Discrimination of Common Strains in Urine by Liquid Chromatography-Ion Mobility-Tandem Mass Spectrometry and Machine Learning.

Accurate identification of bacterial strains in clinical samples is essential to provide an appropriate antibiotherapy to the patient and reduce the prescription of broad-spectrum antimicrobials, leading to antibiotic resistance. In this study, we utilized the combination of a multidimensional analytical technique, liquid chromatography-ion mobility-tandem mass spectrometry (LC-IM-MS/MS), and machine learning to accurately identify and distinguish 11 () strains in artificially contaminated urine samples. Machine learning was utilized on the LC-IM-MS/MS data of the inoculated urine samples to reveal lipid, metabolite, and peptide isomeric biomarkers for the identification of the bacteria strains.

High variability in Factor IX one-stage assay in samples spiked with nonacog beta pegol among different pairs of reagent/detection system.

Introduction: Haemophilia B (HB) is an X-linked hereditary bleeding disorder characterized by coagulation factor IX (FIX) deficiency. To improve the quality of life of patients and adherence to treatment, recombinant factor concentrates modified to extend their half-life have been developed, called extended half-life factors (EHL: extended half-life). Nonacog beta pegol (N9-GP) is a glycopegylated recombinant human FIX molecule that has a half-life of 93 h with a single dose and has shown a higher recovery percentage than other molecules.

Pilot study using the ECHO model to enhance linkage to care for patients with hepatitis C in the custodial setting.

Prisoners in most countries have a higher prevalence of HCV than the general population, but their access to treatment is very limited. Our aim was to evaluate a pilot programme using the ECHO model to enhance linkage to care in patients with HCV in 3 Argentinean prisons between October 2018 and January 2020. All inmates were invited to participate, and data were collected through a personal interview.

Risk management in magnetic resonance: failure mode, effects, and criticality analysis.

The aim of the study was to perform a risk management procedure in "Magnetic Resonance Examination" process in order to identify the critical phases and sources of radiological errors and to identify potential improvement projects including procedures, tests, and checks to reduce the error occurrence risk. In this study we used the proactive analysis "Failure Mode Effects Criticality Analysis," a qualitative and quantitative risk management procedure; has calculated Priority Risk Index (PRI) for each activity of the process; have identified, on the PRI basis, the most critical activities and, for them, have defined improvement projects; and have recalculated the PRI after implementation of improvement projects for each activity. Time stop and audits are performed in order to control the new procedures.