The role of the pharmacist in the management of type 2 diabetes: current insights and future directions

Type 2 diabetes is a chronic disease occurring in ever increasing numbers worldwide. It contributes significantly to the cost of health globally; however, its management remains in the most part less than optimal. Patients must be empowered to self-manage their disease, and they do this in partnership with health care professionals. Whilst the traditional role of the pharmacist has been centered around the supply of medicines and patient counseling, there is an evergrowing body of evidence that pharmacists, through a range of extended services, may contribute positively to the clinical and humanistic outcomes of those with diabetes. Further, these services can be delivered cost-effectively. This paper provides a review of the current evidence supporting the role of pharmacists in diabetes care, whilst providing a commentary of the future roles of pharmacists in this area

Chemical composition of the essential oil of Ocimum tenuiflorum L. (Krishna Tulsi) from North West Karnataka, India

The chemical composition of the essential oil of flowering aerial parts of Ocimum tenuiflorum L. growing in the North West Karnataka, India, was investigated. The hydro-distilled essential oil was analyzed by gas chromatography equipped with flame ionization detector (GC-FID) and gas chromatography coupled with mass spectrometry (GC/MS). Results demonstrated that the oil was found to be rich in phenyl derivative compounds (83.8%). The major compound was identified as methyl eugenol (82.9%) among twenty-six compounds, comprising 98.9% of the total oil

Long Time Scale Ensemble Methods in Molecular Dynamics: Ligand–Protein Interactions and Allostery in SARS-CoV-2 Targets

We subject a series of five protein-ligand systems which contain important SARS-CoV-2 targets, 3-chymotrypsin-like protease (3CLPro), papain-like protease, and adenosine ribose phosphatase, to long time scale and adaptive sampling molecular dynamics simulations. By performing ensembles of ten or twelve 10 μs simulations for each system, we accurately and reproducibly determine ligand binding sites, both crystallographically resolved and otherwise, thereby discovering binding sites that can be exploited for drug discovery. We also report robust, ensemble-based observation of conformational changes that occur at the main binding site of 3CLPro due to the presence of another ligand at an allosteric binding site explaining the underlying cascade of events responsible for its inhibitory effect. Using our simulations, we have discovered a novel allosteric mechanism of inhibition for a ligand known to bind only at the substrate binding site. Due to the chaotic nature of molecular dynamics trajectories, regardless of their temporal duration individual trajectories do not allow for accurate or reproducible elucidation of macroscopic expectation values. Unprecedentedly at this time scale, we compare the statistical distribution of protein-ligand contact frequencies for these ten/twelve 10 μs trajectories and find that over 90% of trajectories have significantly different contact frequency distributions. Furthermore, using a direct binding free energy calculation protocol, we determine the ligand binding free energies for each of the identified sites using long time scale simulations. The free energies differ by 0.77 to 7.26 kcal/mol across individual trajectories depending on the binding site and the system. We show that, although this is the standard way such quantities are currently reported at long time scale, individual simulations do not yield reliable free energies. Ensembles of independent trajectories are necessary to overcome the aleatoric uncertainty in order to obtain statistically meaningful and reproducible results. Finally, we compare the application of different free energy methods to these systems and discuss their advantages and disadvantages. Our findings here are generally applicable to all molecular dynamics based applications and not confined to the free energy methods used in this study

Avoiding Treatment Interruptions: What Role Do Australian Community Pharmacists Play?

OBJECTIVE: To explore the reported practice of Australian community pharmacists when dealing with medication supply requests in absence of a valid prescription. METHODS: Self-administered questionnaire was posted to 1490 randomly selected community pharmacies across all Australian states and territories. This sample was estimated to be a 20% of all Australian community pharmacies. RESULTS: Three hundred eighty five pharmacists participated in the study (response rate achieved was 27.9% (there were 111 undelivered questionnaires). Respondents indicated that they were more likely to provide medications to regular customers without a valid prescription compared to non-regular customers (p<0.0001). However, supply was also influenced by the type of prescription and the medication requested. In the case of type of prescription (Standard, Authority or Private) this relates to the complexity/probability of obtaining a valid prescription from the prescriber at a later date (i.e. supply with an anticipated prescription). Decisions to supply and/or not supply related to medication type were more complex. For some cases, including medication with potential for abuse, the practice and/or the method of supply varied significantly according to age and gender of the pharmacist, and pharmacy location (p<0.05). CONCLUSIONS: Although being a regular customer does not guarantee a supply, results of this study reinforce the importance for patients having a regular pharmacy, where pharmacists were more likely to continue medication supply in cases of patients presenting without a valid prescription. We would suggest, more flexible legislation should be implemented to allow pharmacists to continue supplying of medication when obtaining a prescription is not practical

