Prenatal exposure to air pollutants and childhood atopic dermatitis and allergic rhinitis adopting machine learning approaches: 14-year follow-up birth cohort study

Advancing birth cohort studies using administrative and other research-independent data repositories: Opportunities and challenges

The birth cohort study design is an essential epidemiologic tool for investigating the developmental origins of health and disease. Birth cohorts have greatly improved the etiologic understanding of asthma and allergic diseases, setting the stage for advancements in translational interventions. Increasingly, investigators leverage data repositories that have been compiled and maintained independently of research investigations (administrative data) to establish large birth cohorts or to augment data generated through active participant interaction. In many cases, administrative data can greatly enhance the capacity of birth cohorts to achieve their scientific goals. However, investigators must be wary of common pitfalls and carefully consider whether administrative data are well suited to the scientific inquiry. This article reviews the strengths and challenges of using administrative data and the pragmatic solutions that have been developed to optimize their use in birth cohorts. As birth cohorts continue to play an important role in understanding the etiology of early-life disease, unleashing the power of administrative data will greatly assist in this scientific process.

Atopic dermatitis: a comprehensive updated review of this intriguing disease with futuristic insights

Atopic dermatitis (AD) is a paradigmatic prevalent, long-lasting, and inflammatory skin condition with a diverse range of clinical manifestations. The etiology and clinical symptoms of AD are influenced by complex pathophysiological processes, which involve a strong genetic component, epidermal dysfunction, and immunological dysregulation, and a strong influence of other physiological and environmental factors. The FDA has approved targeted and well-tolerated immunomodulators including biologics like dupilumab and crisaborole, and small molecules such as baricitinib, as novel therapies for AD. They effectively treat AD but are too expensive for most patients. The review provides an update on the state of knowledge of AD pathogenesis, discusses the available diagnostic and scoring indices, and provides a scientific foundation for treatment methods for AD. This review also presents data on clinical efficacy of innovative treatments' considering recent guidelines, emphasizing the newest medications and ongoing trials. Finally, the new implication of artificial intelligence (AI) in AD management is explored, where AI can speed up diagnosis and therapy. The PubMed, Google Scholar, and ScienceDirect databases were used for this review.

Artificial Intelligence: A Snapshot of Its Application in Chronic Inflammatory and Autoimmune Skin Diseases

Immuno-correlated dermatological pathologies refer to skin disorders that are closely associated with immune system dysfunction or abnormal immune responses. Advancements in the field of artificial intelligence (AI) have shown promise in enhancing the diagnosis, management, and assessment of immuno-correlated dermatological pathologies. This intersection of dermatology and immunology plays a pivotal role in comprehending and addressing complex skin disorders with immune system involvement. The paper explores the knowledge known so far and the evolution and achievements of AI in diagnosis; discusses segmentation and the classification of medical images; and reviews existing challenges, in immunological-related skin diseases. From our review, the role of AI has emerged, especially in the analysis of images for both diagnostic and severity assessment purposes. Furthermore, the possibility of predicting patients' response to therapies is emerging, in order to create tailored therapies.

Physical influences on the skin barrier and pathogenesis of allergy

PURPOSE OF REVIEW

As the incidence of allergic conditions has increased in recent decades, the effects of climate change have been implicated. There is also increased knowledge on the effects of other physical influences, such as scratching and Staphylococcus aureus . The skin barrier is the first line of defense to the external environment, so understanding the ways that these factors influence skin barrier dysfunction is important.

RECENT FINDINGS

Although the impact on environmental exposures has been well studied in asthma and other allergic disorders, there is now more literature on the effects of temperature, air pollution, and detergents on the skin barrier. Factors that cause skin barrier dysfunction include extreme temperatures, air pollution (including greenhouse gases and particulate matter), wildfire smoke, pollen, scratching, S. aureus, and detergents.

SUMMARY

Understanding the ways that external insults affect the skin barrier is important to further understand the mechanisms in order to inform the medical community on treatment and prevention measures for atopic conditions.

Global warming and implications for epithelial barrier disruption and respiratory and dermatologic allergic diseases

Global warming has direct and indirect effects, as well as short- and long-term impacts on the respiratory and skin barriers. Extreme temperature directly affects the airway epithelial barrier by disrupting the structural proteins and by triggering airway inflammation and hyperreactivity. It enhances tidal volume and respiratory rate by affecting the thermoregulatory system, causing specific airway resistance and reflex bronchoconstriction via activation of bronchopulmonary vagal C fibers and upregulation of transient receptor potential vanilloid (TRPV) 1 and TRPV4. Heat shock proteins are activated under heat stress and contribute to both epithelial barrier dysfunction and airway inflammation. Accordingly, the frequency and severity of allergic rhinitis and asthma have been increasing. Heat activates TRPV3 in keratinocytes, causing the secretion of inflammatory mediators and eventually pruritus. Exposure to air pollutants alters the expression of genes that control skin barrier integrity and triggers an immune response, increasing the incidence and prevalence of atopic dermatitis. There is evidence that extreme temperature, heavy rains and floods, air pollution, and wildfires increase atopic dermatitis flares. In this narrative review, focused on the last 3 years of literature, we explore the effects of global warming on respiratory and skin barrier and their clinical consequences.

