Improving Blood Donor Diversity Through Focused Recruitment Interventions

Study Design: Beginning in May 2016, the Jefferson Blood Donor Center began collecting donor self-identified race/ethnicity: White, Black or African American, Hispanic, Asian or Pacific Islander, American Indian or Alaskan Native, Multiracial, Other, Unknown (Figure 1). We retrospectively quantified the racial/ethnic groups represented in each month’s donor population. In January 2017,the following intervention strategies were implemented: Emailing donors who self-identified as part of a racial/ethnic minority group Contacting racially/ethnically-focused student groups to organize blood drives with the Jefferson Blood Donor Center Partnering with the Jefferson Medical Oncology Society MarrowthonDrive to encourage blood donations Presentation to the local chapter of the National Association of Hispanic Nurses Interventions still to come Featuring the Jefferson Blood Donor Center in the Office of Diversity and Inclusion’s Diversity Newsletter The quantification of racial/ethnic groups were stratified to pre-intervention months and post-intervention months. Poster presented at Thomas Jefferson University Hospital Housestaff Quality Improvement and Patient Safety conference.https://jdc.jefferson.edu/patientsafetyposters/1038/thumbnail.jp

Campus PRISM: A Report on Promoting Restorative Initiatives for Sexual Misconduct on College Campuses

The Campus PRISM Project (Promoting Restorative Initiatives for Sexual Misconduct) includes an international team of researchers and practitioners who are deeply invested in reducing sexual and gender-based violence by exploring how a restorative approach may provide more healing and better accountability

Point-of-care versus central testing of hemoglobin during large volume blood transfusion.

BACKGROUND: Point-of-care (POC) hemoglobin testing has the potential to revolutionize massive transfusion strategies. No prior studies have compared POC and central laboratory testing of hemoglobin in patients undergoing massive transfusions. METHODS: We retrospectively compared the results of our point-of-care hemoglobin test (EPOC®) to our core laboratory complete blood count (CBC) hemoglobin test (Sysmex XE-5000™) in patients undergoing massive transfusion protocols (MTP) for hemorrhage. One hundred seventy paired samples from 90 patients for whom MTP was activated were collected at a single, tertiary care hospital between 10/2011 and 10/2017. Patients had both an EPOC® and CBC hemoglobin performed within 30 min of each other during the MTP. We assessed the accuracy of EPOC® hemoglobin testing using two variables: interchangeability and clinically significant differences from the CBC. The Clinical Laboratory Improvement Amendments (CLIA) proficiency testing criteria defined interchangeability for measurements. Clinically significant differences between the tests were defined by an expert panel. We examined whether these relationships changed as a function of the hemoglobin measured by the EPOC® and specific patient characteristics. RESULTS: Fifty one percent (86 of 170) of paired samples\u27 hemoglobin results had an absolute difference of ≤7 and 73% (124 of 170) fell within ±1 g/dL of each other. The mean difference between EPOC® and CBC hemoglobin had a bias of - 0.268 g/dL (p = 0.002). When the EPOC® hemoglobin was \u3c 7 g/dL, 30% of the hemoglobin values were within ±7, and 57% were within ±1 g/dL. When the measured EPOC® hemoglobin was ≥7 g/dL, 55% of the EPOC® and CBC hemoglobin values were within ±7, and 76% were within ±1 g/dL. EPOC® and CBC hemoglobin values that were within ±1 g/dL varied by patient population: 77% for cardiac surgery, 58% for general surgery, and 72% for non-surgical patients. CONCLUSIONS: The EPOC® device had minor negative bias, was not interchangeable with the CBC hemoglobin, and was less reliable when the EPOC® value was \u3c 7 g/dL. Clinicians must consider speed versus accuracy, and should check a CBC within 30 min as confirmation when the EPOC® hemoglobin is \u3c 7 g/dL until further prospective trials are performed in this population

