Hasil untuk "Immunologic diseases. Allergy"

Gabsik Yang, J. Seok, H. Kang et al.

Atopic dermatitis (AD) is a common and relapsing skin disease that is characterized by skin barrier dysfunction, inflammation, and chronic pruritus. While AD was previously thought to occur primarily in children, increasing evidence suggests that AD is more common in adults than previously assumed. Accumulating evidence from experimental, genetic, and clinical studies indicates that AD expression is a precondition for the later development of other atopic diseases, such as asthma, food allergies, and allergic rhinitis. Although the exact mechanisms of the disease pathogenesis remain unclear, it is evident that both cutaneous barrier dysfunction and immune dysregulation are critical etiologies of AD pathology. This review explores recent findings on AD and the possible underlying mechanisms involved in its pathogenesis, which is characterized by dysregulation of immunological and skin barrier integrity and function, supporting the idea that AD is a systemic disease. These findings provide further insights for therapeutic developments aiming to repair the skin barrier and decrease inflammation.

Aurélien Trompette, J. Pernot, Olaf Perdijk et al.

Barrier integrity is central to the maintenance of healthy immunological homeostasis. Impaired skin barrier function is linked with enhanced allergen sensitization and the development of diseases such as atopic dermatitis (AD), which can precede the development of other allergic disorders, for example, food allergies and asthma. Epidemiological evidence indicates that children suffering from allergies have lower levels of dietary fibre-derived short-chain fatty acids (SCFA). Using an experimental model of AD-like skin inflammation, we report that a fermentable fibre-rich diet alleviates systemic allergen sensitization and disease severity. The gut-skin axis underpins this phenomenon through SCFA production, particularly butyrate, which strengthens skin barrier function by altering mitochondrial metabolism of epidermal keratinocytes and the production of key structural components. Our results demonstrate that dietary fibre and SCFA improve epidermal barrier integrity, ultimately limiting early allergen sensitization and disease development. The Graphical Abstract was designed using Servier Medical Art images (https://smart.servier.com).

A. Oli, Wilson Okechukwu Obialor, M. Ifeanyichukwu et al.

Abstract The use of vaccines have resulted in a remarkable improvement in global health. It has saved several lives, reduced treatment costs and raised the quality of animal and human lives. Current traditional vaccines came empirically with either vague or completely no knowledge of how they modulate our immune system. Even at the face of potential vaccine design advance, immune-related concerns (as seen with specific vulnerable populations, cases of emerging/re-emerging infectious disease, pathogens with complex lifecycle and antigenic variability, need for personalized vaccinations, and concerns for vaccines' immunological safety -specifically vaccine likelihood to trigger non-antigen-specific responses that may cause autoimmunity and vaccine allergy) are being raised. And these concerns have driven immunologists toward research for a better approach to vaccine design that will consider these challenges. Currently, immunoinformatics has paved the way for a better understanding of some infectious disease pathogenesis, diagnosis, immune system response and computational vaccinology. The importance of this immunoinformatics in the study of infectious diseases is diverse in terms of computational approaches used, but is united by common qualities related to host–pathogen relationship. Bioinformatics methods are also used to assign functions to uncharacterized genes which can be targeted as a candidate in vaccine design and can be a better approach toward the inclusion of women that are pregnant into vaccine trials and programs. The essence of this review is to give insight into the need to focus on novel computational, experimental and computation-driven experimental approaches for studying of host–pathogen interactions and thus making a case for its use in vaccine development.

Xiaozhen Lin, Naishen Qin, Baoxia He et al.

BackgroundProgressive multifocal leukoencephalopathy (PML), a rare and often fatal JC virus–mediated disease, is a significant concern in immunocompromised patients.ObjectiveFollowing READUS-PV guidelines, this study evaluated disproportionality signals for PML associated with specific drugs and underlying diseases using the FDA Adverse Event Reporting System (FAERS).MethodsWe identified PML cases in FAERS (2004 Q1–2024 Q4) and excluded those associated with HIV/AIDS. For drugs with ≥3 PML reports, disproportionality was assessed using the reporting odds ratio (ROR) and proportional reporting ratio (PRR), reported with 95% confidence intervals and χ² statistics, respectively. Subgroup analyses were conducted by age, sex, reporting region, and patient outcome. We also characterized the spectrum of underlying diseases and time to onset (TTO).ResultsWe analyzed 6,864 PML reports; in a sensitivity analysis excluding cases with TTO ≤60 days, 6,258 reports remained. Fifty-four drugs showed significant signals in primary analysis with the exception of acalabrutinib in the analysis restricted to 6,258 cases, including established high-risk agents and potential novel associations. Notably, we observed signals with four monoclonal antibodies (daratumumab, elotuzumab, epcoritamab, and isatuximab); isatuximab had no previous mentions in regulatory labels or published literature to our knowledge. Among established agents, natalizumab had the highest number of reports (n=1,848; ROR 40.7), and rituximab also showed a strong signal (n=1,296; ROR 41.8). PML was most frequently reported in multiple sclerosis (32.28%) and B-cell non-Hodgkin lymphomas (9.44%). TTO varied by agent; natalizumab showed the longest median TTO (44.0 months; 95% CI: 41.8–46.7). Median TTO for antineoplastic drugs (13.6 months; 95% CI: 11.5–15.9) was significantly shorter than for non-antineoplastic drugs (42.4 months; 95% CI: 39.7–44.1).ConclusionsThese findings reinforce established and emerging PML reporting signals with immunomodulatory therapies and support heightened pharmacovigilance—particularly for novel monoclonal antibodies used in hematologic malignancies.

