Background: The heterogeneity and lack of validation of existing severity scores for food allergic reactions limit standardization of case management and research advances. We aimed to develop and validate a severity score for food allergic reactions.Methods: Following a multidisciplinary experts consensus, it was decided to develop a food allergy severity score (FASS) with ordinal (oFASS) and numerical (nFASS) formats. oFASS with 3 and 5 grades were generated through expert consensus, and nFASS by mathematical modeling. Evaluation was performed in the EuroPrevall outpatient clinic cohort (8232 food reactions) by logistic regression with request of emergency care and medications used as outcomes. Discrimination, classification, and calibration were calculated. Bootstrapping internal validation was followed by external validation (logistic regression) in 5 cohorts (3622 food reactions). Correlation of nFASS with the severity classification done by expert allergy clinicians by Best‐Worst Scaling of 32 food reactions was calculated.Results: oFASS and nFASS map consistently, with nFASS having greater granularity. With the outcomes emergency care, adrenaline and critical medical treatment, oFASS and nFASS had a good discrimination (receiver operating characteristic area under the curve [ROC‐AUC]>0.80), classification (sensitivity 0.87–0.92, specificity 0.73–0.78), and calibration. Bootstrapping over ROC‐AUC showed negligible biases (1.0 × 10⁻⁶–1.23 × 10⁻³). In external validation, nFASS performed best with higher ROC‐AUC. nFASS was strongly correlated (R 0.89) to best‐worst scoring of 334 expert clinicians.Conclusion: FASS is a validated and reliable method to measure severity of food allergic reactions. The ordinal and numerical versions that map onto each other are suitable for use by different stakeholders in different settings.

Allergic diseases are one of the most common chronic conditions and their prevalence is on the rise. Environmental exposure, primarily prenatal and early life influences, affect the risk for the development and specific phenotypes of allergic diseases via epigenetic mechanisms. Exposure to pollutants, microorganisms and parasites, tobacco smoke and certain aspects of diet are known to drive epigenetic changes that are essential for immune regulation (e.g., the shift toward T helper 2-Th2 cell polarization and decrease in regulatory T-cell (Treg) differentiation). DNA methylation and histone modifications can modify immune programming related to either pro-allergic interleukin 4 (IL-4), interleukin 13 (IL-13) or counter-regulatory interferon γ (IFN-γ) production. Differential expression of small non-coding RNAs has also been linked to the risk for allergic diseases and associated with air pollution. Certain exposures and associated epigenetic mechanisms play a role in the susceptibility to allergic conditions and specific clinical manifestations of the disease, while others are thought to have a protective role against the development of allergic diseases, such as maternal and early postnatal microbial diversity, maternal helminth infections and dietary supplementation with polyunsaturated fatty acids and vitamin D. Epigenetic mechanisms are also known to be involved in mediating the response to common treatment in allergic diseases, for example, changes in histone acetylation of proinflammatory genes and in the expression of certain microRNAs are associated with the response to inhaled corticosteroids in asthma. Gaining better insight into the epigenetic regulation of allergic diseases may ultimately lead to significant improvements in the management of these conditions, earlier and more precise diagnostics, optimization of current treatment regimes, and the implementation of novel therapeutic options and prevention strategies in the near future.

Osteoarthritis (OA) is a degenerative joint disease accompanied by pain and loss of function. Adipose tissue harbors mesenchymal stem/stromal cells (MSC), or medicinal signaling cells as suggested by Caplan (Caplan, 2017), used in autologous transplantation in many clinical settings. The aim of the study was to characterize a stromal vascular fraction from microfragmented lipoaspirate (SVF-MLA) applied for cartilage treatment in OA and compare it to that of autologous lipoaspirate (SVF-LA). Samples were first stained using a DuraClone SC prototype tube for the surface detection of CD31, CD34, CD45, CD73, CD90, CD105, CD146 and LIVE/DEAD Yellow Fixable Stain for dead cell detection, followed by DRAQ7 cell nuclear dye staining, and analyzed by flow cytometry. In SVF-LA and SVF-MLA samples, the following population phenotypes were identified within the CD45⁻ fraction: CD31⁺CD34⁺CD73^±CD90^±CD105^±CD146^± endothelial progenitors (EP), CD31⁺CD34⁻CD73^±CD90^±CD105⁻CD146^± mature endothelial cells, CD31⁻CD34⁻CD73^±CD90⁺CD105⁻CD146⁺ pericytes, CD31⁻CD34⁺CD73^±CD90⁺CD105⁻CD146⁺ transitional pericytes, and CD31⁻CD34⁺CD73^highCD90⁺CD105⁻CD146⁻ supra-adventitial-adipose stromal cells (SA-ASC). The immunophenotyping profile of SVF-MLA was dominated by a reduction of leukocytes and SA-ASC, and an increase in EP, evidencing a marked enrichment of this cell population in the course of adipose tissue microfragmentation. The role of EP in pericyte-primed MSC-mediated tissue healing, as well as the observed hormonal implication, is yet to be investigated.

Asthma in children is a heterogeneous disease manifested by various phenotypes and endotypes. The level of disease control, as well as the effectiveness of anti-inflammatory treatment, is variable and inadequate in a significant portion of patients. By applying machine learning algorithms, we aimed to predict the treatment success in a pediatric asthma cohort and to identify the key variables for understanding the underlying mechanisms. We predicted the treatment outcomes in children with mild to severe asthma (N = 365), according to changes in asthma control, lung function (FEV1 and MEF50) and FENO values after 6 months of controller medication use, using Random Forest and AdaBoost classifiers. The highest prediction power is achieved for control- and, to a lower extent, for FENO-related treatment outcomes, especially in younger children. The most predictive variables for asthma control are related to asthma severity and the total IgE, which were also predictive for FENO-based outcomes. MEF50-related treatment outcomes were better predicted than the FEV1-based response, and one of the best predictive variables for this response was hsCRP, emphasizing the involvement of the distal airways in childhood asthma. Our results suggest that asthma control- and FENO-based outcomes can be more accurately predicted using machine learning than the outcomes according to FEV1 and MEF50. This supports the symptom control-based asthma management approach and its complementary FENO-guided tool in children. T2-high asthma seemed to respond best to the anti-inflammatory treatment. The results of this study in predicting the treatment success will help to enable treatment optimization and to implement the concept of precision medicine in pediatric asthma treatment.

The atopic march encompasses a sequence of allergic conditions, including atopic dermatitis, food allergy, allergic rhinitis, and asthma, that frequently develop in a sequential pattern within the same individual. It was introduced as a conceptual framework aimed at elucidating the developmental trajectory of allergic conditions during childhood. Following the introduction of this concept, it was initially believed that the atopic march represented the sole and definitive trajectory of the development of allergic diseases. However, this perspective evolved with the emergence of new longitudinal studies, which revealed that the evolution of allergic diseases is far more intricate. It involves numerous immunological pathological mechanisms and may not align entirely with the traditional concept of the atopic march. The objective of our review is to portray the atopic march alongside other patterns in the development of childhood allergic diseases, with a specific emphasis on the potential for a personalized approach to the prevention, diagnosis, and treatment of atopic conditions.