Serological markers of exocrine pancreatic function are differentially informative for distinguishing individuals progressing to type 1 diabetes.

Introduction: Altered serum levels of growth hormones, adipokines, and exocrine pancreas enzymes have been individually linked with type 1 diabetes (T1D). We collectively evaluated seven such biomarkers, combined with islet autoantibodies (AAb) and genetic risk score (GRS2), for their utility in predicting AAb/T1D status.

Research Design And Methods: Cross-sectional serum samples (n=154 T1D, n=56 1AAb+, n=77 ≥2AAb+, n=256 AAb-) were assessed for IGF1, IGF2, adiponectin, leptin, amylase, lipase, and trypsinogen (n=543, age range 2.

Characterizing cell-type spatial relationships across length scales in spatially resolved omics data.

Spatially resolved omics (SRO) technologies enable the identification of cell types while preserving their organization within tissues. Application of such technologies offers the opportunity to delineate cell-type spatial relationships, particularly across different length scales, and enhance our understanding of tissue organization and function. To quantify such multi-scale cell-type spatial relationships, we present CRAWDAD, Cell-type Relationship Analysis Workflow Done Across Distances, as an open-source R package.

Leveraging artificial intelligence and machine learning to accelerate discovery of disease-modifying therapies in type 1 diabetes.

Progress in developing therapies for the maintenance of endogenous insulin secretion in, or the prevention of, type 1 diabetes has been hindered by limited animal models, the length and cost of clinical trials, difficulties in identifying individuals who will progress faster to a clinical diagnosis of type 1 diabetes, and heterogeneous clinical responses in intervention trials. Classic placebo-controlled intervention trials often include monotherapies, broad participant populations and extended follow-up periods focused on clinical endpoints. While this approach remains the 'gold standard' of clinical research, efforts are underway to implement new approaches harnessing the power of artificial intelligence and machine learning to accelerate drug discovery and efficacy testing.

Unique lymphocyte transcriptomic profiles in septic patients with chronic critical illness.

Introduction: Despite continued improvement in post-sepsis survival, long term morbidity and mortality remain high. Chronic critical illness (CCI), defined as persistent inflammation and organ injury requiring prolonged intensive care, is a harbinger of poor long-term outcomes in sepsis survivors. Current dogma states that sepsis survivors are immunosuppressed, particularly in CCI.

Characteristics of autoantibody-positive individuals without high-risk HLA-DR4-DQ8 or HLA-DR3-DQ2 haplotypes.

Aims/hypothesis: Many studies of type 1 diabetes pathogenesis focus on individuals with high-risk HLA haplotypes. We tested the hypothesis that, among islet autoantibody-positive individuals, lacking HLA-DRB1*04-DQA1*03-DQB1*0302 (HLA-DR4-DQ8) and/or HLA-DRB1*0301-DQA1*0501-DQB1*0201 (HLA-DR3-DQ2) is associated with phenotypic differences, compared with those who have these high-risk HLA haplotypes.

Methods: We classified autoantibody-positive relatives of individuals with type 1 diabetes into four groups based on having both HLA-DR4-DQ8 and HLA-DR3-DQ2 (DR3/DR4; n=1263), HLA-DR4-DQ8 but not HLA-DR3-DQ2 (DR4/non-DR3; n=2340), HLA-DR3-DQ2 but not HLA-DR4-DQ8 (DR3/non-DR4; n=1607) and neither HLA-DR3-DQ2 nor HLA-DR4-DQ8 (DRX/DRX; n=1294).