Allergen Immunotherapy: Current and Future Trends

Allergen immunotherapy (AIT) is the sole disease-modifying treatment for allergic rhinitis; it prevents rhinitis from progressing to asthma and lowers medication use. AIT against mites, insect venom, and certain kinds of pollen is effective. The mechanism of action of AIT is based on inducing immunological tolerance characterized by increased IL-10, TGF-β, and IgG4 levels and Treg cell counts. However, AIT requires prolonged schemes of administration and is sometimes associated with adverse reactions. Over the last decade, novel forms of AIT have been developed, focused on better allergen identification, structural modifications to preserve epitopes for B or T cells, post-traductional alteration through chemical processes, and the addition of adjuvants. These modified allergens induce clinical-immunological effects similar to those mentioned above, increasing the tolerance to other related allergens but with fewer side effects. Clinical studies have shown that molecular AIT is efficient in treating grass and birch allergies. This article reviews the possibility of a new AIT to improve the treatment of allergic illness.

Selective cannabis strain allergy in a patient presenting with a local allergic reaction

Background Cannabis use is growing domestically due to recent legalization in many jurisdictions. There are two main species of cannabis, Cannabis sativa and Cannabis indica, and thousands of different commercially available cannabis strains. Although there are multiple reports of cannabis allergy in the literature, to our knowledge, there is no prior published report of selective cannabis strain allergy. Case presentation A 31-year-old male was referred for allergy assessment due to several episodes of localized pruritus and erythema after direct contact with various strains of cannabis. He had noted that the severity of his reaction appeared to be strain dependent. He developed a severe local reaction involving bilateral periorbital edema shortly after coming into direct contact with one particular strain of cannabis. He denied any adverse symptoms after inhalation of cannabis. Fresh skin prick testing was performed to various strains of cannabis and had positive testing to the three of the five tested strains. Conclusions We believe this is the first reported case of selective cannabis strain allergy based on patient history and skin prick testing. This case report outlines the variability in different strains of cannabis and stresses the importance of further research into cannabis allergen identification. Multiple cannabis allergens should be included and incorporated into commercial extracts when they become routinely available.

Mass spectrometric analysis of digesta does not improve the allergenicity assessment of GM crops

An investigation of the potential allergenicity of newly expressed proteins in genetically modified (GM) crops comprises part of the assessment of GM crop safety. However, allergenicity is not completely predictable from a definitive assay result or set of protein characteristics, and scientific opinions regarding the data that should be used to assess allergenicity are continuously evolving. Early studies supported a correlation between the stability of a protein exposed to digestive enzymes such as pepsin and the protein’s status as a potential allergen, but over time the conclusions of these earlier studies were not confirmed. Nonetheless, many regulatory authorities, including the European Food Safety Authority (EFSA), continue to require digestibility analyses as a component of GM crop risk assessments. Moreover, EFSA has recently investigated the use of mass spectrometry (MS), to make digestion assays more predictive of allergy risk, because it can detect and identify small undigested peptides. However, the utility of MS is questionable in this context, since known allergenic peptides are unlikely to exist in protein candidates intended for commercial development. These protein candidates are pre-screened by the same bioinformatics processes that are normally used to identify MS targets. Therefore, MS is not a standalone allergen identification method and also cannot be used to predict previously unknown allergenic epitopes. Thus, the suggested application of MS for analysis of digesta does not improve the poor predictive power of digestion assays in identifying allergenic risk.

A Comparative Analysis of Novel Deep Learning and Ensemble Learning Models to Predict the Allergenicity of Food Proteins

Traditional food allergen identification mainly relies on in vivo and in vitro experiments, which often needs a long period and high cost. The artificial intelligence (AI)-driven rapid food allergen identification method has solved the above mentioned some drawbacks and is becoming an efficient auxiliary tool. Aiming to overcome the limitations of lower accuracy of traditional machine learning models in predicting the allergenicity of food proteins, this work proposed to introduce deep learning model—transformer with self-attention mechanism, ensemble learning models (representative as Light Gradient Boosting Machine (LightGBM) eXtreme Gradient Boosting (XGBoost)) to solve the problem. In order to highlight the superiority of the proposed novel method, the study also selected various commonly used machine learning models as the baseline classifiers. The results of 5-fold cross-validation showed that the area under the receiver operating characteristic curve (AUC) of the deep model was the highest (0.9578), which was better than the ensemble learning and baseline algorithms. But the deep model need to be pre-trained, and the training time is the longest. By comparing the characteristics of the transformer model and boosting models, it can be analyzed that, each model has its own advantage, which provides novel clues and inspiration for the rapid prediction of food allergens in the future.

A Comparative Analysis of Novel Deep Learning Models and Ensemble Learning Models to Predict the Allergenicity of Food Allergens

Traditional food allergen identification mainly relies on in vivo and in vitro experiments, which often needs a long period and high cost. The artificial intelligence (AI)-driven rapid food allergen identification method has solved the two drawbacks and is becoming an efficient auxiliary tool. Aiming to overcome the limitations of lower accuracy of traditional machine learning models in predicting the allergenicity of food allergens, this work proposed to introduce transformer deep learning model with self-attention mechanism and ensemble learning model (representative as Light Gradient Boosting Machine (LightGBM) and eXtreme Gradient Boosting (XGBoost)) to solve the problem. In order to highlight the superiority of the proposed novel method, the study also selected various commonly used machine learning models as the baseline classifiers. The results of 5-fold cross-validation found that the AUC of the deep model was the highest (0.9400), which was better than the ensemble learning and baseline algorithms. But it needed to be pre-trained, and the training cost was highest. By comparing the characteristics of transformer model and boosting models, it can be analyzed that the two types of models have their own advantages, which provides novel clues and inspiration for the rapid prediction of food allergens in the future.

Antigen array for serological diagnosis and novel allergen identification in severe equine asthma

Severe equine asthma (sEA), which closely resembles human asthma, is a debilitating and performance-limiting allergic respiratory disorder which affects 14% of horses in the Northern Hemisphere and is associated with increased allergen-specific immunoglobulin E (IgE) against a range of environmental proteins. A comprehensive microarray platform was developed to enable the simultaneous detection of allergen-specific equine IgE in serum against a wide range of putative allergenic proteins. The microarray revealed a plethora of novel pollen, bacteria, mould and arthropod proteins significant in the aetiology of sEA. Moreover, the analyses revealed an association between sEA-affected horses and IgE antibodies specific for proteins derived from latex, which has traditionally been ubiquitous to the horse’s environment in the form of riding surfaces and race tracks. Further work is required to establish the involvement of latex proteins in sEA as a potential risk factor. This work demonstrates a novel and rapid approach to sEA diagnosis, providing a platform for tailored management and the development of allergen-specific immunotherapy.

These Labels are Nuts: Challenges to Safe Product Identification for Nut-Allergic Consumers

Allergen information on food labels is not standardized, making allergen avoidance difficult for consumers. This study investigated the speed and accuracy of allergen identification on commercial packaging across different types of warning labels. The results identified packaging label characteristics significantly correlated with faster and more accurate identification of allergens. Standardizing warning and safe-to-consume labels may reduce risk of accidental allergen exposure for consumers managing food allergies.