Impact of Air Pollution on Atopic Dermatitis: A Comprehensive Review

Air pollution is associated with multiple health problems worldwide, contributing to increased morbidity and mortality. Atopic dermatitis (AD) is a common allergic disease, and increasing evidence has revealed a role of air pollution in the development of atopic dermatitis. Air pollutants are derived from several sources, including harmful gases such as nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO), as well as particulate matter (PM) of various sizes, and bioaerosols. Possible mechanisms linking air pollution to atopic dermatitis include damage to the skin barrier through oxidative stress, increased water loss, physicochemical injury, and an effect on skin microflora. Furthermore, oxidative stress triggers immune dysregulation, leading to enhanced sensitization to allergens. There have been multiple studies focusing on the association between various types of air pollutants and atopic dermatitis. Since there are many confounders in the current research, such as climate, synergistic effects of mixed pollutants, and diversity of study population, it is not surprising that inconsistencies exist between different studies regarding AD and air pollution. Still, it is generally accepted that air pollution is a risk factor for AD. Future studies should focus on how air pollution leads to AD as well as effective intervention measures.

The Evolution and Application of Artificial Intelligence in Rhinology: A State of the Art Review

OBJECTIVE

To provide a comprehensive overview on the applications of artificial intelligence (AI) in rhinology, highlight its limitations, and propose strategies for its integration into surgical practice.

DATA SOURCES

Medline, Embase, CENTRAL, Ei Compendex, IEEE, and Web of Science.

REVIEW METHODS

English studies from inception until January 2022 and those focusing on any application of AI in rhinology were included. Study selection was independently performed by 2 authors; discrepancies were resolved by the senior author. Studies were categorized by rhinology theme, and data collection comprised type of AI utilized, sample size, and outcomes, including accuracy and precision among others.

CONCLUSIONS

An overall 5435 articles were identified. Following abstract and title screening, 130 articles underwent full-text review, and 59 articles were selected for analysis. Eleven studies were from the gray literature. Articles were stratified into image processing, segmentation, and diagnostics (n = 27); rhinosinusitis classification (n = 14); treatment and disease outcome prediction (n = 8); optimizing surgical navigation and phase assessment (n = 3); robotic surgery (n = 2); olfactory dysfunction (n = 2); and diagnosis of allergic rhinitis (n = 3). Most AI studies were published from 2016 onward (n = 45).

IMPLICATIONS FOR PRACTICE

This state of the art review aimed to highlight the increasing applications of AI in rhinology. Next steps will entail multidisciplinary collaboration to ensure data integrity, ongoing validation of AI algorithms, and integration into clinical practice. Future research should be tailored at the interplay of AI with robotics and surgical education.

A Scoping Review of Artificial Intelligence Research in Rhinology

BACKGROUND

A considerable volume of possible applications of artificial intelligence (AI) in the field of rhinology exists, and research in the area is rapidly evolving.

OBJECTIVE

This scoping review aims to provide a brief overview of all current literature on AI in the field of rhinology. Further, it aims to highlight gaps in the literature for future rhinology researchers.

METHODS

OVID MEDLINE (1946-2022) and EMBASE (1974-2022) were searched from January 1, 2017 until May 14, 2022 to identify all relevant articles. The Preferred Reporting Items for Systematic Reviews and Meta-analyses Extension for Scoping Reviews checklist was used to guide the review.

RESULTS

A total of 2420 results were identified of which 62 met the eligibility criteria. A further 17 articles were included through bibliography searching, for a total of 79 articles on AI in rhinology. Each year resulted in an increase in the number of publications, from 3 articles published in 2017 to 31 articles published in 2021. Articles were produced by authors from 22 countries with a relative majority coming from the USA (19%), China (19%), and South Korea (13%). Articles were placed into 1 of 5 categories: phenotyping/endotyping (n = 12), radiological diagnostics (n = 42), prognostication (n = 10), non-radiological diagnostics (n = 7), surgical assessment/planning (n = 8). Diagnostic or prognostic utility of the AI algorithms were rated as excellent (n = 29), very good (n = 25), good (n = 7), sufficient (n = 1), bad (n = 2), or was not reported/not applicable (n = 15).

CONCLUSIONS

AI is experiencing an increasingly significant role in rhinology research. Articles are showing high rates of diagnostic accuracy and are being published at an almost exponential rate around the world. Utilizing AI in radiological diagnosis was the most published topic of research, however, AI in rhinology is still in its infancy and there are several topics yet to be thoroughly explored.

