Masking of a ß-thalassemia determinant by a novel δ-globin gene defect [Hb A2-Saurashtra or δ100(G2)Pro→Ser; HBD: c.301C>T] in Cis.

Abstract The molecular basis of ß-thalassemia (ß-thal) syndromes have been well documented, while the spectrum of mutations causing δ-thalassemia (δ-thal) has not been well characterized. δ-Thalassemia has no clinical symptoms but its coinheritance with heterozygous ß-thal may cause misdiagnosis, especially in countries with a high prevalence of ß-thal where prevention programs have been implemented. The coinheritance of ß- and δ-globin mutations in India is not common. This association may interfere with correct diagnosis and genetic counseling of ß-thal in screening programs. Here we report two families showing borderline Hb A2 levels belonging to the Koli Community, indigenous to the Saurashtra Province of Gujarat, India. They were referred to us for thalassemia molecular screening as they had children clinically presenting before 2 years of age and requiring regular blood transfusions. Interestingly, both families carried a novel δ-globin gene mutation at codon 100 (C > T) linked to a polyadenylation (polyA) site [AATAAA > A(-AATAA)] 5 bp deletional ß-thal mutation, never before reported in the Indian population. This report highlights the importance of considering δ-globin gene analysis during ß-thal screening to avoid false-negative results in the detection of at-risk couples. It also highlights how incomplete diagnosis of a borderline or normal Hb A2 level may lead to the probable birth of a ß-thal major (ß-TM) child. This has important implications in prenatal diagnosis.

Hierarchical Representation of Complex Intervention Sequences for Automated Subgroup Analysis in Critical Care Settings.

Our understanding of the impact of interventions in critical care is limited by the lack of techniques that represent and analyze complex intervention spaces applied across heterogeneous patient populations. Existing work has mainly focused on selecting a few interventions and representing them as binary variables, resulting in oversimplification of intervention representation. The goal of this study is to find effective representations of sequential interventions to support intervention effect analysis. To this end, we have developed Hi-RISE (Hierarchical Representation of Intervention Sequences), an approach that transforms and clusters sequential interventions into a latent space, with the resulting clusters used for heterogeneous treatment effect analysis. We apply this approach to the MIMIC III dataset and identified intervention clusters and corresponding subpopulations with peculiar odds of 28-day mortality. Our approach may lead to a better understanding of the subgroup-level effects of sequential interventions and improve targeted intervention planning in critical care settings.