[An alarming health check-up: what a bloody plasma!] / Un bilan alarmant : un plasma sanglant !

A 35-year-old patient with a metabolic pathology was hospitalized for programmed fibroscopy under general anesthesia for investigation and management of portal hypertension. Following the operation, he showed signs of sepsis and was transferred to intensive care unit. Biological analyzes carried out and generated automaton alarms for certain parameters. A visual check of the appearance of the sample revealed an unusual color of plasma. Additional informations obtained from the clinical department did not provide any explanation for this coloration. Additional assays confirmed an overdose of vitamin B12 related to the treatment of his pathology and which is responsible for the interference observed. Hence the interest of checking the reaction curves in the event of suspected interference and, if necessary, of making dilutions in order to reduce the effects on the biological assays.

An adaptation of the LMS method to determine expression variations in profiling data.

One of the major issues in expression profiling analysis still is to outline proper thresholds to determine differential expression, while avoiding false positives. The problem being that the variance is inversely proportional to the log of signal intensities. Aiming to solve this issue, we describe a model, expression variation (EV), based on the LMS method, which allows data normalization and to construct confidence bands of gene expression, fitting cubic spline curves to the Box-Cox transformation. The confidence bands, fitted to the actual variance of the data, include the genes devoid of significant variation, and allow, based on the confidence bandwidth, to calculate EVs. Each outlier is positioned according to the dispersion space (DS) and a P-value is statistically calculated to determine EV. This model results in variance stabilization. Using two Affymetrix-generated datasets, the sets of differentially expressed genes selected using EV and other classical methods were compared. The analysis suggests that EV is more robust on variance stabilization and on selecting differential expression from both rare and strongly expressed genes.