BACKGROUND For young children with peanut allergy, dietary avoidance is the current standard of care. We aimed to assess whether peanut oral immunotherapy can induce desensitisation (an increased allergic reaction threshold while on therapy) or remission (a state of non-responsiveness after discontinuation of immunotherapy) in this population. METHODS We did a randomised, double-blind, placebo-controlled study in five US academic medical centres. Eligible participants were children aged 12 to younger than 48 months who were reactive to 500 mg or less of peanut protein during a double-blind, placebo-controlled food challenge (DBPCFC). Participants were randomly assigned by use of a computer, in a 2:1 allocation ratio, to receive peanut oral immunotherapy or placebo for 134 weeks (2000 mg peanut protein per day) followed by 26 weeks of avoidance, with participants and study staff and investigators masked to group treatment assignment. The primary outcome was desensitisation at the end of treatment (week 134), and remission after avoidance (week 160), as the key secondary outcome, were assessed by DBPCFC to 5000 mg in the intention-to-treat population. Safety and immunological parameters were assessed in the same population. This trial is registered on ClinicalTrials.gov, NCT03345160. FINDINGS Between Aug 13, 2013, and Oct 1, 2015, 146 children, with a median age of 39·3 months (IQR 30·8-44·7), were randomly assigned to receive peanut oral immunotherapy (96 participants) or placebo (50 participants). At week 134, 68 (71%, 95% CI 61-80) of 96 participants who received peanut oral immunotherapy compared with one (2%, 0·05-11) of 50 who received placebo met the primary outcome of desensitisation (risk difference [RD] 69%, 95% CI 59-79; p<0·0001). The median cumulative tolerated dose during the week 134 DBPCFC was 5005 mg (IQR 3755-5005) for peanut oral immunotherapy versus 5 mg (0-105) for placebo (p<0·0001). After avoidance, 20 (21%, 95% CI 13-30) of 96 participants receiving peanut oral immunotherapy compared with one (2%, 0·05-11) of 50 receiving placebo met remission criteria (RD 19%, 95% CI 10-28; p=0·0021). The median cumulative tolerated dose during the week 160 DBPCFC was 755 mg (IQR 0-2755) for peanut oral immunotherapy and 0 mg (0-55) for placebo (p<0·0001). A significant proportion of participants receiving peanut oral immunotherapy who passed the 5000 mg DBPCFC at week 134 could no longer tolerate 5000 mg at week 160 (p<0·001). The participant receiving placebo who was desensitised at week 134 also achieved remission at week 160. Compared with placebo, peanut oral immunotherapy decreased peanut-specific and Ara h2-specific IgE, skin prick test, and basophil activation, and increased peanut-specific and Ara h2-specific IgG4 at weeks 134 and 160. By use of multivariable regression analysis of participants receiving peanut oral immunotherapy, younger age and lower baseline peanut-specific IgE was predictive of remission. Most participants (98% with peanut oral immunotherapy vs 80% with placebo) had at least one oral immunotherapy dosing reaction, predominantly mild to moderate and occurring more frequently in participants receiving peanut oral immunotherapy. 35 oral immunotherapy dosing events with moderate symptoms were treated with epinephrine in 21 participants receiving peanut oral immunotherapy. INTERPRETATION In children with a peanut allergy, initiation of peanut oral immunotherapy before age 4 years was associated with an increase in both desensitisation and remission. Development of remission correlated with immunological biomarkers. The outcomes suggest a window of opportunity at a young age for intervention to induce remission of peanut allergy. FUNDING National Institute of Allergy and Infectious Disease, Immune Tolerance Network.
Plant disease diagnosis is essential to farmers' management choices because plant diseases frequently lower crop yield and product quality. For harvests to flourish and agricultural productivity to boost, grape leaf disease detection is important. The plant disease dataset contains grape leaf diseases total of 9,032 images of four classes, among them three classes are leaf diseases, and the other one is healthy leaves. After rigorous pre-processing dataset was split (70% training, 20% validation, 10% testing), and two pre-trained models were deployed: InceptionV3 and Xception. Xception shows a promising result of 96.23% accuracy, which is remarkable than InceptionV3. Adversarial Training is used for robustness, along with more transparency. Grad-CAM is integrated to confirm the leaf disease. Finally deployed a web application using Streamlit with a heatmap visualization and prediction with confidence level for robust grape leaf disease classification.
