Tag Archives: Phos-tag Biotin

The mock patient that is

The mock patient that is actually investigator has visited these community pharmacies with pressurized metered dose inhaler that is Ventolin®. The investigator asked the community pharmacist to demonstrate the inhaler technique for him. Investigator observed the technique carefully and completed the validated checklist (Bryant et al., 2013) of 8 steps after leaving the pharmacy. Another step has been added after the checklist which states that pharmacist can ask to repeat the steps from patient after demonstration (Giraud and Roche, 2002; Knudsen, 2014). It helps the pharmacist to discover the problems that patient will face while using the product. The Phos-tag Biotin management guidelines also recommend checking this step at each patient visit (EPR-3, 2007).
We did a comparative evaluation of metered-dose inhaler technique demonstration among community Pharmacists in Al Qassim and Al Ahsa regions. Al Ahsa is the major urban center in the eastern region of Saudi Arabia (Khan and Azhar, 2013).

This study has found that majority (93.7%) of community pharmacists failed to demonstrate proper inhalation technique of pMDI inhaler. In this study only 7.3% of pharmacists have demonstrated the proper standardized technique of using pressurized metered dose inhaler whereas only 2.1% pharmacists correctly demonstrated the modified criteria (include step 9) of MDI. The criteria of grading system (Lenney et al., 2000) have been used to understand the knowledge of pharmacists to demonstrate the technique and its effect on aerosol drug delivery. The optimal delivery represents grade A (explained step 1–8), grade B (explained step 5–7) shows some delivery and Grade C is those pharmacists who are unable to explain most of the steps (step 2–7) indicating little or no delivery of the drug to the target point. In this study it was found that 7.3% of pharmacist’s fall in grade A category, 28.1% in grade B and grade C are15.2%. In grade C most of pharmacists were just told to press the canister and take two puffs. One of the most important steps is neglected by pharmacists while dispensing the inhalers. This study has added an additional step (step 9) according to the guidelines (EPR-3, 2007; GINA, 2009) recommending to ask this step to ensure patient understanding of the inhaler technique.
There are a number of studies that have been done to assess the demonstration of proper inhalation technique by pharmacists. In one study about 105 community pharmacists had been approached, out of which only 1 pharmacist (0.9%) was able to demonstrate the technique properly (Osman et al., 2012). Another study (Mickle et al., 1990) evaluated pharmacist practice in patient education when dispensing a metered-dose inhaler. The result shows that only 1 (1.9%) of the 52 pharmacists demonstrated MDI inhalation technique correctly. (Hounkpati et al., 2007) did an assessment of pharmacist’s understanding of the inhalation technique. It revealed that only 27.4% of pharmacists gave a correct answer for all the steps involved.
Community pharmacists are last health care provider to see the patients so it places them at an ideal position to teach inhaler technique to them. Various studies have been done on asthma education given by community pharmacists. In one study the community pharmacists were provided training and then evaluated the impact of pharmacist teaching on patients; it showed reduced hospitalization and improves inhaler technique (Cordina et al., 2001). Similar results have been found in another study that showed improved inhaler technique as a result of pharmacist counseling (Basheti et al., 2005). Another study has used interactive tele-pharmacy video counseling, using compressed video, connecting adolescents in schools with pharmacists; this study showed an improvement in inhaler technique (Bynum et al., 2001). Moreover, organizing asthma education session by pharmacists, physicians or nurses can serve as best adjunct to routine care of the asthmatic patients (Kohler et al., 1995).

