CCL7 Chemokine Is a Marker but Not a Therapeutic Target of Acute Kidney Injury.

Background: Chemokines orchestrate immune cells activation and infiltration during acute kidney injury (AKI).

Objectives: We aim to test whether deletion of C-C chemokine ligand 7 (CCL7), a small chemokine related to CCL2 (MCP-1), may modulate AKI development and progression toward kidney fibrosis.

Method: Expression of CCL7 was quantified in murine cortical tubular (MCT) cells exposed to myoglobin or lipopolysaccharide or submitted to metabolic reprogramming.

Teaching pharmacovigilance to French medical students during the COVID-19 pandemic: Interest of distance learning clinical reasoning sessions.

Background: Considering data from the literature in favor of active educational intervention to teach pharmacovigilance, we describe an innovative model of distance learning clinical reasoning sessions (CRS) of pharmacovigilance with 3rd year medical French students.

Methods: The three main objectives were to identify the elements necessary for the diagnosis of an adverse drug reaction, report an adverse drug reaction and perform drug causality assessment. The training was organized in 3 stages.

Alteration of the maximal renal water excretion capacity with kidney function and age in human.

Purpose: The kidney's capability to concentrate and dilute urine is crucial to maintaining body fluid compartments and plasma osmolality. Advanced age and chronic kidney disease (CKD) result in decreased maximal urine concentration. Less is known regarding urine dilution ability.

Clinical decision-making training using the Script Concordance Test and simulation: A pilot study for pediatric residents.

Background: Each year, new pediatric residents begin their shifts in the pediatric emergency room. While technical skills are often acquired during workshops, non-technical skills such as communication, professionalism, situational awareness, or decision-making are rarely tested. Simulation enables non-technical skills to be developed in situations frequently encountered in pediatric emergencies.

Machine Learning-Based Urine Peptidome Analysis to Predict and Understand Mechanisms of Progression to Kidney Failure.

Introduction: The identification of patients with chronic kidney disease (CKD) at risk of progressing to kidney failure (KF) is important for clinical decision-making. In this study we assesed whether urinary peptidome (UP) analysis may help classify patients with CKD and improve KF risk prediction.

Methods: The UP was analyzed using capillary electrophoresis coupled to mass spectrometry in a case-cohort sample of 1000 patients with CKD stage G3 to G5 from the French CKD-Renal Epidemiology and Information Network (REIN) cohort.