Diagnosing dental diseases from radiographs is time-consuming and challenging due to the subtle nature of diagnostic evidence. Existing methods, which rely on object detection models designed for natural images with more distinct target patterns, struggle to detect dental diseases that present with far less visual support. To address this challenge, we propose {\bf DentalX}, a novel context-aware dental disease detection approach that leverages oral structure information to mitigate the visual ambiguity inherent in radiographs. Specifically, we introduce a structural context extraction module that learns an auxiliary task: semantic segmentation of dental anatomy. The module extracts meaningful structural context and integrates it into the primary disease detection task to enhance the detection of subtle dental diseases. Extensive experiments on a dedicated benchmark demonstrate that DentalX significantly outperforms prior methods in both tasks. This mutual benefit arises naturally during model optimization, as the correlation between the two tasks is effectively captured. Our code is available at https://github.com/zhiqin1998/DentYOLOX.
BackgroundLung squamous cell carcinoma (LUSC) is a leading cause of cancer-related mortality. Although immunotherapy has recently demonstrated clinical benefits, the biological roles of immune-related genes (IRGs) in LUSC remain insufficiently understood.MethodsIn this study, transcriptomic and clinical data from 493 LUSC patients were obtained from The Cancer Genome Atlas (TCGA). IRGs were identified through weighted gene co-expression network analysis, followed by univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression to screen for prognostic genes and establish a risk prediction model. The model’s predictive performance was validated, and the immune landscape associated with distinct risk subgroups was systematically characterized. Expression patterns and clinical significance of the signature genes were further investigated using bioinformatics analysis, quantitative real-time PCR, Western blotting, and immunohistochemistry.ResultsA total of 55 differentially expressed IRGs were identified, among which 8 genes (PSMD1, ANGPTL4, LTBP3, MIF, NFATC3, NR1D2, PLXNB3, and SP1) demonstrated significant prognostic value. A prognostic signature based on these 8 IRGs was established that stratified patients into high- and low-risk groups with distinct survival outcomes, immune landscapes, and enriched pathways. As one of the constituent genes of the risk model, NR1D2 was found to be downregulated in LUSC and associated with poor prognosis. Functional assays indicated that NR1D2 facilitated malignant progression by regulating macrophage polarization and enhancing tumor cell migration.ConclusionThis study establishes a novel IRGs-based prognostic signature with potential utility for risk stratification and individualized immunotherapeutic strategies in LUSC. Furthermore, it also provides valuable insights into the role of NR1D2 in clinical outcomes.
Bosubabu Sambana, Hillary Sunday Nnadi, Mohd Anas Wajid
et al.
Plant diseases pose significant challenges to farmers and the agricultural sector at large. However, early detection of plant diseases is crucial to mitigating their effects and preventing widespread damage, as outbreaks can severely impact the productivity and quality of crops. With advancements in technology, there are increasing opportunities for automating the monitoring and detection of disease outbreaks in plants. This study proposed a system designed to identify and monitor plant diseases using a transfer learning approach. Specifically, the study utilizes YOLOv7 and YOLOv8, two state-ofthe-art models in the field of object detection. By fine-tuning these models on a dataset of plant leaf images, the system is able to accurately detect the presence of Bacteria, Fungi and Viral diseases such as Powdery Mildew, Angular Leaf Spot, Early blight and Tomato mosaic virus. The model's performance was evaluated using several metrics, including mean Average Precision (mAP), F1-score, Precision, and Recall, yielding values of 91.05, 89.40, 91.22, and 87.66, respectively. The result demonstrates the superior effectiveness and efficiency of YOLOv8 compared to other object detection methods, highlighting its potential for use in modern agricultural practices. The approach provides a scalable, automated solution for early any plant disease detection, contributing to enhanced crop yield, reduced reliance on manual monitoring, and supporting sustainable agricultural practices.
Recent advances in artificial intelligence (AI) and multimodal data collection are revolutionizing dermatology. Generative AI and machine learning approaches offer opportunities to enhance the diagnosis and treatment of inflammatory skin diseases, including atopic dermatitis, psoriasis, hidradenitis suppurativa, and autoimmune connective tissue disease. This review examines the current landscape of AI applications for inflammatory skin diseases and explores how generative AI and machine learning methods can advance the field through deep phenotyping, disease heterogeneity characterization, drug development, personalized medicine, and clinical care. We discuss the promises and challenges of these technologies and present a vision for their integration into clinical practice.
Sara H. Mahmoud, Mokhtar Gomaa, Ahmed El Taweel
et al.