Incidence and risk factors of delirium in patients post pancreaticoduodenectomy

AbstractBackgroundPost-operative delirium is an important and common complication of major abdominal surgery characterized by acute confusion with fluctuating consciousness. The aim of this study was to establish the incidence of post-operative delirium in patients undergoing a pancreaticoduodenectomy and to determine the risk factors for its development.MethodsFrom a prospectively maintained database, a retrospective cohort analysis was performed of 50 consecutive patients who underwent a pancreaticoduodenectomy at the National Surgical Centre for Pancreatic Cancer in St. Vincent's University Hospital, Dublin and whose entire post-operative stay was in this institution, between July 2011 and December 2012. Two independent medical practitioners assessed all data and delirium was diagnosed according to criteria of the Diagnostic and Statistical Manual Disorder (DSM), fourth edition. Univariate and multivariate analyses were performed.ResultsSeven patients (14%) developed post-operative delirium. The median onset was on the second post-operative day. Older age was predictive of an increased risk of delirium post-operatively. Those who developed delirium had a significantly increased length of stay (LOS) as well as a significantly increased risk of developing at least a grade 3 complication (Clavien-Dindo classification).ConclusionThis study demonstrates that post-operative delirium is associated with a more complicated recovery after a pancreaticoduodenectomy and that older age is independently predictive of its development. Focused screening may allow targeted preventative strategies to be used in the peri-operative period to reduce complications and costs associated with delirium

Is the pharmacy profession innovative enough?: meeting the needs of Australian residents with chronic conditions and their carers using the nominal group technique

Background Community pharmacies are ideally located as a source of support for people with chronic conditions. Yet, we have limited insight into what innovative pharmacy services would support this consumer group to manage their condition/s. The aim of this study was to identify what innovations people with chronic conditions and their carers want from their ideal community pharmacy, and compare with what pharmacists and pharmacy support staff think consumers want. Methods We elicited ideas using the nominal group technique. Participants included people with chronic conditions, unpaid carers, pharmacists and pharmacy support staff, in four regions of Australia. Themes were identified via thematic analysis using the constant comparison method. Results Fifteen consumer/carer, four pharmacist and two pharmacy support staff groups were conducted. Two overarching themes were identified: extended scope of practice for the pharmacist and new or improved pharmacy services. The most innovative role for Australian pharmacists was medication continuance, within a limited time-frame. Consumers and carers wanted improved access to pharmacists, but this did not necessarily align with a faster or automated dispensing service. Other ideas included streamlined access to prescriptions via medication reminders, electronic prescriptions and a chronic illness card. Conclusions This study provides further support for extending the pharmacist’s role in medication continuance, particularly as it represents the consumer’s voice. How this is done, or the methods used, needs to optimise patient safety. A range of innovative strategies were proposed and Australian community pharmacies should advocate for and implement innovative approaches to improve access and ensure continuity of care

Polymorphisms in the WNK1 gene are asociated with blood pressure variation and urinary potassium excretion

