Artificial intelligence in pediatric allergy research DOI Creative Commons
Daniil Lisik, Rani Basna, Duy-Tai Dinh

et al.

European Journal of Pediatrics, Journal Year: 2024, Volume and Issue: 184(1)

Published: Dec. 21, 2024

Abstract Atopic dermatitis, food allergy, allergic rhinitis, and asthma are among the most common diseases in childhood. They heterogeneous diseases, can co-exist their development, manifest complex associations with other disorders environmental hereditary factors. Elucidating these intricacies by identifying clinically distinguishable groups actionable risk factors will allow for better understanding of which enhance clinical management benefit society affected individuals families. Artificial intelligence (AI) is a promising tool this context, enabling discovery meaningful patterns data. Numerous studies within pediatric allergy have continue to use AI, primarily characterize disease endotypes/phenotypes develop models predict future outcomes. However, implementations used relatively simplistic data from one source, such as questionnaires. In addition, methodological approaches reporting lacking. This review provides practical hands-on guide conducting AI-based including (1) an introduction essential AI concepts techniques, (2) blueprint structuring analysis pipelines (from selection variables interpretation results), (3) overview pitfalls remedies. Furthermore, state-of-the art implementation research, well implications perspectives discussed. Conclusion : solutions undoubtedly transform showcased findings innovative technical solutions, but fully harness potential, methodologically robust more advanced techniques on richer be needed. What Known: • Pediatric allergies common, inflicting substantial morbidity societal costs. The field artificial undergoing rapid increasing various fields medicine research. New: Promising applications been reported, largely lags behind fields, particularly regard algorithms non-tabular lacking computational hampers evidence synthesis critical appraisal. Multi-center collaborations multi-omics rich unstructured utilization deep learning likely provide impactful discoveries.

Language: Английский

Exploring the Activation of the Keap1‐Nrf2‐ARE Pathway by PAHs in Children's Toys DOI Creative Commons

Jonas Lauenstein,

Simon van de Weyer,

Rasha Alsaleh

et al.

Contact Dermatitis, Journal Year: 2025, Volume and Issue: unknown

Published: March 15, 2025

ABSTRACT Background Children are particularly susceptible to environmental pollutants. This study assessed the skin sensitisation risk associated with polycyclic aromatic hydrocarbons (PAHs), prevalent in toys. Objectives To evaluate potential of PAHs using KeratinoSens assay. Methods Individual (acenaphthylene, anthracene, benzo[a]anthracene, benzo[a]pyrene (B[a]P), benzo[b]fluoranthene (B[b]F), benzo[e]pyrene, benzo[g,h,i]perylene, benzo[k]fluoranthene (B[k]F), chrysene, fluoranthene, fluorene, naphthalene, phenanthrene, pyrene and triphenylene) ternary mixtures containing B[a]P were for their ability activate Keap1‐Nrf2‐ARE pathway human keratinocytes. The concentration addition model additive index used predict analyse mixture effects. Results Among individual PAHs, B[k]F demonstrated most potent activation pathway, exhibiting a 34‐fold higher potency relative B[a]P. B[b]F, chrysene also exhibited significant activation, while remaining displayed negligible or weak activation. Notably, PAH synergistic effects, except those composed solely sensitizers. Conclusions provides first assessment sensitization these PAHs. findings suggest that B[k]F, B[b]F may pose than previously thought. Additionally, effects observed highlight importance considering combined exposures when assessing exposure risk.

Language: Английский

Citations

0
Inhalant Mediated Allergy: Immunobiology, Clinical Manifestations and Diagnosis DOI Creative Commons

Ki Lam,

Elaine Y. L. Au, Wai‐Ki Ip

et al.