Xiangfei Liu, Wei Ding, Lu Jiang et al.

SUMOylation, a dynamic regulatory process in post-translational modifications (PTMs) mediated by small ubiquitin-like modifier (SUMO) ligases and deSUMOylases, regulates protein function through reversible lysine conjugation. Emerging evidence has identified tumor-mediated hijacking of SUMOylation in both malignant cells and immune components as a novel immune evasion mechanism. This review represents a comprehensive update on how tumor-intrinsic SUMOylation modulates tumor immunity-related JAK/STAT, MHC-I, NF-κB, IFN-I/II pathways and other key proteins to drive its immune evasion, and immune cell-intrinsic SUMOylation in regulating natural killer (NK) and T cell cytotoxicity, dendritic cell (DC) maturation, and macrophage polarization. Tumor immunotherapy is a new potential strategy for cancer, mainly represented by immune checkpoint inhibitions (ICIs), which exhibits poor efficacy in head and neck squamous cell carcinoma (HNSCC), pancreatic ductal adenocarcinoma (PDAC) and other solid tumors. Targeting SUMOylation of tumors presents high potential to synergistically improve the therapeutic effect of ICIs. Preclinical studies have shed light on the therapeutic potential of the combination of SUMOylation inhibitors such as TAK-981 or 2-D08 with ICIs, thus significantly improving tumor prognosis. As current phase I trials suggest dose-dependent toxicity of TAK-981, there is a need for targeted delivery systems; AI-assisted screening of novel SUMOylation inhibitors (SUMOi) which are FDA approved serves as another potential approach; besides, antibodies against these pivotal SUMOylated molecules in tumors could be conjugated with SUMOi to restore the activity of specific proteins in tumor microenvironment. In all, our review proposes that current or other novel strategies for SUMOylation inhibition stands as a promising adjuvant to immunotherapy for tumor management, thereby potentially contributing to the favorable prognosis of cancer patients.

Benjamin R. Bavinton, James Gray, Andrew E. Grulich

MD Mehraz Hosen, Md. Hasibul Islam

Tomato crop health plays a critical role in ensuring agricultural productivity and food security. Timely and accurate detection of diseases affecting tomato plants is vital for effective disease management. In this study, we propose a deep learning-based approach for Tomato Leaf Disease Detection using two well-established convolutional neural networks (CNNs), namely VGG19 and Inception v3. The experiment is conducted on the Tomato Villages Dataset, encompassing images of both healthy tomato leaves and leaves afflicted by various diseases. The VGG19 model is augmented with fully connected layers, while the Inception v3 model is modified to incorporate a global average pooling layer and a dense classification layer. Both models are trained on the prepared dataset, and their performances are evaluated on a separate test set. This research employs VGG19 and Inception v3 models on the Tomato Villages dataset (4525 images) for tomato leaf disease detection. The models' accuracy of 93.93% with dropout layers demonstrates their usefulness for crop health monitoring. The paper suggests a deep learning-based strategy that includes normalization, resizing, dataset preparation, and unique model architectures. During training, VGG19 and Inception v3 serve as feature extractors, with possible data augmentation and fine-tuning. Metrics like accuracy, precision, recall, and F1 score are obtained through evaluation on a test set and offer important insights into the strengths and shortcomings of the model. The method has the potential for practical use in precision agriculture and could help tomato crops prevent illness early on.

