Household environmental microbiota influences early-life eczema development.

Exposure to a diverse microbial environment during pregnancy and early postnatal period is important in determining predisposition towards allergy. However, the effect of environmental microbiota exposure during preconception, pregnancy and postnatal life on development of allergy in the child has not been investigated so far. In the S-PRESTO (Singapore PREconception Study of long Term maternal and child Outcomes) cohort, we collected house dust during all three critical window periods and analysed microbial composition using 16S rRNA gene sequencing. At 6 and 18 months, the child was assessed for eczema by clinicians. In the eczema group, household environmental microbiota was characterized by presence of human-associated bacteria Actinomyces, Anaerococcus, Finegoldia, Micrococcus, Prevotella and Propionibacterium at all time points, suggesting their possible contributions to regulating host immunity and increasing the susceptibility to eczema. In the home environment of the control group, putative protective effect of an environmental microbe Planomicrobium (Planococcaceae family) was observed to be significantly higher than that in the eczema group. Network correlation analysis demonstrated inverse relationships between beneficial Planomicrobium and human-associated bacteria (Actinomyces, Anaerococcus, Finegoldia, Micrococcus, Prevotella and Propionibacterium). Exposure to natural environmental microbiota may be beneficial to modulate shed human-associated microbiota in an indoor environment.

Keeping Pace with Adolescent Asthma: A Practical Approach to Optimizing Care.

Known for their pre-occupation with body image, self-identity creation, peer acceptance, and risk-taking behaviors, adolescents with asthma face unique challenges. Asthma is a heterogeneous disease and accurate diagnosis requires assessment through detailed clinical history, examination, and objective tests. Diagnostic challenges exist as many adolescents can present with asthma-like symptoms but do not respond to asthma treatment and risk being mis-diagnosed. Under-recognition of asthma symptoms and denial of disease severity must also be addressed. The over-reliance on short-acting beta-agonists in the absence of anti-inflammatory therapy for asthma is now deemed unsafe. Adolescents with mild asthma benefit from symptom-driven treatment with combination inhaled corticosteroids (ICS) and long-acting beta-agonist (LABA) on an as-required basis. For those with moderate-to-persistent asthma requiring daily controller therapy, maintenance and reliever therapy using the same ICS-LABA controller simplifies treatment regimes, while serving to reduce exacerbation risk. A developmentally staged approach based on factors affecting asthma control in early, middle, and late adolescence enables better understanding of the individual's therapeutic needs. Biological, psychological, and social factors help formulate a risk assessment profile in adolescents with difficult-to-treat and severe asthma. Smoking increases risks of developing asthma symptoms, lung function deterioration, and asthma exacerbations. Morbidity associated with e-cigarettes or vaping calls for robust efforts towards smoking and vaping cessation and abstinence. As adolescents progress from child-centered to adult-oriented care, coordination and planning are required to improve their self-efficacy to ready them for transition. Frequent flare-ups of asthma can delay academic attainment and adversely affect social and physical development. In tandem with healthcare providers, community and schools can link up to help shoulder this burden, optimizing care for adolescents with asthma.

Assessing the accuracy of artificial intelligence enabled acoustic analytic technology on breath sounds in children.

Interpretation of breath sounds by auscultation has high inter-observer variability, even when performed by trained healthcare professionals. This can be mitigated by using Artificial Intelligence (AI) acoustic analysis. We aimed to develop and validate a novel breath sounds analysis system using AI-enabled algorithms to accurately interpret breath sounds in children. Subjects from the respiratory clinics and wards were auscultated by two independent respiratory paediatricians blinded to their clinical diagnosis. A novel device consisting of a stethoscope head connected to a smart phone recorded the breath sounds. The audio files were categorised into single label (normal, wheeze and crackles) or multi-label sounds. Together with commercially available breath sounds, an AI classifier was trained using machine learning. Unique features were identified to distinguish the breath sounds. Single label breath sound samples were used to validate the finalised Support Vector Machine classifier. Breath sound samples (73 single label, 20 multi-label) were collected from 93 children (mean age [SD] = 5.40 [4.07] years). Inter-rater concordance was observed in 81 (87.1%) samples. Performance of the classifier on the 73 single label breath sounds demonstrated 91% sensitivity and 95% specificity. The AI classifier developed could identify normal breath sounds, crackles and wheeze in children with high accuracy.

Predictive equation for optimal continuous positive airway pressure in children with obstructive sleep apnoea.

AIM: A subgroup of children with obstructive sleep apnoea (OSA) requires treatment with continuous positive airway pressure (CPAP). This study's aims were: 1) to determine if the optimal CPAP for the treatment of OSA in children correlates with body mass index (BMI); 2) to determine the correlation between polysomnographic variables and optimal CPAP in children with OSA; and 3) to develop a CPAP predictive equation for children with OSA. METHODS: This was a retrospective study of children with OSA who underwent CPAP titration studies. Patients with craniofacial abnormalities (except Down syndrome) and neuromuscular diseases were excluded. Polysomnograms were done using Sandman Elite. Correlations between optimal CPAP, clinical and polysomnographic variables were analysed. A multivariable linear regression model for optimal CPAP was developed. RESULTS: 198 children (mean±sd age 13.1±3.6 years) were studied. Optimal CPAP had a significant positive correlation with age (rho=0.216, p=0.002), obstructive apnoea-hypopnoea index (rho=0.421, p<0.001), 3% oxygen desaturation index (rho=0.417, p<0.001), rapid eye movement respiratory disturbance index (rho=0.378, p<0.001) and BMI z-score (rho=0.160, p=0.024); and a significant negative correlation with arterial oxygen saturation measured by pulse oximetry nadir (rho= -0.333, p<0.001). The predictive equation derived was:Optimal CPAP (cmH2O)=6.486+0.273·age (years)-0.664·adenotonsillectomy(no=1, yes=0)+2.120·Down syndrome (yes=1, no=0)+0.280·BMI z-score. CONCLUSION: The equation developed may help to predict optimal CPAP in children with OSA. Further studies are required to validate this equation and to determine its applicability in different populations.