Building an Online Learning Community for Technology Integration in Education

Our professional learning community (PLC), or the Technology Integration Learning Community (TILC), consists of nine professors from the Fischler College of Education at Nova Southeastern University who embody a wide range of knowledge and skills related to instruction, research, and technology. Our TILC provides a supportive, collaborative, safe, and non-judgmental environment for sharing that knowledge (and questions) about technology tools and ideas that can be used to enhance both instruction and learning. Through a self-study, the TILC developed a framework for members to improve their own effectiveness when working with students enrolled in their courses at both the graduate and undergraduate levels

MetNet: Software to Build and Model the Biogenetic Lattice of Arabidopsis

MetNet (http://www.botany.iastate.edu/∼mash/metnetex/metabolicnetex.html) is publicly available software in development for analysis of genome-wide RNA, protein and metabolite profiling data. The software is designed to enable the biologist to visualize, statistically analyse and model a metabolic and regulatory network map of Arabidopsis, combined with gene expression profiling data. It contains a JAVA interface to an interactions database (MetNetDB) containing information on regulatory and metabolic interactions derived from a combination of web databases (TAIR, KEGG, BRENDA) and input from biologists in their area of expertise. FCModeler captures input from MetNetDB in a graphical form. Sub-networks can be identified and interpreted using simple fuzzy cognitive maps. FCModeler is intended to develop and evaluate hypotheses, and provide a modelling framework for assessing the large amounts of data captured by high-throughput gene expression experiments. FCModeler and MetNetDB are currently being extended to three-dimensional virtual reality display. The MetNet map, together with gene expression data, can be viewed using multivariate graphics tools in GGobi linked with the data analytic tools in R. Users can highlight different parts of the metabolic network and see the relevant expression data highlighted in other data plots. Multi-dimensional expression data can be rotated through different dimensions. Statistical analysis can be computed alongside the visual. MetNet is designed to provide a framework for the formulation of testable hypotheses regarding the function of specific genes, and in the long term provide the basis for identification of metabolic and regulatory networks that control plant composition and development

Eliminating Unnecessary Premedications before Outpatient Transfusions

Aims for Improvement Our aim is to eliminate premedication prior to outpatient transfusions in patients without a prior transfusion reaction by 75% within 1 year

Predicting global distributions of eukaryotic plankton communities from satellite data

プランクトンを宇宙から観測する --衛星データを入力データとする海洋真核微生物群集予測モデルの開発--. 京都大学プレスリリース. 2023-10-19.Satellite remote sensing is a powerful tool to monitor the global dynamics of marine plankton. Previous research has focused on developing models to predict the size or taxonomic groups of phytoplankton. Here, we present an approach to identify community types from a global plankton network that includes phytoplankton and heterotrophic protists and to predict their biogeography using global satellite observations. Six plankton community types were identified from a co-occurrence network inferred using a novel rDNA 18 S V4 planetary-scale eukaryotic metabarcoding dataset. Machine learning techniques were then applied to construct a model that predicted these community types from satellite data. The model showed an overall 67% accuracy in the prediction of the community types. The prediction using 17 satellite-derived parameters showed better performance than that using only temperature and/or the concentration of chlorophyll a. The constructed model predicted the global spatiotemporal distribution of community types over 19 years. The predicted distributions exhibited strong seasonal changes in community types in the subarctic–subtropical boundary regions, which were consistent with previous field observations. The model also identified the long-term trends in the distribution of community types, which suggested responses to ocean warming

High Expression Levels of Total IGF-1R and Sensitivity of NSCLC Cells In Vitro to an Anti-IGF-1R Antibody (R1507)

© 2009 Gong et al

Evidence synthesis as the basis for decision analysis: a method of selecting the best agricultural practices for multiple ecosystem services

