Hasil untuk "Immunologic diseases. Allergy"

Fernando Zhapa-Camacho, Robert Hoehndorf

Motivation: Predicting gene-disease associations (GDAs) is the problem to determine which gene is associated with a disease. GDA prediction can be framed as a ranking problem where genes are ranked for a query disease, based on features such as phenotypic similarity. By describing phenotypes using phenotype ontologies, ontology-based semantic similarity measures can be used. However, traditional semantic similarity measures use only the ontology taxonomy. Recent methods based on ontology embeddings compare phenotypes in latent space; these methods can use all ontology axioms as well as a supervised signal, but are inherently transductive, i.e., query entities must already be known at the time of learning embeddings, and therefore these methods do not generalize to novel diseases (sets of phenotypes) at inference time. Results: We developed INDIGENA, an inductive disease-gene association method for ranking genes based on a set of phenotypes. Our method first uses a graph projection to map axioms from phenotype ontologies to a graph structure, and then uses graph embeddings to create latent representations of phenotypes. We use an explicit aggregation strategy to combine phenotype embeddings into representations of genes or diseases, allowing us to generalize to novel sets of phenotypes. We also develop a method to make the phenotype embeddings and the similarity measure task-specific by including a supervised signal from known gene-disease associations. We apply our method to mouse models of human disease and demonstrate that we can significantly improve over the inductive semantic similarity baseline measures, and reach a performance similar to transductive methods for predicting gene-disease associations while being more general. Availability and Implementation: https://github.com/bio-ontology-research-group/indigena

Chenjie Qiu, Honglei Shi, Honglei Shi et al.

ObjectiveTo analyze the temporal mortality trends and demographic/geographic disparities of autoimmune liver diseases (ALDs) among individuals aged 45 years or older in the U.S. from 1999 to 2023, and provide evidence for targeted prevention and control strategies.MethodsBased on the CDC WONDER database, multiple-cause-mention ALD-related deaths (AIH, PBC, PSC) were identified using ICD-10 codes. Age-adjusted mortality rates (AAMRs), annual percent changes (APC/AAPC) were calculated, and trend analyses were conducted via Joinpoint regression. The study population was restricted to decedents aged ≥45 years due to data availability constraints.ResultsTotal multiple-cause-mention ALD-related deaths increased by 165.05%, with AAMR rising from 1.65 to 2.74 per 100,000 (AAPC = 2.48). Females had a higher AAMR (2.97 per 100,000) than males (2.53 per 100,000) in 2023. The 45–54 age group had the fastest AAPC (2.56), and non-Hispanic Black individuals had the highest AAPC (2.94). The West had the highest AAMR (3.24 per 100,000) in 2023. Based on data from 1999-2020, in 2020, AAMR in nonmetropolitan areas (3.09 per 100,000) surpassed that in metropolitan areas, with widening urban-rural gaps.ConclusionU.S. ALD-related multiple-cause-mention mortality continues to rise among adults aged ≥45 years, with significant disparities across age, race/ethnicity, and urban-rural regions. Improving healthcare access for vulnerable populations and developing new therapies are essential to reduce the disease burden.

Tingsong Xiao, Yao An Lee, Zelin Xu et al.

Disease progression modeling aims to characterize and predict how a patient's disease complications worsen over time based on longitudinal electronic health records (EHRs). For diseases such as type 2 diabetes, accurate progression modeling can enhance patient sub-phenotyping and inform effective and timely interventions. However, the problem is challenging due to the need to learn continuous-time progression dynamics from irregularly sampled clinical events amid patient heterogeneity (e.g., different progression rates and pathways). Existing mechanistic and data-driven methods either lack adaptability to learn from real-world data or fail to capture complex continuous-time dynamics on progression trajectories. To address these limitations, we propose Temporally Detailed Hypergraph Neural Ordinary Differential Equation (TD-HNODE), which represents disease progression on clinically recognized trajectories as a temporally detailed hypergraph and learns the continuous-time progression dynamics via a neural ODE framework. TD-HNODE contains a learnable TD-Hypergraph Laplacian that captures the interdependency of disease complication markers within both intra- and inter-progression trajectories. Experiments on two real-world clinical datasets demonstrate that TD-HNODE outperforms multiple baselines in modeling the progression of type 2 diabetes and related cardiovascular diseases.