Pascal Helson, Arvind Kumar

Despite the diversity and volume of brain data acquired and advanced AI-based algorithms to analyze them, brain features are rarely used in clinics for diagnosis and prognosis. Here we argue that the field continues to rely on cohort comparisons to seek biomarkers, despite the well-established degeneracy of brain features. Using a thought experiment (Brain Swap), we show that more data and more powerful algorithms will not be sufficient to identify biomarkers of brain diseases. We argue that instead of comparing patient versus healthy controls using single data type, we should use multimodal (e.g. brain activity, neurotransmitters, neuromodulators, brain imaging) and longitudinal brain data to guide the grouping before defining multidimensional biomarkers for brain diseases.

Janet Wang, Xin Hu, Yunbei Zhang et al.

Skin Neglected Tropical Diseases (NTDs) impose severe health and socioeconomic burdens in impoverished tropical communities. Yet, advancements in AI-driven diagnostic support are hindered by data scarcity, particularly for underrepresented populations and rare manifestations of NTDs. Existing dermatological datasets often lack the demographic and disease spectrum crucial for developing reliable recognition models of NTDs. To address this, we introduce eSkinHealth, a novel dermatological dataset collected on-site in Côte d'Ivoire and Ghana. Specifically, eSkinHealth contains 5,623 images from 1,639 cases and encompasses 47 skin diseases, focusing uniquely on skin NTDs and rare conditions among West African populations. We further propose an AI-expert collaboration paradigm to implement foundation language and segmentation models for efficient generation of multimodal annotations, under dermatologists' guidance. In addition to patient metadata and diagnosis labels, eSkinHealth also includes semantic lesion masks, instance-specific visual captions, and clinical concepts. Overall, our work provides a valuable new resource and a scalable annotation framework, aiming to catalyze the development of more equitable, accurate, and interpretable AI tools for global dermatology.

Lai Jiang, Xing Ren, Jinyan Yang et al.

BackgroundClear Cell Renal Cell Carcinoma (ccRCC) is the most common type of kidney cancer, characterized by high heterogeneity and complexity. Recent studies have identified mitochondrial defects and autophagy as key players in the development of ccRCC. This study aims to delve into the changes in mitophagic activity within ccRCC and its impact on the tumor microenvironment, revealing its role in tumor cell metabolism, development, and survival strategies.MethodsComprehensive analysis of ccRCC tumor tissues using single cell sequencing and spatial transcriptomics to reveal the role of mitophagy in ccRCC. Mitophagy was determined to be altered among renal clear cells by gene set scoring. Key mitophagy cell populations and key prognostic genes were identified using NMF analysis and survival analysis approaches. The role of UBB in ccRCC was also demonstrated by in vitro experiments.ResultsCompared to normal kidney tissue, various cell types within ccRCC tumor tissues exhibited significantly increased levels of mitophagy, especially renal clear cells. Key genes associated with increased mitophagy levels, such as UBC, UBA52, TOMM7, UBB, MAP1LC3B, and CSNK2B, were identified, with their high expression closely linked to poor patient prognosis. Particularly, the ubiquitination process involving the UBB gene was found to be crucial for mitophagy and its quality control.ConclusionThis study highlights the central role of mitophagy and its regulatory factors in the development of ccRCC, revealing the significance of the UBB gene and its associated ubiquitination process in disease progression.

Longsha Liu, Longsha Liu, Thijs A. van Schaik et al.

Akiko Hashimoto-Tane, Edward P. Bowman, Machie Sakuma et al.

Lymphocyte activation gene (Lag)-3 is an inhibitory co-receptor and target of immune checkpoint inhibitor (ICI) therapy for cancer. The dynamic behavior of Lag-3 was analyzed at the immune synapse upon T-cell activation to elucidate the Lag-3 inhibitory mechanism. Lag-3 formed clusters and co-localized with T-cell receptor microcluster (TCR-MC) upon T-cell activation similar to PD-1. Lag-3 blocking antibodies (Abs) inhibited the co-localization between Lag-3 and TCR-MC without inhibiting Lag-3 cluster formation. Lag-3 also inhibited MHC-II-independent stimulation and Lag-3 Ab, which did not block MHC-II binding could still block Lag-3’s inhibitory function, suggesting that the Lag-3 Ab blocks the Lag-3 inhibitory signal by dissociating the co-assembly of TCR-MC and Lag-3 clusters. Consistent with the combination benefit of PD-1 and Lag-3 Abs to augment T-cell responses, bispecific Lag-3/PD-1 antagonists effectively inhibited both cluster formation and co-localization of PD-1 and Lag-3 with TCR-MC. Therefore, Lag-3 inhibits T-cell activation at TCR-MC, and the target of Lag-3 ICI is to dissociate the co-localization of Lag-3 with TCR-MC.

Martin Awe Akilla, Ignatius Abowini Awinibuno Nchor, Moses Banyeh et al.