Agricultural management practices have impacts not only on crops and livestock, but also on soil, water, wildlife, and ecosystem services. Agricultural research provides evidence about these impacts, but it is unclear how this evidence should be used to make decisions. Two methods are widely used in decision making: evidence synthesis and decision analysis. However, a system of evidence-based decision making that integrates these two methods has not yet been established. Moreover, the standard methods of evidence synthesis have a narrow focus (e.g., the effects of one management practice), but the standard methods of decision analysis have a wide focus (e.g., the comparative effectiveness of multiple management practices). Thus, there is a mismatch between the outputs from evidence synthesis and the inputs that are needed for decision analysis. We show how evidence for a wide range of agricultural practices can be reviewed and summarized simultaneously (“subject-wide evidence synthesis”), and how this evidence can be assessed by experts and used for decision making (“multiple-criteria decision analysis”). We show how these methods could be used by The Nature Conservancy (TNC) in California to select the best management practices for multiple ecosystem services in Mediterranean-type farmland and rangeland, based on a subject-wide evidence synthesis that was published by Conservation Evidence (www.conservationevidence.com). This method of “evidence-based decision analysis” could be used at different scales, from the local scale (farmers deciding which practices to adopt) to the national or international scale (policy makers deciding which practices to support through agricultural subsidies or other payments for ecosystem services). We discuss the strengths and weaknesses of this method, and we suggest some general principles for improving evidence synthesis as the basis for multi-criteria decision analysis

Relations between lipoprotein(a) concentrations, LPA genetic variants, and the risk of mortality in patients with established coronary heart disease: a molecular and genetic association study

Background: Lipoprotein(a) concentrations in plasma are associated with cardiovascular risk in the general population. Whether lipoprotein(a) concentrations or LPA genetic variants predict long-term mortality in patients with established coronary heart disease remains less clear. Methods: We obtained data from 3313 patients with established coronary heart disease in the Ludwigshafen Risk and Cardiovascular Health (LURIC) study. We tested associations of tertiles of lipoprotein(a) concentration in plasma and two LPA single-nucleotide polymorphisms ([SNPs] rs10455872 and rs3798220) with all-cause mortality and cardiovascular mortality by Cox regression analysis and with severity of disease by generalised linear modelling, with and without adjustment for age, sex, diabetes diagnosis, systolic blood pressure, BMI, smoking status, estimated glomerular filtration rate, LDL-cholesterol concentration, and use of lipid-lowering therapy. Results for plasma lipoprotein(a) concentrations were validated in five independent studies involving 10 195 patients with established coronary heart disease. Results for genetic associations were replicated through large-scale collaborative analysis in the GENIUS-CHD consortium, comprising 106 353 patients with established coronary heart disease and 19 332 deaths in 22 studies or cohorts. Findings: The median follow-up was 9·9 years. Increased severity of coronary heart disease was associated with lipoprotein(a) concentrations in plasma in the highest tertile (adjusted hazard radio [HR] 1·44, 95% CI 1·14–1·83) and the presence of either LPA SNP (1·88, 1·40–2·53). No associations were found in LURIC with all-cause mortality (highest tertile of lipoprotein(a) concentration in plasma 0·95, 0·81–1·11 and either LPA SNP 1·10, 0·92–1·31) or cardiovascular mortality (0·99, 0·81–1·2 and 1·13, 0·90–1·40, respectively) or in the validation studies. Interpretation: In patients with prevalent coronary heart disease, lipoprotein(a) concentrations and genetic variants showed no associations with mortality. We conclude that these variables are not useful risk factors to measure to predict progression to death after coronary heart disease is established. Funding: Seventh Framework Programme for Research and Technical Development (AtheroRemo and RiskyCAD), INTERREG IV Oberrhein Programme, Deutsche Nierenstiftung, Else-Kroener Fresenius Foundation, Deutsche Stiftung für Herzforschung, Deutsche Forschungsgemeinschaft, Saarland University, German Federal Ministry of Education and Research, Willy Robert Pitzer Foundation, and Waldburg-Zeil Clinics Isny