Hasil untuk "Immunologic diseases. Allergy"

Khalid A. AlMeshari, Dieter C. Broering, Dalia A. Obeid et al.

BackgroundKidney Paired Donation (KPD) programs expand transplant opportunities for immunologically incompatible donor-recipient pairs. This study describes the operational framework and clinical outcomes of a high-volume, single-center KPD program, which became the highest-volume center globally in 2024.MethodsWe analyzed all kidney transplants performed through our KPD program between January and December 2024. The program aimed to achieve full HLA and ABO compatibility for incompatible pairs, while also incorporating additional strategies: inclusion of compatible pairs to improve HLA matching, acceptance of ABO quasi-compatible matches (e.g., A2 donors to O or B recipients), low-risk HLA-incompatible matching for HLA-incompatible candidates with cPRA >80%, and ABO-incompatible matching for those with cPRA >95%.ResultsA total of 135 patients (121 adults, 14 pediatrics) underwent KPD-facilitated transplantation, including 69 HLA-incompatible (51.1%), 37 ABO-incompatible (27.4%), and 29 compatible (21.5%) pairs. Females comprised 60.7% of the cohort, with a significantly higher proportion in the HLA-incompatible group (p < 0.001). HLA-incompatible recipients were older than others (mean age 42.5 years, p < 0.001). Most transplants (93.3%) occurred through 2- to 5-way closed chains, with the remainder via domino chains (6.7%). At baseline, 25% of patients were very highly sensitized (cPRA ≥95%) HLA- incompatible recipients, and ABO-incompatible recipients were blood group O individuals whose intended donors had A1 or B blood groups (high risk combinations). Following matching, 70% of patients achieved full HLA and ABO compatibility, while 30% underwent transplantation with acceptable immunologic risk (i.e. low-risk HLA incompatibility and/or ABO incompatibility). Early post-transplant outcomes were favorable, with a mean serum creatinine of 87.2 µmol/L. Acute rejection occurred in 6.7% of patients, antibody-mediated rejection in 0.7%, and graft loss in 0.7%.ConclusionOur single-center experience demonstrates the feasibility and effectiveness of a high-volume KPD program in overcoming immunologic barriers to kidney transplantation. Strategic inclusion of compatible pairs, ABO quasi-compatible matching, low-risk HLA-incompatible, and ABO-incompatible matchings significantly increased access for difficult-to-match recipients. This model may serve as a replicable framework for other high-capacity transplant centers seeking to expand transplant access and improve outcomes for complex patient populations.

Simon Vikár, Attila Mócsai, Attila Mócsai et al.

Pemphigoid diseases, such as bullous pemphigoid and epidermolysis bullosa acquisita, are severe organ-specific autoimmune diseases characterized by subepidermal skin blistering with increasing incidence in recent years. Although there have been substantial advances in understanding the pathomechanism of these diseases in the last decades, and the first specific therapy targeting the IL-4 and IL-13 pathway (dupilumab) has been approved by the FDA for bullous pemphigoid, further research is needed to eventually improve patient care. The characteristics of pemphigoid diseases include the formation of immune complexes and their recognition by Fcγ-receptors, as well as the development of a characteristic inflammatory cytokine microenvironment in the skin of the affected patients. Several non-receptor tyrosine kinases are involved in these events, playing a very important role in various signaling processes of immune cells. While certain Src-family kinases and the Syk tyrosine kinase play a very important role in signaling by Fcγ-receptors, JAK-family kinases are crucial players in the signaling of various cytokine receptors including, among others, the receptors of IL-4 and IL-13. The inhibition of these tyrosine kinases with small molecule inhibitors is an emerging therapeutic option in the treatment of an increasing number of immune-mediated diseases. Moreover, numerous studies have been conducted to examine proteins (including PLCγ2 and CARD9) in signal transduction following Fcγ-receptor activation in in vitro and in vivo experimental pemphigoid models, and an increasing number of case studies involving JAK inhibitors report the successful application of these drugs in various pemphigoid diseases. This review summarizes our current understanding of the therapeutically most promising tyrosine kinase signaling pathways in the pathogenesis of pemphigoid diseases.