Ananya Joshi, George Khoury, Christodoulas Floudas

Alzheimer's disease is characterized by dangerous amyloid plaques formed by deposits of the protein Beta-Amyloid aggregates in the brain. The specific amino acid sequence that is responsible for the aggregates of Beta-Amyloid is lys-leu-val-phe-phe (KLVFF). KLVFF aggregation inhibitors, which we design in this paper, prevent KLVFF from binding with itself to form oligomers or fibrils (and eventually plaques) that cause neuronal death. Our binder-blocker peptides are designed such that, on one side, they bind strongly to KLVFF, and on the other side, they disrupt critical interactions, thus preventing aggregation. Our methods use optimization techniques and molecular simulations and identify 10 candidate sequences for trial of the 3.2 million possible sequences. This approach for inhibitor identification can be generalized to other diseases characterized by protein aggregation, such as Parkinson's, Huntington's, and prion diseases.

Catarina Canastra, Cátia Pesquita

Discovery gene-disease links is important in biology and medicine areas, enabling disease identification and drug repurposing. Machine learning approaches accelerate this process by leveraging biological knowledge represented in ontologies and the structure of knowledge graphs. Still, many existing works overlook ontologies explicitly representing diseases, missing causal and semantic relationships between them. The gene-disease association problem naturally frames itself as a link prediction task, where embedding algorithms directly predict associations by exploring the structure and properties of the knowledge graph. Some works frame it as a node-pair classification task, combining embedding algorithms with traditional machine learning algorithms. This strategy aligns with the logic of a machine learning pipeline. However, the use of negative examples and the lack of validated gene-disease associations to train embedding models may constrain its effectiveness. This work introduces a novel framework for comparing the performance of link prediction versus node-pair classification tasks, analyses the performance of state of the art gene-disease association approaches, and compares the different order-based formalizations of gene-disease association prediction. It also evaluates the impact of the semantic richness through a disease-specific ontology and additional links between ontologies. The framework involves five steps: data splitting, knowledge graph integration, embedding, modeling and prediction, and method evaluation. Results show that enriching the semantic representation of diseases slightly improves performance, while additional links generate a greater impact. Link prediction methods better explore the semantic richness encoded in knowledge graphs. Although node-pair classification methods identify all true positives, link prediction methods outperform overall.

Olga V. Kovaleva, Madina A. Rashidova, Vasiliy V. Sinyov et al.

The anti-tumor role of the immune system has long been associated with interferon-γ-mediated activation of immune cells and their ability to recognize and eliminate transformed cells. Fundamental principles of tumor immunoediting describe a dynamic interplay between the immune system and neoplastic cells, wherein immune pressure can paradoxically shape tumor evolution. Within this context, macrophages, natural killer cells, and T lymphocytes are central effectors of anti-tumor immunity. Traditionally, macrophages exhibiting M1 phenotype are characterized by high cytotoxic potential and considered important contributors to tumor eradication. In contrast, M2-polarized tumor-associated macrophages are associated with immune suppression and tumor progression. However, recent evidence challenges this binary paradigm. It is increasingly evident that M1 macrophages, while initially exerting anti-tumor effects, can also promote tumor progression by applying sustained cytotoxic pressure that selects for more malignant and immune-resistant tumor clones. This phenomenon represents an unexpected and overlooked contribution of cytotoxic macrophages to tumor progression. In this review, we examine the complex, context-dependent function of M1 macrophages and reassess current strategies aimed at enhancing their cytotoxicity. While such approaches may offer short-term benefits, they risk driving clonal selection of aggressive, immune-evasive tumor cells. Therefore, we propose a paradigm shift: instead of promoting M1 polarization alone, therapeutic strategies should consider the broader consequences of macrophage–tumor interactions. A nuanced understanding of macrophage plasticity and tumor dynamics is essential for designing effective immunotherapies. Recognizing the paradoxical role of M1 macrophages is critical to avoiding unintended support of tumor evolution and improving treatment outcomes.

