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INTRODUCTION Various meta-analyses have shown that e-learning is as effective as traditional methods of continuing professional education. However, there are some disadvantages to e-learning, such as possible technical problems, the need for greater self-discipline, cost involved in developing programmes and limited direct interaction. Currently, most strategies for teaching amplitude-integrated electroencephalography (aEEG) in neonatal intensive care units (NICUs) worldwide depend on traditional teaching methods. METHODSWe implemented a programme that utilised an integrated approach to e-learning. The programme consisted of three sessions of supervised protected time e-learning in an NICU. The objective and subjective effectiveness of the approach was assessed through surveys administered to participants before and after the programme. RESULTSA total of 37 NICU staff (32 nurses and 5 doctors) participated in the study. 93.1% of the participants appreciated the need to acquire knowledge of aEEG. We also saw a statistically significant improvement in the subjective knowledge score (p = 0.041) of the participants. The passing rates for identifying abnormal aEEG tracings (defined as ≥ 3 correct answers out of 5) also showed a statistically significant improvement (from 13.6% to 81.8%, p < 0.001). Among the participants who completed the survey, 96.0% felt the teaching was well structured, 77.8% felt the duration was optimal, 80.0% felt that they had learnt how to systematically interpret aEEGs, and 70.4% felt that they could interpret normal aEEG with confidence.CONCLUSION An integrated approach to e-learning can help improve subjective and objective knowledge of aEEG.

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SummaryExposure 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.

AimA 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.MethodsThis 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.Results198 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.ConclusionThe 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.