ABSTRACT
The well-known Greulich and Pyle (GP) method of bone age assessment (BAA) relies on comparing a hand X-ray against templates of discrete maturity classes collected in an atlas. Automated methods have recently shown great success with BAA, especially using deep learning. In this perspective, we first review the success and limitations of various automated BAA methods. We then offer a novel hypothesis: When networks predict bone age that is not aligned with a GP reference class, it is not simply statistical error (although there is that as well); they are picking up nuances in the hand X-ray that lie "outside that class." In other words, trained networks predict distributions around classes. This raises a natural question: How can we further understand the reasons for a prediction to deviate from the nominal class age? We claim that segmental aging, that is, ratings based on characteristic bone groups can be used to qualify predictions. This so-called segmental GP method has excellent properties: It can not only help identify differential maturity in the hand but also provide a systematic way to extend the use of the current GP atlas to various other populations.
ABSTRACT
Background: Non-genetic factors like microbial dysbiosis may be contributing to the increasing incidence/progression of type 1 diabetes mellitus (T1DM). Objectives: To analyse the gut microbiota profile in Indian children with T1DM and its effect on glycaemic control. Methodology: Faecal samples of 29 children with T1DM were collected and faecal microbial DNA was extracted and subjected to 16S rRNA (ribosomal RNA) sequencing and further analysis. Results: The dominant phyla in children with T1DM were Firmicutes and Bacteroidetes. Butyrate-producing bacteria Blautia and Ruminococcus showed a significant negative correlation with the glycosylated haemoglobin (HbA1C) levels (p < 0.05). Coprococcus and Propionibacterium were important negative predictors of glycaemic control (p < 0.05). Conclusion: Our study suggests that Indian children with T1DM have a distinct gut microbiome taxonomic composition and that short-chain fatty acid-producing bacteria like Ruminococcus and Blautia (butyrate-producing) may play an important role in the glycaemic control of subjects with T1DM.