Guillaume Sarrabayrouse, Corentin Joulain, Stéphanie Bessoles et al.

A number of drug treatments are known to alter the dialogue between the gut microbiota and the immune system components in the digestive mucosa. Alterations in intestinal homeostasis are now well known to affect peripheral immune responses and favor the occurrence of a number of pathologies such as allergies and cancers. Erythropoietin’s known pleiotropic effects might explain the adverse events sometimes observed in anemic patients treated by erythropoiesis-stimulating agents (ESA). However, the impact of this therapeutic cytokine on the homeostasis of the intestinal tract has not previously been investigated in detail. By studying a mouse model of erythropoietin (EPO) supplementation for 28 days, we observed EPO-induced dysbiosis of the fecal microbiota characterized by a greater bacterial load, lower bacterial diversity and taxonomic changes. With regard to the mucosal immune system, an analysis of leukocyte populations in the small intestine and colon treatment revealed low proportions of ileal CD4 lymphocyte subpopulations (Treg, Tr17 and Th17 cells), IgA-secreting plasma cells, and a major macrophage subpopulation, involved in the control of lymphocyte responses. Our results provide for the first time a descriptive analysis of intestinal EPO’s regulatory properties and raise questions about the involvement of EPO-induced alterations in the microbiota and the gut immune effectors in the control of intestinal and peripheral immune responses.

Hui Tang, Binsheng Fu, Qing Yang et al.

ObjectiveTo explore the clinical experience of split liver transplantation (SLT) as a salvage treatment for acute graft failure (AGF) caused by immune checkpoint inhibitors (ICIs).MethodsThe clinical data of one hepatocellular carcinoma (HCC) patient who underwent two liver transplants were retrospectively reviewed.ResultsThe patient received multiple PD-1/PD-L1 inhibitor treatments, with the last one administered 16 days prior to the first transplant. On postoperative day 7, there was a rapid increase in transaminases, indicating acute rejection, which was treated with additional Rabbit anti-human thymocyte immunoglobulin(ATG). On day 14, the patient presented with fatigue, shortness of breath, and abdominal distension. An ultrasound revealed reversed portal vein flow and significant liver enlargement. Given the patient’s deteriorating condition, a rescue second liver transplant (complete right lobe split liver transplantation with middle hepatic vein bipartition/reconstruction) was performed on day 16. The anti-rejection regimen included ATG, Baliximab, Rituximab, glucocorticoids, and intravenous immunoglobulin (IVIG). Postoperative pathology indicated acute liver failure due to humoral rejection. The patient has since been followed for over 12 months, with stable liver function and no signs of rejection or tumor recurrence.ConclusionsThis case highlights the need for cautious use of ICIs before liver transplantation and supports SLT as an effective option in cases of AGF.

Fernando Pérez-Calvo, Francisco Jover-Diaz, Elisabet Delgado-Sánchez et al.

Chuang Liu, Qixiang Kang, Xuanci Wang et al.

IntroductionDucks rank among the most important sources of animal protein globally, yet hepatic and splenic hemorrhage and necrosis in Muscovy ducks present a critical challenge to the poultry industry. The causes behind such diseases are often multifaceted, involving both established and newly emerging pathogens. MethodsIn this study, we leveraged metatranscriptomic sequencing to profile the intestinal viral communities of healthy and diseased Muscovy ducks from a Guangdong Province farm that experienced a hepatic and splenic hemorrhage in June 2024. ResultsOur findings revealed marked differences in viral community profiles between the two groups, with the diseased cohort exhibiting higher α-diversity. Taxonomic analyses across multiple levels uncovered significant variations in viral composition, including shifts in phylums like Uroviricota and families such as Demerecviridae. At the genus and species levels, several bacteriophages and eukaryotic viruses displayed differential abundance. Notably, Avian orthoreovirus was detected exclusively in diseased ducks, with a specific novel duck reovirus (NDRV) validated via RT-qPCR as a potential contributor to hepatic and splenic pathogenesis. In contrast, known pathogens such as Duck hepatitis A virus (DHAV) and Fowl adenovirus serotype 4 (FAdV-4) were not detected. DiscussionThis study constitutes the first comprehensive analysis of the Muscovy duck gut virome, highlighting NDRV as a potential causative agent and emphasizing the utility of metatranscriptomics in pathogen discovery.