Abstract Live bird markets (LBMs) are considered hotspots for Avian Influenza Viruses (AIVs). In such markets, AIVs pose threats to both poultry and public health. Within LBMs, AIVs spread through various routes, including direct contact, environmental contamination, and aerosol transmission. Unique factors in Egyptian LBMs, such as the coexistence of wild and domestic birds, increase transmission risks between birds as well as spill-overs into exposed humans. Understanding the transmission dynamics of AIVs is vital for implementing effective control measures. We conducted a study in four Egyptian LBMs located in Mediterranean coast cities from November 2021 to March 2023. In this study we tested 3,971 samples from poultry, wild birds, and the environment, out of which 692 (17.4%) were positive for AIV. Poultry exhibited a higher prevalence (42.2%) than wild birds (34.4%). Environmental samples, including water (30.8%), surfaces (17.2%), and air (18.2%), also tested positive for AIV. Diverse AIV subtypes, including H5N1, H9N2, H5/H9 co-infection, and H5N8, were detected among avian species and the environment. Temporal analysis revealed fluctuating IAV positivity rates from November 2021 to March 2023. These results emphasize the importance of continuous surveillance, resource allocation, and multisectoral collaboration to protect poultry and human health, and prevent novel influenza strains’ emergence in Egyptian LBMs.
Pumpkin is a vital crop cultivated globally, and its productivity is crucial for food security, especially in developing regions. Accurate and timely detection of pumpkin leaf diseases is essential to mitigate significant losses in yield and quality. Traditional methods of disease identification rely heavily on subjective judgment by farmers or experts, which can lead to inefficiencies and missed opportunities for intervention. Recent advancements in machine learning and deep learning offer promising solutions for automating and improving the accuracy of plant disease detection. This paper presents a comprehensive analysis of state-of-the-art Convolutional Neural Network (CNN) models for classifying diseases in pumpkin plant leaves. Using a publicly available dataset of 2000 highresolution images, we evaluate the performance of several CNN architectures, including ResNet, DenseNet, and EfficientNet, in recognizing five classes: healthy leaves and four common diseases downy mildew, powdery mildew, mosaic disease, and bacterial leaf spot. We fine-tuned these pretrained models and conducted hyperparameter optimization experiments. ResNet-34, DenseNet-121, and EfficientNet-B7 were identified as top-performing models, each excelling in different classes of leaf diseases. Our analysis revealed DenseNet-121 as the optimal model when considering both accuracy and computational complexity achieving an overall accuracy of 86%. This study underscores the potential of CNNs in automating disease diagnosis for pumpkin plants, offering valuable insights that can contribute to enhancing agricultural productivity and minimizing economic losses.
The emerging research shows that lncRNAs are associated with a series of complex human diseases. However, most of the existing methods have limitations in identifying nonlinear lncRNA-disease associations (LDAs), and it remains a huge challenge to predict new LDAs. Therefore, the accurate identification of LDAs is very important for the warning and treatment of diseases. In this work, multiple sources of biomedical data are fully utilized to construct characteristics of lncRNAs and diseases, and linear and nonlinear characteristics are effectively integrated. Furthermore, a novel deep learning model based on graph attention automatic encoder is proposed, called HGATELDA. To begin with, the linear characteristics of lncRNAs and diseases are created by the miRNA-lncRNA interaction matrix and miRNA-disease interaction matrix. Following this, the nonlinear features of diseases and lncRNAs are extracted using a graph attention auto-encoder, which largely retains the critical information and effectively aggregates the neighborhood information of nodes. In the end, LDAs can be predicted by fusing the linear and nonlinear characteristics of diseases and lncRNA. The HGATELDA model achieves an impressive AUC value of 0.9692 when evaluated using a 5-fold cross-validation indicating its superior performance in comparison to several recent prediction models. Meanwhile, the effectiveness of HGATELDA in identifying novel LDAs is further demonstrated by case studies. the HGATELDA model appears to be a viable computational model for predicting LDAs.
