Lactobacillus rhamnosus GG effectively suppresses bone loss in a mouse model of induced PD irrespective of the mode of administration.

Certain facial characteristics were associated with higher or lower pass rates with regard to fit testing, and fit testers were able to select a suitable respirator on the basis of a visual assessment in the majority of cases. For the fit tester, training and experience were important factors; however, for the HCW being fitted, prior experience in respirator use was not an important factor.

BackgroundMicroscopic evaluation of urine is inconsistently performed in veterinary clinics. The IDEXX SediVue Dx® Urine Sediment Analyzer (SediVue) recently was introduced for automated analysis of canine and feline urine and may facilitate performance of urinalyses in practice.ObjectiveCompare the performance of the SediVue with manual microscopy for detecting clinically relevant numbers of cells and 2 crystal types.SamplesFive‐hundred thirty urine samples (82% canine, 18% feline).MethodsFor SediVue analysis (software versions [SW] 1.0.0.0 and 1.0.1.3), uncentrifuged urine was pipetted into a cartridge. Images were captured and processed using a convolutional neural network algorithm. For manual microscopy, urine was centrifuged to obtain sediment. To determine sensitivity and specificity of the SediVue compared with manual microscopy, thresholds were set at ≥5/high power field (hpf) for red blood cells (RBC) and white blood cells (WBC) and ≥1/hpf for squamous epithelial cells (sqEPI), non‐squamous epithelial cells (nsEPI), struvite crystals (STR), and calcium oxalate dihydrate crystals (CaOx Di).ResultsThe sensitivity of the SediVue (SW1.0.1.3) was 85%‐90% for the detection of RBC, WBC, and STR; 75% for CaOx Di; 71% for nsEPI; and 33% for sqEPI. Specificity was 99% for sqEPI and CaOx Di; 87%‐90% for RBC, WBC, and nsEPI; and 84% for STR. Compared to SW1.0.0.0, SW1.0.1.3 had increased sensitivity but decreased specificity. Performance was similar for canine versus feline and fresh versus stored urine samples.Conclusions and Clinical ImportanceThe SediVue exhibits good agreement with manual microscopy for the detection of most formed elements evaluated, but improvement is needed for epithelial cells.

We conducted a quality improvement project aimed at increasing the frequency of mobilisation in our ICU. We designed a four-part quality improvement project comprising: an audit documenting the baseline frequency of mobilisation; a staff survey evaluating perceptions of the barriers to mobilisation; identification of barriers that were amenable to change and implementation of strategies to address these; and a follow-up audit to determine their effectiveness. The setting was a tertiary care, urban, public hospital ICU in South Australia. All patients admitted to the ICU during the two audit periods were included in the audits, while all permanent/semi-permanent ICU staff were eligible for inclusion in the staff survey. We found that patient-and institution-related factors had the greatest impact on the mobilisation of patients in our ICU. Barriers identified as being amenable to change included insufficient staff education about the benefits of mobilisation, poor interdisciplinary communication and lack of leadership regarding mobilisation. Various strategies were implemented to address these barriers over a three-month period. Multivariable analyses showed that three out of four mobility outcomes did not significantly change between the baseline and follow-up audits, with a significant difference in favour of the baseline audit found for the fourth mobility outcome (maximum level of mobility). We concluded that implementing relatively simple measures to improve staff education, interdisciplinary communication and leadership regarding early progressive mobilisation was ineffective at improving mobility outcomes for patients in a large tertiary-level Australian ICU. Other strategies, such as changing sedation practices and/or increasing staffing, may be required to improve mobility outcomes of these patients.

Studies assessing dietary intake and its relationship to metabolic phenotype are emerging, but limited. The aims of the study are to identify dietary patterns in Australian adults, and to determine whether these dietary patterns are associated with metabolic phenotype and obesity. Cross-sectional data from the Australian Bureau of Statistics 2011 Australian Health Survey was analysed. Subjects included adults aged 45 years and over (n = 2415). Metabolic phenotype was determined according to criteria used to define metabolic syndrome (0–2 abnormalities vs. 3–7 abnormalities), and additionally categorized for obesity (body mass index (BMI) ≥30 kg/m2 vs. BMI <30 kg/m2). Dietary patterns were derived using factor analysis. Multivariable models were used to assess the relationship between dietary patterns and metabolic phenotype, with adjustment for age, sex, smoking status, socio-economic indexes for areas, physical activity and daily energy intake. Twenty percent of the population was metabolically unhealthy and obese. In the fully adjusted model, for every one standard deviation increase in the Healthy dietary pattern, the odds of having a more metabolically healthy profile increased by 16% (odds ratio (OR) 1.16; 95% confidence interval (CI): 1.04, 1.29). Poor metabolic profile and obesity are prevalent in Australian adults and a healthier dietary pattern plays a role in a metabolic and BMI phenotypes. Nutritional strategies addressing metabolic syndrome criteria and targeting obesity are recommended in order to improve metabolic phenotype and potential disease burden.

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Abnormal breathing patterns can follow anesthesia in infants after surgical repair of pyloric stenosis. Occasionally, these patterns can be associated with desaturation. New-onset postoperative apnea was not seen with a remifentanil-based anesthetic.

SummaryWe assessed the value of the Edmonton Obesity Staging System (EOSS) compared with the body mass index (BMI) for determining associations with use of health services and pharmacotherapies in a nationally representative sample of participants in the 2011–2013 Australian Health Survey. A subsample of participants aged 18 years or over, with at least overweight (BMI ≥ 25 kg/m2) or central obesity (waist measurement of ≥102 cm for men; ≥88 cm for women), and who had provided physical measurements (n = 9730) were selected for analysis. For statistical significance of each predictor, we used logistic regression for model comparisons with the BMI and EOSS separately, and adjusted for covariates. For relative explanatory ability, we used the Nagelkerke pseudo R2, receiver operating characteristic curve, and area under curve statistic. The EOSS was significantly better than the BMI for predicting polypharmacy and most of the health service use variables. Conversely, the BMI was significantly better than the EOSS for predicting having discussed lifestyle changes relevant to weight loss with the primary care physician. Clinicians, health care professionals, consumers, and policy makers should consider the EOSS a more accurate predictor of polypharmacy and health service use than the BMI in adults with overweight or obesity.