Tag Archives: GW788388

GW788388 br Dopamine and retinal function DA is a major

Dopamine and retinal function
DA is a major neurotransmitter in the vertebrate retina, and its cellular localization and functions are similar in organisms as diverse as fish and primates (Djamgoz et al., 1997; Masson et al., 1993). In the retina, DA originates in one class of amacrine cells (ACs) and in interplexiform cells. It is transmitted via standard synaptic transmission, as well as by volume transmission, where it can diffuse up to 3mm through retinal GW788388 tissue to potentially influence every type of retinal neuron, as all have receptors for DA (Yazulla and Studholme, 1995). One important function of DA in the retina is to weaken the gap junctions that couple horizontal cells (Piccolino et al., 1984; Teranishi et al., 1983). Because horizontal cells (HCs) pool the activity of photoreceptor cells across space, this DA-related uncoupling leads to significant reduction of the normally large HC receptive fields (Xin and Bloomfield, 2000), and to increased sensitivity to local (relative to contextual) stimulation (Brandies and Yehuda, 2008). A second effect of the uncoupling of HCs is reduced interaction between neurons signaling light and dark portions of space, leading to enhanced center responses (and reduced effects of surround responses) (Hedden and Dowling, 1978).
The DA receptor types found in the retina are the same as those found in the GW788388 (e.g., D1–D5), and can be roughly divided into D1 and D2 types (Brandies and Yehuda, 2008). Endogenous DA inhibits suprathreshold rod-mediated ON and OFF responses. However, with cones, DA increases excitation for ON responses, and increases inhibition for OFF responses in most cases (Popova and Kupenova, 2013). It is thought that D2 receptors are primarily involved in generating ON responses, whereas D1 receptors are mainly responsible for OFF responses (Popova and Kupenova, 2013). As a result of these asymmetries, excess DA at D2 receptors can lead to hyper-intense color perception (as seen, for example, in early schizophrenia, see below), whereas reduced DA can lead to reductions in color perception (as seen, for example, in Parkinson’s disease, see below).

Other neurotransmitters and retinal function
Glutamate is the major neurotransmitter in the vertebrate retina (de Souza et al., 2012), is the only output neurotransmitter of the photoreceptors (Copenhagen and Jahr, 1989), and is also released by bipolar and ganglion cells (Massey and Miller, 1990) (i.e., cells providing the feedforward sweep of information within and out of the retina). Moreover, glutamate output from photoreceptors is gated by the NMDA-type of glutamate receptor (Copenhagen and Jahr, 1989). This is potentially relevant to vision in schizophrenia since: 1) various lines of evidence converge in indicating that schizophrenia is characterized by NMDA dysregulation and NMDA receptor hypoactivity, leading to increased glutamate release, and this evidence forms the basis of a leading theory of the etiology of the condition (Kantrowitz and Javitt, 2010a, b; Moghaddam and Javitt, 2012; Olney and Farber, 1995); 2) NMDA dysregulation is known to cause increased DA release (Javitt, 2007), and to produce visual distortions, hallucinations, and performance on psychophysical tests of vision that resemble those found in schizophrenia (Phillips and Silverstein, 2003; Uhlhaas et al., 2007); 3) NMDA antagonists such as ketamine can cause pathological changes in the retina, including retinal hypoxia and cell death (Antal, 1979), and can cause reductions in the early (sensory) visual P1 evoked potential (Lalonde et al., 2006), which is also found in schizophrenia where it is related to level of positive symptoms (Gonzalez-Hernandez et al., 2014); and 4) many schizophrenia patients have diabetes, in many cases due to antipsychotic medication-related metabolic syndrome and weight gain, and diabetes is associated with increased retinal glutamate and retinopathy (Kowluru et al., 2001). To date, it has not been established that changes in glutamate function exist in the retina of people with schizophrenia (and therefore that any such changes contribute to visual functioning in people with schizophrenia). However, given the evidence noted here, we believe this is as fruitful an area to explore as that of retinal DA changes and vision in schizophrenia. One question that is particularly relevant is whether the glutamatergic input to, or from, two types of retinal cells in particular, midget and parasol cells, is altered. While the functional properties of these cell types continue to be topics of study, it is generally agreed that midget bipolar and ganglion cells have small receptive fields, are involved in color processing, and project into the LGN parvocellular pathway (Kolb, 1995; Kolb and Marshak, 2003). In contrast, parasol ganglion cells, which receive input from DB2 and DB3 type bipolar cells (Jacoby et al., 2000), have large receptive fields, project to the LGN magnocellular pathway, and play a role in contrast sensitivity (Crook et al., 2014). The latter has been found to be variously underactive or overactive in schizophrenia, depending on the task, and phase of illness (Butler et al., 2001; Javitt, 2009; Kelemen et al., 2013; McClure, 2001; Schechter et al., 2003), but retinal contributions to these effects have yet to be described.