Plant diseases pose significant threats to agriculture. It necessitates proper diagnosis and effective treatment to safeguard crop yields. To automate the diagnosis process, image segmentation is usually adopted for precisely identifying diseased regions, thereby advancing precision agriculture. Developing robust image segmentation models for plant diseases demands high-quality annotations across numerous images. However, existing plant disease datasets typically lack segmentation labels and are often confined to controlled laboratory settings, which do not adequately reflect the complexity of natural environments. Motivated by this fact, we established PlantSeg, a large-scale segmentation dataset for plant diseases. PlantSeg distinguishes itself from existing datasets in three key aspects. (1) Annotation type: Unlike the majority of existing datasets that only contain class labels or bounding boxes, each image in PlantSeg includes detailed and high-quality segmentation masks, associated with plant types and disease names. (2) Image source: Unlike typical datasets that contain images from laboratory settings, PlantSeg primarily comprises in-the-wild plant disease images. This choice enhances the practical applicability, as the trained models can be applied for integrated disease management. (3) Scale: PlantSeg is extensive, featuring 11,400 images with disease segmentation masks and an additional 8,000 healthy plant images categorized by plant type. Extensive technical experiments validate the high quality of PlantSeg's annotations. This dataset not only allows researchers to evaluate their image classification methods but also provides a critical foundation for developing and benchmarking advanced plant disease segmentation algorithms.
Arnon Elizur, Shirel Rachel‐Jossefi, Marianna Rachmiel
et al.
Abstract Background The effect of the amount of transient cow's milk formula (CMF) consumed during the first days of life on IgE‐cow's milk allergy (IgE‐CMA) is unknown. Methods A cohort of 58 patients with IgE‐CMA was identified from a large scale population‐based study of 13,019 infants followed from birth. A group of 116 infants matched for sex and breastfeeding only duration (beyond the nursery period), and another random group of 259 healthy infants were used as controls. Parents were interviewed and the infants' medical records were searched to assess CMF consumption in the nursery. Results While 96% of the mothers of the 174 infants (58 with Cow's milk allergy and 116 controls) reported on exclusive breastfeeding during the stay in the nursery, CMF consumption was documented in 96 (55%) of the infants. Of those, most (57; 59%) received one to three feedings, 20 (21%) received four to nine feedings, and 19 (20%) received ≥10 feedings. Fewer formula feeds (1–3) were significantly more common in the allergic group than ≥4 feeds (p = 0.0003) and no feeds at all (p = 0.02) compared to controls (n = 116). Of those exclusively breastfed in the nursery, 13/23 allergic infants (57%) introduced CMF at age 105–194 days (the period with highest‐risk for IgE‐CMA) compared to 33/98 (34%) from the random control group (n = 259) (p = 0.04). Conclusions Most infants end up receiving few CMF feeds in the nursery. Transient CMF in the nursery is associated with increased risk of IgE‐CMA.
BACKGROUND Activated phosphoinositide 3-kinase δ syndrome (APDS) 2 (p110δ-activating mutations causing senescent T cells, lymphadenopathy, and immunodeficiency [PASLI]-R1), a recently described primary immunodeficiency, results from autosomal dominant mutations in PIK3R1, the gene encoding the regulatory subunit (p85α, p55α, and p50α) of class IA phosphoinositide 3-kinases. OBJECTIVES We sought to review the clinical, immunologic, and histopathologic phenotypes of APDS2 in a genetically defined international patient cohort. METHODS The medical and biological records of 36 patients with genetically diagnosed APDS2 were collected and reviewed. RESULTS Mutations within splice acceptor and donor sites of exon 11 of the PIK3R1 gene lead to APDS2. Recurrent upper respiratory tract infections (100%), pneumonitis (71%), and chronic lymphoproliferation (89%, including adenopathy [75%], splenomegaly [43%], and upper respiratory tract lymphoid hyperplasia [48%]) were the most common features. Growth retardation was frequently noticed (45%). Other complications were mild neurodevelopmental delay (31%); malignant diseases (28%), most of them being B-cell lymphomas; autoimmunity (17%); bronchiectasis (18%); and chronic diarrhea (24%). Decreased serum IgA and IgG levels (87%), increased IgM levels (58%), B-cell lymphopenia (88%) associated with an increased frequency of transitional B cells (93%), and decreased numbers of naive CD4 and naive CD8 cells but increased numbers of CD8 effector/memory T cells were predominant immunologic features. The majority of patients (89%) received immunoglobulin replacement; 3 patients were treated with rituximab, and 6 were treated with rapamycin initiated after diagnosis of APDS2. Five patients died from APDS2-related complications. CONCLUSION APDS2 is a combined immunodeficiency with a variable clinical phenotype. Complications are frequent, such as severe bacterial and viral infections, lymphoproliferation, and lymphoma similar to APDS1/PASLI-CD. Immunoglobulin replacement therapy, rapamycin, and, likely in the near future, selective phosphoinositide 3-kinase δ inhibitors are possible treatment options.
