Comparison of pharmacist and public views and experiences of community pharmacy medicines-related services in England

Background: Services provided by community pharmacists designed to support people using medicines are increasing. In England, two national services exist: Medicine Use Reviews (MUR) and New Medicines Service (NMS). Very few studies have been conducted seeking views of the public, rather than service users, on willingness to use these services or expectations of these services, or determined whether views align with pharmacist perceptions. Objective: To compare the perceptions of pharmacists and the general public on medicines-related services, particularly MUR and NMS services. Methods: Two parallel surveys were conducted in one area of England: one involved the general public and was administered using a street survey, and the other was a postal survey of community pharmacists. Similar questionnaires were used, seeking views of services, awareness, reasons for using services, and perceived benefits. Results: Response rates were 47.2% (1,000/2,012 approached) for the public and 40.8% (341/836) for pharmacists. Few people had experienced a discussion in a private consultation room or were aware of the two formal services, although their willingness to use them was high. Pharmacists estimated time spent on service provision as 10 minutes for MUR and 12 minutes for NMS, which aligned with acceptability to both pharmacists and the public. Pharmacists underestimated the willingness of the public to wait for an informal discussion or to make appointments for formal services. Both pharmacists and the public had high expectations that services would be beneficial in terms of increasing knowledge and understanding, but public expectations and experiences of services helping to sort out problems fell well below pharmacists’ perceptions. People who had experienced a pharmacy service had different perceptions of pharmacists. Conclusion: Views differed regarding why people use services and key aspects of service delivery. For services to improve, the pharmacy profession needs a better awareness of what the public, especially those with potential to benefit from services, view as acceptable and desirable

Medicine-related services in community pharmacy: public preferences for pharmacy attributes and promotional methods and comparison with pharmacists' perceptions

Background: Public awareness of pharmacy services designed to support use of medicines is low, yet little is known about how the public view promotion of these services or their preferences for the attributes of pharmacies from which they would like to receive them. Objective: To compare the public’s preferred attributes of pharmacies and methods for promoting medicine-related services with community pharmacists’ perceptions of their customers’ views. Method: Parallel surveys of the general public, using a street survey, and community pharmacists, via a postal survey in South East England. Results: Response rates were: public 47.2% (1000/2012) and pharmacists 40.8% (341/836) respectively. Pharmacists’ perceptions of customer preferences for using the same pharmacy, independent ownership and personal knowledge of the pharmacist were higher than actual public preferences. More pharmacists also thought approachability and previous good service would be important than the public. The public’s desires for long opening hours and for a pharmacy with a good relationship with their doctor’s surgery was higher than pharmacists believed. The majority of the public prefer not to interrupt a pharmacist busy in the dispensary, which was not perceived by pharmacists as a factor. Pharmacists’ perceptions aligned more with the preferences of regular medicines users and frequent pharmacy users. Both groups viewed direct recommendation as the most effective approach for promoting pharmacy services, particularly by doctors and pharmacy staff. Pharmacists’ expectations of the effectiveness of posters and mass media methods were much higher than those of the public. Conclusions: Pharmacists and pharmacy owners must ensure good relationships with local medical practices to enable them to maximise opportunities for using the promotional methods judged most effective in encouraging use of medicine-related services. Staff must be approachable and enable access to pharmacists ensuring perceptions of pharmacist busyness are not a deterrent

Automatic Spectroscopic Data Categorization by Clustering Analysis (ASCLAN): A Data-Driven Approach for Distinguishing Discriminatory Metabolites for Phenotypic Subclasses

We propose a novel data-driven approach aiming to reliably distinguish discriminatory metabolites from nondiscriminatory metabolites for a given spectroscopic data set containing two biological phenotypic subclasses. The automatic spectroscopic data categorization by clustering analysis (ASCLAN) algorithm aims to categorize spectral variables within a data set into three clusters corresponding to noise, nondiscriminatory and discriminatory metabolites regions. This is achieved by clustering each spectral variable based on the r(2) value representing the loading weight of each spectral variable as extracted from a orthogonal partial least-squares discriminant (OPLS-DA) model of the data set. The variables are ranked according to r(2) values and a series of principal component analysis (PCA) models are then built for subsets of these spectral data corresponding to ranges of r(2) values. The Q(2)X value for each PCA model is extracted. K-means clustering is then applied to the Q(2)X values to generate two clusters based on minimum Euclidean distance criterion. The cluster consisting of lower Q(2)X values is deemed devoid of metabolic information (noise), while the cluster consists of higher Q(2)X values is then further subclustered into two groups based on the r(2) values. We considered the cluster with high Q(2)X but low r(2) values as nondiscriminatory, while the cluster with high Q(2)X and r(2) values as discriminatory variables. The boundaries between these three clusters of spectral variables, on the basis of the r(2) values were considered as the cut off values for defining the noise, nondiscriminatory and discriminatory variables. We evaluated the ASCLAN algorithm using six simulated (1)H NMR spectroscopic data sets representing small, medium and large data sets (N = 50, 500, and 1000 samples per group, respectively), each with a reduced and full resolution set of variables (0.005 and 0.0005 ppm, respectively). ASCLAN correctly identified all discriminatory metabolites and showed zero false positive (100% specificity and positive predictive value) irrespective of the spectral resolution or the sample size in all six simulated data sets. This error rate was found to be superior to existing methods for ascertaining feature significance: univariate t test by Bonferroni correction (up to 10% false positive rate), Benjamini-Hochberg correction (up to 35% false positive rate) and metabolome wide significance level (MWSL, up to 0.4% false positive rate), as well as by various OPLS-DA parameters: variable importance to projection, (up to 15% false positive rate), loading coefficients (up to 35% false positive rate), and regression coefficients (up to 39% false positive rate). The application of ASCLAN was further exemplified using a widely investigated renal toxin, mercury II chloride (HgCl2) in rat model. ASCLAN successfully identified many of the known metabolites related to renal toxicity such as increased excretion of urinary creatinine, and different amino acids. The ASCLAN algorithm provides a framework for reliably differentiating discriminatory metabolites from nondiscriminatory metabolites in a biological data set without the need to set an arbitrary cut off value as applied to some of the conventional methods. This offers significant advantages over existing methods and the possibility for automation of high-throughput screening in "omics" data

