Antibiotic repeat prescriptions: are patients not re-filling them properly?

Objective: This study aimed to explore patients’ utilization of repeat prescriptions for antibiotics indicated in upper respiratory tract infections (URTI). An emphasis was placed on whether the current system of repeat prescriptions contributes to patients self-diagnosing infections and if so, identify the common reasons for this. Methods: This is a prospective study of self-reported use of repeat antibiotic prescriptions by pharmacy consumers presenting with repeat prescriptions for antibiotics commonly indicated in URTIs. Data were collected via self-completed surveys in Perth metropolitan pharmacies. Results: A total of 123 respondents participated in this study from 19 Perth metropolitan pharmacies. Of the respondents, approximately a third of them (33.9%) presented to the pharmacy to fill their antibiotic repeat prescription one month or more from the time the original prescription was written (i.e. time when original diagnosis was made by a doctor). Over two thirds of respondents indicated to not have consulted their doctor prior to presenting to the pharmacy to have their antibiotic repeat prescription dispensed (i.e. 68.3%). The most common reasons for this were that their ‘doctor had told them to take the second course’ (38%), followed by potential self-diagnosis (29%), i.e. ‘they had the same symptoms as the last time they took the antibiotics’. Approximately one third (33.1%) of respondents indicated they ‘were not told what the repeat prescription was needed for’ when they were originally prescribed the antibiotic. Respondents who presented to fill their repeat prescription more than 2 weeks after the original prescription written were more likely not have consulted their doctor (p = 0.006, 95% CI [1.16, 2.01]) and not to know why their repeat was needed (p = 0.010, 95% CI [1.07,2.18]).Conclusions: Findings of this study suggested that the current 12 month validity of antibiotics repeat prescriptions is potentially contributing to patients’ self-diagnosis of URTIs and therefore potential misuse of antibiotics. This may be contributing to the rise of antimicrobial resistance. The study also outlines some common reasons for patients potentially self-diagnosing URTIs when using repeat prescriptions. Larger Australian studies are needed to confirm these findings

Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients

Background: Volume of distribution is an important pharmacokinetic property that indicates the extent of a drug's distribution in the body tissues. This paper addresses the problem of how to estimate the apparent volume of distribution at steady state (Vss) of chemical compounds in the human body using decision tree-based regression methods from the area of data mining (or machine learning). Hence, the pros and cons of several different types of decision tree-based regression methods have been discussed. The regression methods predict Vss using, as predictive features, both the compounds' molecular descriptors and the compounds' tissue:plasma partition coefficients (Kt:p) - often used in physiologically-based pharmacokinetics. Therefore, this work has assessed whether the data mining-based prediction of Vss can be made more accurate by using as input not only the compounds' molecular descriptors but also (a subset of) their predicted Kt:p values. Results: Comparison of the models that used only molecular descriptors, in particular, the Bagging decision tree (mean fold error of 2.33), with those employing predicted Kt:p values in addition to the molecular descriptors, such as the Bagging decision tree using adipose Kt:p (mean fold error of 2.29), indicated that the use of predicted Kt:p values as descriptors may be beneficial for accurate prediction of Vss using decision trees if prior feature selection is applied. Conclusions: Decision tree based models presented in this work have an accuracy that is reasonable and similar to the accuracy of reported Vss inter-species extrapolations in the literature. The estimation of Vss for new compounds in drug discovery will benefit from methods that are able to integrate large and varied sources of data and flexible non-linear data mining methods such as decision trees, which can produce interpretable models. Figure not available: see fulltext. © 2015 Freitas et al.; licensee Springer

Gender Differences in Aspirin use Among Adults With Coronary Heart Disease in the United States