Houda Belhad, Asmae Bourbia, Salma Boughanja

Chronic diseases, such as cardiovascular disease, diabetes, chronic kidney disease, and thyroid disorders, are the leading causes of premature mortality worldwide. Early detection and intervention are crucial for improving patient outcomes, yet traditional diagnostic methods often fail due to the complex nature of these conditions. This study explores the application of machine learning (ML) and deep learning (DL) techniques to predict chronic disease and thyroid disorders. We used a variety of models, including Logistic Regression (LR), Random Forest (RF), Gradient Boosted Trees (GBT), Neural Networks (NN), Decision Trees (DT) and Native Bayes (NB), to analyze and predict disease outcomes. Our methodology involved comprehensive data pre-processing, including handling missing values, categorical encoding, and feature aggregation, followed by model training and evaluation. Performance metrics such ad precision, recall, accuracy, F1-score, and Area Under the Curve (AUC) were used to assess the effectiveness of each model. The results demonstrated that ensemble methods like Random Forest and Gradient Boosted Trees consistently outperformed. Neutral Networks also showed superior performance, particularly in capturing complex data patterns. The findings highlight the potential of ML and DL in revolutionizing chronic disease prediction, enabling early diagnosis and personalized treatment strategies. However, challenges such as data quality, model interpretability, and the need for advanced computational techniques in healthcare to improve patient outcomes and reduce the burden of chronic diseases. This study was conducted as part of Big Data class project under the supervision of our professors Mr. Abderrahmane EZ-ZAHOUT and Mr. Abdessamad ESSAIDI.

Tingyi Wu, Tingyi Wu, Qianqian Ji et al.

PurposeTo investigate the influence of aqueous humor cytokines levels on the failure of illuminated microcatheter-assisted circumferential trabeculotomy (MAT) in open-angle glaucoma (OAG) patients.MethodsThis was a prospective case series with a follow-up period of 24 months. General information and ocular examinations were recorded. Aqueous humor was collected at the time of surgery. Eight aqueous humor cytokines were analyzed: CCL2, VCAM-1, ICAM-1, IL-6, IL-8, IL-10, CXCL10 and G-CSF. Bioinformatics analysis was used to explore the protein network and the possible pathways related to OAG. Surgical failure was defined as a requirement for glaucoma reoperation or intraocular pressure (IOP) greater than 21mmHg with more than 3 topical antiglaucoma medications at 24-month follow-up.ResultsSixty-five eyes were enrolled (58 success and 7 failure). The levels of CCL2, ICAM-1, IL-6 and CXCL10 in aqueous humor were significantly higher in the surgical failure group (P = 0.024, 0.002, 0.022 and 0.008, respectively). A higher percentage of secondary glaucoma (P < 0.001), younger age (P = 0.019), worse preoperative BCVA (P = 0.022), higher preoperative IOP (P = 0.022) and more preoperative topical antiglaucoma medications (P = 0.029) were significantly observed in the surgical failure group. Bioinformatics analysis identified 4 hub proteins, including CCL2, CXCL10, IL-6 and CXCR3, and demonstrated the potential role of chemokine signaling pathway in MAT surgical outcome.ConclusionHigher concentrations of CCL2, ICAM-1, IL-6 and CXCL10 in the aqueous humor were related to the failure of MAT surgery in OAG patients, and chemokine signaling pathway might be associated with the surgical outcome.

Masato Tamari, Aaron M. Ver Heul

Type 2 inflammation has a major role in barrier tissues such as the skin and airways and underlies common conditions including atopic dermatitis (AD) and asthma. Cytokines including interleukin 4 (IL-4), IL-5, and IL-13 are key immune signatures of type 2 inflammation and are the targets of multiple specific therapeutics for allergic diseases. Despite shared core immune mechanisms, the distinct structures and functions of the skin and airways lead to unique therapeutic responses. It is increasingly recognized that the nervous system has a major role in sensing and directing inflammatory processes. Indeed, crosstalk between type 2 immune activation and somatosensory functions mediates tissue-specific signatures such as itching in the skin. However, neuroimmune interactions are shaped by distinct neuronal and immune landscapes, and differ between the skin and airways. In the skin, dorsal root ganglia-derived neurons mediate pruritus via type 2 cytokines and neurogenic inflammation by mast cell or basophil activation. Conversely, vagal ganglia-derived neurons regulate airway immune responses by releasing neuropeptides/neurotransmitters such as calcitonin gene-related peptides, neuromedin U, acetylcholine, and noradrenaline. Sensory neuron-derived vasoactive intestinal peptide forms a feedback loop with IL-5, amplifying eosinophilic inflammation in the airways, a mechanism that is absent in the skin. These differences influence the efficacy of cytokine-targeted therapies. For instance, IL-4/IL-13-targeted therapies like dupilumab demonstrate efficacy in AD and allergic airway diseases, whereas IL-5-targeted therapies are effective in eosinophilic asthma but not AD. Understanding these neuroimmune interactions underscores the need for tailored therapeutic approaches to address allergic diseases where barrier tissues are involved.

