The importance of allergen immunotherapy (AIT) is multifaceted, encompassing both clinical and quality-of-life improvements and cost-effectiveness in the long term. Key mechanisms of allergen tolerance induced by AIT include changes in memory type allergen-specific T- and B-cell responses towards a regulatory phenotype with decreased Type 2 responses, suppression of allergen-specific IgE and increased IgG1 and IgG4 , decreased mast cell and eosinophil numbers in allergic tissues and increased activation thresholds. The potential of novel patient enrolment strategies for AIT is taking into account recent advances in biomarkers discoveries, molecular allergy diagnostics and mobile health applications contributing to a personalized approach enhancement that can increase AIT efficacy and compliance. Artificial intelligence can help manage and interpret complex and heterogeneous data, including big data from omics and non-omics research, potentially predict disease subtypes, identify biomarkers and monitor patient responses to AIT. Novel AIT preparations, such as synthetic compounds, innovative carrier systems and adjuvants, are also of great promise. Advances in clinical trial models, including adaptive, complex and hybrid designs as well as real-world evidence, allow more flexibility and cost reduction. The analyses of AIT cost-effectiveness show a clear long-term advantage compared to pharmacotherapy. Important research questions, such as defining clinical endpoints, biomarkers of patient selection and efficacy, mechanisms and the modulation of the placebo effect and alternatives to conventional field trials, including allergen exposure chamber studies are still to be elucidated. This review demonstrates that AIT is still in its growth phase and shows immense development prospects.

Hot topics in allergen immunotherapy, 2023: Current status and future perspective.

In addition to the challenge of meeting global demand for food production, there are increasing concerns about food safety and the need to protect consumer health from the negative effects of foodborne allergies. Certain bio‐molecules (usually proteins) present in food can act as allergens that trigger unusual immunological reactions, with potentially life‐threatening consequences. The relentless working lifestyles of the modern era often incorporate poor eating habits that include readymade prepackaged and processed foods, which contain additives such as peanuts, tree nuts, wheat, and soy‐based products, rather than traditional home cooking. Of the predominant allergenic foods (soybean, wheat, fish, peanut, shellfish, tree nuts, eggs, and milk), peanuts (Arachis hypogaea) are the best characterized source of allergens, followed by tree nuts (Juglans regia, Prunus amygdalus, Corylus avellana, Carya illinoinensis, Anacardium occidentale, Pistacia vera, Bertholletia excels), wheat (Triticum aestivum), soybeans (Glycine max), and kidney beans (Phaseolus vulgaris). The prevalence of food allergies has risen significantly in recent years including chance of accidental exposure to such foods. In contrast, the standards of detection, diagnosis, and cure have not kept pace and unfortunately are often suboptimal. In this review, we mainly focus on the prevalence of allergies associated with peanut, tree nuts, wheat, soybean, and kidney bean, highlighting their physiological properties and functions as well as considering research directions for tailoring allergen gene expression. In particular, we discuss how recent advances in molecular breeding, genetic engineering, and genome editing can be used to develop potential low allergen food crops that protect consumer health.

Prospects for developing allergen-depleted food crops.

This chapter explores AI's influence on pharmaceutical sciences, highlighting its enhancement of traditional design methodologies. It explores AI's transformational role in key sectors, including drug discovery, virtual screening, and drug formulation development. AI's ability to efficiently identify potential drug candidates from large chemical libraries and its use of optimization algorithms in the selection of suitable excipients and dosage forms are discussed. The chapter also emphasizes AI's significance in improving pharmaceutical manufacturing processes through parameter refinement, quality outcome prediction, and real-time anomaly detection. The integration of traditional design methods with AI ensures robust, reliable, AI-driven processes that are compliant with regulations. In conclusion, the chapter highlights the potential of AI in pharmaceutical sciences and the importance of its integration with traditional design methods. This approach empowers scientists to innovate, speed up drug development, and improve patient outcomes. 

