Mouse Anti Chicken vimentin Hybridoma [I6]

Four Providencia rettgeri isolates and one Providencia stuartii isolate were obtained from urine samples of five patients in 2018 in Japan. All of the isolates were resistant to imipenem and meropenem, and three were highly resistant to both carbapenems, with MICs of 512 mu g/ml. The three highly carbapenem-resistant isolates harbored blaIMP-70, encoding a variant of IMP-1 metallo-beta-lactamase with two amino acid substitutions (Val67Phe and Phe87Val), and the other two harbored bla(IMP-1) and bla(IMP-11), respectively. Whole-genome sequencing revealed that an isolate harbored two copies of bla(IMP-1) on the chromosome and that the other four harbored a copy of bla(IMP-11) or bla(IMP-70) in a plasmid. Expression of bla(IMP-70) conferred carbapenem resistance in Escherichia coli. Recombinant IMP-70 and an IMP-1 variant with Val67Phe but without Phe87Val had significant higher hydrolytic activities against meropenem than recombinant IMP-1, indicating that an amino acid substitution of Val67Phe affects increased activities against meropenem in IMP-70. These results suggest that Providencia spp. become more highly resistant to carbapenems by acquisition of two copies of bla(IMP-1) or by mutation of blaIMP genes with amino acid substitutions, such as bla(IMP-70).

Background The histological differentiation of individual types of vascular anomalies (VA), such as lymphatic malformations (LM), hemangioma (Hem), paraganglioma (PG), venous malformations (VeM), arteriovenous malformations (AVM), pyogenic granulomas (GP), and (not otherwise classified) vascular malformations (VM n.o.c.) is frequently difficult due to the heterogeneity of these anomalies. The aim of the study was to evaluate digital image analysis as a method for VA stratification Methods A total of 40 VA tissues were examined immunohistologically using a selection of five vascular endothelial-associated markers (CD31, CD34, CLDN5, PDPN, VIM). The staining results were documented microscopically followed by digital image analyses based quantification of the candidate-marker-proteins using the open source program ImageJ/Fiji. Results Differences in the expression patterns of the candidate proteins could be detected particularly when deploying the quotient of the quantified immunohistochemical signal values. Deploying signal marker quotients, LM could be fully distinguished from all other tested tissue types. GP achieved stratification from LM, Hem, VM, PG and AVM tissues, whereas Hem, PG, VM and AVM exhibited significantly different signal marker quotients compared with LM and GP tissues. Conclusion Although stratification of different VA from each other was only achieved in part with the markers used, the results of this study strongly support the usefulness of digital image analysis for the stratification of VA. Against the background of upcoming new diagnostic techniques involving artificial intelligence and deep (machine) learning, our data serve as a paradigm of how digital evaluation methods can be deployed to support diagnostic decision making in the field of VAs.