Abstract Background/Aim Immune tolerance in the fetal–maternal junction is maintained by a balance in the Th1/Th2 system. Th1‐type immunity is associated with pro‐inflammatory cytokines and immune checkpoint molecules (ICMs) such as B7‐H1, while Th2‐type immunity is characterized by anti‐inflammatory cytokines and ICMs such as B7‐1. Any imbalance in the Th1/Th2 immune system may lead to adverse pregnancy outcomes such as pre‐eclampsia (PE). Hitherto, the potential of serum B7‐1 and B7‐H1 proteins as early markers of PE has not been explored in the Ghanaian population. Materials and Methods This was a case–control study from May 2020 to April 2022 at the War Memorial and the Upper East Regional Hospitals. The study involved 291 women, including 180 (61.9%) with normotensive pregnancy and 111 (38.1%) with PE. Venous blood samples were collected and assayed for blood cell count, serum interleukins (ILs)‐4, ‐6, ‐12, ‐18, and TNF‐α as well as serum B7‐1 and B7‐H1 proteins. Results The monocyte count (p = .007), the serum levels of IL‐18 (p = .035), TNF‐α (p = .001), and B7‐H1 (p = .006) were significantly higher in PE than in normotensive pregnancy. In addition, the monocyte count (p = .002), the serum levels of IL‐12 (p = .029), TNF‐α (p = .016), and B7‐1 (p = .009) levels were significantly higher in the third trimester than the second trimester PE. In predicting PE, the area under the curve of cytokines and ICMs ranged from 0.51 for IL‐6 to 0.62 for TNF‐α. Conclusion PE may be characterized by a dominant Th1‐type immunity with higher levels of pro‐inflammatory cytokines and B7‐H1 proteins, but these variables may not be suitable for predicting PE.

Guancheng Wan, Zewen Liu, Max S. Y. Lau et al.

Effective epidemic forecasting is critical for public health strategies and efficient medical resource allocation, especially in the face of rapidly spreading infectious diseases. However, existing deep-learning methods often overlook the dynamic nature of epidemics and fail to account for the specific mechanisms of disease transmission. In response to these challenges, we introduce an innovative end-to-end framework called Epidemiology-Aware Neural ODE with Continuous Disease Transmission Graph (EARTH) in this paper. To learn continuous and regional disease transmission patterns, we first propose EANO, which seamlessly integrates the neural ODE approach with the epidemic mechanism, considering the complex spatial spread process during epidemic evolution. Additionally, we introduce GLTG to model global infection trends and leverage these signals to guide local transmission dynamically. To accommodate both the global coherence of epidemic trends and the local nuances of epidemic transmission patterns, we build a cross-attention approach to fuse the most meaningful information for forecasting. Through the smooth synergy of both components, EARTH offers a more robust and flexible approach to understanding and predicting the spread of infectious diseases. Extensive experiments show EARTH superior performance in forecasting real-world epidemics compared to state-of-the-art methods. The code will be available at https://github.com/Emory-Melody/EpiLearn.

Ghazaleh Babanejaddehaki, Aijun An, Manos Papagelis

Infectious diseases occur when pathogens from other individuals or animals infect a person, resulting in harm to both individuals and society as a whole. The outbreak of such diseases can pose a significant threat to human health. However, early detection and tracking of these outbreaks have the potential to reduce the mortality impact. To address these threats, public health authorities have endeavored to establish comprehensive mechanisms for collecting disease data. Many countries have implemented infectious disease surveillance systems, with the detection of epidemics being a primary objective. The clinical healthcare system, local/state health agencies, federal agencies, academic/professional groups, and collaborating governmental entities all play pivotal roles within this system. Moreover, nowadays, search engines and social media platforms can serve as valuable tools for monitoring disease trends. The Internet and social media have become significant platforms where users share information about their preferences and relationships. This real-time information can be harnessed to gauge the influence of ideas and societal opinions, making it highly useful across various domains and research areas, such as marketing campaigns, financial predictions, and public health, among others. This article provides a review of the existing standard methods developed by researchers for detecting outbreaks using time series data. These methods leverage various data sources, including conventional data sources and social media data or Internet data sources. The review particularly concentrates on works published within the timeframe of 2015 to 2022.

Zihao Zhao, Yi Jing, Fuli Feng et al.