Akeem Temitope Otapo, Alice Othmani, Ghazaleh Khodabandelou et al.

The integration of Artificial Intelligence (AI) and the Internet of Medical Things (IoMT) in healthcare, through Machine Learning (ML) and Deep Learning (DL) techniques, has advanced the prediction and diagnosis of chronic diseases. AI-driven models such as XGBoost, Random Forest, CNNs, and LSTM RNNs have achieved over 98\% accuracy in predicting heart disease, chronic kidney disease (CKD), Alzheimer's disease, and lung cancer, using datasets from platforms like Kaggle, UCI, private institutions, and real-time IoMT sources. However, challenges persist due to variations in data quality, patient demographics, and formats from different hospitals and research sources. The incorporation of IoMT data, which is vast and heterogeneous, adds complexities in ensuring interoperability and security to protect patient privacy. AI models often struggle with overfitting, performing well in controlled environments but less effectively in real-world clinical settings. Moreover, multi-morbidity scenarios especially for rare diseases like dementia, stroke, and cancers remain insufficiently addressed. Future research should focus on data standardization and advanced preprocessing techniques to improve data quality and interoperability. Transfer learning and ensemble methods are crucial for improving model generalizability across clinical settings. Additionally, the exploration of disease interactions and the development of predictive models for chronic illness intersections is needed. Creating standardized frameworks and open-source tools for integrating federated learning, blockchain, and differential privacy into IoMT systems will also ensure robust data privacy and security.

Christos Theodoropoulos, Andrei Catalin Coman, James Henderson et al.

The ever-growing volume of biomedical publications creates a critical need for efficient knowledge discovery. In this context, we introduce an open-source end-to-end framework designed to construct knowledge around specific diseases directly from raw text. To facilitate research in disease-related knowledge discovery, we create two annotated datasets focused on Rett syndrome and Alzheimer's disease, enabling the identification of semantic relations between biomedical entities. Extensive benchmarking explores various ways to represent relations and entity representations, offering insights into optimal modeling strategies for semantic relation detection and highlighting language models' competence in knowledge discovery. We also conduct probing experiments using different layer representations and attention scores to explore transformers' ability to capture semantic relations.

Rikke Olund Rasander, Søren Schwartz Sørensen, Søren Schwartz Sørensen et al.

IntroductionAnti-Thymocyte Globulin (ATG) is a cornerstone in immune suppression for solid organ transplantation. The treatment is a delicate balance between complications arising from over-immunosuppression such as infections and cancer versus rejection stemming from under-immunosuppression. CD3+ T-lymphocyte measurements are frequently employed for treatment monitoring. However, this analysis is costly and not always accessible. The aim of this study was to investigate whether the total count of lymphocytes could replace CD3+ T-lymphocyte measurements based on data from our transplantation center combined with a review of the literature. The hypothesis was that the total lymphocyte count could serve as a diagnostic surrogate marker for CD3+ T-lymphocytes.MethodsA retrospective cohort study was conducted, including patients who underwent kidney and/or a pancreas transplantation and received ATG as induction therapy or for rejection treatment. The inclusion criterium was that the total lymphocyte count and CD3+ T-lymphocyte measurements were measured simultaneously on the same day. Additionally, PubMed and Embase were searched up to 18/10/2023 for published studies on solid organ transplantation, ATG, T-lymphocytes, lymphocyte count, and monitoring. In the retrospective cohort study, a total of 91 patients transplanted between 2016 and 2023, with 487 samples, were included. ResultsTotal lymphocyte counts below 0.3 x 109/L had a high sensitivity (86%) as a surrogate marker of CD3+ T-lymphocytes below 0.05 x 109/L, but the specificity was low (52%) for total lymphocyte counts above 0.3 x 109/L as a surrogate marker for CD3+ T-lymphocytes above 0.05 x 109/L. A review of the literature identified seven studies comparing total lymphocyte counts and CD3+ T-lymphocytes in ATG monitoring. These studies supported the use of a low total lymphocyte count as a surrogate marker for CD3+ T-lymphocytes and an indicator to omit ATG treatment. However, there was no consensus regarding high total lymphocyte counts as an indicator for continued treatment.DiscussionResults supports that the total lymphocyte count can be used to omit ATG treatment when below 0.3 x 109/L whereas the CD3+ T-lymphocyte analysis should be reserved for higher total lymphocyte counts to avoid ATG overtreatment.