Clinical Reviews in Allergy & Immunology, Journal Year: 2025, Volume and Issue: 68(1)

Published: April 15, 2025

Abstract Inhalant allergen–mediated respiratory diseases, including asthma and allergic rhinitis, have become increasing global health issues. While air pollution is believed to favor sensitization intensify clinical symptoms of allergy, allergen can vary highly with geographical location, climate, lifestyle differences. Pollen higher in European countries, while dust mite more common regions high humidity. Domestic pet on the rising trend industrialized nations, but paradoxical effect intensive cat exposure early childhood also observed. Clinical management inhalant diseases has greatly benefited from immunological mechanistic understanding pathophysiology. In this review, we discuss current knowledge mediated disorders emphasis (1) major immune cells relevant chemokines cytokines effector phase aeroallergen exposure, (2) their manifestation (3) characterization allergens, (4) chemical contributions development (5) diagnosis allergy. Knowledge role Th2 skewing, IgE, basophil, mast cells, eosinophils are fundamental these disorders. Skin test, basophil activation specific IgE component–resolved diagnostics used for facilitate further management. Advances biologics allergen-specific immunotherapy will strategize future approaches care diseases.

Language: Английский

Citations

0
Longitudinal Sensitization Patterns in Childhood and Adolescence DOI Open Access
Jonathan Thorsen, Morten Arendt Rasmussen, Bo Chawes

et al.

Allergy, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 27, 2024

Allergen-specific immunoglobulin E (sIgE) production typically starts in early childhood and develops over time a complex manner that is not fully understood [1]. Some allergens contribute to life sensitization, for example, egg milk [2], while others increase through adolescence, aeroallergens [2]. Concurrent sensitization multiple changing patterns are common. Understanding the temporal development of sIgE can lead identification individuals at risk developing allergic diseases, allowing timely intervention prevent progression allergies related conditions such as asthma atopic dermatitis (AD). Several studies have performed detailed longitudinal measurements [3] or patient clustering traits diseases [4, 5]. However, there lack comprehensively describing with unified statistical models. We previously identified replicated Copenhagen Prospective Studies on Asthma Childhood 2000 birth cohort (COPSAC2000) 411 children born mothers [6]. An unsupervised cluster analysis revealed seven age- allergen-specific from age 0–6 years, differentially associated asthma, rhinitis, AD. All were verified independent BAMSE high reproducibility (R2 > 0.89). now gathered disease outcome data COPSAC2000 13 18 years age. Here, we extend these explore their into adolescence assess associations AD years. In COPSAC2000, blood samples collected ages ½, 1½, 4, 6, 13, against eight five food allergens. Sensitization was defined ≥ 0.35 kUA/L. For definitions AD, please see Online Repository. Of enrolled, 405 (98.5%) had least one valid measurement. these, 240 (59.3%) sensitized allergen; 132 (32.6%) allergens, 209 (51.6%) aeroallergens, 101 (24.9%) both. Individual rates each point shown Figure S1. generally increased peaked earlier. Most new cases aeroallergen appeared where remission both (Figure S2). Using Non-Negative Sparse Parallel Factor (NNS-PARAFAC) model, analyzed (n = 405) three-way array (children, age, allergens), which explained 60% total variation. 1 shows prevalence 0–18 patterns: (1) dog/cat/horse; (2) timothy grass/birch; (3) molds; (4) house dust mite; (5) peanut/wheat flour/mugwort; (6) peanut/soybean; (7) egg/milk/wheat flour. four aeroallergen-driven (1–4) mixed aero−/food allergen pattern up minor changes thereafter. Food peanut/soybean (pattern 6) stable peaking flour 7) then decreased by observed positive direction association between all except 7 (egg/milk/wheat flour) 2), although many significant. Adjusting potential confounders did alter results noteworthily. The strongest (dog/cat/horse) three outcomes (ORs 3.75–4.80, p-values < 0.004). 1, 2, 3, 5 significant, 4 (house mite) borderline 6–7 not. only positively showed an inverse association. These findings highlight dynamic nature its impact time. Further discussion available expanded prospective cohort. develop differently stronger when including later points. By understanding distinct long-term health outcomes, clinicians make more informed decisions about management prevention lifestyle interventions, avoidance, medical targeted immunotherapy. A.-M.M.S. has written first draft manuscript. M.A.R. J.T. analyses. co-authors provided important intellectual input contributed considerably analyses interpretation data. corresponding author full access final responsibility decision submit publication. reports speaking fee AstraZeneca. ThermoFisher participating advisory board ALK. funding agencies any role design conduct study; collection, management, data; preparation, review, approval No pharmaceutical company involved study. support this study request author. publicly due privacy ethical restrictions. Please note: publisher responsible content functionality supporting information supplied authors. Any queries (other than missing content) should be directed article.