Western blotting Control and metformin treated cells were lysed and

Western blotting
Control and metformin-treated Phos-tag Biotin were lysed and 10 μg of protein resolved by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE). Further details are provided in the supplementary materials (see Appendix: Supplementary material).
cDNA microarray
Total RNA was extracted from the two cell lines untreated or treated with 10 mM metformin for 24 h. Purified and labelled cRNA was hybridised to a microarray (Canine V2 [4 × 44K], Agilent Technologies). Detailed procedures and analysis methods are provided in the supplementary materials (see Appendix: Supplementary material).
Cell cycle analysis
Control and metformin-treated cells were trypsinised and fixed with 70% ice-cold ethanol. After fixation, the cells were washed with phosphate buffered saline (PBS) and stained with 50 μg/mL propidium iodide (Sigma-Aldrich), 0.1 mg/mL RNase A (Roche Diagnostics), and 0.05% Triton X-100 (Sigma-Aldrich) for 40 min at 37 °C. The stained cells were immediately analysed (BD FACSCalibur, Becton-Dickinson) and the data were then processed (FlowJo software, TreeStar).
Statistical analysis
All data are shown as means ± standard deviation (SD). The statistical methods and software used were as follows: two-sided Student\’s t test was performed using Excel 2013 (Microsoft), and Dunnett\’s test was performed using R software ( R Development Core Team, 2013) and multcomp R package (Hothorn et al., 2008). The choice of the statistical methods was described in each figure legend. P ≤ 0.05 was considered statistically significant.
In vitro and in vivo anti-tumour effects of metformin on CMGT cells
Metformin decreased the in vitro viability of the cell lines in a time- and concentration-dependent manner (Figs. 1A, B). CHMp-5b was significantly more sensitive than CHMp-13a, with half-maximal inhibitory concentrations (IC50) of 2.2 mM and >20 mM (not determined), respectively, for a 48 h exposure (Figs. 1C, D).
Fig. 1. In vitro and in vivo effect of metformin on canine mammary gland tumour (CMGT) cells. (A-D): In vitro anti-proliferative effect on CMGT cells. Results of viability assays in CHMp-5b (A) and CHMp-13a (B) cell lines exposed to different concentrations of metformin for varying durations. (C) Representative microscopic images of CMGT cells after 24 h of metformin exposure. (D) Concentration response curves generated to estimate the half-maximal inhibitory concentration (IC50) from an independent cell viability assay result (48 h). (E-H): Anti-tumour effect of metformin in a xenograft model. (E) Tumour growth curves for control and metformin (300 mg/kg/day) groups. (F) Macroscopic appearance of resected tumours on day 26. Scale bars, 1 cm. (G) Comparison of the resected tumour weight after removal of the necrotic tissue. (H) Comparison of the number of metastatic nodules in the lung. *P < 0.05 (A, B, Dunnett\’s test, Control vs. treatment; D, E, G, H, t test, CHMp-5b vs. CHMp-13a). MET, metformin.Figure optionsDownload full-size imageDownload as PowerPoint slide

Because the long term use of metformin is devoid

Because the long-term use of metformin is devoid of severe side effects (Ekström et al., 2012) and because metformin inhibits mitochondrial respiratory complex 螜 in a similar manner to rotenone, we hypothesised that metformin has an anti-tumour effect on CMGT cells. The aim of our study was, therefore, to evaluate the anti-tumour effects of metformin on CMGT cells both in vitro and in vivo, and to determine its mechanism of action.
Materials and methods
Cell culture
Two clonal CMGT Phos-tag Biotin cell lines with different phenotypes, CHMp-5b (metastatic) and CHMp-13a (non-metastatic) cells, were used (Murai et al., 2012). The cell lines were maintained in RPMI 1640 medium (Wako Pure Chemical Industries) supplemented with 10% fetal bovine serum (FBS; Life Technologies) and 50 μg/mL gentamicin (Sigma-Aldrich) at 37 °C in a humid Phos-tag Biotin with 5% CO2. All other cell incubation steps used these conditions unless otherwise stated.
Cell viability assay
CMGT cells were seeded at a density of 2000 cells/well in 96-well flat-bottom plates and metformin (1,1-dimethylbiguanide hydrochloride; Sigma-Aldrich; catalogue number D150959) was added at the final concentrations indicated after 24 h. Cell viability was determined using Cell Counting Kit-8 (Dojindo Laboratories). The effect of AMPK activation on cell viability was examined using the AMPK inhibitor Compound C (InSolution Compound C, 10 mM in dimethyl sulfoxide or DMSO, Merck Millipore). Further details are provided in the supplementary materials (see Appendix: Supplementary material).
Immunodeficient mouse xenograft model
The study protocol was approved by The University of Tokyo Animal Care and Use Committee (accession number 14-857; date of approval, 18 Apr 2014). BALB/c-nu/nu mice were inoculated with 5 × 106 CHMp-5b cells. Then, mice were randomly assigned to control (n = 5) and metformin (300 mg/kg/day administered in drinking water; n = 5) groups. The mice were humanely sacrificed after three weeks of treatment. Further details are provided in the supplementary materials (see Appendix: Supplementary material).
Quantification of reactive oxygen species
Cellular reactive oxygen species (ROS) and superoxide anions were quantified using a detection kit (Total ROS/Superoxide Detection Kit, Enzo Life Sciences). Cells were seeded in normal culture flasks and allowed to settle for 24 h. Metformin (0, 1, 5, 10, and 20 mM) was added 6 h prior to analysis. After trypsinisation, cells were labelled according to the manufacturer\’s instructions. Dead cells were gated based on their forward scatter vs. side scatter area profiles. Fluorescence was measured and analysed using a flow cytometer and software (BD FACSCalibur and BD CellQuest Pro software; Becton-Dickinson).
Quantification of ATP production from oxidative phosphorylation
Cells were suspended at a density of 2 × 105 cells/mL in glucose-free RPMI (Sigma-Aldrich) supplemented with 2 g/L galactose (Sigma-Aldrich), and 100 μL of the suspension was added to 96-well plates (Nunc LumiNunc; Thermo Fisher Scientific), followed by incubation for 24 h. Metformin was added and ATP production was quantified after a 2-h incubation period (Mitochondrial ToxGlo Assay, Promega). Luminescence was measured using a plate reader (ARVO MX, Perkin Elmer).

The diversity debit hypothesis would predict the

The Phos-tag Biotin debit hypothesis would predict the parameter of interest, λ   to be negative and statistically significant. We note, however, that government expenditure is likely to be endogenous. It is reasonable to expect that welfare outcomes at district level are influenced by the allocation of public resources, as much as the decision on how to distribute such resources is likely to be influenced by local demands and social needs. The presence of endogeneity would imply that sitsit is correlated with eiteit, and therefore under an OLS framework, Eqn. (2) would yield biased and inconsistent estimates. To test and address the endogeneity problem, we resort to instrumental variable estimators, including two-stage least squares (2SLS), limited information maximum likelihood (LIML), and generalized method of moments (GMM) to obtain, under a pooled cross-sectional setting, the following system of equations:equation(4)sit=αit+βxit+λfi+δzit+μi+ζt+υitsit=αit+βxit+λfi+δzit+μi+ζt+υitequation(5)wit=αit+βxit+ϕŝit+λfi+μi+ζt+υitwhere zitzit is a vector of strictly exogenous instrumental variables that are partially correlated with sitsit, so the coefficient of zitzit is nonzero, i.e., δ≠0δ≠0 and Cov(zit,υit)≠0Cov(zit,υit)≠0, while zitzit is uncorrelated with witwit, so Cov(zit,eit)=0Cov(zit,eit)=0. Finding valid instruments is thus important. We experiment with two approaches: First, we exploit exogenous instrumental variables that have been used previously in the literature. Specifically, we use the logarithm of population, and the distance to the national capital, Lusaka, as external instruments. With regard to the former instrument, Easterly and Rebelo (1993) and Gebregziabher and Niño-Zarazúa (2014) find that the scale of the economy, measured by its population, is an important determinant of fiscal policy in general, and the allocation of social expenditure in particular. Larger populations would have the effect of diminishing the average cost of providing public goods. These scale effects would arise from the nonconvexities associated with the costs of public goods provision. Yet, there is no reason to suspect that a particular district will achieve higher or lower levels of welfare simply because it has more or less people. The second instrument is based on the observation made in previous studies that more remote areas in Zambia tend to receive lower transfers from the central governments. Picazo and Zhao (2009), for instance, find that the most remote and least urbanized areas in Zambia receive the lowest per capita releases in the health sector (De Kemp, Faust, & Leiderer, 2011). This could imply that there is a negative correlation between the distance to the capital city and the bargaining power that rural communities are able to exercise to attract public resources from the center; or that remote districts are sanctioned more frequently in financial terms if they fail to meet formal planning or reporting requirements. It is not entirely clear, however, whether more or less financial resources would necessarily lead to better or worse welfare outcomes, after controlling for poverty and the rural environment. We favor the use of log of population as our preferred instrument, given past evidence about its validity, but also experiment with distance to Lusaka as a secondary instrument.