The Effect of the COVID-19 Pandemic on Non-COVID-19 Deaths: Population-Wide Retrospective Cohort Study.

BACKGROUND: Health care avoidance in the COVID-19 pandemic has been widely reported. Yet few studies have investigated the dynamics of hospital avoidance behavior during pandemic waves and inferred its impact on excess non-COVID-19 deaths. OBJECTIVE: This study aimed to measure the impact of hospital avoidance on excess non-COVID-19 deaths in public hospitals in Hong Kong. METHODS: This was a retrospective cohort study involving 11,966,786 patients examined between January 1, 2016, and December 31, 2021, in Hong Kong. All data were linked to service, treatment, and outcomes. To estimate excess mortality, the 2-stage least squares method was used with daily tallies of emergency department (ED) visits and 28-day mortality. Records for older people were categorized by long-term care (LTC) home status, and comorbidities were used to explain the demographic and clinical attributes of excess 28-day mortality. The primary outcome was actual excess death in 2020 and 2021. The 2-stage least squares method was used to estimate the daily excess 28-day mortality by daily reduced visits. RESULTS: Compared with the prepandemic (2016-2019) average, there was a reduction in total ED visits in 2020 of 25.4% (548,116/2,142,609). During the same period, the 28-day mortality of non-COVID-19 ED deaths increased by 7.82% (2689/34,370) compared with 2016-2019. The actual excess deaths in 2020 and 2021 were 3143 and 4013, respectively. The estimated total excess non-COVID-19 28-day deaths among older people in 2020 to 2021 were 1958 (95% CI 1100-2820; no time lag). Deaths on arrival (DOAs) or deaths before arrival (DBAs) increased by 33.6% (1457/4336) in 2020, while non-DOA/DBAs increased only by a moderate 4.97% (1202/24,204). In both types of deaths, the increases were higher during wave periods than in nonwave periods. Moreover, non-LTC patients saw a greater reduction in ED visits than LTC patients across all waves, by more than 10% (non-LTC: 93,896/363,879, 25.8%; LTC: 7,956/67,090, 11.9%). Most of the comorbidity subsets demonstrated an annualized reduction in visits in 2020. Renal diseases and severe liver diseases saw notable increases in deaths. CONCLUSIONS: We demonstrated a statistical method to estimate hospital avoidance behavior during a pandemic and quantified the consequent excess 28-day mortality with a focus on older people, who had high frequencies of ED visits and deaths. This study serves as an informed alert and possible investigational guideline for health care professionals for hospital avoidance behavior and its consequences.

An artificial intelligence-enabled smartphone app for real-time pressure injury assessment.

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.

Evaluation of Experimental Protocols for Shotgun Whole-Genome Metagenomic Discovery of Antibiotic Resistance Genes.

Shotgun metagenomics has enabled the discovery of antibiotic resistance genes (ARGs). Although there have been numerous studies benchmarking the bioinformatics methods for shotgun metagenomic data analysis, there has not yet been a study that systematically evaluates the performance of different experimental protocols on metagenomic species profiling and ARG detection. In this study, we generated 35 whole genome shotgun metagenomic sequencing data sets for five samples (three human stool and two microbial standard) using seven experimental protocols (KAPA or Flex kits at 50ng, 10ng, or 5ng input amounts; XT kit at 1ng input amount). Using this comprehensive resource, we evaluated the seven protocols in terms of robust detection of ARGs and microbial abundance estimation at various sequencing depths. We found that the data generated by the seven protocols are largely similar. The inter-protocol variability is significantly smaller than the variability between samples or sequencing depths. We found that a sequencing depth of more than 30M is suitable for human stool samples. A higher input amount (50ng) is generally favorable for the KAPA and Flex kits. This systematic benchmarking study sheds light on the impact of sequencing depth, experimental protocol, and DNA input amount on ARG detection in human stool samples.

Quantifying full-length circular RNAs in cancer.

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 with 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, showing the importance of quantifying full-length circRNA isoforms.

Identification and characterization of a novel Epstein-Barr Virus-encoded circular RNA from LMP-2 Gene.

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.

START: a system for flexible analysis of hundreds of genomic signal tracks in few lines of SQL-like queries.

BACKGROUND: A genomic signal track is a set of genomic intervals associated with values of various types, such as measurements from high-throughput experiments. Analysis of signal tracks requires complex computational methods, which often make the analysts focus too much on the detailed computational steps rather than on their biological questions. RESULTS: Here we propose Signal Track Query Language (STQL) for simple analysis of signal tracks. It is a Structured Query Language (SQL)-like declarative language, which means one only specifies what computations need to be done but not how these computations are to be carried out. STQL provides a rich set of constructs for manipulating genomic intervals and their values. To run STQL queries, we have developed the Signal Track Analytical Research Tool (START, http://yiplab.cse.cuhk.edu.hk/start/ ), a system that includes a Web-based user interface and a back-end execution system. The user interface helps users select data from our database of around 10,000 commonly-used public signal tracks, manage their own tracks, and construct, store and share STQL queries. The back-end system automatically translates STQL queries into optimized low-level programs and runs them on a computer cluster in parallel. We use STQL to perform 14 representative analytical tasks. By repeating these analyses using bedtools, Galaxy and custom Python scripts, we show that the STQL solution is usually the simplest, and the parallel execution achieves significant speed-up with large data files. Finally, we describe how a biologist with minimal formal training in computer programming self-learned STQL to analyze DNA methylation data we produced from 60 pairs of hepatocellular carcinoma (HCC) samples. CONCLUSIONS: Overall, STQL and START provide a generic way for analyzing a large number of genomic signal tracks in parallel easily.