Prevalence of Skin Reactions and Self-Reported Allergies in 5 Countries with Their Social Impact Measured through Quality of Life Impairment

A Catastrophic Biodiversity Loss in the Environment Is Being Replicated on the Skin Microbiome: Is This a Major Contributor to the Chronic Disease Epidemic?

There has been a catastrophic loss of biodiversity in ecosystems across the world. A similar crisis has been observed in the human gut microbiome, which has been linked to "all human diseases affecting westernized countries". This is of great importance because chronic diseases are the leading cause of death worldwide and make up 90% of America's healthcare costs. Disease development is complex and multifactorial, but there is one part of the body's interlinked ecosystem that is often overlooked in discussions about whole-body health, and that is the skin microbiome. This is despite it being a crucial part of the immune, endocrine, and nervous systems and being continuously exposed to environmental stressors. Here we show that a parallel biodiversity loss of 30-84% has occurred on the skin of people in the developed world compared to our ancestors. Research has shown that dysbiosis of the skin microbiome has been linked to many common skin diseases and, more recently, that it could even play an active role in the development of a growing number of whole-body health problems, such as food allergies, asthma, cardiovascular diseases, and Parkinson's, traditionally thought unrelated to the skin. Damaged skin is now known to induce systemic inflammation, which is involved in many chronic diseases. We highlight that biodiversity loss is not only a common finding in dysbiotic ecosystems but also a type of dysbiosis. As a result, we make the case that biodiversity loss in the skin microbiome is a major contributor to the chronic disease epidemic. The link between biodiversity loss and dysbiosis forms the basis of this paper's focus on the subject. The key to understanding why biodiversity loss creates an unhealthy system could be highlighted by complex physics. We introduce entropy to help understand why biodiversity has been linked with ecosystem health and stability. Meanwhile, we also introduce ecosystems as being governed by "non-linear physics" principles-including chaos theory-which suggests that every individual part of any system is intrinsically linked and implies any disruption to a small part of the system (skin) could have a significant and unknown effect on overall system health (whole-body health). Recognizing the link between ecosystem health and human health allows us to understand how crucial it could be to maintain biodiversity across systems everywhere, from the macro-environment we inhabit right down to our body's microbiome. Further, in-depth research is needed so we can aid in the treatment of chronic diseases and potentially change how we think about our health. With millions of people currently suffering, research to help mitigate the crisis is of vital importance.

Immunomodulatory effects of nanoparticles on dendritic cells in a model of allergic contact dermatitis: importance of PD-L2 expression

Nanoparticle (NP) skin exposure is linked to an increased prevalence of allergic contact dermatitis. In our prior studies using the mouse contact hypersensitivity (CHS) model, we reported that silica 20 nm (SiO2) NPs suppressed the allergic response and titanium dioxide NPs doped with manganese (mTiO2) exacerbated it. In this work, we conducted in vitro experiments using bone marrow-derived dendritic cells (BMDCs) to study the combinatorial effect of the potent 2,4-dinitrofluorobenzene (DNFB) hapten sensitizer with SiO2 and mTiO2 NPs on BMDC cytotoxicity, cytokine secretion and phenotype using the B7 family ligands. Results show that DNFB and mTiO2 behave similarly and exhibit proinflammatory characteristics while SiO2 promotes a naive phenotype. We observe that the B7-H3 (CD276) ligand is only expressed on CD80 + (B7-1) BMDCs. Results from adoptive transfer CHS studies, combined with BMDC phenotype analysis, point to the importance of PD-L2 expression in modulating the adaptive immune response. This work identifies metrics that can be used to predict the effects of NPs on contact allergy and to guide efforts to engineer cell-based therapies to induce hapten specific immune tolerance.

Immunomodulatory Effects of Nanoparticles on Dendritic Cells in a Model of Allergic Contact Dermatitis - Importance of PD-L2 Expression

Nanoparticle (NP) skin exposure is linked to the increased prevalence of allergic contact dermatitis. In prior studies using the mouse contact hypersensitivity (CHS) model, we reported that silica 20 nm (Si20nm) suppressed the allergic response and TiO2 doped with manganese (mTiO2) exacerbated it. In this work, we conducted in vitro experiments using bone marrow-derived dendritic cells (BMDCs) to study the combinatorial effect of the potent 2, 4-dinitrofluorobenzene (DNFB) hapten sensitizer with Si20nm and mTiO2 NPs on BMDC cytotoxicity, cytokine secretion and phenotype using the B7 family ligands. Results show that DNFB and mTiO2 behave similarly and exhibit proinflammatory characteristics while Si20nm promotes a naive phenotype. We observe that the B7-H3 (CD276) ligand is only expressed on CD80+ (B7-1) BMDC. Results from adoptive transfer CHS studies, combined with BMDC phenotype analysis, point to the importance of PD-L2 expression in modulating the adaptive immune response. This work identifies metrics that can be used to predict the effects of NPs on contact allergy and to guide efforts to engineer cell-based therapies to induce antigen specific immune tolerance.

Prevalence of self-reported food allergy among adults in Jiangxi, China

Background

Food allergy has become an increasingly important public health problem. However, information regarding epidemiological studies of food allergy among Chinese adults is very limited. This study aims to estimate the prevalence of self-reported food allergy among adults in China.

Method

A population-based cross-sectional study was administered to estimate the prevalence of self-reported food allergy on the basis of a face-to-face questionnaire survey. The participants were recruited by cluster random sampling from three prefectures in Jiangxi Province, China.

Results

A total of 12 082 questionnaires were distributed, and 11 935 (98.8%) of completed ones were collected. The prevalence of self-reported food allergy was 4.0% (3.1% in men and 4.8% in women), self-reported doctor-diagnosed food allergy accounted for 1.4%. The most common allergic symptom was skin reaction showing in 63.9% of the participants with self-reported food allergy. The main allergic foods were shrimp, mollusks, and mango, accounting for the prevalence of 39.8%, 20.8%, and 18.7%, respectively. The self-reported food allergy was significantly linked with gender, age group, body height and other allergic conditions.

Conclusions

The prevalence of self-reported food allergy is about 4.0% among adults in China. The three most common allergenic foods were shrimp, mollusks and mango. Gender, age, and other allergic diseases could be contributing factors associated with food allergy in adults. These findings will provide scientific basis for the further research and prevention of food allergy in adults.

Predicting the Skin Sensitization Potential of Small Molecules with Machine Learning Models Trained on Biologically Meaningful Descriptors

In recent years, a number of machine learning models for the prediction of the skin sensitization potential of small organic molecules have been reported and become available. These models generally perform well within their applicability domains but, as a result of the use of molecular fingerprints and other non-intuitive descriptors, the interpretability of the existing models is limited. The aim of this work is to develop a strategy to replace the non-intuitive features by predicted outcomes of bioassays. We show that such replacement is indeed possible and that as few as ten interpretable, predicted bioactivities are sufficient to reach competitive performance. On a holdout data set of 257 compounds, the best model (“Skin Doctor CP:Bio”) obtained an efficiency of 0.82 and an MCC of 0.52 (at the significance level of 0.20). Skin Doctor CP:Bio is available free of charge for academic research. The modeling strategies explored in this work are easily transferable and could be adopted for the development of more interpretable machine learning models for the prediction of the bioactivity and toxicity of small organic compounds.