AI-Driven Decision-Making Applications in Pharmaceutical Sciences

Immuno-correlated dermatological pathologies refer to skin disorders that are closely associated with immune system dysfunction or abnormal immune responses. Advancements in the field of artificial intelligence (AI) have shown promise in enhancing the diagnosis, management, and assessment of immuno-correlated dermatological pathologies. This intersection of dermatology and immunology plays a pivotal role in comprehending and addressing complex skin disorders with immune system involvement. The paper explores the knowledge known so far and the evolution and achievements of AI in diagnosis; discusses segmentation and the classification of medical images; and reviews existing challenges, in immunological-related skin diseases. From our review, the role of AI has emerged, especially in the analysis of images for both diagnostic and severity assessment purposes. Furthermore, the possibility of predicting patients' response to therapies is emerging, in order to create tailored therapies. 

Artificial Intelligence: A Snapshot of Its Application in Chronic Inflammatory and Autoimmune Skin Diseases

Abstract Atopic dermatitis (AD), a complex and heterogeneous chronic inflammatory skin disorder, manifests in a spectrum of clinical subtypes. The application of genomics has elucidated the role of genetic variations in predisposing individuals to AD. Transcriptomics, analyzing gene expression alterations, sheds light on the molecular underpinnings of AD. Proteomics explores the involvement of proteins in AD pathophysiology, while epigenomics examines the impact of environmental factors on gene expression. Lipidomics, which investigates lipid profiles, enhances our understanding of skin barrier functionalities and their perturbations in AD. This review synthesizes insights from these omics approaches, highlighting their collective importance in unraveling the intricate pathogenesis of AD. The review culminates by projecting future trajectories in AD research, particularly the promise of multi‐omics in forging personalized medicine and novel therapeutic interventions. Such an integrated multi‐omics strategy is poised to transform AD comprehension and management, steering towards more precise and efficacious treatment modalities. 

Integrating multi‐omics approaches in deciphering atopic dermatitis pathogenesis and future therapeutic directions

Artificial intelligence (AI) has increasingly been used in healthcare. Given the capacity of AI to handle large data and complex relationships between variables, AI is well suited for applications in healthcare. Recently, AI has been applied to allergy research.In this article, we review how AI technologies have been utilized in basic science and clinical allergy research for asthma, atopic dermatitis, rhinology, adverse reactions to drugs and vaccines, food allergy, anaphylaxis, urticaria, and eosinophilic gastrointestinal disorders. We discuss barriers for AI adoption to improve the care of patients with atopic diseases. These studies demonstrate the utility of applying AI to the field of allergy to help investigators expand their understanding of disease pathogenesis, improve diagnostic accuracy, enable prediction for treatments and outcomes, and for drug discovery. 

/pdf/artificial-intelligence-exploring-the-future-of-innovation-2lzsuyaw.pdf

Artificial Intelligence: Exploring the Future of Innovation in Allergy Immunology

BACKGROUND Nocardia pneumonia shares similar imaging and clinical features with pulmonary tuberculosis and lung neoplasms, but the treatment and anti-infective medication are completely different. Here, we report a case of pulmonary nocardiosis caused by Nocardia cyriacigeorgica (N. cyriacigeorgica), which was misdiagnosed as community-acquired pneumonia (CAP) with repeated fever. CASE SUMMARY A 55-year-old female was diagnosed with community-acquired pneumonia in the local hospital because of repeated fever and chest pain for two months. After the anti-infection treatment failed in the local hospital, the patient came to our hospital for further treatment. Enhanced computed tomography showed multiple patchy, nodular and strip-shaped high-density shadows in both lungs. A routine haematological examination was performed and showed abnormalities in CD19+ B cells and CD4+ T cells. Positive acid-fast bifurcating filaments and branching gram-positive rods were observed in the bronchoalveolar lavage fluid of the patient under an oil microscope, which was identified by matrix-assisted laser desorption ionization-time of flight mass spectrometry as N. cyriacigeorgica. The patient's condition quickly improved after taking 0.96 g compound sulfamethoxazole tablets three times a day. CONCLUSION The antibiotic treatment of Nocardia pneumonia is different from that of common CAP. Attention should be given to the pathogenic examination results of patients with recurrent fever. Nocardia pneumonia is an opportunistic infection. Patients with CD4+ T-cell deficiency should be aware of Nocardia infection.

https://doi.org/10.12998/wjcc.v11.i5.1175

Nocardia cyriacigeorgica infection in a patient with repeated fever and CD4+ T cell deficiency: A case report

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Artificial Intelligence: Exploring the Future of Innovation in Allergy Immunology

Nocardia cyriacigeorgica infection in a patient with repeated fever and CD4+ T cell deficiency: A case report