Epstein-Barr Virus (EBV) establishes a long-term latent infection and is associated with a number of human malignancies that are thought to arise from deregulation of different stages of the viral life cycle. Recently, a large number of microRNAs (miRNAs) have been described for EBV, and it has been suggested that their expression may vary between the different latency states found in normal and malignant tissue. To date, however, no technique has been utilized to comprehensively and quantitatively test this idea by profiling expression of the EBV miRNAs in primary infected tissues. We describe here a multiplex reverse transcription-PCR assay that allows the profiling of 39 of the 40 known mature EBV miRNAs from as little as 250 ng of RNA. With this approach, we present a comprehensive profile of EBV miRNAs in primary nasopharyngeal carcinoma (NPC) tumors including estimates of miRNA copy number per tumor cell. This is the first comprehensive profiling of EBV miRNAs in any EBV-associated tumor. In contrast to previous suggestions, we show that the BART-derived miRNAs are present in a wide range of copy numbers from ≤10 ³ per cell in both primary tumors and the widely used NPC-derived C666-1 cell line. However, we confirm the hypothesis that the BHRF1 miRNAs are not expressed in NPC. Lastly, we demonstrate that EBV miRNA expression in the widely used NPC line C666-1 is, with some caveats, broadly representative of primary NPC tumors.

Background Patients routinely use Twitter to share feedback about their experience receiving healthcare. Identifying and analysing the content of posts sent to hospitals may provide a novel real-time measure of quality, supplementing traditional, survey-based approaches. Objective To assess the use of Twitter as a supplemental data stream for measuring patient-perceived quality of care in US hospitals and compare patient sentiments about hospitals with established quality measures. Design 404 065 tweets directed to 2349 US hospitals over a 1-year period were classified as having to do with patient experience using a machine learning approach. Sentiment was calculated for these tweets using natural language processing. 11 602 tweets were manually categorised into patient experience topics. Finally, hospitals with ≥50 patient experience tweets were surveyed to understand how they use Twitter to interact with patients. Key results Roughly half of the hospitals in the US have a presence on Twitter. Of the tweets directed toward these hospitals, 34 725 (9.4%) were related to patient experience and covered diverse topics. Analyses limited to hospitals with ≥50 patient experience tweets revealed that they were more active on Twitter, more likely to be below the national median of Medicare patients (p<0.001) and above the national median for nurse/patient ratio (p=0.006), and to be a non-profit hospital (p<0.001). After adjusting for hospital characteristics, we found that Twitter sentiment was not associated with Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) ratings (but having a Twitter account was), although there was a weak association with 30-day hospital readmission rates (p=0.003). Conclusions Tweets describing patient experiences in hospitals cover a wide range of patient care aspects and can be identified using automated approaches. These tweets represent a potentially untapped indicator of quality and may be valuable to patients, researchers, policy makers and hospital administrators.

A multidisciplinary journal that focuses on public health and technology, public health informatics, mass media campaigns, surveillance, and innovation in public health practice and research. Also dedicated to rapid open data sharing during epidemics.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet