Sequence- and Interactome-Based Prediction of Viral Protein Hotspots Targeting Host Proteins: A Case Study for HIV Nef

Virus proteins alter protein pathways of the host toward the synthesis of viral particles by breaking and making edges via binding to host proteins. In this study, we developed a computational approach to predict viral sequence hotspots for binding to host proteins based on sequences of viral and host proteins and literature-curated virus-host protein interactome data. We use a motif discovery algorithm repeatedly on collections of sequences of viral proteins and immediate binding partners of their host targets and choose only those motifs that are conserved on viral sequences and highly statistically enriched among binding partners of virus protein targeted host proteins. Our results match experimental data on binding sites of Nef to host proteins such as MAPK1, VAV1, LCK, HCK, HLA-A, CD4, FYN, and GNB2L1 with high statistical significance but is a poor predictor of Nef binding sites on highly flexible, hoop-like regions. Predicted hotspots recapture CD8 cell epitopes of HIV Nef highlighting their importance in modulating virus-host interactions. Host proteins potentially targeted or outcompeted by Nef appear crowding the T cell receptor, natural killer cell mediated cytotoxicity, and neurotrophin signaling pathways. Scanning of HIV Nef motifs on multiple alignments of hepatitis C protein NS5A produces results consistent with literature, indicating the potential value of the hotspot discovery in advancing our understanding of virus-host crosstalk

Pharmacist charting in the preadmission clinic of a Sydney teaching hospital: A pilot study

© 2017 The Society of Hospital Pharmacists of Australia. Aim: To trial a pharmacist charting service, comprising medication charting, in the preadmission clinic (PAC) at aSydney teaching hospital. Methods: A prospective pre/post-trial was conducted comprising a 1-month baseline audit and a 1-month trial of pharmacist charting (i.e. pharmacists’ preparation of patients’ medication charts during routine consultations). Purpose-designed data collection forms were used to document: pharmacist and doctor consultation times, time taken by pharmacists to prepare medication charts, and completeness and accuracy of the prescribed medication charts. A semi-structured survey was used to elicit feedback from PAC staff regarding the pharmacist charting service; the data were thematically analysed using manual, inductive coding. Results: Seventy-two medication charts were completed by PAC pharmacists during the 1-month trial. Completeness of charts improved post-intervention (5.4 vs 80.6%, p < 0.001), as did the accuracy of charts (proportion of charts with inaccuracies:41.1 vs 1.4%,p < 0.001); only one (1.4%) pharmacist-prescribed medication chart was identified as having an inaccuracy. Thechanges in mean consultation times per patient for doctors and pharmacists, respectively, changed from pre- to post-intervention as follows: pharmacists 18.9 ± 6.5 min to 20.6 ± 8.3 min (p = NS); and doctors 25.0 ± 9.6 min to 19.0 ± 6.4 min(p < 0.001). A statistically significant relationship was found between pharmacist consultation time and patients’ numbers of medications (p < 0.001) and age group (p = 0.004). Conclusion: Pharmacist charting in the PAC has been shown to improve medication chart completeness and accuracy, helping to ensure medication safety in the hospital setting. A further, long-term trial will help confirm the clinical benefits of such a service

Implementing a pharmacist charting service in the pre-admission clinic

Aim: To implement a pharmacist charting service in the preadmission clinic (PAC) and describe a preparatory process for the initiation of this service. Method: Stage 1: a proposal for a pharmacist charting service in the PAC was devised via an iterative process with an advisory panel. Stage 2: feedback on the proposal was obtained from key staff via a survey comprising 15 linear scale statements (0 = strongly disagree to 10 = strongly agree) and open-ended questions. Stage 3: baseline data were collected on existing PAC service outcomes, e.g. consultation times, accuracy of medication charts. Results: Stage 1: a service protocol was developed following positive feedback. Stage 2: most staff strongly agreed that a pharmacist charting service would improve the efficiency and workflow in the PAC (median 8; n = 19) and that PAC pharmacists were competent and skilled to chart medications (median 9). Pharmacists perceived that the proposed service would increase their workload (median 10), consultation times (median 9) and medicolegal responsibilities (median 7) while the opposite was reported by the doctors (median 2, 2, and 3, respectively). Stage 3: mean baseline consultation times for pharmacists and doctors were 18.6 and 25 minutes, respectively. Most (95%) of the 56 analysed medication charts were incomplete (at least 1 piece of information missing) while 41% had at least one or more inaccuracies. Conclusion: There are benefits of a pharmacist charting service. This is the first step toward exploring models for pharmacist prescribing