Escherichia coli biofilm formation and dispersion under hydrodynamic conditions on metal surfaces

The aim of this study was to analyze the impact of hydrodynamic forces on the multiplication of E. coli, and biofilm formation and dispersion. The experiments were provided in a flow chamber simulating a cleaning-in-place system. Biofilm biomass was measured using a crystal violet dye method. The results show that hydrodynamic forces affect not only biofilm formation and dispersion but the multiplication of E. coli in the first place. We found more biofilm biomass on the rough surface than on the smooth one. The results of the biofilm formation test show that laminar flow promotes the biofilm growth over 72 h, meanwhile turbulent flow after 48 h causes decrease in biomass. The results of the biofilm dispersion test, in contrast, show that laminar flow removed less biofilm from both materials that turbulent flow did. Therefore, taking into account these findings in cleaning-in-place technology can substantially reduce E. coli multiplication and biofilm formation.     

Genomewide profiling of copy‐number alteration in monoclonal gammopathy of undetermined significance

Monoclonal gammopathy of undetermined significance (MGUS) is a benign condition with an approximate 1% annual risk of symptomatic plasma cell disorder development, mostly to multiple myeloma (MM). We performed genomewide screening of copy‐number alterations (CNAs) in 90 MGUS and 33 MM patients using high‐density DNA microarrays. We identified CNAs in a smaller proportion of MGUS (65.6%) than in MM (100.0%, P = 1.31 × 10^?5) and showed median number of CNAs is lower in MGUS (3, range 0–22) than in MM (13, range 4–38, P = 1.82 × 10^?10). In the MGUS cohort, the most frequent losses were located at 1p (5.6%), 6q (6.7%), 13q (30.0%), 14q (14.4%), 16q (8.9%), 21q (5.6%), and gains at 1q (23.3%), 2p (6.7%), 6p (13.3%), and Xq (7.8%). Hyperdiploidy was detected in 38.9% of MGUS cases, and the most frequent whole chromosome gains were 3 (25.6%), 5 (23.3%), 9 (37.8%), 15 (23.3%), and 19 (32.2%). We also identified CNAs such as 1p, 6q, 8p, 12p, 13q, 16q losses, 1q gain and hypodiploidy, which are potentially associated with an adverse prognosis in MGUS. In summary, we showed that MGUS is similar to MM in that it is a genetically heterogeneous disorder, but overall cytogenetic instability is lower than in MM, which confirms that genetic abnormalities play important role in monoclonal gammopathies.     

Steroid hormone regulation of C. elegans and Drosophila aging and life history

In the last two decades it has become clear that hormones and gene mutations in endocrine signaling pathways can exert major effects on lifespan and related life history traits in worms, flies, mice, and other organisms. While most of this research has focused on insulin/insulin-like growth factor-1 signaling, a peptide hormone pathway, recent work has shown that also lipophilic hormones play an important role in modulating lifespan and other life history traits. Here we review how steroid hormones, a particular group of lipophilic hormones, affect life history traits in the nematode worm (Caenorhabditis elegans) and the fruit fly (Drosophila melanogaster), with a particular focus on longevity. Interestingly, a comparison suggests that parallel endocrine principles might be at work in worms and flies in these species and that steroid hormones interact with the gonad to affect lifespan.     

Generating automated kidney transplant biopsy reports combining molecular measurements with ensembles of machine learning classifiers

We previously reported a system for assessing rejection in kidney transplant biopsies using microarray‐based gene expression data, the Molecular Microscope^® Diagnostic System (MMDx). The present study was designed to optimize the accuracy and stability of MMDx diagnoses by replacing single machine learning classifiers with ensembles of diverse classifier methods. We also examined the use of automated report sign‐outs and the agreement between multiple human interpreters of the molecular results. Ensembles generated diagnoses that were both more accurate than the best individual classifiers, and nearly as stable as the best, consistent with expectations from the machine learning literature. Human experts had ≈93% agreement (balanced accuracy) signing out the reports, and random forest‐based automated sign‐outs showed similar levels of agreement with the human experts (92% and 94% for predicting the expert MMDx sign‐outs for T cell–mediated (TCMR) and antibody‐mediated rejection (ABMR), respectively). In most cases disagreements, whether between experts or between experts and automated sign‐outs, were in biopsies near diagnostic thresholds. Considerable disagreement with histology persisted. The balanced accuracies of MMDx sign‐outs for histology diagnoses of TCMR and ABMR were 73% and 78%, respectively. Disagreement with histology is largely due to the known noise in histology assessments (ClinicalTrials.gov NCT01299168).     

Heartworm on the rise—new insights into Dirofilaria immitis epidemiology

Parasitology Research - Until recently Dirofilaria immitis, the causative agent of serious canine heartworm disease, has been detected relatively infrequently in Central Europe in comparison with...     

Therapeutic Alliance: What Can be Learned from Case Studies?

Two different video-taped short-term psychotherapies were rated on therapeutic alliance for 27 sessions. The ratings were based on two methods: the Penn Helping Alliance Scales (Penn-HAS) and the Therapeutic Alliance Rating Scale (TARS). Both scales provide separate scores for the therapist and the patient. After checking the reliabilities of the ratings, the strength and course of the alliance formation are compared within and across therapies. Furthermore it was hypothesized that the alliance is structured differently in the two therapies. This hypothesis is tested by making content-specific item comparisons across the therapies for the Penn-HAS and by a comparison of the factor structure over time (factor analytic P-technique) for the TARS.