A previous study showed that of all DRPs are

A previous study showed that 50–80% of all DRPs are predictable (Viktil and Blix, 2008) and thus pharmacist interventions might resolve and prevent DRPs leading GW788388 to a significant impact in DRP reduction; however, they were not enough to address all problems identified. Regarding other DRPs (indication, effectiveness, and adherence) no expressive GW788388 was observed as that presented for safety DRPs. These aspects might happen due to few pharmacist-physician interventions conducted (n=9; 4.46%). Health team interventions were proposed to reduce indication and effectiveness DRPs, though these are the most complex interventions to be implemented for the pharmacy staff because many patients do not want a new drug added to their therapy regimen since they already take a large amount of tablets daily.
Although it is well known that a clinical pharmacist must be present at prescription time and actively participate in clinical case discussions in regard to the pharmacist-physician interventions (Blix et al., 2006; Kucukarslan et al., 2003; Viktil and Blix, 2008), our interventions were performed by medical record entries and a verbal approach. Nonetheless, our study had 100% of the pharmacist-physician interventions accepted suggesting that this combined method could also be effective and should be explored in the future.
As our data show, pharmacists should intercede with clinician teams in cases of HAART intolerance and with patients to instruct them on how to adequately take antiretroviral drugs, encouraging therapy adherence. Knowing that barriers to treatment adherence are complex and diverse but could also be related to patients’ cultural attitudes and beliefs (Bolsewicz et al., 2015) and it increases when patients have a greater understanding about their own health (Blix et al., 2004; de Lyra et al., 2007; de Oliveira, 2011; Strand et al., 2004), we performed interventions to enhance patient knowledge in relation to HIV/AIDS, HAART, and laboratory tests.
In the present study, we observed a significant increase in CD4 counts for both groups. However, the intervention group presented with a mean CD4 increase of 154.66cells/mm3 while the control group had an increase of 83.8cells/mm3 (p=0.401) by the end of one year. Additionally, after one year of follow-up there were 9 (20.93%) patients in the intervention group and 6 (13.95%) in the control group with CD4 counts >500cells/mm3 and an undetectable viral load. These data demonstrate the clinical relevance of the pharmacist interventions in the intervention group.
The viral load parameter did not demonstrate a statistical difference; however, the viral load reduction was greater in the intervention group, resulting in a mean reduction of 23.52×103RNAcopies/mL, while in the control group there was 6.23×103RNAcopies/mL. Viral load quantification is an important marker of HAART adherence (Bonner et al., 2013). Previous studies had shown that the pharmacist, through pharmaceutical care and interventions, could improve adherence which would help with reducing the viral load (Henderson et al., 2011; Nevo et al., 2015; Reis et al., 2016; Saberi et al., 2012).

