RESUMEN
Pyruvate kinase (PK) is a key enzyme of anaerobic glycolysis. The genetic heterogeneity of PK deficiency (PKD) is high, and over 400 unique variants have been identified. Twenty-nine patients who had been diagnosed as PKD genetically in seven distinct paediatric haematology departments were evaluated. Fifteen of 23 patients (65.2%) had low PK levels. The PK:hexokinase ratio had 100% sensitivity for PKD diagnosis, superior to PK enzyme assay. Two novel intronic variants (c.695-1G>A and c.694+43C>T) have been described. PKD should be suspected in patients with chronic non-spherocytic haemolytic anaemia, even if enzyme levels are falsely normal. Total PKLR gene sequencing is necessary for the characterization of patients with PKD and for genetic counselling.
Asunto(s)
Anemia Hemolítica Congénita no Esferocítica , Intrones , Piruvato Quinasa , Errores Innatos del Metabolismo del Piruvato , Humanos , Piruvato Quinasa/deficiencia , Piruvato Quinasa/genética , Masculino , Femenino , Errores Innatos del Metabolismo del Piruvato/genética , Niño , Preescolar , Anemia Hemolítica Congénita no Esferocítica/genética , Turquía , Lactante , Adolescente , MutaciónRESUMEN
Plasma medicine is gaining attraction in the medical field, particularly the use of cold atmospheric plasma (CAP) in biomedicine. The chemistry of the plasma is complex, and the reactive oxygen species (ROS) within it are the basis for the biological effect of CAP on the target. Understanding how the oxidative power of ROS responds to diverse plasma parameters is vital for standardizing the effective application of CAP. The proven applicability of machine learning (ML) in the field of medicine is encouraging, as it can also be applied in the field of plasma medicine to correlate the oxidative strength of plasma-treated water (PTW) according to different parameters. In this study, plasma-treated water was mixed with potassium iodide-starch reagent for color formation that could be linked to the oxidative capacity of PTW. Corresponding images were captured resulting from the exposure of the color-forming agent to water treated with plasma for different time points. Several ML models were trained to distinguish the color changes sourced by the oxidative strength of ROS. The AdaBoost Classifier (ABC) algorithm demonstrated better performance among the classification models used by extracting color-based features from the images. Our results, with a test accuracy of 63.5%, might carry a potential for future standardization in the field of plasma medicine with an automated system that can be created to interpret the oxidative properties of ROS in different plasma treatment parameters via ML.