Stress and food allergy: mechanistic considerations

Historical and social science perspectives on food allergy

This article provides an overview of the insights social scientists, historians and other health humanities scholars have made to our understanding of food allergies. It shows how humanities and social science scholars have tended to address three pivotal issues related to food allergies: first, they have addressed the epidemiology of food allergies, including the apparent rise in the rate of food allergies and the emergence of theories that purport to explain why food allergies may be increasing. These include theories related to changes in food consumption and the hygiene hypothesis. Second, humanities and social science scholars have researched how risks related to food allergies have been constructed, understood, experienced and mitigated. Third, humanities and social science scholars have investigated the experiences of food allergy sufferers and those who care for them, providing valuable qualitative insights that can inform how we respond to food allergy and our understanding of the aetiology of food allergy. The article concludes with three recommendations. First, there should be a more interdisciplinary approach to food allergy research that involves social scientists and health humanities scholars. Second, humanities and social sciences scholars should be more willing to unpack and scrutinise the theories put forward to explain the aetiology of food allergies, rather than accepting them at face value. And finally, humanities and social sciences scholars can play a major role in ensuring that the experiences of patients and their carers are articulated and fed into debates about food allergy, including its causes and how to respond to it.

Food allergies and food-induced anaphylaxis: role of cofactors

Food allergies and food-induced anaphylaxis are important health problems. Several cofactors modulating the onset of anaphylaxis have been identified. In the presence of cofactors, allergic reactions may be induced at lower doses of food allergens and/or become severe. Exercise and concomitant infections are well-documented cofactors of anaphylaxis in children. Other factors such as consumption of nonsteroidal anti-inflammatory drugs, alcohol ingestion, and stress have been reported. Cofactors reportedly play a role in approximately 30% of anaphylactic reactions in adults and 14%-18.3% in children. Food-dependent exercise-induced anaphylaxis (FDEIA) is the best-studied model of cofactor-induced anaphylaxis. Wheat-dependent exercise-induced anaphylaxis, the most common FDEIA condition, has been studied the most. The mechanisms of action of cofactors have not yet been fully identified. This review aims to educate clinicians on recent developments in the role of cofactors and highlight the importance of recognizing cofactors in food allergies and food-induced anaphylaxis.

Skin and Psychosomatics - Psychodermatology today

Modern psychodermatology relies on the bio-psycho-social disease model in psychosomatics, according to which biological, psychological and social factors (on various levels, from molecules to the biosphere) play a major role in the disease pathogenesis through complex, non-linear interactions over the entire disease course. It is nowadays experimentally proven that "emotions get into the skin". Recent research shows close anatomical, physiological and functional connections between skin and nervous system, already known to be ontogenetically related. These connections are reflected in many skin diseases where psychological and somatic etiological factors are closely intertwined. A holistic approach by the physician should do justice to this interdependence; biological, psychological and social factors should be adequately taken into account when taking anamnesis, making a diagnosis and choosing a therapy. The "visibility" of the skin organ bestows dermatology a special position among the various other clinical subjects, and renders a holistic, psychosomatic approach to the patient that is particularly important. The life course belongs also to modern psychodermatological approaches. Based on the modern psychodermatology concept, other corresponding sub-areas such as psychogastroenterology, psychocardiology etc. have emerged. After the theoretical part of this article, some selected skin diseases are discussed in more detail from the psychosomatic point of view.

Building the Concept of Nurturing Resilience

THEORETICAL PRINCIPLES

Children and adolescents are faced with ubiquitous daily stressors. When faced with various stressors, they will either recover or stay distressed. For those unable to recover, acute daily stressors have potential to turn chronic with long term negative mental health outcomes. Nurturing resilience is one way to prevent distress from turning to mental health disorders. With the ability of adolescents and children to access nurses with various specialties, it is imperative nurses understand the nuances of resilience and the core elements to nurture resilience in youth.

PHENOMENA ADDRESSED

The purpose of building the concept of nurturing resilience is to clarify the meaning of this concept in children and adolescents, develop a conceptual definition of nurturing resilience, and provide a conceptual model of nurturing resilience based on the building process. The concept was formulated utilizing Liehr and Smith's practice-based approach to concept building.

RESEARCH LINKAGES

Concept building defines nurturing resilience as developing secure connections with another to support self-acceptance that aids in tempering reactivity when faced with environmental hardships. Resilience is a protective mechanism which keeps youth balanced while promoting positive mental health when faced with adversities by allowing one to bounce back after adversity to an original, fundamental state. Understanding patterns and processes of resilience enables nurses to nurture resilience. Nurturing resilience provides adolescents and children the ability to change in response to difficult circumstances; in turn, mental distress is reduced, and mental health of youth are optimized.

Machine learning approach yields epigenetic biomarkers of food allergy: A novel 13-gene signature to diagnose clinical reactivity

BACKGROUND

Current laboratory tests are less than 50% accurate in distinguishing between people who have food allergies (FA) and those who are merely sensitized to foods, resulting in the use of expensive and potentially dangerous Oral Food Challenges. This study presents a purely-computational machine learning approach, conducted using DNA Methylation (DNAm) data, to accurately diagnose food allergies and potentially find epigenetic targets for the disease.

METHODS AND RESULTS

An unbiased feature-selection pipeline was created that narrowed down 405,000+ potential CpG biomarkers to 18. Machine-learning models that utilized subsets of this 18-feature aggregate achieved perfect classification accuracy on completely hidden test cohorts (on an 8-fold hidden dataset). Ensemble classification was also shown to be effective for this High Dimension Low Sample Size (HDLSS) DNA methylation dataset. The efficacy of these machine learning classifiers and the 18 CpGs was further validated by their high accuracy on a large number of hidden data permutations, where the samples in the training, cross-validation, and hidden sets were repeatedly randomly allocated. The 18-CpG signature mapped to 13 genes, on which biological insights were collected. Notably, many of the FA-discriminating genes found in this study were strongly associated with the immune system, and seven of the 13 genes were previously associated with FA.

CONCLUSIONS

Previous studies have also created highly-accurate classifiers for this dataset, using both data-driven and a priori biological insights to construct a 96-CpG signature. This research builds on previous work because it uses a completely computational approach to obtain a perfect classification accuracy while using only 18 highly discriminating CpGs (0.005% of the total available features). In machine learning, simpler models, as used in this study, are generally preferred over more complex ones (other things being equal). Lastly, the completely data-driven methodology presented in this research eliminates the need for a priori biological information and allows for generalizability to other DNAm classification problems.