Gastro-oesophageal reflux: rare presentation of Sotos syndrome in a neonate.

Sotos syndrome is a disorder characterised by distinctive facial features, excessive growth during childhood and intellectual disability. While these criteria apply to children and adults, they fall short when applied to neonates. Hyperbilirubinaemia, large for gestational age, hypotonia and seizures, along with cardiac and renal anomalies, are known to be common presentations in neonates. Reports have also added hyperinsulinaemic hypoglycaemia as a presenting feature of Sotos syndrome in neonates. Here, we report a case of Sotos syndrome in a neonate who presented in the neonatal period with recurrent apnoeic episodes with hypotonia, which were later attributed to severe gastro-oesophageal reflux.

Estimating the impact of health systems factors on antimicrobial resistance in priority pathogens.

OBJECTIVES: Antimicrobial resistance (AMR) is the next big pandemic that threatens humanity. The One Health approach to AMR requires quantification of interactions between health, demographic, socioeconomic, environmental, and geopolitical factors to design interventions. This study is focused on learning health system factors on global AMR. METHODS: This study analysed longitudinal data (2004-2017) of AMR having 6 33 820 isolates from 70 middle and high-income countries. We integrated AMR data with the Global Burden of Disease (GBD), Governance (WGI), and Finance data sets to find AMR's unbiased and actionable determinants. We chose a Bayesian decision network (BDN) approach within the causal modelling framework to quantify determinants of AMR. Further, we integrated Bayesian networks' global knowledge discovery approach with discriminative machine learning to predict individual-level antibiotic susceptibility in patients. RESULTS: From MAR (multiple antibiotic resistance) scores, we found a non-uniform spread pattern of AMR. Components-level analysis revealed that governance, finance, and disease burden variables strongly correlate with AMR. From the Bayesian network analysis, we found that access to immunization, obstetric care, and government effectiveness are strong, actionable factors in reducing AMR, confirmed by what-if analysis. Finally, our discriminative machine learning models achieved an individual-level AUROC (Area under receiver operating characteristic curve) of 0.94 (SE = 0.01) and 0.89 (SE = 0.002) to predict Staphylococcus aureus resistance to ceftaroline and oxacillin, respectively. CONCLUSION: Causal machine learning revealed that immunisation strategies and quality of governance are vital, actionable interventions to reduce AMR.