Faruk Ahmed, Md. Taimur Ahad, Yousuf Rayhan Emon

Tea leaf diseases are a major challenge to agricultural productivity, with far-reaching implications for yield and quality in the tea industry. The rise of machine learning has enabled the development of innovative approaches to combat these diseases. Early detection and diagnosis are crucial for effective crop management. For predicting tea leaf disease, several automated systems have already been developed using different image processing techniques. This paper delivers a systematic review of the literature on machine learning methodologies applied to diagnose tea leaf disease via image classification. It thoroughly evaluates the strengths and constraints of various Vision Transformer models, including Inception Convolutional Vision Transformer (ICVT), GreenViT, PlantXViT, PlantViT, MSCVT, Transfer Learning Model & Vision Transformer (TLMViT), IterationViT, IEM-ViT. Moreover, this paper also reviews models like Dense Convolutional Network (DenseNet), Residual Neural Network (ResNet)-50V2, YOLOv5, YOLOv7, Convolutional Neural Network (CNN), Deep CNN, Non-dominated Sorting Genetic Algorithm (NSGA-II), MobileNetv2, and Lesion-Aware Visual Transformer. These machine-learning models have been tested on various datasets, demonstrating their real-world applicability. This review study not only highlights current progress in the field but also provides valuable insights for future research directions in the machine learning-based detection and classification of tea leaf diseases.

Ayush Garg, Julia Lu, Anika Maji

Oral diseases such as periodontal (gum) diseases and dental caries (cavities) affect billions of people across the world today. However, previous state-of-the-art models have relied on X-ray images to detect oral diseases, making them inaccessible to remote monitoring, developing countries, and telemedicine. To combat this overuse of X-ray imagery, we propose a lightweight machine learning model capable of detecting calculus (also known as hardened plaque or tartar) in RGB images while running efficiently on low-end devices. The model, a modified MobileNetV3-Small neural network transfer learned from ImageNet, achieved an accuracy of 72.73% (which is comparable to state-of-the-art solutions) while still being able to run on mobile devices due to its reduced memory requirements and processing times. A ResNet34-based model was also constructed and achieved an accuracy of 81.82%. Both of these models were tested on a mobile app, demonstrating their potential to limit the number of serious oral disease cases as their predictions can help patients schedule appointments earlier without the need to go to the clinic.

Jessica S. Velasco, Jomer V. Catipon, Edmund G. Monilar et al.

Automatic classification of skin disease plays an important role in healthcare especially in dermatology. Dermatologists can determine different skin diseases with the help of an android device and with the use of Artificial Intelligence. Deep learning requires a lot of time to train due to the number of sequential layers and input data involved. Powerful computer involving a Graphic Processing Unit is an ideal approach to the training process due to its parallel processing capability. This study gathered images of 7 types of skin disease prevalent in the Philippines for a skin disease classification system. There are 3400 images composed of different skin diseases like chicken pox, acne, eczema, Pityriasis rosea, psoriasis, Tinea corporis and vitiligo that was used for training and testing of different convolutional network models. This study used transfer learning to skin disease classification using pre-trained weights from different convolutional neural network models such as VGG16, VGG19, MobileNet, ResNet50, InceptionV3, Inception-ResNetV2, Xception, DenseNet121, DenseNet169, DenseNet201 and NASNet mobile. The MobileNet model achieved the highest accuracy, 94.1% and the VGG16 model achieved the lowest accuracy, 44.1%.