C. Akdis, M. Akdiş

Substantial progress in understanding mechanisms of immune regulation in allergy, asthma, autoimmune diseases, tumors, organ transplantation and chronic infections has led to a variety of targeted therapeutic approaches. Allergen-specific immunotherapy (AIT) has been used for 100 years as a desensitizing therapy for allergic diseases and represents the potentially curative and specific way of treatment. The mechanisms by which allergen-AIT has its mechanisms of action include the very early desensitization effects, modulation of T- and B-cell responses and related antibody isotypes as well as inhibition of migration of eosinophils, basophils and mast cells to tissues and release of their mediators. Regulatory T cells (Treg) have been identified as key regulators of immunological processes in peripheral tolerance to allergens. Skewing of allergen-specific effector T cells to a regulatory phenotype appears as a key event in the development of healthy immune response to allergens and successful outcome in AIT. Naturally occurring FoxP3+ CD4+CD25+ Treg cells and inducible type 1 Treg (Tr1) cells contribute to the control of allergen-specific immune responses in several major ways, which can be summarized as suppression of dendritic cells that support the generation of effector T cells; suppression of effector Th1, Th2 and Th17 cells; suppression of allergen-specific IgE, and induction of IgG4; suppression of mast cells, basophils and eosinophils and suppression of effector T cell migration to tissues. New strategies for immune intervention will likely include targeting of the molecular mechanisms of allergen tolerance and reciprocal regulation of effector and regulatory T cell subsets.

Salini Mohanty, Jui-Hua Tsai, Ning Ning et al.

As higher-valent pneumococcal conjugate vaccines (PCVs) become available for pediatric populations in the US, it is important to understand healthcare provider (HCP) preferences for and acceptability of PCVs. US HCPs (pediatricians, family medicine physicians and advanced practitioners) completed an online, cross-sectional survey between March and April 2023. HCPs were eligible if they recommended or prescribed vaccines to children age <24 months, spent ≥25% of their time in direct patient care, and had ≥2 y of experience in their profession. The survey included a discrete choice experiment (DCE) in which HCPs selected preferred options from different hypothetical vaccine profiles with systematic variation in the levels of five attributes. Relative attribute importance was quantified. Among 548 HCP respondents, the median age was 43.2 y, and the majority were male (57.9%) and practiced in urban areas (69.7%). DCE results showed that attributes with the greatest impact on HCP decision-making were 1) immune response for the shared serotypes covered by PCV13 (31.4%), 2) percent of invasive pneumococcal disease (IPD) covered by vaccine serotypes (21.3%), 3) acute otitis media (AOM) label indication (20.3%), 4) effectiveness against serotype 3 (17.6%), and 5) number of serotypes in the vaccine (9.5%). Among US HCPs, the most important attribute of PCVs was comparability of immune response for PCV13 shared serotypes, while the number of serotypes was least important. Findings suggest new PCVs eliciting high immune responses for serotypes that contribute substantially to IPD burden and maintaining immunogenicity against serotypes in existing PCVs are preferred by HCPs.

Farha Naz, Mohd Arish, Imtaiyaz Hassan

Timothy G. Wanninger, Timothy G. Wanninger, Omar A. Saldarriaga et al.

