The use of SDS-PAGE scanning of spent dialysate to assess uraemic toxin removal by dialysis.

BACKGROUND: Uraemic toxins in the 8 to 60 kDa molecular weight range have been attracting increasing attention in dialysis therapy. However, there are no available standardized methods to evaluate their removal. Using new filtering membranes, we evaluated SDS-PAGE of spent dialysate to assess cut-off ranges and removal capacities into dialysate, while also measuring classical markers of dialyser function. METHODS: Eighteen dialysis patients were washed out for 2 weeks with FX 100 (Helixone(®)), followed by randomization to Xevonta Hi 23 (Amembris(®)) or FX dialysers for 2 weeks, then crossed over for an additional 2 weeks, and finally placed on Xenium 210 (Purema(®)) for 2 weeks. SDS-PAGE scanning of the removed proteins contained in the spent dialysate was performed during all dialysis sessions. Total mass of urea, creatinine, total proteins, beta 2 microglobulin (ß2m), retinol-binding protein (RBP) and albumin were measured. The reduction rates of serum urea, creatinine, ß2m, leptin, RBP, alpha 1-antitrypsin, albumin and total proteins were also determined. RESULTS: SDS-PAGE scanning identified four major protein peaks (10-18, 20-22.5, 23-30 and 60-80 kDa molecular weight) and showed clear differences in the amounts of removed proteins between the dialysers, particularly in the 20-22.5, 23-30 and 60-80 kDa ranges. Total mass of removed ß2m, RBP and albumin were in agreement with SDS-PAGE, while serum assays showed differing results. CONCLUSIONS: SDS-PAGE scanning provided a good characterization of protein patterns in the spent dialysate; it extended and agreed with protein determinations and allowed a better assessment of dialyser performance in removing 10 to 80 kDa molecular weight substances. It also identified differences between the three mainly filtrating polysulfone dialysers that were not detected with blood measurements.

Use of spent dialysate analysis to estimate blood levels of uraemic solutes without blood sampling: urea.

BACKGROUND: Urea kinetic modelling-based methods are widely used to assess dialysis efficacy. However, they require blood sampling and are susceptible to a number of errors, mainly from the calculated parameters (particularly V). Spent dialysate determinations have been used and have been shown to be reliable and simple to use. In this study, we associated dialysate-based and clearance determinations along with Kt/V to estimate blood urea levels. METHODS: Urea kinetic modelling, continuous sampling of spent dialysate and ionic dialysance were determined in 18 stable dialysis patients during 126 dialysis sessions. Mean blood urea levels were estimated as follows: mean urea level = spent dialysate - urea mass/(dialysance T). Blood urea levels before and after dialysis were calculated based on the same determinations and extended formulae. RESULTS: Estimated mean urea level was significantly correlated with measured mean blood urea level (R(2) = 0.957; P < 0.0001), and Bland and Altman analysis showed significant agreement between estimated and measured levels. Estimated and measured blood urea levels were also correlated before and after dialysis (R(2) = 0.972 , P < 0.0001 and R(2) = 0.903 , P < 0.0001, respectively), with good agreement for both blood urea before and after dialysis and their respective estimates. CONCLUSIONS: Blood urea levels may be reliably estimated from the total mass of urea removed in the dialysate and the dialysance measured during dialysis. Coupling both measurements allows a precise monitoring of dialysis efficacy and a specific evaluation of the patient's urea metabolism status. Technical dysfunctions and patient variations may be easily identified using this approach without blood sampling.

[Molecular mechanisms involved in kidney ischemia-reperfusion]. / Mécanismes moléculaires impliqués dans l'ischémie-reperfusion rénale.

Acute renal failure (ARF) is still associated with high mortality. It is the consequence of a set of phenomena leading to a low glomerular filtration rate resulting, at least partly, from a misregulation of renal blood flow resulting itself from injuries at the epithelial and endothelial level. The outer medulla seems to be the region of the kidney the most affected by ischemia. Investigation at the histological level reveals a partial destruction of the renal epithelium generated by necrosis and/or apoptosis, loss of cell polarity, cell desquamation into the lumen and endothelial cell swelling. The recent advances in the comprehension of this pathology underline the major role of inflammation, which is probably responsible for the worsening and the persistence of ARF. Studies at the molecular level have pinpointed the implication of many signalling pathways such as apoptosis, G-protein signalling, various receptor and kinase activation. The characterisation of the molecular events involved in ARF should help in our approaches to prevent and treat ARF. The understanding of the adaptation mechanisms to ischemic stress (conditioning) is probably one of the most promising research area of this field in terms of medical applications.

Foxn1 is required for tissue assembly and desmosomal cadherin expression in the hair shaft.

The mouse nude mutation inactivates the gene encoding the Foxn1 transcription factor, causing defective hair morphogenesis. Here, we show for the first time that Foxn1 is required for proper assembly of the hair medulla, and we identify Foxn1-regulated genes by transcript profiling. One such gene encodes the desmosomal cadherin, Dsc2. Significantly, Foxn1-dependent Dsc2 expression is restricted to the hair medulla, and within these cells, Dsc2 protein is predominantly localized to specialized adhesion junctions between the cortex and the medulla. Our results reveal Foxn1 as an essential regulator of tissue assembly in the growing hair shaft and implicate Dsc2 as a downstream effector of this activity.

Reducing the variability in cDNA microarray image processing by Bayesian inference.

MOTIVATION: Gene expression levels are obtained from microarray experiments through the extraction of pixel intensities from a scanned image of the slide. It is widely acknowledged that variabilities can occur in expression levels extracted from the same images by different users with the same software packages. These inconsistencies arise due to differences in the refinement of the placement of the microarray 'grids'. We introduce a novel automated approach to the refinement of grid placements that is based upon the use of Bayesian inference for determining the size, shape and positioning of the microarray 'spots', capturing uncertainty that can be passed to downstream analysis. RESULTS: Our experiments demonstrate that variability between users can be significantly reduced using the approach. The automated nature of the approach also saves hours of researchers' time normally spent in refining the grid placement.