Food Allergy and Anaphylaxis Research

Gut microbial composition modulates food-specific CD4+ T cells in food allergy

This study shows that gut microbial composition affects food allergen-specific CD4+ T cells, leading to differences in food allergy severity between mouse strains.

Microbial metabolism of food allergens determines the severity of IgE-mediated anaphylaxis

This study shows that gut and oral bacteria metabolize peanut allergens, affecting IgE-binding and anaphylaxis severity in allergic individuals.

Dynamic Regulation of CD300b, and CD300f on Myeloid Cells in Oral Immunotherapy

This study examines changes in CD300b and CD300f expression on myeloid cells during oral immunotherapy and finds their dynamic regulation correlates with treatment response and immune tolerance.

Proteomic characterization of major fish allergy responsive protein parvalbumins in Hilsa (Tenualosa ilisha): A commercially important fish in Southeast Asia

This study characterizes parvalbumin isoforms in Hilsa fish using proteomic methods, finding higher PRVB levels in young fish and lower PRVB compared to other freshwater fish.

Identification of potential inflammation markers for outgrowth of cow's milk allergy

The paper identifies potential inflammation markers in saliva samples of children with cow's milk allergy (CMA) that correlate with resolution or persistence of the allergy.

Leveraging mRNA platform for the development of vaccines against egg allergy

This study develops mRNA vaccines against egg allergy using a mouse model and shows they prevent anaphylaxis by inducing allergen-blocking antibodies and shifting immune responses.

NetAllergen, a random forest model integrating MHC-II presentation propensity for improved allergenicity prediction

NetAllergen improves allergenicity prediction by integrating MHC-II presentation propensity, outperforming existing methods when sequence similarity to known allergens is low.

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

This paper compares deep learning and ensemble learning models for predicting food protein allergenicity, finding that the transformer model achieved the highest AUC of 0.9578.

Novel allergen discovery through comprehensive de novo transcriptomic analyses of 5 shrimp species

The paper identifies known and novel allergens in five shrimp species through transcriptomic analysis, revealing potential targets for improved diagnostics and immunotherapeutics.