BackgroundThe inflammatory macrophage response contributes to severe Ebola virus disease, with liver and lung injury in humans.ObjectiveWe sought to further define the activation status of hepatic and pulmonary macrophage populations in Ebola virus disease.MethodsWe compared liver and lung tissue from terminal Ebola virus (EBOV)-infected and uninfected control cynomolgus macaques challenged via the conjunctival route. Gene and protein expression was quantified using the nCounter and GeoMx Digital Spatial Profiling platforms. Macrophage phenotypes were further quantified by digital pathology analysis.ResultsHepatic macrophages in the EBOV-infected group demonstrated a mixed inflammatory/non-inflammatory profile, with upregulation of CD163 protein expression, associated with macrophage activation syndrome. Hepatic macrophages also showed differential expression of gene sets related to monocyte/macrophage differentiation, antigen presentation, and T cell activation, which were associated with decreased MHC-II allele expression. Moreover, hepatic macrophages had enriched expression of genes and proteins targetable with known immunomodulatory therapeutics, including S100A9, IDO1, and CTLA-4. No statistically significant differences in M1/M2 gene expression were observed in hepatic macrophages compared to controls. The significant changes that occurred in both the liver and lung were more pronounced in the liver.ConclusionThese data demonstrate that hepatic macrophages in terminal conjunctivally challenged cynomolgus macaques may express a unique inflammatory profile compared to other macaque models and that macrophage-related pharmacologically druggable targets are expressed in both the liver and the lung in Ebola virus disease.

Auvick Chandra Bhowmik, Md. Taimur Ahad, Yousuf Rayhan Emon

Jamun leaf diseases pose a significant threat to agricultural productivity, negatively impacting both yield and quality in the jamun industry. The advent of machine learning has opened up new avenues for tackling these diseases effectively. Early detection and diagnosis are essential for successful crop management. While no automated systems have yet been developed specifically for jamun leaf disease detection, various automated systems have been implemented for similar types of disease detection using image processing techniques. This paper presents a comprehensive review of machine learning methodologies employed for diagnosing plant leaf diseases through image classification, which can be adapted for jamun leaf disease detection. It meticulously assesses the strengths and limitations of various Vision Transformer models, including Transfer learning model and vision transformer (TLMViT), SLViT, SE-ViT, IterationViT, Tiny-LeViT, IEM-ViT, GreenViT, and PMViT. Additionally, the paper reviews models such as Dense Convolutional Network (DenseNet), Residual Neural Network (ResNet)-50V2, EfficientNet, Ensemble model, Convolutional Neural Network (CNN), and Locally Reversible Transformer. These machine-learning models have been evaluated on various datasets, demonstrating their real-world applicability. This review not only sheds light on current advancements in the field but also provides valuable insights for future research directions in machine learning-based jamun leaf disease detection and classification.

Yannick Feld, Alexander K. Hartmann

We numerically study disease dynamics that lead to the disease switching from one host species to another, resulting in diseases gaining the ability to infect, e.g., humans. Unlike previous studies that focused on branching processes starting with the first infected humans, we begin by considering a disease pathogen that initially cannot infect humans. We model the entire process, starting from an infection in the animal population, including mutations that eventually enable the disease to cause an epidemic outbreak in the human population. We use an SIR model on a network consisting of 132 dog and 1320 human nodes, with a single parameter representing the gene of the pathogen. We use numerical large-deviation techniques, specifically the $1/t$ Wang-Landau algorithm, to calculate the potentially very small probability of the host switching event. With this approach we are able to resolve probabilities as small as $10^{-120}$. Additionally the $1/t$ Wang-Landau algorithm allows us to obtain the complete probability density function $P(C)$ of the cumulative fraction $C$ of infected humans, which is an indicator for the severity of the disease in the human population. We also calculate correlations of $C$ with selected quantities $q$ that characterize the outbreak. Due to the application of the rare-event algorithm, this is possible for the entire range of $C$ values.

Gauri Naik, Nandini Narvekar, Dimple Agarwal et al.

