We present here click here the first study on the UVB response and the antioxidant enzymatic defense of Acinetobacter HAAW

isolates Ver3, Ver5 and Ver7 from Lake Verde and N40 from Lake Negra. Bacterial strains Ver3, Ver5 and Ver7 were isolated from Andean Lake Verde and strain N40 from Lake Negra (Ordoñez et al., 2009). Both lakes are located at 4400 m above sea level in Catamarca, Argentina. All four strains belong to the Extremophile Strain Collection from the Laboratory of the Andean Lakes Microbiology Research (http://www.limla.com.ar). Three strains from the German Collection of Microorganisms and Cell Cultures (DSMZ) –A. baumannii DSM 30007, Acinetobacter johnsonii DSM 6963 and Acinetobacter lwoffii DSM 2403 – were included in the assays for comparison. Escherichia coli DH5α strain was used as a control for in situ SOD inhibition assay as described below. All cultures were grown in Luria–Bertani (LB) broth, supplemented with 2% agar for solid medium when applicable. The 16S rRNA gene sequences from 34 Acinetobacter strains used in this work were obtained from the National Center for Biotechnology www.selleckchem.com/products/ly2157299.html Information (NCBI), (the corresponding accession numbers are: AM778686.1, AM410706.2, AM778688.1, AF509828.1, AM778690.1, X81663.1, AM778696.1, EU337121.1, AF509825.1, AJ293694.1, AF509826.1, AJ293693.1, GU388381.1, AJ626712.1, NR_028853.1, AJ303013.1, FJ907197.1, AJ295007.1,

EU661706.1, FJ608110.1, FJ860867.1, EF204273.1, GQ200824.1, DQ289068.1, FN563422.1, EF204280.1, FN563420.1, GU083586.1, X81660.1, FJ263924.1, AF509827.1, FN393792.1, NR_028851.1 and X81665.1.) The 16S rRNA gene sequences of the four isolates studied here were amplified previously using universal primers (F-27: AGAGTTTGATAMTGGCTCAG, R-1492: TACGGYTACCTTGTTACGACTT) and sequenced as described (Ordoñez et al., 2009). Nucleotide database searches were performed at NCBI using the blast network service. To construct the phylogenetic

trees, the sequences were aligned in the clustal x 2.0.9 program, which is a Windows interface for the clustal w multiple sequence alignment program (Larkin et al., 2007). treeview x version 0.5.0 was used to display phylogenies. All positions containing gaps and missing data were eliminated from the dataset manually. Analyses were performed by the neighbor-joining Metalloexopeptidase (NJ) distance method within the same program (Saitou & Nei, 1987). Confidence limits to the inferences obtained by NJ were placed by the bootstrap procedure. Bacterial cultures collected at an OD600 nm of 0.4 were subjected to serial dilutions. Aliquots of 10 μL were then loaded onto LB agar plates, supplemented with methyl viologen (MV) (0.15 mM) or hydrogen peroxide (H2O2) (0.35 mM) when indicated. To evaluate tolerance to UV, plates were exposed to 9 × 103 J m−2 radiation using UVB lamps (maximum emission 302 nm, Bio-Rad Life Science) as light source.

For US pharmacists (all sectors, including community), work overload was one of the factors that most contributed to job stress for pharmacists generally.[55] Other US research suggested that community pharmacists wanted to spend less time dispensing and on business management and more time on consultation and drug-use management. This was true of community pharmacists working in both chain and independent pharmacy settings.[56] Results from another study showed that prescriptions Target Selective Inhibitor Library molecular weight dispensed personally by community pharmacists had increased since

the year 2000.[57] Svarstad et al. also reported that increased community pharmacy busyness reduced the likelihood of any pharmacist

communication to patients (talking to patients, oral information giving, assessment of understanding).[58] Papers identified used a range of methods to research the subjects of pharmacist workload, job satisfaction and stress. Various limitations are to be noted. Two studies used questionnaires to collect information from pharmacists.[43,46] Questionnaires in all the studies identified were previously validated. However, non-response bias has the potential to affect study outcomes on the basis that non-responders may be characteristically different to Selleckchem Forskolin those who do respond. McCann et al. stated that non-response bias in their quantitative study[46] (questionnaire response rate 39%) should not be overlooked. Bond et al. followed up non-responders over the telephone, giving a high overall response rate (71%) helping to reduce