Language: Английский

Citations

0
Social inequalities in childhood asthma DOI Creative Commons
Angela Pinot de Moira, Adnan Ćustović

World Allergy Organization Journal, Journal Year: 2024, Volume and Issue: 17(12), P. 101010 - 101010

Published: Dec. 1, 2024

Language: Английский

Citations

0
Association of urinary eosinophilic protein X at age 3 years and subsequent persistence of wheezing and asthma diagnosis in adolescence DOI Creative Commons

Iso Precious Oloyede,

Anhar Ullah, Clare Murray

et al.

Pediatric Allergy and Immunology, Journal Year: 2024, Volume and Issue: 35(12)

Published: Dec. 1, 2024

Abstract Background Wheezing is common in early life, but most children stop wheezing by school age. However, the prediction of course through childhood difficult. Objective To investigate whether urinary EPX (a marker eosinophil activation) at age 3 years may be useful for wheeze persistence and future asthma diagnosis. Methods U‐EPX was measured (radioimmunoassay) 906 participants population‐based birth cohort. Children attended follow‐ups to 16 years. We discriminative ability u‐EPX other factors predicting diagnosis using receiver operating characteristic [ROC] curves. Results Of 613 with follow‐up information 16, 511 had data on years; those; 133 (21.7%) asthma. Based longitudinal data, were assigned clusters: No (NWZ), transient (ETW), late‐onset (LOW), intermittent (INT) persistent (PEW). levels differed significantly between different clusters ( p = .003), characterised symptoms having higher u‐EPX. In whole cohort, best performing classification model included sex, u‐EPX, sensitisation (area under curve (AUC) 0.82, 95% CI: 0.76–0.88). allergic alone similar predictive power (AUC [95%CI]: 0.64 [0.58–0.71] 0.65 [0.60–0.71]). The among doctor‐confirmed first child's years, gestational maternal atopy (AUC: 0.76, 95%CI: 0.67–0.85). Conclusions Early‐life a non‐invasive adolescence.

Language: Английский

Citations

0
Artificial intelligence in pediatric allergy research DOI Creative Commons
Daniil Lisik, Rani Basna, Duy-Tai Dinh

et al.

European Journal of Pediatrics, Journal Year: 2024, Volume and Issue: 184(1)

Published: Dec. 21, 2024

Abstract Atopic dermatitis, food allergy, allergic rhinitis, and asthma are among the most common diseases in childhood. They heterogeneous diseases, can co-exist their development, manifest complex associations with other disorders environmental hereditary factors. Elucidating these intricacies by identifying clinically distinguishable groups actionable risk factors will allow for better understanding of which enhance clinical management benefit society affected individuals families. Artificial intelligence (AI) is a promising tool this context, enabling discovery meaningful patterns data. Numerous studies within pediatric allergy have continue to use AI, primarily characterize disease endotypes/phenotypes develop models predict future outcomes. However, implementations used relatively simplistic data from one source, such as questionnaires. In addition, methodological approaches reporting lacking. This review provides practical hands-on guide conducting AI-based including (1) an introduction essential AI concepts techniques, (2) blueprint structuring analysis pipelines (from selection variables interpretation results), (3) overview pitfalls remedies. Furthermore, state-of-the art implementation research, well implications perspectives discussed. Conclusion : solutions undoubtedly transform showcased findings innovative technical solutions, but fully harness potential, methodologically robust more advanced techniques on richer be needed. What Known: • Pediatric allergies common, inflicting substantial morbidity societal costs. The field artificial undergoing rapid increasing various fields medicine research. New: Promising applications been reported, largely lags behind fields, particularly regard algorithms non-tabular lacking computational hampers evidence synthesis critical appraisal. Multi-center collaborations multi-omics rich unstructured utilization deep learning likely provide impactful discoveries.

Language: Английский

Citations

0