Unraveling Uncertainty: The Impact of Biological and Analytical Variation on the Prediction Uncertainty of Categorical Prediction Models.

Background: Interest in prediction models, including machine learning (ML) models, based on laboratory data has increased tremendously. Uncertainty in laboratory measurements and predictions based on such data are inherently intertwined. This study developed a framework for assessing the impact of biological and analytical variation on the prediction uncertainty of categorical prediction models.

A comprehensive survey of artificial intelligence adoption in European laboratory medicine: current utilization and prospects.

Background: As the healthcare sector evolves, Artificial Intelligence's (AI's) potential to enhance laboratory medicine is increasingly recognized. However, the adoption rates and attitudes towards AI across European laboratories have not been comprehensively analyzed. This study aims to fill this gap by surveying European laboratory professionals to assess their current use of AI, the digital infrastructure available, and their attitudes towards future implementations.

The trajectory of functional status of patients with kidney failure choosing conservative kidney management.

Purpose: In older patients with kidney failure (KF) starting dialysis, there is a high rate of functional decline. Not much is known about the functional trajectory of patients receiving conservative kidney management (CKM). Therefore, the aim of this study is to assess this functional trajectory and explore clinical parameters associated with functional decline.

Machine learning for risk stratification in the emergency department (MARS-ED) study protocol for a randomized controlled pilot trial on the implementation of a prediction model based on machine learning technology predicting 31-day mortality in the emergency department.

Background: Many prediction models have been developed to help identify emergency department (ED) patients at high risk of poor outcome. However, these models often underperform in clinical practice and their actual clinical impact has hardly ever been evaluated. We aim to perform a clinical trial to investigate the clinical impact of a prediction model based on machine learning (ML) technology.

Explainable Machine Learning Models for Rapid Risk Stratification in the Emergency Department: A Multicenter Study.

Background: Risk stratification of patients presenting to the emergency department (ED) is important for appropriate triage. Diagnostic laboratory tests are an essential part of the workup and risk stratification of these patients. Using machine learning, the prognostic power and clinical value of these tests can be amplified greatly.

Head-to-head comparison of 19 prediction models for short-term outcome in medical patients in the emergency department: a retrospective study.

Introduction: Prediction models for identifying emergency department (ED) patients at high risk of poor outcome are often not externally validated. We aimed to perform a head-to-head comparison of the discriminatory performance of several prediction models in a large cohort of ED patients.

Methods: In this retrospective study, we selected prediction models that aim to predict poor outcome and we included adult medical ED patients.

High-Sensitivity Cardiac Troponin I and T Kinetics Differ following Coronary Bypass Surgery: A Systematic Review and Meta-Analysis.

Background: Cardiac troponin I and T are both used for diagnosing myocardial infarction (MI) after coronary artery bypass grafting (CABG), also known as type 5 MI (MI-5). Different MI-5 definitions have been formulated, using multiples of the 99th percentile upper reference limit (10×, 35×, or 70× URL), with or without supporting evidence. These definitions are arbitrarily chosen based on conventional assays and do not differentiate between troponin I and T.

Proportions of circulating transitional B cells associate with MRI activity in interferon beta-treated multiple sclerosis patients.

B-cells contribute to MS pathogenesis. The association of circulating B-cell phenotypes with combined unique active lesions (CUA) on MRI at 48 weeks follow-up was investigated in 50 interferon beta-treated MS patients. Transitional B-cell proportions were lower in participants with CUA at week 0 and 48 [p = 0.

Machine learning-based glucose prediction with use of continuous glucose and physical activity monitoring data: The Maastricht Study.

Background: Closed-loop insulin delivery systems, which integrate continuous glucose monitoring (CGM) and algorithms that continuously guide insulin dosing, have been shown to improve glycaemic control. The ability to predict future glucose values can further optimize such devices. In this study, we used machine learning to train models in predicting future glucose levels based on prior CGM and accelerometry data.

Greater daily glucose variability and lower time in range assessed with continuous glucose monitoring are associated with greater aortic stiffness: The Maastricht Study.

Aims: CVD is the main cause of morbidity and mortality in individuals with diabetes. It is currently unclear whether daily glucose variability contributes to CVD. Therefore, we investigated whether glucose variability is associated with arterial measures that are considered important in CVD pathogenesis.

Cardiac Troponin T: The Impact of Posttranslational Modifications on Analytical Immunoreactivity in Blood up to the Excretion in Urine.

Cardiac troponin T (cTnT) is a sensitive and specific biomarker for detecting cardiac muscle injury. Its concentration in blood can be significantly elevated outside the normal reference range under several pathophysiological conditions. The classical analytical method in routine clinical analysis to detect cTnT in serum or plasma is a single commercial immunoassay, which is designed to quantify the intact cTnT molecule.

A comparison of machine learning models versus clinical evaluation for mortality prediction in patients with sepsis.

Introduction: Patients with sepsis who present to an emergency department (ED) have highly variable underlying disease severity, and can be categorized from low to high risk. Development of a risk stratification tool for these patients is important for appropriate triage and early treatment. The aim of this study was to develop machine learning models predicting 31-day mortality in patients presenting to the ED with sepsis and to compare these to internal medicine physicians and clinical risk scores.

Testing for IgA anti-tissue transglutaminase in routine clinical practice: Requesting behaviour in relation to prevalence of positive results.

Objective: Due to the high awareness of coeliac disease and improvement of serological tests, the number of requested laboratory tests has increased substantially over the years. In the current study we have evaluated the requesting behaviour of distinct clinical disciplines in relation to the prevalence of positive results and in the context of existing guidelines.

Methods: Data were retrospectively extracted from the laboratory information system over a time-span of 5 years in a tertiary hospital and compared with the situation in a secondary hospital.

Dendrobium orchids carrying antisense ACC oxidase: small changes in flower morphology and a delay of bud abortion, flower senescence, and abscission of flowers.

Four Dendrobium Sonia 'Earsakul' lines were generated by insertion of one, two or three antisense copies of a Carica papaya gene encoding 1-aminocyclopropane-1-carboxylic acid oxidase (CpACO). Whole vegetative plants of the transgenic lines showed about 50% of the basal ethylene production rate, while the increase in ethylene production in floral buds during opening and open flowers prior to visible senescence was delayed. Detailed analysis of more than 100 parameters in flowering plants showed no effect of antisense ACO on plant morphology and coloration, except for shorter length and width of some of the sepals and petals.

Identification and Characterization of Cardiac Troponin T Fragments in Serum of Patients Suffering from Acute Myocardial Infarction.

Background: Cardiac troponin T (cTnT) is the preferred biomarker for the diagnosis of acute myocardial infarction (AMI). It has been suggested that cTnT is present predominantly in fragmented forms in human serum following AMI. In this study, we have used a targeted mass spectrometry assay and epitope mapping using Western blotting to confirm this hypothesis.