possible bias.[43] The use of work diaries or subjective evaluation as a method of recording pharmacists’ work patterns was reported by several of the studies identified. Participants’ perception of time spent on certain aspects of their jobs was skewed, intentionally or unintentionally. One study reported differences between actual work completed and estimated work, some of the differences having statistical significance.[39] Observational studies were used in some of the research described in this review. Observations are subject to the Hawthorne effect, where participants modify their behaviour in response to being observed.[59] Immune system Although quantifying the Hawthorne effect in such studies remains difficult, observations are still key for investigating pharmacists’ workload, especially given the differences in perceived workload and actual workload identified in this review.[39] Much of the data presented in this review were collected several years prior to the introduction of the 2005 CPCF in England and Wales. Only seven out of the 13 studies identified were post 2005; three of these were in Northern Ireland where the contractual framework is different to England and Wales.

We used proteomics to characterize the insoluble subproteome of C. difficile strain 630. Gel-based LC-MS analysis led to the identification of 2298 peptides;

provalt analysis with a false discovery rate set at 1% concatenated this list to 560 unique peptides, resulting Selleck Dasatinib in 107 proteins being positively identified. These were functionally classified and physiochemically characterized and pathway reconstruction identified a variety of central anaerobic metabolic pathways, including glycolysis, mixed acid fermentation and short-chain fatty acid metabolism. Additionally, the metabolism of a variety of amino acids was apparent, including the reductive branch of the leucine fermentation pathway, from which we identified seven of the eight enzymes. Increasing proteomics data sets should – in conjunction with other ‘omic’ technologies – allow the construction of models for ‘normal’ metabolism in C. difficile 630. This would be a significant initial step towards a full systems understanding of this clinically important microorganism. The Gram-positive spore-forming anaerobe Clostridium difficile, first described by Hall & O’Toole (1935), has become recognized as the leading cause of infectious

diarrhoeal in hospital patients worldwide over the last three decades (Riley, 1998; Sebaihia et al., 2007). Two factors are significant in the increased prevalence of C. difficile infection (CDI): the increase in the use of broad-spectrum antibiotics, including find more cephalosporins Phospholipase D1 and aminopenicillins (Poutanen & Simor, 2004), and the widely reported contamination of the hospital environment by C. difficile spores (Durai, 2007). Antibiotic-associated diarrhoeal and colitis were well established soon after antibiotics became available, with C. difficile being identified as the major cause of antibiotic-associated diarrhoeal and as the nearly exclusive cause of potentially life-threatening pseudomembranous colitis in 1978 (Bartlett, 2006). Clostridium difficile’s well-documented antibiotic resistance results in its persistence when the normal gut microbial communities are disturbed or eradicated by antibiotic

therapy, following which C. difficile spores germinate, producing vegetative cells, which, upon proliferation, secrete the organism’s two major virulence factors – toxin A and toxin B. As the major virulence factors, the toxins have been studied extensively in order to dissect C. difficile virulence mechanisms and they are the primary markers for the diagnosis of CDI (reviewed extensively elsewhere – e.g. Voth & Ballard, 2005; Jank et al., 2007; Lyras et al., 2009). The toxins lead to the development of symptoms associated with CDI, ranging from mild, self-limiting watery diarrhoeal, to mucosal inflammation, high fever and pseudomembranous colitis (Bartlett & Gerding, 2008). Recently, a new epidemic of C. difficile, associated with the emergence of a single hypervirulent strain of C.

Here, we describe protozoan features that affect their EX 527 manufacturer ability to grow on secondary-metabolite-producing bacteria, and examine whether different bacterial secondary metabolites affect protozoa similarly. We investigated the growth of nine different soil protozoa on six different Pseudomonas strains, including the four secondary-metabolite-producing Pseudomonas fluorescens DR54 and CHA0, Pseudomonas chlororaphis MA342 and Pseudomonas sp. DSS73, as well as the two nonproducers P. fluorescens DSM50090T and P. chlororaphis ATCC43928. Secondary metabolite producers affected protozoan growth

differently. In particular, bacteria with extracellular secondary metabolites seemed more inhibiting than bacteria with membrane-bound metabolites. Interestingly, protozoan response seemed to correlate with high-level protozoan taxonomy, and amoeboid taxa tolerated a broader range of Pseudomonas strains than did the non-amoeboid

taxa. This stresses the importance of studying both protozoan and bacterial characteristics in order to understand bacterial defence mechanisms and potentially improve survival of bacteria introduced into the environment, for example for biocontrol purposes. Protozoan grazing increases bacterial turnover of organic matter and reduces bacterial biomass (Rønn et al., 2002; Bonkowski, 2004; Christensen et al., 2006). Furthermore, particular BIBW2992 in vivo protozoa consume different bacteria to different extents (Rønn et al., 2001, 2002; Mohapatra & Fukami, 2004; Pickup et al., 2007). Factors that presumably affect bacterial susceptibility to grazing include cell size, speed of movement, extent of biofilm production, and the composition of the bacterial envelope (Matz & Kjelleberg, 2005). Bacteria that produce secondary metabolites may likewise be less suitable as protozoan food (Rønn et al., Vorinostat solubility dmso 2001; Andersen & Winding, 2004; Matz et al., 2004; Jousset et al., 2006; Pedersen et al., 2009). The genus

Pseudomonas is interesting in this context as it includes strains that produce a wide range of secondary metabolites (Haas & Défago, 2005). Protozoa can discriminate between different food items (e.g. Jürgens & DeMott, 1995; Boenigk et al., 2001; Jezbera et al., 2006; Pedersen et al., 2009) and therefore only ingest some bacterial strains. Hence, protozoa graze different taxonomic groups of bacteria differently (Matz et al., 2004). Still, we know only little about how protozoan features correlate with which bacteria they can ingest and hence digest. Here, we focus on protozoan characteristics; thus, we hypothesize that protozoan taxonomic affiliation (Adl et al., 2007) can be used to predict which bacteria they can subsist on, depending upon the bacterial production of secondary metabolites. Thus, we hope to find protozoan characteristics that correlate with their ability to grow on specific bacteria.

One tracking sequence continued for 120 s and was repeated four times with a resting interval of 5 min. The order of the symmetric and asymmetric conditions was counterbalanced across participants. In each tracking condition, TMS was delivered for a total of 40 times when the target line passed the 6 N level. The TMS trigger was randomized across the incremental and decremental phases in the left thumb abduction force (i.e. the interstimulus interval was either 10 or 15 s). A practice tracking session without TMS was conducted three times prior to beginning each tracking condition. To clarify whether TMS-induced force disturbance

and Talazoparib chemical structure TCI were modulated in association with unimanual force regulation, we designed the first control experiment in which the participants were instructed to keep the left side force constant at 6 N and to track the target line with only the right side force. The left side force and electromyographic

(EMG) activity were averaged separately with reference to the TMS trigger during the right side tracking phase. The effect of TMS on the left tonic force and EMG activity were compared between the force incremental and decremental phases of the right side force. The second control experiment was designed to investigate whether excitation of the crossed corticospinal tract (CST) was always accompanied by excitation of the transcallosal pathway. To this end, the participants also performed unimanual CAL-101 purchase tracking on the left side in addition to the two bimanual conditions (symmetric and asymmetric). TMS Immune system was initially delivered at an intensity of 1.5 times the RMT during the unimanual condition (i.e. the right thumb was relaxed). During the second and third trials, one of the bimanual

conditions was conducted in a counterbalanced order across the participants. The TMS intensity during the bimanual conditions was adjusted so that the size of the MEP in the right APB was equivalent to that during the unimanual condition (approximately 0.8 × RMT). By comparing the results from the unimanual and bimanual conditions, we evaluated the magnitude of the transcallosal effect elicited by different stimulus intensities under almost equivalent excitabilities of the crossed CST. Bilateral thumb abductor forces were measured using strain gauges (type KFWS; Kyowa Dengyo Co., Ltd, Japan) attached to the metal pressure plates. The force signal was amplified (DC 5 kHz, gain × 106), displayed concurrently on an oscilloscope for visual feedback, and stored on a computer with a sampling rate of 1 kHz using a CED 1401 A/D converter (Cambridge Electrical Design, UK). The stored force signal was low-pass filtered (Butterworth filter, two-order, 30-Hz cut-off) for offline analysis. To evaluate the tracking performance, the tracking accuracy and tracking synchrony were calculated in an 8-s pre-stimulus period.

This type of temporally restricted feeding (RF) schedule synchronises circadian oscillators in the limbic forebrain (Amir et al., 2004; Lamont et al., 2005; Waddington Lamont et al., 2007) and can induce a diurnal rhythm of clock gene protein expression in the dorsomedial nucleus of the hypothalamus (DMH; (Verwey et al., 2007). Ghrelin is a stomach peptide that acts in the brain to regulate energy balance (Kojima et al., 1999; Tschop et al., 2000; Toshinai et al., 2001). Ghrelin is secreted in response to fasting and hypoglycemia, and causes feeding when administered either peripherally or centrally (Tschop et al., 2000; Toshinai et al., 2001). Importantly, plasma ghrelin levels increase

before, and are rapidly reduced following, a meal, suggesting AZD2281 manufacturer a role in meal initiation (Cummings et al., 2001; Toshinai et al., 2001; Sanchez et al., 2004; Drazen et al., 2006). The effects Selleck BTK inhibitor of ghrelin are mediated through the growth hormone secretagogue receptor (GHSR), found in brain regions associated with feeding and the regulation of circadian rhythms. For example, the message for GHSR is found in the SCN of rats, primates and, to a lesser extent, mice (Guan et al., 1997; Mitchell et al., 2001; Zigman et al., 2006). Ghrelin receptors are also found in brain regions stimulated in anticipation of scheduled

meals (Angeles-Castellanos et al., 2004). These data suggest that ghrelin may play a role in circadian timing mechanisms, particularly entrainment to food availability. The latter hypothesis has been supported by studies showing

that GHSR-knockout (KO) mice show attenuated anticipatory locomotor activity on an RF schedule (Blum et al., 2009; LeSauter et al., 2009), and cFOS expression is reduced in many brain areas in response to RF (Blum et al., 2009; Lamont et al., 2012). Moreover, and in spite of evidence for the presence of the ghrelin receptor in the circadian system, the role of ghrelin on circadian rhythms remains to be studied in detail. Here we looked for the presence Acyl CoA dehydrogenase of GHSR in the circadian system of mice using GHSR-KO mice with a LacZ reporter inserted into the promoter of the GHSR gene. To further investigate the circadian phenotype of animals lacking the ghrelin receptor, analyses of running wheel activity and neuronal activation were performed under various lighting conditions. KO and WT mice were placed under a 12 : 12 h light : dark schedule (LD), constant darkness (DD) or constant light (LL); they were killed at different intervals to observe circadian rhythms of cFos expression. We also examined circadian rhythms of GHSR-KO and WT mice under conditions of DD and LL, and the ability of these animals to entrain to scheduled meals under these lighting conditions. Mice with targeted mutations to the ghrelin receptor gene (GHSR-KO) and their WT littermates were bred at the Carleton University Department of Neuroscience animal facilities.

, 2004). However, lack of the HEXXH consensus motif does not automatically exclude membership buy Palbociclib of camelysin in the

zinc metalloprotease family, of which His, Glu, Asp and Arg are possible zinc ligands (Barrett, 1998). Thus, camelysin belongs to the metalloproteases, showing the typical strong inhibition by metal chelators (Fricke et al., 1995), but it is insensitive to phosphoramidon or zincov, which are the strongest inhibitors of neutral metalloproteinases of the thermolysin-type (clan MA) (Rawlings & Barrett, 1993). Metalloprotease camelysin prefers cleavage sites at the Leu–Gly or Leu–Ala bond, which are in front of aliphatic and hydrophilic amino acid residues (-OH, -SO3H amido group), avoiding bulky aromatic residues. Thus, these cleavage sites have a broad protein specificity; all kinds of casein are cleaved as well as acid-soluble collagen, globin and ovalbumin, and intact insulin is only destroyed to a small extent (Fricke

et al., 2001). Metalloprotease camelysin isolated from B. thuringiensis ssp. israelensis (Bti) exhibited maximal activity against the substrate azocasein at a temperature of 37 °C and pH 7.5. However, the enzyme activity remained high at basic pH values (8–10) (Nisnevitch et al., 2010). The immune inhibitor BIBW2992 in vivo A (InhA) metalloprotease, which has similarities to the Bacillus thermoproteolyticus thermolysin, the Pseudomonas aeruginosa elastase and the protease E-15 from Serratia, could specifically cleave antibacterial proteins

produced by the insect host (Lövgren et al., 1990; Grandvalet et al., 2001). It was previously reported that InhA is toxic to adult Drosophila (Sidén et al., 1979). The goal of this study was to investigate the role of the metalloproteases of B. thuringiensis Clomifene acrystalliferous strain XBU001. We addressed the issue by deleting the calY gene in the chromosome of B. thuringiensis, and then complementing it. The InhA protein was not expressed in strain KCTF in which the calY gene was deleted. However, the InhA was expressed when the metalloprotease camelysin was complementary in the strain KCTF. This is first report that camelysin can positively regulate the expression of the InhA protein. The bacterial strains and plasmids used in this study are shown in Table 1. Strain KCTF12 (Liu et al., 2008) has a 3.9-kb fragment of cry1Ac integrated in the chromosome derived from B. thuringiensis acrystalliferous strain XBU001 (Hu et al., 2004). It was routinely cultured at 30 °C in Luria–Bertani (LB) medium. Bacillus thuringiensis strains were cultured in fermentation medium for sporulation (Ding et al., 2009). For subcloning, Escherichia coli GB2005 (Fu et al., 2008a, b) was grown at 37 °C in LB medium. Ampicillin (100 μg mL−1), chloramphenicol (5 μg mL−1) or erythromycin (25 μg mL−1) were added to propagate plasmids. Plasmid pUC18 was used for routine cloning and subcloning experiments.

1E). In both conditions, subjects equally improved from training to retrieval testing (F1,14 = 13.83 and P = 0.002, for ‘training/retrieval’ main effect). Performance on the digit span test measuring working memory capacity, and the word fluency test measuring the capability for retrieval from long-term memory, also did not differ between conditions (Table 2). Total sleep time was very similar during the tSOS and sham stimulation

conditions (74.1 ± 3.3 vs. 76.2 ± 3.4 min; Table 3), and 4-min intervals of (sham) stimulation also occurred equally often (7.60 ± 0.18 vs. 7.53 ± 0.21 selleck kinase inhibitor intervals; Table 3). In most cases (n = 13), subjects were woken after the end of the first REM sleep period. Visual scoring of arousals during the (sham) stimulation periods showed that the number of arousals was, on average, slightly lower during the stimulation condition than during the sham condition (mean ± SEM: 7.27 ± 1.35

vs. 8.93 ± 1.68; P = 0.16), but did not significantly differ between the two conditions. During the 4-min intervals of stimulation, endogenous SWA cannot be separated from the induced tSOS sine wave stimulation signal covering the same frequency band (Fig. 2A). However, after high-pass filtering, an analysis of spindle activity during ongoing stimulation was possible. The corresponding statistical anova included factors representing the stimulation period and the different electrode sites, as well as Venetoclax an additional phase factor (discriminating up-phases and down-phases of the tSOS sine wave signal). In Pz, induction of SWA by tSOS

was acutely accompanied by distinct increases in a broad frequency range of 8–20 Hz during the anodal up-phases of the oscillating Rucaparib mouse stimulation, as compared with the down-phases of the stimulation signal (F1,14 = 88.45 and P < 0.001 for the 9–15-Hz frequency band; Fig. 2B). This phase-coupling of EEG activity to the tSOS signal covering both the low 9–12-Hz and high 12–15-Hz spindle frequency bands was, for fast spindle activity, most pronounced during the first and third stimulation periods (F5,70 = 3.82 and P = 0.011 for the phase × stimulation period interaction). Exploratory analyses indicated that this phase-coupling also extended to the faster (15–20 Hz) beta frequency band (F1,14 = 72.0 and P < 0.001 for main effect of phase; F5,70 = 2.61 and P = 0.059, for the phase × stimulation period interaction). There was no systematic difference in EEG power in the slow and fast spindle bands or the adjacent beta band (calculated across the entire periods of acute stimulation) from those in the corresponding periods of the sham condition. Analyses of the 1-min stimulation-free intervals immediately following the 4-min intervals of tSOS (vs. sham stimulation) included factors representing the stimulation period and, in the case of the EEG power, the different electrode sites. This analysis revealed a clear tSOS-induced increase in SWS.

The different cecum contents were pooled (cecum extract) and used to study their effect on the different bacterial strains throughout this work. Bifidobacterium animalis ssp. lactis IPLA4549, B. animalis ssp. lactis IPLAR2, Bifidobacterium bifidum LMG11041T, Bifidobacterium longum ssp. longum NCIMB8809, Lactobacillus acidophilus

DSM20079T, Lactobacillus casei ssp. rhamnosus GG (ATCC53103), Lactobacillus delbrueckii ssp. delbrueckii IPLAlb101, and Lactobacillus reuteri DSM20016T were routinely grown at 37 °C in MRS broth (Difco®; Becton Dickinson, Franklin Lakes, NJ) supplemented with 0.05% (w/v) l-cysteine (MRSC) (Sigma Chemical Co., St. Louis, MO). Lactococcus lactis ssp. cremoris MG1363 and Streptococcus Nutlin-3a thermophilus LMG18311 were propagated on M17 broth (Difco®; Becton Dickinson) supplemented selleck kinase inhibitor with 1% (w/v) glucose (GM17) at 30 °C. All cultures were incubated in anaerobic jars (Anaerocult A System; Merck KGaA, Darmstadt, Germany). The environmental conditions of the large intestine were simulated by supplementing the growth media with 0.1% or 1.0% (v/v) cecum extract. Overnight cultures of the different bacterial strains were used to inoculate (1% v/v) 50 mL of fresh media containing 0%, 0.1%, or 1.0% (v/v) sterilized cecum extract. Cultures were made in triplicate from three independent precultures;

cells were harvested at different phases of the growth curve, depending on the experiment. With this setup, bacteria check details enter stationary phase of growth after 7–10 h of growth, depending on the strain. No apparent inhibitory effect on growth was observed after addition of 1.0% (v/v) cecum extract. Precipitation of extracellular proteins was performed as described previously (Sánchez et al., 2009b). Fifty milliliter aliquots of fresh MRSC or GM17 broth containing 0%, 0.1%, or 1.0% (v/v) cecum extract were inoculated (1% v/v) from an overnight culture of the different bacterial

strains. Cultures were allowed to enter stationary phase of growth; cells were harvested by centrifugation (9300 g, 4 °C, 10 min). Supernatants were then filtered (0.45 μm). Sodium deoxycholate 10 mg (Sigma) was added and mixed, and the resulting solution was incubated at 4 °C for 30 min. Chilled trichloroacetic acid (TCA; Sigma) was added at a final concentration of 6% (w/v), and proteins were allowed to precipitate at 4 °C for 2 h. Proteins were recovered by centrifugation (9300 g, 4 °C, 10 min); pellets were washed twice with 2 mL of chilled acetone (Sigma). Pellets were allowed to dry at room temperature, and proteins were resolubilized by ultrasonication (Ultrasonic bath; Deltasonic, Meaux, France) in 200 μL of 1× Laemmli buffer for 10 min (Laemmli, 1970).

Data were entered onto SPPS (v.21), with results analysed using descriptive statistics. Smad2 phosphorylation The questions derived from the Morisky tool were used to generate an adherence score for each patient, with scores of 2 or more representing high knowledge and motivation for anticoagulation adherence. Seventy-one of seventy-eight approached patients completed the questionnaire; fifty-seven (80%) were prescribed

warfarin, most commonly for atrial fibrillation, with fifty-one patients (72%) having been on treatment for >28 days. Eight patients (11%) reported occasionally missing their anticoagulation medicine and the majority (sixty-seven patients, 94%) were confident they took their anticoagulant correctly. Twenty-seven patients (38%) said they did not know about the long term benefits of taking anticoagulant therapy. The same number stated that they had concerns about their anticoagulation medication, with possible side-effects and long-term damage to health most commonly cited. Sixty-four patients responded to the questions required for a Morisky score to be calculated (Table 1). Table 1 Morisky scores for patients completing the questionnaire; higher scores indicate greater adherence Morisky

score 3 4 5 6 N/A N (%) 2 (2.5) 4 (5.5) 24 (34) 34 (48) 7 (10) Pharmacists believed that 20% of patients required further this website adherence support, however

no significant differences were found in the Morisky scores of these patients and those patients considered Amisulpride adherent by the pharmacist. Clinic pharmacists reported using information from the questionnaire for thirty-one (44%) consultations. Our findings suggest that the majority of patients attending the anticoagulation clinic had high knowledge and motivation to adhere to their anticoagulant therapy. Some patients expressed concerns surrounding treatment, possibly reflected by the similar number of patients who were relatively new to anticoagulation therapy. Several patients were thought to need targeted adherence support by the pharmacist, but this was not reflected by their Morisky scores from the questionnaire. This mismatch warrants further exploration in a larger study. The tool may not be practical for administration to all patients in clinic, but could be used to determine non-adherent patients and possible reasons for their non-adherence. 1. Cutler DM, Everett W. Thinking outside the pillbox – medication adherence as a priority for healthcare reform. NEJM 2010; 362: 1553–1555 2. Morisky DE, Green LW, Levine DM. Concurrent and predictive validity of a self-reported measure of medication adherence. Med Care 1986; 24: 67–74 C. Beea, S. Gardinera, G. Maya,b, D.