Malloyrohde1070

s between raters. CONCLUSIONS The present study demonstrates that ICF categories can be translated into clinical practice. Collaboration between clinicians and researchers would further enhance the implementation of the ICF in Japan.BACKGROUND Previous studies have shown that the genus Moraxella is commonly present in the nasal microbiota of swine. RESULTS In this study, 51 isolates of Moraxella were obtained from nasal swabs from 3 to 4 week old piglets, which represented 26 different fingerprintings by enterobacterial repetitive intergenic consensus (ERIC)-PCR. Whole 16S rRNA gene sequencing allowed the identification at species level of the Moraxella spp. isolates. The majority of the field strains were identified as Moraxella pluranimalium, but Moraxella porci was also detected. In addition, a cluster of 7 strains did not group with any described Moraxella species, probably representing a new species. Subsequent phenotypic characterization indicated that strains of Moraxella pluranimalium were mainly sensitive to serum complement, while the cluster representing the putative new species was highly resistant. Biofilm formation capacity was very variable among the Moraxella spp. isolates, while adherence to epithelial cell lines was similar among selected strains. Additionally, variability was also observed in the association of selected strains to porcine alveolar macrophages. Antimicrobial tests evidenced the existence of multidrug-resistance in the strains. CONCLUSIONS In summary, phenotypic characterization revealed heterogeneity among Moraxella strains from the nasal cavity of piglets. Strains with pathogenic potential were detected as well as those that may be commensal members of the nasal microbiota. However, the role of Moraxella in porcine diseases and health should be further evaluated.BACKGROUND In the Phase III INPULSIS® trials, treatment of patients with idiopathic pulmonary fibrosis (IPF) with nintedanib significantly reduced the annual rate of decline in forced vital capacity (FVC) versus placebo, consistent with slowing disease progression. However, nintedanib was not associated with a benefit in health-related quality of life (HRQoL) assessed using the St George's respiratory questionnaire (SGRQ). We aimed to further examine the impact of IPF progression on HRQoL and symptoms, and to explore the effect of nintedanib on HRQoL in patients from the INPULSIS® trials stratified by clinical factors associated with disease progression. METHODS Patient-reported outcome (PRO) data from the INPULSIS® trials were included in three post hoc analyses. Two analyses used the pooled data set to examine PRO changes from baseline to week 52 according to 1) decline in FVC and 2) occurrence of acute exacerbations. In the third analysis, patients were stratified based on clinical indicators of disease pral and activity scores). Phenazine methosulfate CONCLUSIONS In patients with advanced IPF, compared with placebo, nintedanib slowed deterioration in HRQoL and symptoms as assessed by several PROs. HRQoL measures have a higher responsiveness to change in advanced disease and may lack sensitivity to capture change in patients with less-advanced IPF.BACKGROUND Implantoplasty is an option in peri-implantitis treatment, but little is known about the effect on the soft tissue. The aim of the study was to characterize surface roughness following experimental implantoplasty and to examine its effect on human fibroblast growth and secretion of selected proteins. METHODS Titanium grade IV coins were mechanically treated with six different rotating bur sequences; diamond burs or carbide burs alone, or followed by either Arkansas stone bur or silicone burs. Machined and rough-surface sandblasted, acid-etched (SLA) coins were used as control. The surface topography was characterized by scanning electron microscope and profilometer. Human gingival fibroblasts from two donors were cultured on the coins to quantify the effect on cell morphology, growth, and protein secretion by confocal microscopy and multiplex immunoassay. RESULTS All surface roughness parameters were lower for the surfaces treated with experimental implantoplasty than for the SLA surface, and the sequence of carbide burs followed by silicone burs rendered the least rough surface of the test groups. The implantoplasty procedures changed the elemental composition of the titanium surface. High surface roughness showed a weak to moderate negative correlation to fibroblast growth, but induced a higher secretion of VEGF, IL-6 and MCP-3 to the cell medium compared to the least rough surfaces of the test groups. At day 30 fibronectin levels were higher in the SLA group. CONCLUSIONS The surface roughness following implantoplasty demonstrated a weak to moderate negative correlation with the growth of fibroblasts. The addition of Arkansas stone and silicon burs to the experimental implantoplasty bur protocol rendered an initial increase in fibroblast growth. Implantoplasty altered the elemental composition of the titanium surface, and had an effect on the fibroblast cytokine secretion and fibronectin levels.BACKGROUND Automated machine-learning systems are able to de-identify electronic medical records, including free-text clinical notes. Use of such systems would greatly boost the amount of data available to researchers, yet their deployment has been limited due to uncertainty about their performance when applied to new datasets. OBJECTIVE We present practical options for clinical note de-identification, assessing performance of machine learning systems ranging from off-the-shelf to fully customized. METHODS We implement a state-of-the-art machine learning de-identification system, training and testing on pairs of datasets that match the deployment scenarios. We use clinical notes from two i2b2 competition corpora, the Physionet Gold Standard corpus, and parts of the MIMIC-III dataset. RESULTS Fully customized systems remove 97-99% of personally identifying information. Performance of off-the-shelf systems varies by dataset, with performance mostly above 90%. Providing a small labeled dataset or large unlabeled dataset allows for fine-tuning that improves performance over off-the-shelf systems.