Precision Medicine in Childhood Asthma: Omic Studies of Treatment Response

Attitudes on Artificial Intelligence use in Pediatric Care From Parents of Hospitalized Children

INTRODUCTION

Artificial intelligence (AI) may benefit pediatric healthcare, but it also raises ethical and pragmatic questions. Parental support is important for the advancement of AI in pediatric medicine. However, there is little literature describing parental attitudes toward AI in pediatric healthcare, and existing studies do not represent parents of hospitalized children well.

METHODS

We administered the Attitudes toward Artificial Intelligence in Pediatric Healthcare, a validated survey, to parents of hospitalized children in a single tertiary children's hospital. Surveys were administered by trained study personnel (11/2/2021-5/1/2022). Demographic data were collected. An Attitudes toward Artificial Intelligence in Pediatric Healthcare score, assessing openness toward AI-assisted medicine, was calculated for seven areas of concern. Subgroup analyses were conducted using Mann-Whitney U tests to assess the effect of race, gender, education, insurance, length of stay, and intensive care unit (ICU) admission on AI use.

RESULTS

We approached 90 parents and conducted 76 surveys for a response rate of 84%. Overall, parents were open to the use of AI in pediatric medicine. Social justice, convenience, privacy, and shared decision-making were important concerns. Parents of children admitted to an ICU expressed the most significantly different attitudes compared to parents of children not admitted to an ICU.

CONCLUSIONS

Parents were overall supportive of AI-assisted healthcare decision-making. In particular, parents of children admitted to ICU have significantly different attitudes, and further study is needed to characterize these differences. Parents value transparency and disclosure pathways should be developed to support this expectation.

Winds of change a tale of: asthma and microbiome

The role of the microbiome in asthma is highlighted, considering its influence on immune responses and its connection to alterations in asthmatic patients. In this context, we review the variables influencing asthma phenotypes from a microbiome perspective and provide insights into the microbiome's role in asthma pathogenesis. Previous cohort studies in patients with asthma have shown that the presence of genera such as Bifidobacterium, Lactobacillus, Faecalibacterium, and Bacteroides in the gut microbiome has been associated with protection against the disease. While, the presence of other genera such as Haemophilus, Streptococcus, Staphylococcus, and Moraxella in the respiratory microbiome has been implicated in asthma pathogenesis, indicating a potential link between microbial dysbiosis and the development of asthma. Furthermore, respiratory infections have been demonstrated to impact the composition of the upper respiratory tract microbiota, increasing susceptibility to bacterial diseases and potentially triggering asthma exacerbations. By understanding the interplay between the microbiome and asthma, valuable insights into disease mechanisms can be gained, potentially leading to the development of novel therapeutic approaches.

Severe Asthma and Biological Therapies: Now and the Future

Recognition of phenotypic variability in pediatric asthma allows for a more personalized therapeutic approach. Knowledge of the underlying pathophysiological and molecular mechanisms (endotypes) of corresponding biomarkers and new treatments enables this strategy to progress. Biologic therapies for children with severe asthma are becoming more relevant in this sense. The T2 phenotype is the most prevalent in childhood and adolescence, and non-T2 phenotypes are usually rare. This document aims to review the mechanism of action, efficacy, and potential predictive and monitoring biomarkers of biological drugs, focusing on the pediatric population. The drugs currently available are omalizumab, mepolizumab, benralizumab, dupilumab, and 1ezepelumab, with some differences in administrative approval prescription criteria between the U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA). Previously, we described the characteristics of severe asthma in children and its diagnostic and therapeutic management.