Eye diseases have posed significant challenges for decades, but advancements in technology have opened new avenues for their detection and treatment. Machine learning and deep learning algorithms have become instrumental in this domain, particularly when combined with Optical Coherent Technology (OCT) imaging. We propose a novel method for efficient detection of eye diseases from OCT images. Our technique enables the classification of patients into disease free (normal eyes) or affected by specific conditions such as Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), or Drusen. In this work, we introduce an end to end web application that utilizes machine learning and deep learning techniques for efficient eye disease prediction. The application allows patients to submit their raw OCT scanned images, which undergo segmentation using a trained custom UNet model. The segmented images are then fed into an ensemble model, comprising InceptionV3 and Xception networks, enhanced with a self attention layer. This self attention approach leverages the feature maps of individual models to achieve improved classification accuracy. The ensemble model's output is aggregated to predict and classify various eye diseases. Extensive experimentation and optimization have been conducted to ensure the application's efficiency and optimal performance. Our results demonstrate the effectiveness of the proposed approach in accurate eye disease prediction. The developed web application holds significant potential for early detection and timely intervention, thereby contributing to improved eye healthcare outcomes.

Hania Arain, Tina Patel, Nicoleta Mureanu et al.

BackgroundGestational diabetes (GDM) affects approximately 14% of pregnancies globally and is associated with short- and long-term complications for both the mother and child. In addition, GDM has been linked to chronic low-grade inflammation with recent research indicating a potential immune dysregulation in pathophysiology and a disparity in regulatory T cells.ObjectiveThis systematic review and meta-analysis aimed to determine whether there is an association between GDM and the level of Tregs in the peripheral blood.MethodsLiterature searches were conducted in PubMed, Embase, and Ovid between the 7th and 14th of February 2022. The inclusion criteria were any original studies published in the English language, measuring differentiated Tregs in women with GDM compared with glucose-tolerant pregnant women. Meta-analysis was performed between comparable Treg markers. Statistical tests were used to quantify heterogeneity: τ2, χ2, and I2. Study quality was assessed using a modified version of the Newcastle-Ottawa scale.ResultsThe search yielded 223 results: eight studies were included in the review and seven in the meta-analysis (GDM = 228, control = 286). Analysis of Tregs across all trimesters showed significantly lower Treg numbers in women with GDM (SMD, −0.76; 95% CI, −1.37, −0.15; I2 = 90%). This was reflected in the analysis by specific Treg markers (SMD −0.55; 95% CI, −1.04, −0.07; I2 = 83%; third trimester, five studies). Non-significant differences were found within subgroups (differentiated by CD4+FoxP3+, CD4+CD127−, and CD4+CD127−FoxP3) of both analyses.ConclusionGDM is associated with lower Treg numbers in the peripheral maternal blood. In early pregnancy, there is clinical potential to use Treg levels as a predictive tool for the subsequent development of GDM. There is also a potential therapeutic intervention to prevent the development of GDM by increasing Treg populations. However, the precise mechanism by which Tregs mediate GDM remains unclear.Systematic review registrationhttps://www.crd.york.ac.uk/prospero, identifier CRD42022309796.

Peng Song, Peng Song, Ke Cao et al.

IntroductionInnate lymphoid cells (ILCs) are key components of the immune system, yet the similarity and distinction of the properties across tissues under homeostasis, inflammation and tumor process remain elusive.MethodsHere we performed integrative inference of ILCs to reveal their transcriptional profiles and heterogeneity from single-cell genomics. We collected a large number of ILCs from human six different tissues which can represent unique immune niches (circulation, lymphoid tissue, normal and inflamed mucosa, tumor microenvironment), to systematically address the transcriptional imprinting.ResultsILCs are profoundly imprinted by their organ of residence, and tissue-specific distinctions are apparent under pathological conditions. In the hepatocellular carcinoma microenvironment, we identified intermediate c-kit+ ILC2 population, and lin-CD127- NK-like cells that expressed markers of cytotoxicity including CCL5 and IFNG. Additionally, CD127+CD94+ ILC1s were preferentially enriched in inflamed ileum from patients with Crohn’s disease.DiscussionThese analyses depicted a comprehensive characterization of ILC anatomical distribution and subset heterogeneity, and provided a base line for future temporal or spatial studies focused on tissue-specific ILC-mediated immunity.

L. Bielory, L. Delgado, C. Katelaris et al.