overdiagnosis after 2012 remains yet largely unknown, the extent of overdiagnosis indicates an urgent need to closely monitor its evolution worldwide and the impact of recent guidelines.

Tolerogenic dendritic cells (tolDCs) are assessed as immunomodulatory adjuvants to regulate autoimmunity. The underlying gene expression endorsing their regulatory features remains ill-defined. Using deep mRNA sequencing, we compared transcriptomes of 1,25-dihydroxyvitaminD/dexametasone-modulated tolDCs with that of non-modulated mature inflammatory DCs (mDCs). Differentially expressed genes controlled cellular interactions, metabolic pathways and endorse tolDCs with the capacity to regulate cell activation through nutrient and signal deprivation, collectively gearing tolDCs into tolerogenic immune regulators. Gene expression differences correlated with protein expression, designating low CD86 and high CD52 on the cell surface as superior discriminators between tolDCs and mDCs. Of 37 candidate genes conferring risk to developing type 1 diabetes (T1D), 11 genes differentially expressed in tolDCs and mDCs regulated immune response and antigen-presenting activity. Differential-expressed transcripts of candidate risk loci for T1D suggest a role of these 'risk genes' in immune regulation, which targeting may modulate the genetic contribution to autoimmunity.

Type 1 diabetes results from selective destruction of insulin-producing pancreatic β-cells by a progressive autoimmune process. Type 1 diabetes proves very heterogeneous in pathology, disease progression and efficacy of therapeutic intervention. Indeed, several immunotherapies that appear ineffective for the entire treated patient population in fact look promising in subgroups of patients. It therefore seems inconceivable that one standard therapy will provide the golden bullet of disease intervention. Instead, personalized medicine may improve immune intervention efficacy rates. We discuss the effect of disease heterogeneity on treatment outcome of immunotherapies, identifying apparent gaps in our understanding of treatment efficacy in subgroups of Type 1 diabetic patients as well as identifying future opportunities for immunotherapy.

This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. OBJECTIVES:To analyze the available literature on the performance of artificial intelligence-generated clinical models for the prediction of serious life-threatening events in non-ICU adult patients and evaluate their potential clinical usage. DATA SOURCES:The PubMed database was searched for relevant articles in English literature from January 1, 2000, to January 23, 2022. Search terms, including artificial intelligence, machine learning, deep learning, and deterioration, were both controlled terms and free-text terms. STUDY SELECTION:We performed a systematic search reporting studies that showed performance of artificial intelligence-based models with outcome mortality and clinical deterioration. DATA EXTRACTION:Two review authors independently performed study selection and data extraction. Studies with the same outcome were grouped, namely mortality and various forms of deterioration (including ICU admission, adverse events, and cardiac arrests). Meta-analysis was planned in case sufficient data would be extracted from each study and no considerable heterogeneity between studies was present. DATA SYNTHESIS:In total, 45 articles were included for analysis, in which multiple methods of artificial intelligence were used. Twenty-four articles described models for the prediction of mortality and 21 for clinical deterioration. Due to heterogeneity of study characteristics (patient cohort, outcomes, and prediction models), meta-analysis could not be performed. The main reported measure of performance was the area under the receiver operating characteristic (AUROC) (n = 38), of which 33 (87%) had an AUROC greater than 0.8. The highest reported performance in a model predicting mortality had an AUROC of 0.935 and an area under the precision-recall curve of 0.96. CONCLUSIONS:Currently, a growing number of studies develop and analyzes artificial intelligence-based prediction models to predict critical illness and deterioration. We show that artificial intelligence-based prediction models have an overall good performance in predicting deterioration of patients. However, external validation of existing models and its performance in a clinical setting is highly recommended.

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