The effect of greenness on allergic rhinitis outcomes in children and adolescents: A systematic review and meta-analysis

BACKGROUND

The relationship between greenness and health emerges as new public health concern. More published studies from multiple areas have explored the relationship between greenness and allergic rhinitis (AR) in children and adolescents. This study aims to determine the association between greenness and allergic rhinitis by systematic review and meta-analysis, in order to provide a more comprehensive assessment of the impact of greenness on AR in children and adolescents.

METHODS

The relative literature was systematically searched in PubMed, Embase, and Web of science lastly on September 25, 2022. Terms related to greenness and allergic rhinitis were used for searching. Summary effect estimates of greenness on AR in children and adolescents were calculated for per 10 % increase of greenness exposure with different buffer sizes by random-effects model.

RESULTS

A total of 579 studies were screened, and fourteen studies from Europe, Asia and North America were finally included. Most greenness exposure were measured by normalized difference vegetation index (NDVI). Enhanced vegetation index, outdoor-green environmental score and existed to measuring different greenness types. Greenness surrounding residences and schools were assessed. The overall effect of greenness on primary outcome was 1.00 (95%CI = 0.99-1.00). Most effect estimates of greenness were included in the NDVI-500 m group, and the pooled OR was 0.99 (95%CI = 0.97-1.01). No significant pooled estimates were found in analyses with study locations.

CONCLUSION

This study indicates no significant association between greenness exposure and AR in children and adolescents. Various exposure measures and conversion of data may affect the results of this meta-analysis. More precise assessment of personal greenness exposure in well-designed prospective studies are vital for drawing a definite association in future. Furthermore, greenness exposure surrounding schools should be paid considerable attention for its effect on AR in school-aged children and adolescents.

Air Pollution and Atopic Dermatitis, from Molecular Mechanisms to Population-Level Evidence: A Review

Atopic dermatitis (AD) has increased in prevalence to become the most common inflammatory skin condition globally, and geographic variation and migration studies suggest an important role for environmental triggers. Air pollution, especially due to industrialization and wildfires, may contribute to the development and exacerbation of AD. We provide a comprehensive, multidisciplinary review of existing molecular and epidemiologic studies on the associations of air pollutants and AD symptoms, prevalence, incidence, severity, and clinic visits. Cell and animal studies demonstrated that air pollutants contribute to AD symptoms and disease by activating the aryl hydrocarbon receptor pathway, promoting oxidative stress, initiating a proinflammatory response, and disrupting the skin barrier function. Epidemiologic studies overall report that air pollution is associated with AD among both children and adults, though the results are not consistent among cross-sectional studies. Studies on healthcare use for AD found positive correlations between medical visits for AD and air pollutants. As the air quality worsens in many areas globally, it is important to recognize how this can increase the risk for AD, to be aware of the increased demand for AD-related medical care, and to understand how to counsel patients regarding their skin health. Further research is needed to develop treatments that prevent or mitigate air pollution-related AD symptoms.

Potential biomarkers of atopic dermatitis

Atopic dermatitis (AD) is a chronic, recurrent inflammatory skin disease with a wide range of heterogeneity. Accurate biomarkers or predictors are the keys to instructing personalized tailored precise treatment. The development of technology such as transcriptomics, genomics, and proteomics provides novel insights into the possibility to find potential biomarkers. Meanwhile, emerging minimally invasive methods such as tape stripping were used to reveal different profiles of patients' skin without biopsy. Several potential biomarkers or predictors have been found. In this review, we summarized the current development of potential biomarkers of AD. Nitric oxide synthase 2/inducible nitric oxide synthase (NOS2/iNOS), human beta-defensin-2 (hBD-2), and matrix metalloproteinases 8/9 (MMP8/9) may be the candidate biomarkers for AD diagnosis. Filaggrin (FLG) gene mutation increased the occurrence risk of AD. Fatty-acid-binding protein 5 (FABP5) may serve as an effective biomarker for the atopic march (AM). Squamous cell carcinoma antigen 2 (SCCA2), serum thymus and activation-regulated chemokine (TARC), cutaneous T-cell-attracting chemokine (CTACK), eosinophil-derived neurotoxin (EDN), macrophage-derived chemokine (MDC), lactate dehydrogenase (LDH), and interleukin (IL)-18 can be the candidate biomarkers for disease severity monitoring. IL-17, IL-23, IL-33, and indoleamine 2,3-dioxygenase (IDO1) can be used as predictive biomarkers for AD comorbidities. LDH, TARC, pulmonary and activation-regulated chemokine (PARC), periostin, IL-22, eotaxin-1/3, and IL-8 may be the candidate biomarkers for monitoring treatment effects. There are still unmet needs and a long way to go for more convenient, non-invasive, and effective predictors and biomarkers to better guide personalized precise treatment.