Interprofessional Student Hotspotting Project

Background: IPE and Practice-based Learning It is well established that there is a need for Interprofessional Education (IPE) in the current landscape of health education1 and students that participate in IPE enjoy these experiences and develop skills in interprofessional teamwork that prepare them for future team-based practice.2,3,4 IPE, however, is not standardized. It ranges from programs that teach theoretical frameworks in a classroom setting to others that allow interprofessional teams of students to work together in a clinical setting.5,6 The second form is referred to as practice-based learning (PrBL).3 PrBL is now considered by experts as one of the most effective teaching methods.3,6 Unfortunately, due to the lack of consistency of IPE programs and the lack of standardization in IPE research, there is inconsistency in data regarding the best IPE method.6,

Daptomycin Non-Susceptible MRSA Bacteremia: A Case Report

Background Staphylococcus aureus1, 2, 3 One of the most common pathogens causing community-acquired and nosocomial infections Has rapidly developed resistance to many antibiotics: Daptomycin 2 Bactericidal cyclic lipopeptide antibiotic Possesses negative charge which attracts calcium to form cationic complex Interacts with negatively charged phospholipid heads on bacterial cell membranes, leading to membrane depolarization and cell death Daptomycin non-susceptible (DNS) S. aureus 2, 4, 5 Extremely rare - About 60 clinical cases reported Defined by an MIC greater than 1 mcg/mL Potential mechanisms include: – Changes in cell membrane and cell wall structure alter daptomycin’s permeability2 Overexpression and dysregulation of dltA transcription increases D-alanylated teichoic acid content in the cell wall mprF mutation leads to partially neutral charge of cell membrane – Vancomycin intermediate S. aureus (VISA) and vancomycin resistant S. aureus (VRSA) may predispose patients to develop DNS S. aureus2 Have seen increased resistance with lower doses4, 5 – 4 to 6 mg/kg/day has higher rates of DNS S. aureus – Experts recommend doses ≥ 8mg/kg/day especially for bacteremiahttps://jdc.jefferson.edu/pharmacyposters/1000/thumbnail.jp

Development of a method for generating SNP interaction-aware polygenic risk scores for radiotherapy toxicity

Aim: To identify the effect of single nucleotide polymorphism (SNP) interactions on the risk of toxicity following radiotherapy (RT) for prostate cancer (PCa) and propose a new method for polygenic risk score incorporating SNP-SNP interactions (PRSi). Materials and methods: Analysis included the REQUITE PCa cohort that received external beam RT and was followed for 2 years. Late toxicity endpoints were: rectal bleeding, urinary frequency, haematuria, nocturia, decreased urinary stream. Among 43 literature-identified SNPs, the 30% most strongly associated with each toxicity were tested. SNP-SNP combinations (named SNP-allele sets) seen in >= 10% of the cohort were condensed into risk (RS) and protection (PS) scores, respectively indicating increased or decreased toxicity risk. Performance of RS and PS was evaluated by logistic regression. RS and PS were then combined into a single PRSi evaluated by area under the receiver operating characteristic curve (AUC). Results: Among 1,387 analysed patients, toxicity rates were 11.7% (rectal bleeding), 4.0% (urinary frequency), 5.5% (haematuria), 7.8% (nocturia) and 17.1% (decreased urinary stream). RS and PS combined 8 to 15 different SNP-allele sets, depending on the toxicity endpoint. Distributions of PRSi differed significantly in patients with/without toxicity with AUCs ranging from 0.61 to 0.78. PRSi was better than the classical summed PRS, particularly for the urinary frequency, haematuria and decreased urinary stream endpoints. Conclusions: Our method incorporates SNP-SNP interactions when calculating PRS for radiotherapy toxicity. Our approach is better than classical summation in discriminating patients with toxicity and should enable incorporating genetic information to improve normal tissue complication probability models. (C) 2021 The Authors. Published by Elsevier B.V