Conclusion

Funding

Acknowledgments

Introduction
Plants are a major source of phytochemicals for drug discovery and for laboratory synthesis of drugs. About 80% of the world population is using medicinal plants as their major source for medication in primary health care, and about 120 plant derived compounds are used in western medicine. Phytochemicals can be used as small-molecule drug precursors, which can be converted into drugs by chemical modification, ex. 10-deacetylbaccatin, isolated from Taxus baccata, is used in the semisynthetic method to produce paclitaxel. Many synthetic analogues have been made such as analgesics based on morphine, and local anesthetics based on cocaine. The pharmaceutical industry is searching for new, renewable sources of drugs, very often plant-based drugs, because the use of herbs or a combination of herbs and synthetic drugs can reduce toxicities and maximize therapeutic outcomes (Verpoorte, 2000; Salim et al., 2008; Dhanani et al., in press). A promising source of plant-based biologically active compounds is the Eleutherococcus Maxim. genus, found in eastern Asia and far western Russia. The major secondary metabolites present in Eleutherococcus are phenols, such as eleutherosides (derivatives of lignans, coumarins, phenylpropanoids), flavonoids, phenolic acids, and anthocyanins (Fig. 1). The E. senticosus products attract global attention as a novel medicinal plant and since a few years, have become popular as dietary supplement in the United States and European countries. Imported products of this plant have become available in North America, with a market share of 3.1% of the $12 billion medicinal herbal industry (Załuski et al., 2010; Watson, 2003). The 1994 DSHEA (Dietary Supplement Health and Education Act) regulation allows a direct commercialization of E. senticosus as a supplement for consumption in the United States without the regulation of the FDA (Food and Drug Administration) (Arouca and Grassi-Kassisse, 2013). Preparations of the roots of E. senticosus are given in cases of asthenia with weakness and fatigue, e.g., in convalescence. This indication has been officially accepted by the Community Herbal Monograph on Eleutherococcus senticosus (Rupr et Maxim) Maxim Radix (EMEA/HMPC/244569/2006), published by the European Medicines Agency. The fruits have been used for a long time as an ingredient of the fermented wine, the leaves as a tonic, as a functional beverage commercially marketed for reducing liver damage and accelerating alcohol detoxification.

br Statistical Analysis br Descriptive statistics are

Statistical Analysis

Descriptive statistics are expressed as mean ± standard deviation for continuous variables and as frequency and percentages for nominal variables. A paired t test was used to compare the excursion and peak motion speed between the right GW788388 and the left diaphragm. The associations between the excursions of the diaphragms and participants\’ characteristics were evaluated by means of the Pearson\’s correlation coefficient and a simple linear regression or Student\’s t test depending on the type of variable (ie, continuous or nominal variable). Continuous variables were height, weight, BMI, tidal volume, vital capacity (VC, %VC), forced expiratory volume (FEV1, FEV1%, and %FEV1), and nominal variables were gender and smoking history. The robustness of the results of the univariate analyses was assessed with multiple linear regression models. The significance level for all tests was 5% (two sided). All data were analyzed using a commercially available software program (JMP; version 12, SAS, Cary, NC, USA).

Results

Participants\’ Characteristics

Table 1 shows the clinical characteristics of all the participants (n = 172).

Excursions and Peak Motion Speeds of the Bilateral Diaphragm

Univariate Analysis of Associations Between the Diaphragmatic Excursions and Participants\’ Demographics

Figure 3. Estimated regression line of the excursion of the diaphragm on BMI or tidal volume. (a) Association between BMI and excursion of the right diaphragm. (b) Association between BMI and excursion of the left diaphragm. (c) Association between tidal volume and excursion of the right diaphragm. (d) Association between tidal volume and excursion of the left diaphragm. Lines show estimated regression (a–d). All scatterplots show correlations (P < 0.05). BMI, body mass index.Figure optionsDownload full-size imageDownload high-quality image (226 K)Download as PowerPoint slide

Multivariate Analysis of Associations Between the Excursions and Participants\’ Demographics

Multiple linear regression analysis using all variables as factors (Model 1) demonstrated Polyadenylation weight, BMI, and tidal volume were independently associated with the bilateral excursion of the diaphragms (all P < 0.05) after adjusting for other clinical variables, including age, gender, smoking history, height, VC, %VC, FEV1, FEV1%, and %FEV1. There were no significant associations between the excursion of the diaphragms and variables including age, gender, smoking history, height, VC, %VC, FEV1, FEV1%, and %FEV1 (Table 4). Additionally, a multiple linear regression model using age, gender, BMI, tidal volume, VC, FEV1, and smoking history as factors (Model 2) was also fit as a sensitivity analysis, taking into account the correlation among variables (eg, BMI, height, and weight; VC and %VC; FEV1, FEV1%, and %FEV1). Model 2 (Supplementary Data S1) gave results consistent with Model 1 (Table 4): higher BMI and higher tidal volume were independently associated with the increased bilateral excursion of the diaphragms (all P < 0.05). The adjusted R2 in Model 1 was numerically higher than that in Model 2 (right, 0.19 vs. 0.16, respectively; left, 0.16 vs. 0.13, respectively).