BACKGROUND: Aspirin reduces mortality for men and women with coronary heart disease (CHD). Previous research suggests women with acute coronary syndromes receive less aggressive care, including less frequent early administration of aspirin. The presence of gender differences in aspirin use for secondary prevention is less clear. OBJECTIVE: To determine if a gender difference exists in the use of aspirin for secondary prevention among individuals with CHD. DESIGN: We analyzed data from the nationally representative 2000–2002 Medical Expenditure Panel Surveys to determine the prevalence of regular aspirin use among men and women with CHD. PARTICIPANTS: Participants, 1,869, 40 years and older who reported CHD or prior myocardial infarction. RESULTS: Women were less likely than men to use aspirin regularly (62.4% vs 75.6%, p < .001) even after adjusting for demographic, socioeconomic and clinical characteristics (adjusted OR = 0.62, 95% CI, 0.48–0.79). This difference narrowed but remained significant when the analysis was limited to those without self-reported contraindications to aspirin (79.8% vs 86.4%, P = .002, adjusted OR = 0.68, 95% CI, 0.48–0.97). Women were more likely than men to report contraindications (20.5% vs 12.5%, P < .001). Differences in aspirin use were greater between women and men with private health insurance (61.8% vs 79.0%, P < .001, adjusted OR = 0.48, 95% CI, 0.35–0.67) than among those with public coverage (62.5% vs 70.7%, P = .04, adjusted OR = 0.74, 95% CI, 0.50–1.11) (P < .001 for gender–insurance interaction). CONCLUSION: We found a gender difference in aspirin use among patients with CHD not fully explained by differences in patient characteristics or reported contraindications. These findings suggest a need for improved secondary prevention of cardiovascular events for women with CHD

Dopamine Modulates the Rest Period Length without Perturbation of Its Power Law Distribution in Drosophila melanogaster

We analyzed the effects of dopamine signaling on the temporal organization of rest and activity in Drosophila melanogaster. Locomotor behaviors were recorded using a video-monitoring system, and the amounts of movements were quantified by using an image processing program. We, first, confirmed that rest bout durations followed long-tailed (i.e., power-law) distributions, whereas activity bout durations did not with a strict method described by Clauset et al. We also studied the effects of circadian rhythm and ambient temperature on rest bouts and activity bouts. The fraction of activity significantly increased during subjective day and at high temperature, but the power-law exponent of the rest bout distribution was not affected. The reduction in rest was realized by reduction in long rest bouts. The distribution of activity bouts did not change drastically under the above mentioned conditions. We then assessed the effects of dopamine. The distribution of rest bouts became less long-tailed and the time spent in activity significantly increased after the augmentation of dopamine signaling. Administration of a dopamine biosynthesis inhibitor yielded the opposite effects. However, the distribution of activity bouts did not contribute to the changes. These results suggest that the modulation of locomotor behavior by dopamine is predominantly controlled by changing the duration of rest bouts, rather than the duration of activity bouts

Influence of metals and metalloids on the composition and fluorescence quenching of the extracellular polymeric substances produced by the polymorphic fungus <i>Aureobasidium pullulans</i>

Aureobasidium pullulansis a ubiquitous and widely distributed fungus in the environment, and exhibits substantial tolerance against toxic metals. However, the interactions between metals and metalloids with the copious extracellular polymeric substances (EPS) produced byA. pullulansand possible relationships to tolerance are not well understood. In this study, it was found that mercury (Hg) and selenium (Se), as selenite, not only significantly inhibited growth ofA. pullulansbut also affected the composition of produced EPS. Lead (Pb) showed little influence on EPS yield or composition. The interactions of EPS fromA. pullulanswith the tested metals and metalloids depended on the specific element and their concentration. Fluorescence intensity measurements of the EPS showed that the presence of metal(loid)s stimulated the production of extracellular tryptophan-like and aromatic protein-like substances. Examination of fluorescence quenching and calculation of binding constants revealed that the fluorescence quenching process for Hg; arsenic (As), as arsenite; and Pb to EPS were mainly governed by static quenching which resulted in the formation of a stable non-fluorescent complexes between the EPS and metal(loid)s. Se showed no significant interaction with the EPS according to fluorescence quenching. These results provide further understanding of the interactions between metals and metalloids and EPS produced by fungi and their contribution to metal(loid) tolerance

Splitting or lumping? A conservation dilemma exemplified by the critically endangered Dama Gazelle (Nanger dama)

