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This prospective study sought to evaluate potential savings of radiation dose to medical staff using real-time dosimetry coupled with visual radiation dose feedback during angiographic interventions. For this purpose, we analyzed a total of 214 angiographic examinations that consisted of chemoembolizations and several other types of therapeutic interventions. The Unfors RaySafe i2 dosimeter was worn by the interventionalist at chest height over the lead protection. A total of 110 interventions were performed with real-time radiation dosimetry allowing the interventionalist to react upon higher x-ray exposure and 104 examinations served as the comparative group without real-time radiation monitoring. By using the real-time display during interventions, the overall mean operator radiation dose decreased from 3.67 (IQR, 0.95–23.01) to 2.36 μSv (IQR, 0.52–12.66) (−36%; p = 0.032) at simultaneously reduced operator exposure time by 4.5 min (p = 0.071). Dividing interventions into chemoembolizations and other types of therapeutic interventions, radiation dose decreased from 1.31 (IQR, 0.46-3.62) to 0.95 μSv (IQR, 0.53-3.11) and from 24.39 (IQR, 12.14-63.0) to 10.37 μSv (IQR, 0.85-36.84), respectively, using live-screen dosimetry (p ≤ 0.005). Radiation dose reductions were also observed for the participating assistants, indicating that they could also benefit from real-time visual feedback dosimetry during interventions (−30%; p = 0.039). Integration of real-time dosimetry into clinical processes might be useful in reducing occupational radiation exposure time during angiographic interventions. The real-time visual feedback raised the awareness of interventionalists and their assistants to the potential danger of prolonged radiation exposure leading to the adoption of radiation-sparing practices. Therefore, it might create a safer environment for the medical staff by keeping the applied radiation exposure as low as possible.
A toolbox for the generation of chemical probes for Baculovirus IAP Repeat containing proteins
(2022)
E3 ligases constitute a large and diverse family of proteins that play a central role in regulating protein homeostasis by recruiting substrate proteins via recruitment domains to the proteasomal degradation machinery. Small molecules can either inhibit, modulate or hijack E3 function. The latter class of small molecules led to the development of selective protein degraders, such as PROTACs (PROteolysis TArgeting Chimeras), that recruit protein targets to the ubiquitin system leading to a new class of pharmacologically active drugs and to new therapeutic options. Recent efforts have focused on the E3 family of Baculovirus IAP Repeat (BIR) domains that comprise a structurally conserved but diverse 70 amino acid long protein interaction domain. In the human proteome, 16 BIR domains have been identified, among them promising drug targets such as the Inhibitors of Apoptosis (IAP) family, that typically contain three BIR domains (BIR1, BIR2, and BIR3). To date, this target area lacks assay tools that would allow comprehensive evaluation of inhibitor selectivity. As a consequence, the selectivity of current BIR domain targeting inhibitors is unknown. To this end, we developed assays that allow determination of inhibitor selectivity in vitro as well as in cellulo. Using this toolbox, we have characterized available BIR domain inhibitors. The characterized chemical starting points and selectivity data will be the basis for the generation of new chemical probes for IAP proteins with well-characterized mode of action and provide the basis for future drug discovery efforts and the development of PROTACs and molecular glues.
Background and Aims: Monocyte chemotactic protein-1 (MCP-1) is a potent chemoattractant for monocytes. It is involved in pathogenesis of several inflammatory diseases. Hepatic MCP-1 is a readout of macrophage activation. While inflammation is a major driver of liver disease progression, the origin and role of circulating MCP-1 as a biomarker remains unclear.
Methods: Hepatic CC-chemokine ligand 2 (CCL2) expression and F4/80 staining for Kupffer cells were measured and correlated in a mouse model of chronic liver disease (inhalative CCl4 for 7 weeks). Next, hepatic RNA levels of CCL2 were measured in explanted livers of 39 patients after transplantation and correlated with severity of disease. Changes in MCP-1 were further evaluated in a rat model of experimental cirrhosis and acute-on-chronic liver failure (ACLF). Finally, we analyzed portal and hepatic vein levels of MCP-1 in patients receiving transjugular intrahepatic portosystemic shunt insertion for complications of portal hypertension.