WNK1 - a serine/threonine kinase involved in electrolyte homeostasis and blood pressure (BP) control - is an excellent candidate gene for essential hypertension (EH). We and others have previously reported association between WNK1 and BP variation. Using tag SNPs (tSNPs) that capture 100% of common WNK1 variation in HapMap, we aimed to replicate our findings with BP and to test for association with phenotypes relating to WNK1 function in the British Genetics of Hypertension (BRIGHT) study case-control resource (1700 hypertensive cases and 1700 normotensive controls). We found multiple variants to be associated with systolic blood pressure, SBP (7/28 tSNPs min-p = 0.0005), diastolic blood pressure, DBP (7/28 tSNPs min-p = 0.002) and 24 hour urinary potassium excretion (10/28 tSNPs min-p = 0.0004). Associations with SBP and urine potassium remained significant after correction for multiple testing (p = 0.02 and p = 0.01 respectively). The major allele (A) of rs765250, located in intron 1, demonstrated the strongest evidence for association with SBP, effect size 3.14 mmHg (95%CI:1.23–4.9), DBP 1.9 mmHg (95%CI:0.7–3.2) and hypertension, odds ratio (OR: 1.3 [95%CI: 1.0–1.7]).We genotyped this variant in six independent populations (n = 14,451) and replicated the association between rs765250 and SBP in a meta-analysis (p = 7×10−3, combined with BRIGHT data-set p = 2×10−4, n = 17,851). The associations of WNK1 with DBP and EH were not confirmed. Haplotype analysis revealed striking associations with hypertension and BP variation (global permutation p10 mmHg reduction) and risk for hypertension (OR<0.60). Our data indicates that multiple rare and common WNK1 variants contribute to BP variation and hypertension, and provide compelling evidence to initiate further genetic and functional studies to explore the role of WNK1 in BP regulation and EH

Predicting disease risk areas through co-production of spatial models: the example of Kyasanur Forest Disease in India’s forest landscapes

Zoonotic diseases affect resource-poor tropical communities disproportionately, and are linked to human use and modification of ecosystems. Disentangling the socio-ecological mechanisms by which ecosystem change precipitates impacts of pathogens is critical for predicting disease risk and designing effective intervention strategies. Despite the global “One Health” initiative, predictive models for tropical zoonotic diseases often focus on narrow ranges of risk factors and are rarely scaled to intervention programs and ecosystem use. This study uses a participatory, co-production approach to address this disconnect between science, policy and implementation, by developing more informative disease models for a fatal tick-borne viral haemorrhagic disease, Kyasanur Forest Disease (KFD), that is spreading across degraded forest ecosystems in India. We integrated knowledge across disciplines to identify key risk factors and needs with actors and beneficiaries across the relevant policy sectors, to understand disease patterns and develop decision support tools. Human case locations (2014–2018) and spatial machine learning quantified the relative role of risk factors, including forest cover and loss, host densities and public health access, in driving landscape-scale disease patterns in a long-affected district (Shivamogga, Karnataka State). Models combining forest metrics, livestock densities and elevation accurately predicted spatial patterns in human KFD cases (2014–2018). Consistent with suggestions that KFD is an “ecotonal” disease, landscapes at higher risk for human KFD contained diverse forest-plantation mosaics with high coverage of moist evergreen forest and plantation, high indigenous cattle density, and low coverage of dry deciduous forest. Models predicted new hotspots of outbreaks in 2019, indicating their value for spatial targeting of intervention. Co-production was vital for: gathering outbreak data that reflected locations of exposure in the landscape; better understanding contextual socio-ecological risk factors; and tailoring the spatial grain and outputs to the scale of forest use, and public health interventions. We argue this inter-disciplinary approach to risk prediction is applicable across zoonotic diseases in tropical settings

Bayesian lasso binary quantile regression

In this paper, a Bayesian hierarchical model for variable selection and estimation in the context of binary quantile regression is proposed. Existing approaches to variable selection in a binary classification context are sensitive to outliers, heteroskedasticity or other anomalies of the latent response. The method proposed in this study overcomes these problems in an attractive and straightforward way. A Laplace likelihood and Laplace priors for the regression parameters are proposed and estimated with Bayesian Markov Chain Monte Carlo. The resulting model is equivalent to the frequentist lasso procedure. A conceptional result is that by doing so, the binary regression model is moved from a Gaussian to a full Laplacian framework without sacrificing much computational efficiency. In addition, an efficient Gibbs sampler to estimate the model parameters is proposed that is superior to the Metropolis algorithm that is used in previous studies on Bayesian binary quantile regression. Both the simulation studies and the real data analysis indicate that the proposed method performs well in comparison to the other methods. Moreover, as the base model is binary quantile regression, a much more detailed insight in the effects of the covariates is provided by the approach. An implementation of the lasso procedure for binary quantile regression models is available in the R-package bayesQR