Evaluation of current patient-based real-time quality control in clinical chemistry testing.

Introduction: To perform simulation studies on patient-based real-time quality control (PBRTQC) for aspartate aminotransferase (AST), iron (Fe), potassium (K), and thyrotropin (thyroid stimulating hormone, TSH) analytes, focusing on optimizing systematic error detection while minimizing data loss.

Methods: Clinical laboratory data for the four analytes were analyzed using various truncation methods. Among these methods, truncation limits corresponding to fixed percentiles (e.

Relationship between serum levels of ANGPTL8, Apo C2, and human placental lactogen (hPL) in patients with gestational diabetes mellitus: Interaction of LPL regulators with hPL, a possible contributing factor to insulin resistance.

Introduction: Gestational diabetes mellitus (GDM) is defined as glucose intolerance during pregnancy. We aimed to investigate the potential effects of betatrophin and ApoC2 in GDM, focusing on their roles in LPL (lipoprotein lipase) regulation and their relationship with hPL to elucidate the possible impact of hPL on lipid metabolism and its potential contribution to the development of GDM.

Methods: Thirty pregnant women with normal glucose tolerance and 29 with gestational diabetes mellitus (diagnosed by 75g OGTT between 24 and 28 weeks) were included in the study.

Machine learning-based clinical decision support using laboratory data.

Artificial intelligence (AI) and machine learning (ML) are becoming vital in laboratory medicine and the broader context of healthcare. In this review article, we summarized the development of ML models and how they contribute to clinical laboratory workflow and improve patient outcomes. The process of ML model development involves data collection, data cleansing, feature engineering, model development, and optimization.