Inhibition of lincRNA-Cox2 alleviates apoptosis and inflammatory injury of lipopolysaccharide-stimulated human bronchial epithelial cells via the Nrf2/HO-1 axis

This study mainly explored the role and mechanism of lincRNA-Cox2 in inflammatory injury of human bronchial epithelial cells. BEAS-2B cells were stimulated with lipopolysaccharide to establish an in vitro inflammatory injury model. Real-time polymerase chain reaction was used to detect lincRNA-Cox2 expression in LPS-stimulated BEAS-2B. Cell viability and apoptosis of cells were assessed using CCK-8 and Annexin V-PI double staining. The contents of inflammatory factors were determined by enzyme-linked immunosorbent assay kits. The protein levels of nuclear factor erythrocyte 2-related factor 2 and haem oxygenase 1 protein levels were measured by Western blot. The results showed that lincRNA-Cox2 was upregulated in LPS-stimulated BEAS-2B cells. lincRNA-Cox2 knockdown inhibited apoptosis and the release of tumour necrosis factor alpha, interleukin 1beta (IL-1β), IL-4, IL-5, and IL-13 in BEAS-2B cells. lincRNA-Cox2 overexpression had the opposite effect. lincRNA-Cox2 knockdown also inhibited LPS-induced oxidative damage in BEAS-2B cells. Further mechanistic studies showed that inhibition of lincRNA-Cox2 upregulated the levels of Nrf2 and HO-1, and si-Nrf2 reversed the effects of si-lincRNA-Cox2. In conclusion, lincRNA-Cox2 knockdown inhibited BEAS-2B apoptosis and the level of inflammatory factors by activating the Nrf2/HO-1 pathway.

Parental Attitudes toward Artificial Intelligence-Driven Precision Medicine Technologies in Pediatric Healthcare

Precision medicine relies upon artificial intelligence (AI)-driven technologies that raise ethical and practical concerns. In this study, we developed and validated a measure of parental openness and concerns with AI-driven technologies in their child's healthcare. In this cross-sectional survey, we enrolled parents of children <18 years in 2 rounds for exploratory (n = 418) and confirmatory (n = 386) factor analysis. We developed a 12-item measure of parental openness to AI-driven technologies, and a 33-item measure identifying concerns that parents found important when considering these technologies. We also evaluated associations between openness and attitudes, beliefs, personality traits, and demographics. Parents (N = 804) reported mean openness to AI-driven technologies of M = 3.4/5, SD = 0.9. We identified seven concerns that parents considered important when evaluating these technologies: quality/accuracy, privacy, shared decision making, convenience, cost, human element of care, and social justice. In multivariable linear regression, parental openness was positively associated with quality (beta = 0.23), convenience (beta = 0.16), and cost (beta = 0.11), as well as faith in technology (beta = 0.23) and trust in health information systems (beta = 0.12). Parental openness was negatively associated with the perceived importance of shared decision making (beta = -0.16) and being female (beta = -0.12). Developers might support parental openness by addressing these concerns during the development and implementation of novel AI-driven technologies.

The Genomics and Metagenomics of Asthma Severity (GEMAS) Study: Rationale and Design

Asthma exacerbations are a major contributor to the global disease burden, but no significant predictive biomarkers are known. The Genomics and Metagenomics of Asthma Severity (GEMAS) study aims to assess the role of genomics and the microbiome in severe asthma exacerbations. Here, we present the design of GEMAS and the characteristics of patients recruited from March 2018 to March 2020. Different biological samples and demographic and clinical variables were collected from asthma patients recruited by allergy and pulmonary medicine units in several hospitals from Spain. Cases and controls were defined by the presence/absence of severe asthma exacerbations in the past year (oral corticosteroid use, emergency room visits, and/or asthma-related hospitalizations). A total of 137 cases and 120 controls were recruited. After stratifying by recruitment location (i.e., Canary Islands and Basque Country), cases and controls did not differ for most demographic and clinical variables (p > 0.05). However, cases showed a higher proportion of characteristics inherent to asthma exacerbations (impaired lung function, severe disease, uncontrolled asthma, gastroesophageal reflux, and use of asthma medications) compared to controls (p < 0.05). Similar results were found after stratification by recruitment unit. Thereby, asthma patients enrolled in GEMAS are balanced for potential confounders and have clinical characteristics that support the phenotype definition. GEMAS will improve the knowledge of potential biomarkers of asthma exacerbations.