinations of exponential (when the hyperbolic function is written via exponential). Therefore, our work shows that when modelling a disease the choice of mathematical representation is crucial, in particular to model the onset of an epidemic.
Image segmentation plays a vital role in the medical field by isolating organs or regions of interest from surrounding areas. Traditionally, segmentation models are trained on a specific organ or a disease, limiting their ability to handle other organs and diseases. At present, few advanced models can perform multi-organ or multi-disease segmentation, offering greater flexibility. Also, recently, prompt-based image segmentation has gained attention as a more flexible approach. It allows models to segment areas based on user-provided prompts. Despite these advances, there has been no dedicated work on prompt-based interactive multi-organ and multi-disease segmentation, especially for Chest X-rays. This work presents two main contributions: first, generating doodle prompts by medical experts of a collection of datasets from multiple sources with 23 classes, including 6 organs and 17 diseases, specifically designed for prompt-based Chest X-ray segmentation. Second, we introduce Prompt2SegCXR, a lightweight model for accurately segmenting multiple organs and diseases from Chest X-rays. The model incorporates multi-stage feature fusion, enabling it to combine features from various network layers for better spatial and semantic understanding, enhancing segmentation accuracy. Compared to existing pre-trained models for prompt-based image segmentation, our model scores well, providing a reliable solution for segmenting Chest X-rays based on user prompts.
Previous foundation models for fundus images were pre-trained with limited disease categories and knowledge base. Here we introduce a knowledge-rich vision-language model (RetiZero) that leverages knowledge from more than 400 fundus diseases. For RetiZero's pretraining, we compiled 341,896 fundus images paired with texts, sourced from public datasets, ophthalmic literature, and online resources, encompassing a diverse range of diseases across multiple ethnicities and countries. RetiZero exhibits remarkable performance in several downstream tasks, including zero-shot disease recognition, image-to-image retrieval, AI-assisted clinical diagnosis,few-shot fine-tuning, and internal- and cross-domain disease identification. In zero-shot scenarios, RetiZero achieves Top-5 accuracies of 0.843 for 15 diseases and 0.756 for 52 diseases. For image retrieval, it achieves Top-5 scores of 0.950 and 0.886 for the same sets, respectively. AI-assisted clinical diagnosis results show that RetiZero's Top-3 zero-shot performance surpasses the average of 19 ophthalmologists from Singapore, China, and the United States. RetiZero substantially enhances clinicians' accuracy in diagnosing fundus diseases, in particularly rare ones. These findings underscore the value of integrating the RetiZero into clinical settings, where various fundus diseases are encountered.
Recent advances in prototypical learning have shown remarkable potential to provide useful decision interpretations associating activation maps and predictions with class-specific training prototypes. Such prototypical learning has been well-studied for various single-label diseases, but for quite relevant and more challenging multi-label diagnosis, where multiple diseases are often concurrent within an image, existing prototypical learning models struggle to obtain meaningful activation maps and effective class prototypes due to the entanglement of the multiple diseases. In this paper, we present a novel Cross- and Intra-image Prototypical Learning (CIPL) framework, for accurate multi-label disease diagnosis and interpretation from medical images. CIPL takes advantage of common cross-image semantics to disentangle the multiple diseases when learning the prototypes, allowing a comprehensive understanding of complicated pathological lesions. Furthermore, we propose a new two-level alignment-based regularisation strategy that effectively leverages consistent intra-image information to enhance interpretation robustness and predictive performance. Extensive experiments show that our CIPL attains the state-of-the-art (SOTA) classification accuracy in two public multi-label benchmarks of disease diagnosis: thoracic radiography and fundus images. Quantitative interpretability results show that CIPL also has superiority in weakly-supervised thoracic disease localisation over other leading saliency- and prototype-based explanation methods.
