Epstein-Barr virus (EBV) has been recently found to generate novel circular RNAs (circRNAs) through backsplicing. However, comprehensive catalogs of EBV circRNAs in other cell lines and their functional characterization are still lacking. In this study, we have identified a list of putative EBV circRNAs in GM12878, an EBV-transformed lymphoblastoid cell line, with a significant majority encoded from the EBV latent genes. A novel EBV circRNA derived from the exon 5 of LMP-2 gene which exhibited highest prevalence, was further validated using RNase R assay and Sanger sequencing. This circRNA, which we term circLMP-2_e5, can be universally detected in a panel of EBV-positive cell lines modelling different latency programs. It ranges from lower expression in nasopharyngeal carcinoma (NPC) cells to higher expression in B cells, and is localized to both the cytoplasm and the nucleus. We provide evidence that circLMP-2_e5 is expressed concomitantly with its cognate linear LMP-2 RNA upon EBV lytic reactivation, and may be produced as a result of exon skipping, with its circularization possibly occurring without the involvement of cis elements in the short flanking introns. Furthermore, we show that circLMP-2_e5 is not involved in regulating cell proliferation, host innate immune response, its linear parental transcripts, or EBV lytic reactivation. Taken together, our study expands the current repertoire of putative EBV circRNAs, broadens our understanding of the biology of EBV circRNAs, and lays the foundation for further investigation of their function in the EBV life cycle and disease development.

Circular RNAs (circRNAs) are abundantly expressed in cancer. Their resistance to exonucleases enables them to have potentially stable interactions with different types of biomolecules. Alternative splicing can create different circRNA isoforms that have different sequences and unequal interaction potentials. The study of circRNA function thus requires knowledge of complete circRNA sequences. Here we describe psirc, a method that can identify full-length circRNA isoforms and quantify their expression levels from RNA sequencing data. We confirm the effectiveness and computational efficiency of psirc using both simulated and actual experimental data. Applying psirc on transcriptome profiles from nasopharyngeal carcinoma and normal nasopharynx samples, we discover and validate circRNA isoforms differentially expressed between the two groups. Compared to the assumed circular isoforms derived from linear transcript annotations, some of the alternatively spliced circular isoforms have 100 times higher expression and contain substantially fewer microRNA response elements, demonstrating the importance of quantifying full-length circRNA isoforms.

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The management of chronic wounds in the elderly such as pressure injury (also known as bedsore or pressure ulcer) is increasingly important in an ageing population. Accurate classification of the stage of pressure injury is important for wound care planning. Nonetheless, the expertise required for staging is often not available in a residential care home setting. Artificial-intelligence (AI)-based computer vision techniques have opened up opportunities to harness the inbuilt camera in modern smartphones to support pressure injury staging by nursing home carers. In this paper, we summarise the recent development of smartphone or tablet-based applications for wound assessment. Furthermore, we present a new smartphone application (app) to perform real-time detection and staging classification of pressure injury wounds using a deep learning-based object detection system, YOLOv4. Based on our validation set of 144 photos, our app obtained an overall prediction accuracy of 63.2%. The per-class prediction specificity is generally high (85.1%–100%), but have variable sensitivity: 73.3% (stage 1 vs. others), 37% (stage 2 vs. others), 76.7 (stage 3 vs. others), 70% (stage 4 vs. others), and 55.6% (unstageable vs. others). Using another independent test set, 8 out of 10 images were predicted correctly by the YOLOv4 model. When deployed in a real-life setting with two different ambient brightness levels with three different Android phone models, the prediction accuracy of the 10 test images ranges from 80 to 90%, which highlight the importance of evaluation of mobile health (mHealth) application in a simulated real-life setting. This study details the development and evaluation process and demonstrates the feasibility of applying such a real-time staging app in wound care management.