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This article presents seven challenges of collecting primary (i.e., firsthand) data from commercially sexually exploited children (CSEC). We drew on our research team's experience collecting longitudinal data from 28 CSEC survivors with a 12-month follow-up period. We used both face-to-face and electronic group brainstorming methods to nominate a list of research-related challenges. The two main themes that were identified were challenges that can limit data quality and concerns about the impact of research on participants, researchers, and others. The three challenges related to data quality are (1) the age of the research participants; (2) questions about obtaining informed consent from parents or guardians; and (3) the over-interrogation of CSEC youth. The four challenges related to concerns about the impact of research were (4) concerns that research participation may further exploit youth; (5) staying in the role of researcher and refraining from providing advocacy; (6) secondary trauma and burnout experienced by research staff; and (7) the additional burden that research and data collection may place on the advocates and direct service providers. Because the process of collecting data from CSEC youth can be complicated, and rife with ethical and practical challenges, we have relayed our experiences with seven specific research-related challenges in order to stimulate discourse and further progress in the field.  相似文献   
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The International Agency for Research on Cancer has classified diesel engine exhaust (DEE) as a human lung carcinogen. Given that inflammation is suspected to be an important underlying mechanism of lung carcinogenesis, we evaluated the relationship between DEE exposure and the inflammatory response using data from a cross‐sectional molecular epidemiology study of 41 diesel engine testing workers and 46 unexposed controls. Repeated personal exposure measurements of PM2.5 and other DEE constituents were taken for the diesel engine testing workers before blood collection. Serum levels of six inflammatory biomarkers including interleukin (IL)‐1, IL‐6, IL‐8, tumor necrosis factor (TNF)‐α, macrophage inflammatory protein (MIP)‐1β, and monocyte chemotactic protein (MCP)‐1 were analyzed in all subjects. Compared to unexposed controls, concentrations of MIP‐1β were significantly reduced by ~37% in DEE exposed workers (P < 0.001) and showed a strong decreasing trend with increasing PM2.5 concentrations in all subjects (Ptrend < 0.001) as well as in exposed subjects only (Ptrend = 0.001). Levels of IL‐8 and MIP‐1β were significantly lower in workers in the highest exposure tertile of PM2.5 (>397 µg/m3) compared to unexposed controls. Further, significant inverse exposure‐response relationships for IL‐8 and MCP‐1 were also found in relation to increasing PM2.5 levels among the DEE exposed workers. Given that IL‐8, MIP‐1β, and MCP‐1 are chemokines that play important roles in recruitment of immunocompetent cells for immune defense and tumor cell clearance, the observed lower levels of these markers with increasing PM2.5 exposure may provide insight into the mechanism by which DEE promotes lung cancer. Environ. Mol. Mutagen. 59:144–150, 2018. © 2017 Wiley Periodicals, Inc.  相似文献   
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Bacteria influence site‐specific disease etiology and the host's ability to metabolize xenobiotics, such as polycyclic aromatic hydrocarbons (PAHs). Lung cancer in Xuanwei, China has been attributed to PAH‐rich household air pollution from burning coal. This study seeks to explore the role of lung microbiota in lung cancer among never smoking Xuanwei women and how coal burning may influence these associations. DNA from sputum and buccal samples of never smoking lung cancer cases (n = 8, in duplicate) and controls (n = 8, in duplicate) in two Xuanwei villages was extracted using a multi‐step enzymatic and physical lysis, followed by a standardized clean‐up. V1‐V2 regions of 16S rRNA genes were PCR‐amplified. Purified amplicons were sequenced by 454 FLX Titanium pyrosequencing and high‐quality sequences were evaluated for diversity and taxonomic membership. Bacterial diversity among cases and controls was similar in buccal samples (P = 0.46), but significantly different in sputum samples (P = 0.038). In sputum, Granulicatella (6.1 vs. 2.0%; P = 0.0016), Abiotrophia (1.5 vs. 0.085%; P = 0.0036), and Streptococcus (40.1 vs. 19.8%; P = 0.0142) were enriched in cases compared with controls. Sputum samples had on average 488.25 species‐level OTUs in the flora of cases who used smoky coal (PAH‐rich) compared with 352.5 OTUs among cases who used smokeless coal (PAH‐poor; P = 0.047). These differences were explained by the Bacilli species (Streptococcus infantis and Streptococcus anginosus). Our small study suggests that never smoking lung cancer cases have differing sputum microbiota than controls. Further, bacteria found in sputum may be influenced by environmental exposures associated with the type of coal burned in the home. Environ. Mol. Mutagen. 55:643–651, 2014. © 2014 Wiley Periodicals, Inc.  相似文献   
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We report a patient with generalized smooth muscle hamartoma who presented with many of the variety of congenital anomalies that have been reported in babies with multiple symmetric circumferential rings of folded skin known as Michelin tire baby (MTB) syndrome, but our patient did not show the MTB phenotype. This constellation of findings in the absence of the MTB phenotype has not been previously reported.  相似文献   
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The Mid-South Clinical Data Research Network (CDRN) encompasses three large health systems: (1) Vanderbilt Health System (VU) with electronic medical records for over 2 million patients, (2) the Vanderbilt Healthcare Affiliated Network (VHAN) which currently includes over 40 hospitals, hundreds of ambulatory practices, and over 3 million patients in the Mid-South, and (3) Greenway Medical Technologies, with access to 24 million patients nationally. Initial goals of the Mid-South CDRN include: (1) expansion of our VU data network to include the VHAN and Greenway systems, (2) developing data integration/interoperability across the three systems, (3) improving our current tools for extracting clinical data, (4) optimization of tools for collection of patient-reported data, and (5) expansion of clinical decision support. By 18 months, we anticipate our CDRN will robustly support projects in comparative effectiveness research, pragmatic clinical trials, and other key research areas and have the capacity to share data and health information technology tools nationally.  相似文献   
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