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The effectiveness of community-based participatory research (CBPR) partnerships to address health inequities is well documented. CBPR integrates knowledge and perspectives of diverse communities throughout the research process, following principles that emphasize trust, power sharing, co-learning, and mutual benefits. However, institutions and funders seldom provide the time and resources needed for the critical stage of equitable partnership formation and development.
Since 2011, the Detroit Urban Research Center, collaborating with other entities, has promoted the development of new community–academic research partnerships through two grant programs that combine seed funding with capacity building support from community and academic instructors/mentors experienced in CBPR. Process and outcomes were evaluated using mixed methods.
From 2011 to 2021, 50 partnerships received grants ranging from $2,500 to $30,000, totaling $605,000. Outcomes included equitable partnership infrastructure and processes, innovative pilot research, translation of findings to interventions and policy change, dissemination to multiple audiences, new proposals and projects, and sustained community–academic research partnerships. All partnerships continued beyond the program; over half secured additional funding.
Keys to success included participation as community–academic teams, dedicated time for partnership/relationship development, workshops to develop equity-based skills, relationships, and projects, expert community–academic instructor guidance, and connection to additional resources. Findings demonstrate that small amounts of seed funding for newly forming community–academic partnerships, paired with capacity building support, can provide essential time and resources needed to develop diverse, inclusive, equity-focused CBPR partnerships. Building such support into funding initiatives and through academic institutions can enhance impact and sustainability of translational research toward advancing health equity.
Cancers are fundamentally caused by genomic changes in the cancer cells that lead to their uncontrolled growth (Balmain et al., 2003; Stratton et al., 2009). Understanding these changes, which include DNA copy number alterations, is an intense focus of current research into the causes of, and potential therapies for, every type of cancer. Major research projects, such as the Cancer Genome Atlas (TCGA) project (The Cancer Genome Atlas Research Network, 2008), aim to comprehensively catalog all genomic changes in cancer. This chapter discusses the problem of interpreting copy number data, specifically in the context of cancer research.
To measure copy number, whole-genome genotyping array assays hybridize sample DNA to oligonucleotides deposited on the array. Modern designs use synthetic oligonucleotides to measure copy number at frequent intervals along the genome, especially in regions of known copy number variation. Modern arrays also include many probes that target both alleles of a large number of common single-nucleotide polymorphisms (SNPs). These platforms are therefore widely used in genotyping studies. Array-based assays available for measuring genome-wide copy number include arrays from Illumina, Sentrix, Agilent, and Affymetrix. Data from next-generation sequencing of DNA can also be used to detect copy number alterations and is rapidly becoming cost competitive with array-based platforms.
Molecular inversion probe (MIP) arrays (Wang et al., 2007, 2009; Ji and Welch, 2009) are another platform that can be used for large-scale copy number analysis and genotyping. MIP technology uses less DNA, can handle lower quality DNA, has a greater dynamic range, has higher quality markers, and better separates allelic information than other array-based approaches.
To demonstrate that nosocomial transmission of vancomycin-resistant enterococci (VRE) can be terminated and endemicity prevented despite widespread dissemination of an epidemic strain in a large tertiary-care referral hospital.
Two months after the index case was detected in the intensive care unit, 68 patients became either infected or colonized with an epidemic strain of vanB vancomycin-resistant Enterococcus faecium despite standard infection control procedures. The following additional interventions were then introduced to control the outbreak: (1) formation of a VRE executive group; (2) rapid laboratory identification (30 to 48 hours) using culture and polymerase chain reaction detection of vanA and vanB resistance genes; (3) mass screening of all hospitalized patients with isolation of carriers and cohorting of contacts; (4) environmental screening and increased cleaning; (5) electronic flagging of medical records of contacts; and (6) antibiotic restrictions (third-generation cephalosporins and vancomycin).
A total of 19,658 patient and 24,396 environmental swabs were processed between July and December 2001. One hundred sixty-nine patients in 23 wards were colonized with a single strain of vanB vancomycin-resistant E. faecium. Introducing additional control measures rapidly brought the outbreak under control. Hospital-wide screening found 39 previously unidentified colonized patients, with only 7 more nonsegregat-ed patients being detected in the next 2 months. The outbreak was terminated within 3 months at a cost of $2.7 million (Australian dollars).
Despite widespread dissemination of VRE in a large acute care facility, eradication was achievable by a well-resourced, coordinated, multifaceted approach and was in accordance with good clinical governance.
The Maqarin site, Jordan is being studied as a natural analogue of a cementitious radioactive waste repository. The microbiology has been studied and diverse microbial populations capable of tolerating alkaline pH were detected at all sampling localities. Dissolved organic carbon was identified as the potentially most important reductant with sulphate identified as the main oxidant, both supplying energy for microbial life. Calculations on upper limits of microbial numbers were made with a microbiology code (MGSE) using existing information but the results are overestimates when compared with field observations. This indicates that the model is very conservative and that more information on, for example, carbon sources is required.
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