Integrative Modeling of Quantitative Plasma Lipoprotein, Metabolic, and Amino Acid Data Reveals a Multiorgan Pathological Signature of SARS-CoV-2 Infection.

The metabolic effects of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection on human blood plasma were characterized using multiplatform metabolic phenotyping with nuclear magnetic resonance (NMR) spectroscopy and liquid chromatography-mass spectrometry (LC-MS). Quantitative measurements of lipoprotein subfractions, α-1-acid glycoprotein, glucose, and biogenic amines were made on samples from symptomatic coronavirus disease 19 (COVID-19) patients who had tested positive for the SARS-CoV-2 virus (n = 17) and from age- and gender-matched controls (n = 25). Data were analyzed using an orthogonal-projections to latent structures (OPLS) method and used to construct an exceptionally strong (AUROC = 1) hybrid NMR-MS model that enabled detailed metabolic discrimination between the groups and their biochemical relationships. Key discriminant metabolites included markers of inflammation including elevated α-1-acid glycoprotein and an increased kynurenine/tryptophan ratio. There was also an abnormal lipoprotein, glucose, and amino acid signature consistent with diabetes and coronary artery disease (low total and HDL Apolipoprotein A1, low HDL triglycerides, high LDL and VLDL triglycerides), plus multiple highly significant amino acid markers of liver dysfunction (including the elevated glutamine/glutamate and Fischer's ratios) that present themselves as part of a distinct SARS-CoV-2 infection pattern. A multivariate training-test set model was validated using independent samples from additional SARS-CoV-2 positive patients and controls. The predictive model showed a sensitivity of 100% for SARS-CoV-2 positivity. The breadth of the disturbed pathways indicates a systemic signature of SARS-CoV-2 positivity that includes elements of liver dysfunction, dyslipidemia, diabetes, and coronary heart disease risk that are consistent with recent reports that COVID-19 is a systemic disease affecting multiple organs and systems. Metabolights study reference: MTBLS2014

Development and validation of an ultra?performance liquid chromatography quadrupole time of flight mass spectrometry method for rapid quantification of free amino acids in human urine

An ultra-performance liquid chromatography quadrupole time of flight mass spectrometry (UPLC-qTOFMS)method using hydrophilic interaction liquid chromatography was developed and validated for simultaneous quantification of 18 free amino acids in urine with a total acquisition time including the column re-equilibration of less than 18 min per sample. This method involves simple sample preparation steps which consisted of 15 times dilution with acetonitrile to give a final composition of 25 % aqueous and 75 % acetonitrile without the need of any derivatization. The dynamic range for our calibration curve is approximately two orders of magnitude (120-fold from the lowest calibration curve point) with good linearity (r2 ? 0.995 for all amino acids). Good separation of all amino acids as well as good intra- and inter-day accuracy (<15 %) and precision (<15 %) were observed using three quality control samples at a concentration of low, medium and high range of the calibration curve. The limits of detection (LOD) and lower limit of quantification of our method were ranging from approximately 1–300 nM and 0.01–0.5 µM, respectively. The stability of amino acids in the prepared urine samples was found to be stable for 72 h at 4 °C, after one freeze thaw cycle and for up to 4 weeks at ?80 °C. We have applied this method to quantify the content of 18 free amino acids in 646 urine samples from a dietary intervention study. We were able to quantify all 18 free amino acids in these urine samples, if they were present at a level above the LOD. We found our method to be reproducible (accuracy and precision were typically <10 % for QCL, QCM and QCH) and the relatively high sample throughput nature of this method potentially makes it a suitable alternative for the analysis of urine samples in clinical setting

Statistical HOmogeneous Cluster SpectroscopY (SHOCSY): an optimized statistical approach for clustering of ¹H NMR spectral data to reduce interference and enhance robust biomarkers selection.