Abeer Dyoub, Ivan Letteri

In this study, we investigated the application of bio-inspired optimization algorithms, including Genetic Algorithm, Particle Swarm Optimization, and Whale Optimization Algorithm, for feature selection in chronic disease prediction. The primary goal was to enhance the predictive accuracy of models streamline data dimensionality, and make predictions more interpretable and actionable. The research encompassed a comparative analysis of the three bio-inspired feature selection approaches across diverse chronic diseases, including diabetes, cancer, kidney, and cardiovascular diseases. Performance metrics such as accuracy, precision, recall, and f1 score are used to assess the effectiveness of the algorithms in reducing the number of features needed for accurate classification. The results in general demonstrate that the bio-inspired optimization algorithms are effective in reducing the number of features required for accurate classification. However, there have been variations in the performance of the algorithms on different datasets. The study highlights the importance of data pre-processing and cleaning in ensuring the reliability and effectiveness of the analysis. This study contributes to the advancement of predictive analytics in the realm of chronic diseases. The potential impact of this work extends to early intervention, precision medicine, and improved patient outcomes, providing new avenues for the delivery of healthcare services tailored to individual needs. The findings underscore the potential benefits of using bio-inspired optimization algorithms for feature selection in chronic disease prediction, offering valuable insights for improving healthcare outcomes.

Johanna Hol Fosse, Adriana Magalhaes Santos Andresen, Frieda Betty Ploss et al.

Many sialic acid-binding viruses express a receptor-destroying enzyme (RDE) that removes the virus-targeted receptor and limits viral interactions with the host cell surface. Despite a growing appreciation of how the viral RDE promotes viral fitness, little is known about its direct effects on the host. Infectious salmon anemia virus (ISAV) attaches to 4-O-acetylated sialic acids on Atlantic salmon epithelial, endothelial, and red blood cell surfaces. ISAV receptor binding and destruction are effectuated by the same molecule, the haemagglutinin esterase (HE). We recently discovered a global loss of vascular 4-O-acetylated sialic acids in ISAV-infected fish. The loss correlated with the expression of viral proteins, giving rise to the hypothesis that it was mediated by the HE. Here, we report that the ISAV receptor is also progressively lost from circulating erythrocytes in infected fish. Furthermore, salmon erythrocytes exposed to ISAV ex vivo lost their capacity to bind new ISAV particles. The loss of ISAV binding was not associated with receptor saturation. Moreover, upon loss of the ISAV receptor, erythrocyte surfaces became more available to the lectin wheat germ agglutinin, suggesting a potential to alter interactions with endogenous lectins of similar specificity. The pruning of erythrocyte surfaces was inhibited by an antibody that prevented ISAV attachment. Furthermore, recombinant HE, but not an esterase-silenced mutant, was sufficient to induce the observed surface modulation. This links the ISAV-induced erythrocyte modulation to the hydrolytic activity of the HE and shows that the observed effects are not mediated by endogenous esterases. Our findings are the first to directly link a viral RDE to extensive cell surface modulation in infected individuals. This raises the questions of whether other sialic acid-binding viruses that express RDEs affect host cells to a similar extent, and if such RDE-mediated cell surface modulation influences host biological functions with relevance to viral disease.

Nazek Noureddine, M. Chałubiński, P. Wawrzyniak

Abstract The respiratory epithelium constitutes the physical barrier between the human body and the environment, thus providing functional and immunological protection. It is often exposed to allergens, microbial substances, pathogens, pollutants, and environmental toxins, which lead to dysregulation of the epithelial barrier and result in the chronic inflammation seen in allergic diseases and asthma. This epithelial barrier dysfunction results from the disturbed tight junction formation, which are multi-protein subunits that promote cell–cell adhesion and barrier integrity. The increasing interest and evidence of the role of impaired epithelial barrier function in allergy and asthma highlight the need for innovative approaches that can provide new knowledge in this area. Here, we review and discuss the current role and mechanism of epithelial barrier dysfunction in developing allergic diseases and the effect of current allergy therapies on epithelial barrier restoration.