UAVs emerge as the optimal carriers for visual weed iden?tification and integrated pest and disease management in crops. How?ever, the absence of specialized datasets impedes the advancement of model development in this domain. To address this, we have developed the Pests and Diseases Tree dataset (PDT dataset). PDT dataset repre?sents the first high-precision UAV-based dataset for targeted detection of tree pests and diseases, which is collected in real-world operational environments and aims to fill the gap in available datasets for this field. Moreover, by aggregating public datasets and network data, we further introduced the Common Weed and Crop dataset (CWC dataset) to ad?dress the challenge of inadequate classification capabilities of test models within datasets for this field. Finally, we propose the YOLO-Dense Pest (YOLO-DP) model for high-precision object detection of weed, pest, and disease crop images. We re-evaluate the state-of-the-art detection models with our proposed PDT dataset and CWC dataset, showing the completeness of the dataset and the effectiveness of the YOLO-DP. The proposed PDT dataset, CWC dataset, and YOLO-DP model are pre?sented at https://github.com/RuiXing123/PDT_CWC_YOLO-DP.
Hepatocellular carcinoma (HCC) is a malignant tumor with high recurrence and metastasis rates and poor prognosis. Basement membrane is a ubiquitous extracellular matrix and is a key physical factor in cancer metastasis. Therefore, basement membrane-related genes may be new targets for the diagnosis and treatment of HCC. We systematically analyzed the expression pattern and prognostic value of basement membrane-related genes in HCC using the TCGA-HCC dataset, and constructed a new BMRGI based on WGCNA and machine learning. We used the HCC single-cell RNA-sequencing data in GSE146115 to describe the single-cell map of HCC, analyzed the interaction between different cell types, and explored the expression of model genes in different cell types. BMRGI can accurately predict the prognosis of HCC patients and was validated in the ICGC cohort. In addition, we also explored the underlying molecular mechanisms and tumor immune infiltration in different BMRGI subgroups, and confirmed the differences in response to immunotherapy in different BMRGI subgroups based on the TIDE algorithm. Then, we assessed the sensitivity of HCC patients to common drugs. In conclusion, our study provides a theoretical basis for the selection of immunotherapy and sensitive drugs in HCC patients. Finally, we also considered CTSA as the most critical basement membrane-related gene affecting HCC progression. In vitro experiments showed that the proliferation, migration and invasion abilities of HCC cells were significantly impaired when CTSA was knocked down.
IntroductionOvulation dysfunction is now a widespread cause of infertility around the world. Although the impact of immune cells in human reproduction has been widely investigated, systematic understanding of the changes of the immune atlas under female ovulation remain less understood.MethodsHere, we generated single cell transcriptomic profiles of 80,689 PBMCs in three representative statuses of ovulation dysfunction, i.e., polycystic ovary syndrome (PCOS), primary ovarian insufficiency (POI) and menopause (MENO), and identified totally 7 major cell types and 25 subsets of cells.ResultsOur study revealed distinct cluster distributions of immune cells among individuals of ovulation disorders and health. In patients with ovulation dysfunction, we observed a significant reduction in populations of naïve CD8 T cells and effector memory CD4 T cells, whereas circulating NK cells and regulatory NK cells increased.DiscussionOur results highlight the significant contribution of cDC-mediated signaling pathways to the overall inflammatory response within ovulation disorders. Furthermore, our data demonstrated a significant upregulation of oxidative stress in patients with ovulation disorder. Overall, our study gave a deeper insight into the mechanism of PCOS, POI, and menopause, which may contribute to the better diagnosis and treatments of these ovulatory disorder.
Armando D. Diaz Gonzalez, Kevin S. Hughes, Songhui Yue
et al.
