Loss-of-function GHSR variants are associated with short stature and low IGF-I.

Context: The growth hormone (GH) secretagogue receptor, encoded by GHSR, is expressed on somatotrophs of the pituitary gland. Stimulation with its ligand ghrelin, as well as its constitutive activity, enhances GH secretion. Studies in knock-out mice suggest that heterozygous loss-of-function of GHSR is associated with decreased GH response to fasting, but patient observations in small case reports have been equivocal.

Dyslipidaemia as a target for atherosclerotic cardiovascular disease prevention in children with type 1 diabetes: lessons learned from familial hypercholesterolaemia.

In the last few decades, atherosclerotic cardiovascular disease (ASCVD) risk has decreased dramatically among individuals affected by familial hypercholesterolaemia (FH) as a result of the early initiation of statin treatment in childhood. Contemporaneously important improvements in care for people with diabetes have also been made, such as the prevention of mortality from acute diabetic complications. However, individuals with type 1 diabetes still have a two to eight times higher risk of death than the general population.

A case study of support for girls' access to primary school in Ghana.

The Strategic Approach to Girls' Education (STAGE) project developed and implemented an intervention that helped marginalized out-of-school girls in the northern regions of Ghana enter and be successful in primary school. STAGE builds on the Government of Ghana's Complementary Basic Education policy, which supports an accelerated learning program that provides literacy and numeracy classes in mother tongue to out-of-school girls between 8 and 14 years of age. This article reviews the literature that informed the design of STAGE, describes the intervention, reports on the impact on its participants, and suggests a model for replicating this intervention in Ghana and adapting it for implementation in other countries.

The diagnostic value of nasal microbiota and clinical parameters in a multi-parametric prediction model to differentiate bacterial versus viral infections in lower respiratory tract infections.

Background: The ability to accurately distinguish bacterial from viral infection would help clinicians better target antimicrobial therapy during suspected lower respiratory tract infections (LRTI). Although technological developments make it feasible to rapidly generate patient-specific microbiota profiles, evidence is required to show the clinical value of using microbiota data for infection diagnosis. In this study, we investigated whether adding nasal cavity microbiota profiles to readily available clinical information could improve machine learning classifiers to distinguish bacterial from viral infection in patients with LRTI.