L. Samuelov, O. Sarig, R. Harmon et al.

H. McSorley, J. Hewitson, R. Maizels

Ryan K. Tan, Yang Liu, Lei Xie

Many multi-genic systemic diseases such as neurological disorders, inflammatory diseases, and the majority of cancers do not have effective treatments yet. Reinforcement learning powered systems pharmacology is a potentially effective approach to design personalized therapies for untreatable complex diseases. In this survey, state-of-the-art reinforcement learning methods and their latest applications to drug design are reviewed. The challenges on harnessing reinforcement learning for systems pharmacology and personalized medicine are discussed. Potential solutions to overcome the challenges are proposed. In spite of successful application of advanced reinforcement learning techniques to target-based drug discovery, new reinforcement learning strategies are needed to address systems pharmacology-oriented personalized de novo drug design.

Hanan Alahmadi, Håvard Rue, Janet van Niekerk

Statistical analysis based on quantile regression methods is more comprehensive, flexible, and less sensitive to outliers when compared to mean regression methods. When the link between different diseases are of interest, joint disease mapping is useful for measuring directional correlation between them. Most studies study this link through multiple correlated mean regressions. In this paper we propose a joint quantile regression framework for multiple diseases where different quantile levels can be considered. We are motivated by the theorized link between the presence of Malaria and the gene deficiency G6PD, where medical scientist have anecdotally discovered a possible link between high levels of G6PD and lower than expected levels of Malaria initially pointing towards the occurrence of G6PD inhibiting the occurrence of Malaria. This link cannot be investigated with mean regressions and thus the need for flexible joint quantile regression in a disease mapping framework. Our joint quantile disease mapping model can be used for linear and non-linear effects of covariates by stochastic splines, since we define it as a latent Gaussian model. We perform Bayesian inference of this model using the INLA framework embedded in the R software package INLA. Finally, we illustrate the applicability of model by analyzing the malaria and G6PD deficiency incidences in 21 African countries using linked quantiles of different levels.

Tuan Truong, Matthias Lenga, Antoine Serrurier et al.

Audio-based classification techniques on body sounds have long been studied to aid in the diagnosis of respiratory diseases. While most research is centered on the use of cough as the main biomarker, other body sounds also have the potential to detect respiratory diseases. Recent studies on COVID-19 have shown that breath and speech sounds, in addition to cough, correlate with the disease. Our study proposes Fused Audio Instance and Representation (FAIR) as a method for respiratory disease detection. FAIR relies on constructing a joint feature vector from various body sounds represented in waveform and spectrogram form. We conducted experiments on the use case of COVID-19 detection by combining waveform and spectrogram representation of body sounds. Our findings show that the use of self-attention to combine extracted features from cough, breath, and speech sounds leads to the best performance with an Area Under the Receiver Operating Characteristic Curve (AUC) score of 0.8658, a sensitivity of 0.8057, and a specificity of 0.7958. Compared to models trained solely on spectrograms or waveforms, the use of both representations results in an improved AUC score, demonstrating that combining spectrogram and waveform representation helps to enrich the extracted features and outperforms the models that use only one representation.

Àlex Giménez-Romero, Federico Vazquez, Cristóbal López et al.

Emerging marine infectious diseases pose a substantial threat to marine ecosystems and the conservation of their biodiversity. Compartmental models of epidemic transmission in marine sessile organisms, available only recently, are based on non-spatial descriptions in which space is homogenised and parasite mobility is not explicitly accounted for. However, in realistic scenarios epidemic transmission is conditioned by the spatial distribution of hosts and the parasites mobility patterns, calling for a explicit description of space. In this work we develop a spatially-explicit individual-based model to study disease transmission by waterborne parasites in sessile marine populations. We investigate the impact of spatial disease transmission through extensive numerical simulations and theoretical analysis. Specifically, the effects of parasite mobility into the epidemic threshold and the temporal progression of the epidemic are assessed. We show that larger values of pathogen mobility imply more severe epidemics, as the number of infections increases, and shorter time-scales to extinction. An analytical expression for the basic reproduction number of the spatial model is derived as function of the non-spatial counterpart, which characterises a transition between a disease-free and a propagation phase, in which the disease propagates over a large fraction of the system.