No abstract

The preanalytical performance of a stat laboratory in our setting is favorable and complies with international quality specifications.

The aim of this article is to show how thevpower of statistics and cheminformatics can be combined, in R, using two packages: rcdk and cluster.We describe the role of clustering methods for identifying similar structures in a group of 23 molecules according to their fingerprints. The most commonly used method is to group the molecules using a "score" obtained by measuring the average distance between them. This score reflects the similarity/non-similarity between compounds and helps us identify active or potentially toxic substances through predictive studies.Clustering is the process by which the common characteristics of a particular class of compounds are identified. For clustering applications, we are generally measure the molecular fingerprint similarity with the Tanimoto coefficient. Based on the molecular fingerprints, we calculated the molecular distances between the methotrexate molecule and the other 23 molecules in the group, and organized them into a matrix. According to the molecular distances and Ward 's method, the molecules were grouped into 3 clusters. We can presume structural similarity between the compounds and their locations in the cluster map. Because only 5 molecules were included in the methotrexate cluster, we considered that they might have similar properties and might be further tested as potential drug candidates.

No abstract

PurposeThe purpose of this study was to identify risk factors for extensively drug-resistant (XDR) Acinetobacter baumannii (AB) and XDR Proteeae association in the largest intensive care unit (ICU) in Western Romania.Materials and methodsThis retrospective case-controlled study was conducted between January 2016 and December 2016 in the ICU of the “Pius Brînzeu” County Emergency Clinical Hospital of Timi oara. Data were collected, in strict confidentiality, from the electronic database of the Microbiology Laboratory and the hospital’s electronic medical records. Risk factors were ș investigated by logistic regression. Independent variables with P≤0.05 and OR >1 (95% CI >1) in the univariate analysis were entered into multivariate sequenced analysis.FindingsThe incidence density of coinfection with XDR AB and XDR Proteeae was 5.31 cases per 1,000 patient-days. Independent risk factors for the association of XDR AB and XDR Proteeae were represented by the presence of tracheostomy and naso-/orogastric nutrition ≥ 8 days. In addition, pressure ulcers were independent predictive factors for infections with all three infection types. Previous antibiotic therapy was an independent risk factor for the acquisition of XDR-AB strains, alone or in association, while the prolonged hospitalization in the ICU, blood transfusion, and hemodialysis appear as independent risk factors for single infections.ConclusionThis association of XDR AB and XDR Proteeae may well not be limited to our hospital or our geographical area.