Published biomedical information has and continues to rapidly increase. The recent advancements in Natural Language Processing (NLP), have generated considerable interest in automating the extraction, normalization, and representation of biomedical knowledge about entities such as genes and diseases. Our study analyzes germline abstracts in the construction of knowledge graphs of the of the immense work that has been done in this area for genes and diseases. This paper presents SimpleGermKG, an automatic knowledge graph construction approach that connects germline genes and diseases. For the extraction of genes and diseases, we employ BioBERT, a pre-trained BERT model on biomedical corpora. We propose an ontology-based and rule-based algorithm to standardize and disambiguate medical terms. For semantic relationships between articles, genes, and diseases, we implemented a part-whole relation approach to connect each entity with its data source and visualize them in a graph-based knowledge representation. Lastly, we discuss the knowledge graph applications, limitations, and challenges to inspire the future research of germline corpora. Our knowledge graph contains 297 genes, 130 diseases, and 46,747 triples. Graph-based visualizations are used to show the results.
Hiroki Murai, Makoto Irahara, Mayumi Sugimoto
et al.
Background: IgE-mediated egg allergy is a common food allergy worldwide. Patients with egg allergy are known to easily achieve tolerance compared to other allergens such as nuts. Oral food challenge (OFC) is often performed on patients diagnosed with or suspected of having IgE-mediated food allergy, but whether hen's egg OFC is useful in IgE-dependent egg allergy patients to avoid complete elimination remains unknown. Methods: We identified articles in which OFCs were performed in Japanese patients diagnosed with or suspected of having IgE-mediated egg allergy. We evaluated whether the OFCs were useful to avoid the complete elimination of eggs by assessing the following: (1) the number of patients who could avoid complete elimination; (2) the number of patients who experienced serious adverse events (SAEs); or (3) adverse events (AEs); (4) improvement in quality of life (QOL); and (5) immunological changes. Results: Fifty-nine articles were selected in the study; all the references were case series or case studies in which OFC was compared to pre-challenge conditions. The overall negative ratio against egg OFC was 62.7%, but an additional 71.9% of OFC-positive patients could take eggs when expanded to partial elimination. Of the 4182 cases, 1146 showed AEs in the OFC, and two cases reached an SAE. Two reports showed an improvement in QOL and immunological changes, although the evidence was weak. Conclusions: OFCs against eggs may be useful to avoid complete elimination, but medical professionals should proceed with the test safely and carefully.
BackgroundIgA nephropathy (IgAN) is the most frequent glomerulonephritis in inflammatory bowel disease (IBD). However, the inter-relational mechanisms between them are still unclear. This study aimed to explore the shared gene effects and potential immune mechanisms in IgAN and IBD.MethodsThe microarray data of IgAN and IBD in the Gene Expression Omnibus (GEO) database were downloaded. The differential expression analysis was used to identify the shared differentially expressed genes (SDEGs). Besides, the shared transcription factors (TFs) and microRNAs (miRNAs) in IgAN and IBD were screened using humanTFDB, HMDD, ENCODE, JASPAR, and ChEA databases. Moreover, weighted gene co-expression network analysis (WGCNA) was used to identify the shared immune-related genes (SIRGs) related to IgAN and IBD, and R software package org.hs.eg.db (Version3.1.0) were used to identify common immune pathways in IgAN and IBD.ResultsIn this study, 64 SDEGs and 28 SIRGs were identified, and the area under the receiver operating characteristic curve (ROC) of 64 SDEGs was calculated and two genes (MVP, PDXK) with high area under the curve (AUC) in both IgAN and IBD were screened out as potential diagnostic biomarkers. We then screened 3 shared TFs (SRY, MEF2D and SREBF1) and 3 miRNAs (hsa-miR-146, hsa-miR-21 and hsa-miR-320), and further found that the immune pathways of 64SDEGs, 28SIRGs and 3miRNAs were mainly including B cell receptor signaling pathway, FcγR-mediated phagocytosis, IL-17 signaling pathway, toll-like receptor signaling pathway, TNF signaling pathway, TRP channels, T cell receptor signaling pathway, Th17 cell differentiation, and cytokine-cytokine receptor interaction.ConclusionOur work revealed the differentiation of Th17 cells may mediate the abnormal humoral immunity in IgAN and IBD patients and identified novel gene candidates that could be used as biomarkers or potential therapeutic targets.