Results: In this mouse model of fibrotic hepatitis, hepatic expression of CCL2 (P = 0.009) and the amount of F4/80 positive cells in the liver (P < 0.001) significantly increased after induction of hepatitis by CCl4 compared to control animals. Moreover, strong correlation of hepatic CCL2 expression and F4/80 positive cells were seen (P = 0.023). Furthermore, in human liver explants, hepatic transcription levels of CCL2 correlated with the MELD score of the patients, and thus disease severity (P = 0.007). The experimental model of ACLF in rats revealed significantly higher levels of MCP-1 plasma (P = 0.028) and correlation of hepatic CCL2 expression (R = 0.69, P = 0.003). Particularly, plasma MCP-1 levels did not correlate with peripheral blood monocyte CCL2 expression. Finally, higher levels of MCP-1 were observed in the hepatic compared to the portal vein (P = 0.01) in patients receiving TIPS. Similarly, a positive correlation of MCP-1 with Child-Pugh score was observed (P = 0.018). Further, in the presence of ACLF, portal and hepatic vein levels of MCP-1 were significantly higher compared to patients without ACLF (both P = 0.039).
Conclusion: Circulating levels of MCP-1 mainly derive from the injured liver and are associated with severity of liver disease. Therefore, liver macrophages contribute significantly to disease progression. Circulating MCP-1 may reflect the extent of hepatic macrophage activation.
In Eurotransplant kidney allocation system (ETKAS), candidates can be considered unlimitedly for repeated re‐transplantation. Data on outcome and benefit are indeterminate. We performed a retrospective 15‐year patient and graft outcome data analysis from 1464 recipients of a third or fourth or higher sequential deceased donor renal transplantation (DDRT) from 42 transplant centers. Repeated re‐DDRT recipients were younger (mean 43.0 vs. 50.2 years) compared to first DDRT recipients. They received grafts with more favorable HLA matches (89.0% vs. 84.5%) but thereby no statistically significant improvement of patient and graft outcome was found as comparatively demonstrated in 1st DDRT. In the multivariate modeling accounting for confounding factors, mortality and graft loss after 3rd and ≥4th DDRT (P < 0.001 each) and death with functioning graft (DwFG) after 3rd DDRT (P = 0.001) were higher as compared to 1st DDRT. The incidence of primary nonfunction (PNF) was also significantly higher in re‐DDRT (12.7%) than in 1st DDRT (7.1%; P < 0.001). Facing organ shortage, increasing waiting time, and considerable mortality on dialysis, we question the current policy of repeated re‐DDRT. The data from this survey propose better HLA matching in first DDRT and second DDRT and careful selection of candidates, especially for ≥4th DDRT.
Understanding how epigenetic variation in non-coding regions is involved in distal gene-expression regulation is an important problem. Regulatory regions can be associated to genes using large-scale datasets of epigenetic and expression data. However, for regions of complex epigenomic signals and enhancers that regulate many genes, it is difficult to understand these associations. We present StitchIt, an approach to dissect epigenetic variation in a gene-specific manner for the detection of regulatory elements (REMs) without relying on peak calls in individual samples. StitchIt segments epigenetic signal tracks over many samples to generate the location and the target genes of a REM simultaneously. We show that this approach leads to a more accurate and refined REM detection compared to standard methods even on heterogeneous datasets, which are challenging to model. Also, StitchIt REMs are highly enriched in experimentally determined chromatin interactions and expression quantitative trait loci. We validated several newly predicted REMs using CRISPR-Cas9 experiments, thereby demonstrating the reliability of StitchIt. StitchIt is able to dissect regulation in superenhancers and predicts thousands of putative REMs that go unnoticed using peak-based approaches suggesting that a large part of the regulome might be uncharted water.