You will be shown an abstract from a biomedical research paper. Given this abstract, your task is to extract all unique entities of the following types: ["Chemical"].

Please return the output as a JSON object of the format: {"Chemical": ["Arsenic", ...]}. The keys should be entity types and values should be lists of extracted entities belonging to the corresponding type. If you cannot find entities belonging to a specific type, the value should be [].

Only output the JSON object and do not include any additional text.

Abstract:

Meta-immunological profiling of children with type 1 diabetes identifies new biomarkers to monitor disease progression. Type 1 diabetes is characterized by autoimmune destruction of pancreatic β-cells in genetically susceptible individuals. Triggers of islet autoimmunity, time course and the precise mechanisms responsible for the progressive β-cell failure are not completely understood. The recent escalation of obesity in affluent countries has been suggested to contribute to the increased incidence of type 1 diabetes. Understanding the link between metabolism and immune tolerance could lead to the identification of new markers for the monitoring of disease onset and progression. We studied several immune cell subsets and factors with high metabolic impact as markers associated with disease progression in high-risk subjects, type 1 diabetes patients at onset, 12 and 24 months after diagnosis. A multiple correlation matrix among different parameters was evaluated statistically and assessed visually on two-dimensional graphs. Markers to predict residual β-cell function up to one year after diagnosis were identified in multivariate logistic regression models. The metaimmunological profile changed significantly over time in patients and a specific signature that associated with worsening disease was identified. A multivariate logistic regression model measuring age, body mass index (BMI), fasting C-peptide, number of circulating CD3(+)CD16(+)CD56(+) cells and the percentage of CD1c(+)CD19(-)CD14(-)CD303(-) type 1 myeloid dendritic cells (mDC1s) at disease onset had a significant predictive value. The identification of a specific meta-immunological profile associated to disease status may contribute to understand the basis of diabetes progression.
{"Chemical": []}