Medication recommendation systems have gained significant attention in healthcare as a means of providing tailored and effective drug combinations based on patients' clinical information. However, existing approaches often suffer from fairness issues, as recommendations tend to be more accurate for patients with common diseases compared to those with rare conditions. In this paper, we propose a novel model called Robust and Accurate REcommendations for Medication (RAREMed), which leverages the pretrain-finetune learning paradigm to enhance accuracy for rare diseases. RAREMed employs a transformer encoder with a unified input sequence approach to capture complex relationships among disease and procedure codes. Additionally, it introduces two self-supervised pre-training tasks, namely Sequence Matching Prediction (SMP) and Self Reconstruction (SR), to learn specialized medication needs and interrelations among clinical codes. Experimental results on two real-world datasets demonstrate that RAREMed provides accurate drug sets for both rare and common disease patients, thereby mitigating unfairness in medication recommendation systems.

Thien-Minh Le, Jukka-Pekka Onnela

Infectious disease modeling is used to forecast epidemics and assess the effectiveness of intervention strategies. Although the core assumption of mass-action models of homogeneously mixed population is often implausible, they are nevertheless routinely used in studying epidemics and provide useful insights. Network models can account for the heterogeneous mixing of populations, which is especially important for studying sexually transmitted diseases. Despite the abundance of research on mass-action and network models, the relationship between them is not well understood. Here, we attempt to bridge the gap by first identifying a spreading rule that results in an exact match between disease spreading on a fully connected network and the classic mass-action models. We then propose a method for mapping epidemic spread on arbitrary networks to a form similar to that of mass-action models. We also provide a theoretical justification for the procedure. Finally, we show the advantages of the proposed methods using synthetic data that is based on an empirical network. These findings help us understand when mass-action models and network models are expected to provide similar results and identify reasons when they do not.

Georgia S. Brennan, Alain Goriely

Neurodegenerative diseases are associated with the assembly of specific proteins into oligomers and fibrillar aggregates. At the brain scale, these protein assemblies can diffuse through the brain and seed other regions, creating an autocatalytic protein progression. The growth and transport of these assemblies depend on various mechanisms that can be targeted therapeutically. Here, we use spatially-extended nucleation-aggregation-fragmentation models for the dynamics of prion-like neurodegenerative protein-spreading in the brain to study the effect of different drugs on whole-brain Alzheimer's disease progression.

Gerold Schmitt-Ulms, Xinzhu Wang, Joel Watts et al.

Decades after their initial observation in prion-infected brain tissues, the identities of virus-like dense particles, varicose tubules, and oval bodies containing parallel bands and fibrils have remained elusive. Our recent work revealed that a phenotype of dilation of the endoplasmic reticulum (ER), most notable for the perinuclear space (PNS), contributes to spongiform degeneration. To assess the significance of this phenotype for the etiology of prion diseases, we explored whether it can be functionally linked to other neuropathological hallmarks observed in these diseases, as this would indicate it to be a central event. Having surveyed the neuropathological record and other distant literature niches, we propose a model in which pathogenic forms of the prion protein poison raft domains, including essential Na+, K+-ATPases (NKAs) embedded within them, thereby triggering an ER-centered cellular rescue program coordinated by the unfolded protein response (UPR). The execution of this program stalls general protein synthesis, causing the deterioration of synaptic spines. As the disease progresses, cells selectively increase sterol biosynthesis, along with ribosome and ER biogenesis. These adaptive rescue attempts cause morphological changes to the ER which manifest as ER dilation or ER hypertrophy in a manner that is influenced by Ca2+ influx into the cell. The nuclear-to-cytoplasmic transport of mRNAs and tRNAs interrupts in late stage disease, thereby depriving ribosomes of supplies and inducing them to aggregate into a paracrystalline form. In support of this model, we share previously reported data, whose features are consistent with the interpretation that 1) the phenotype of ER dilation is observed in major prion diseases, 2) varicose tubules and oval bodies represent ER hypertrophy, and 3) virus-like dense particles are paracrystalline aggregates of inactive ribosomes.

K. Miyake, Junya Ito, H. Karasuyama

Basophils are the rarest granulocytes and have long been overlooked in immunological research due to their rarity and similarities with tissue-resident mast cells. In the last two decades, non-redundant functions of basophils have been clarified or implicated in a broad spectrum of immune responses, particularly by virtue of the development of novel analytical tools for basophils. Basophils infiltrate inflamed tissues of patients with various disorders, even though they circulate in the bloodstream under homeostatic conditions. Depletion of basophils results in the amelioration or exaggeration of inflammation, depending on models of disease, indicating basophils can play either beneficial or deleterious roles in a context-dependent manner. In this review, we summarize the recent findings of basophil pathophysiology under various conditions in mice and humans, including allergy, autoimmunity, tumors, tissue repair, fibrosis, and COVID-19. Further mechanistic studies on basophil biology could lead to the identification of novel biomarkers or therapeutic targets in a broad range of diseases.