Managers of threatened species often face the dilemma of whether to keep populations separate to conserve local adaptations and minimize the risk of outbreeding, or whether to manage populations jointly to reduce loss of genetic diversity and minimise inbreeding. In this study we examine genetic relatedness and diversity in three of the five last remaining wild populations of dama gazelle and a number of captive populations, using mtDNA control region and cytochrome b data. Despite the sampled populations belonging to the three putative subspecies, which are delineated according to phenotypes and geographical location, we find limited evidence for phylogeographical structure within the data and no genetic support for the putative subspecies. In the light of these data we discuss the relevance of inbreeding depression, outbreeding depression, adaptive variation, genetic drift, and phenotypic variation to the conservation of the dama gazelle and make some recommendations for its future conservation management. The genetic data suggest that the best conservation approach is to view the dama gazelle as a single species without subspecific divisions

Overexpression of c-erbB2 is an independent marker of resistance to endocrine therapy in advanced breast cancer

The present study investigated the interaction between c-erbB2 overexpression and the response to first-line endocrine therapy in patients with advanced breast cancer. The primary tumours of 241 patients who were treated at first relapse with endocrine therapy were assessed for overexpression of c-erbB2 by immunohistochemistry. c-erbB2 was overexpressed in 76 (32%) of primary breast cancers and did not correlate with any other prognostic factor. The overall response to treatment and time to progression were significantly lower in patients with c-erbB2-positive tumours compared to those that were c-erbB2-negative (38% vs 56%, P = 0.02; and 4.1 months vs 8.7 months, P < 0.001, respectively). In multivariate analysis, c-erbB2 status was the most significant predictive factor for a short time to progression (P = 0.0009). In patients with ER-positive primary tumours treated at relapse with tamoxifen (n = 170), overexpression of c-erbB2 was associated with a significantly shorter time to progression (5.5 months vs 11.2 months, P < 0.001). In conclusion, overexpression of c-erbB2 in the primary tumour is an independent marker of relative resistance to first-line endocrine therapy in patients with advanced breast cancer. In patients with ER-positive primary tumours, the overexpression of c-erbB2 defines a subgroup less likely to respond to endocrine therapy. © 1999 Cancer Research Campaig

The Deep Dementia Phenotyping (DEMON) Network: A global platform for innovation using data science and artificial intelligence.

This is the final version. Available from Wiley via the DOI in this record. BACKGROUND: The increasing availability of large high-dimensional data from experimental medicine, population-based and clinical cohorts, clinical trials, and electronic health records has the potential to transform dementia research. Our ability to make best use of this rich data will depend on utilisation of advanced machine learning and artificial intelligence (AI) techniques and collaboration across disciplinary and geographic boundaries. METHOD: The Deep Dementia Phenotyping (DEMON) Network launched in 20191 to support the growing interest in machine learning and AI. Led by Director Prof David Llewellyn and Deputy Director Dr Janice Ranson, the leadership team additionally includes 5 Theme Leads and 14 Working Group Leads, supported by an international Steering Committee of world-leading academics. Core funding is provided by Alzheimer's Research UK, the Alan Turing Institute and the University of Exeter, with additional support from strategic partners including the UK Dementia Research Institute and the Alzheimer's Society. Grand Challenges were established at a National Strategy Workshop in June 2020. Multidisciplinary Working Groups were formed to coordinate practical activities in seven key areas: Genetics and omics, experimental medicine, drug discovery and trials optimisation, biomarkers, imaging, dementia prevention, and applied models and digital health. Additional Special Interest Groups coordinate topic specific collaborations. RESULT: Membership on 4th February 2022 comprised 1,321 individuals from 61 countries across 6 continents (see Figure). Areas of expertise include dementia research (904; 68%), data science (692; 52%), clinical practice (244; 18%), industry (162; 12%), and regulation (26; 2%). Individual membership is free, and regular knowledge transfer events are provided including a monthly seminar series, talks and workshops, training, networking, and early career development. Each Working Group meets monthly, with multiple grants, reviews, and original research articles in progress. Eight state of the science position papers are in preparation, resulting from a Symposium held in April 2021. In January 2022, 110 early career researchers participated in the Network's flagship event 'NEUROHACK', a 4-day competitive global hackathon, with pilot grants awarded to those generating the most innovative solutions. CONCLUSION: The DEMON Network is a rapidly growing global platform for innovation that is supporting the global dementia research community to collaborate. Find out more at demondementia.com