We propose a novel statistical approach to improve the reliability of (1)H NMR spectral analysis in complex metabolic studies. The Statistical HOmogeneous Cluster SpectroscopY (SHOCSY) algorithm aims to reduce the variation within biological classes by selecting subsets of homogeneous (1)H NMR spectra that contain specific spectroscopic metabolic signatures related to each biological class in a study. In SHOCSY, we used a clustering method to categorize the whole data set into a number of clusters of samples with each cluster showing a similar spectral feature and hence biochemical composition, and we then used an enrichment test to identify the associations between the clusters and the biological classes in the data set. We evaluated the performance of the SHOCSY algorithm using a simulated (1)H NMR data set to emulate renal tubule toxicity and further exemplified this method with a (1)H NMR spectroscopic study of hydrazine-induced liver toxicity study in rats. The SHOCSY algorithm improved the predictive ability of the orthogonal partial least-squares discriminatory analysis (OPLS-DA) model through the use of "truly" representative samples in each biological class (i.e., homogeneous subsets). This method ensures that the analyses are no longer confounded by idiosyncratic responders and thus improves the reliability of biomarker extraction. SHOCSY is a useful tool for removing irrelevant variation that interfere with the interpretation and predictive ability of models and has widespread applicability to other spectroscopic data, as well as other "omics" type of data

Characterization of metabolic responses to healthy diets and the association with blood pressure: application to the Optimal Macronutrient Intake Trial for Heart Health (OmniHeart), a Randomized Control Study

Background: Inter-individual variation in the response to diet is common but the underlying mechanism for such variation is unclear. Objective: The objective of this study was to use a metabolic profiling approach to identify a panel of urinary metabolites representing individuals demonstrating typical (homogeneous) metabolic responses to healthy diets, and subsequently to define the association of these metabolites with improvement of risk factors for cardiovascular diseases (CVD). Design: 24-h urine samples from 158 participants, with pre-hypertension and stage 1 hypertension collected at baseline and following the consumption of a carbohydrate-rich, a protein-rich and a monounsaturated fat-rich healthy diet (6-weeks per diet) in a randomized, crossover study, were analyzed by proton (1H) nuclear magnetic resonance (NMR) spectroscopy. Urinary metabolite profiles were interrogated to identify typical and variable responses to each diet. We quantified the differences in absolute excretion of metabolites distinguishing between dietary comparisons within the typical response groups and established their associations with CVD risk factors using linear regression. Results: Globally all three diets induced a similar pattern of change in the urinary metabolic profiles for the majority of participants (60.1%). Diet-dependent metabolic variation was not significantly associated with total cholesterol or low density lipoprotein cholesterol levels. However, blood pressure (BP) was found to be significantly associated with six urinary metabolites reflecting: dietary intake (proline-betaine [inverse], carnitine [direct]); gut microbial co-metabolites (hippurate [direct], 4-cresyl sulfate [inverse], phenylacetylglutamine [inverse]), and tryptophan metabolism (N-methyl-2-pyridone-5-carboxamide [inverse]). A dampened clinical response was observed in some individuals with variable metabolic responses, which could be attributed to non-adherence to diet (up to 25.3%), variation in gut microbiome activity (7.6%) or a combination of both (7.0%). Conclusion: These data indicate inter-individual variations in BP in response to dietary change and highlight the potential influence of the gut microbiome in mediating this relationship. This approach provides a framework for stratification of individuals undergoing dietary management

Metabolic phenotyping for discovery of urinary biomarkers of diet, xenobiotics and blood pressure in the INTERMAP Study: an overview

The etiopathogenesis of cardiovascular diseases (CVDs) is multifactorial. Adverse blood pressure (BP) is a major independent risk factor for epidemic CVD affecting ~40% of the adult population worldwide and resulting in significant morbidity and mortality. Metabolic phenotyping of biological fluids has proven its application in characterizing low-molecular-weight metabolites providing novel insights into gene-environmental-gut microbiome interaction in relation to a disease state. In this review, we synthesize key results from the INTERnational study of MAcro/micronutrients and blood Pressure (INTERMAP) Study, a cross-sectional epidemiologic study of 4680 men and women aged 40-59 years from Japan, the People's Republic of China, the United Kingdom and the United States. We describe the advancements we have made regarding the following: (1) analytical techniques for high-throughput metabolic phenotyping; (2) statistical analyses for biomarker identification; (3) discovery of unique food-specific biomarkers; and (4) application of metabolome-wide association studies to gain a better understanding into the molecular mechanisms of cross-cultural and regional BP differences

Development of combined chemometric and NMR spectroscopic methods to enhance molecular biomarker discovery in human populations

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