Eosinophilic airway inflammation is one of the cardinal features of allergic airway diseases such as atopic asthma and allergic rhinitis. These childhood‐onset conditions are mediated by allergen and allergen‐specific IgE and often accompanied by other allergic diseases including food allergy and eczema. They can develop consecutively in the same patient, which is referred to as an allergic march. In contrast, some phenotypes of asthma, nonsteroidal anti‐inflammatory drugs‐exacerbated airway disease (N‐ERD), chronic rhinosinusitis with nasal polyps (CRSwNP)/eosinophilic CRS and allergic bronchopulmonary aspergillosis/mycosis (ABPA/ABPM) are adult‐onset airway diseases, which are characterized by prominent peripheral blood eosinophilia. Most of these conditions, except for ABPA/ABPM, are nonatopic, and the coexistence of multiple diseases, including an adult‐onset eosinophilic systemic disease, eosinophilic granulomatosis with polyangiitis (EGPA), is common. In this review, we focus on eosinophil biology, genetics and clinical characteristics and the pathophysiology of adult‐onset eosinophilic asthma, N‐ERD, CRSwNP/eosinophilic CRS, ABPA/ABPM and EGPA, while exploring the common genetic, immunological and pathological conditions among these adult‐onset eosinophilic diseases.
Antibodies of IgG4 subclass are exceptional players of the immune system, as they are considered to be immunologically inert and functionally monovalent, and as such may be part of classical tolerance mechanisms. IgG4 antibodies are found in a range of different diseases, including IgG4-related diseases, allergy, cancer, rheumatoid arthritis, helminth infection and IgG4 autoimmune diseases, where they may be pathogenic or protective. IgG4 autoimmune diseases are an emerging new group of diseases that are characterized by pathogenic, antigen-specific autoantibodies of IgG4 subclass, such as MuSK myasthenia gravis, pemphigus vulgaris and thrombotic thrombocytopenic purpura. The list of IgG4 autoantigens is rapidly growing and to date contains 29 candidate antigens. Interestingly, IgG4 autoimmune diseases are restricted to four distinct organs: 1) the central and peripheral nervous system, 2) the kidney, 3) the skin and mucous membranes and 4) the vascular system and soluble antigens in the blood circulation. The pathogenicity of IgG4 can be validated using our classification system, and is usually excerted by functional blocking of protein-protein interaction.
BACKGROUND Feline allergic diseases present as challenging problems for clinicians, not least because of the number of reaction patterns of the feline skin, none of which are specific for allergy. Furthermore, there is some controversy over the nomenclature that should be used in their description. OBJECTIVES To review the literature, assess the status of knowledge of the topic and the extent to which these diseases could be categorized as atopic in nature, and make recommendations concerning nomenclature. METHODS Atopic diseases in humans and cats were researched. A comparison then was made of the essential features in the two species. RESULTS There were sufficient similarities between human atopic diseases and the manifestations of feline diseases of presumed allergic aetiology to justify the use of "atopic" to describe some of the feline conditions affecting the skin, respiratory and gastrointestinal tract. However, none of the allergic skin diseases showed features consistent with atopic dermatitis as described in man and the dog. CONCLUSIONS AND CLINICAL IMPORTANCE The term "Feline Atopic Syndrome" (FAS) is proposed to encompass allergic diseases of the skin, gastrointestinal tract and respiratory tract, and "Feline atopic skin syndrome" (FASS) proposed to describe allergic skin disease associated with environmental allergies. We are not aware of any adverse food reactions in cats that are attributable to causes other than immunological reactions against the food itself. We therefore propose an aetiological definition of "Food Allergy" (FA) to describe such cases.
In a highly simplified view, a disease can be seen as the phenotype emerging from the interplay of genetic predisposition and fluctuating environmental stimuli. We formalize this situation in a minimal model, where a network (representing cellular regulation) serves as an interface between an input layer (representing environment) and an output layer (representing functional phenotype). Genetic predisposition for a disease is represented as a loss of function of some network nodes. Reduced, but non-zero, output indicates disease. The simplicity of this genetic disease model and its deep relationship to percolation theory allows us to understand the interplay between disease, network topology and the location and clusters of affected network nodes. We find that our model generates two different characteristics of diseases, which can be interpreted as chronic and acute diseases. In its stylized form, our model provides a new view on the relationship between genetic mutations and the type and severity of a disease.