Background Coronavirus disease 2019 (COVID-19) has become a public health emergency. The widely used reverse transcription–polymerase chain reaction (RT-PCR) method has limitations for clinical diagnosis and treatment. Methods A total of 323 samples from 76 COVID-19–confirmed patients were analyzed by droplet digital PCR (ddPCR) and RT-PCR based 2 target genes (ORF1ab and N). Nasal swabs, throat swabs, sputum, blood, and urine were collected. Clinical and imaging data were obtained for clinical staging. Results In 95 samples that tested positive by both methods, the cycle threshold (Ct) of RT-PCR was highly correlated with the copy number of ddPCR (ORF1ab gene, R2 = 0.83; N gene, R2 = 0.87). Four (4/161) negative and 41 (41/67) single-gene positive samples tested by RT-PCR were positive according to ddPCR with viral loads ranging from 11.1 to 123.2 copies/test. The viral load of respiratory samples was then compared and the average viral load in sputum (17 429 ± 6920 copies/test) was found to be significantly higher than in throat swabs (2552 ± 1965 copies/test, P < .001) and nasal swabs (651 ± 501 copies/test, P < .001). Furthermore, the viral loads in the early and progressive stages were significantly higher than that in the recovery stage (46 800 ± 17 272 vs 1252 ± 1027, P < .001) analyzed by sputum samples. Conclusions Quantitative monitoring of viral load in lower respiratory tract samples helps to evaluate disease progression, especially in cases of low viral load.

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Hepatitis B virus (HBV) causes acute and chronic hepatitis and hepatocellular carcinoma. Small interfering RNA (siRNA) and lamivudine have been shown to have anti-HBV effects through different mechanisms. However, assessment of the genome-wide effects of siRNA and lamivudine on HBV-producing cell lines has not been reported, which may provide a clue to interrogate the HBV-cell interaction and to evaluate the siRNA's side effect as a potential drug. In the present study, we designed seven siRNAs based on the conserved HBV sequences and tested their effects on the expression of HBV genes following sorting of siRNA-positive cells. Among these seven siRNAs, siRNA-1 and siRNA-7 were found to effectively suppress HBV gene expression. We further addressed the global gene expression changes in stable HBV-producing cells induced by siRNA-1 and siRNA-7 by use of human genome-wide oligonucleotide microarrays. Data from the gene expression profiling indicated that siRNA-1 and siRNA-7 altered the expression of 54 and 499 genes, respectively, in HepG2.2.15 cells, which revealed that different siRNAs had various patterns of gene expression profiles and suggested a complicated influence of siRNAs on host cells. We further observed that 18 of these genes were suppressed by both siRNA-1 and siRNA-7. Interestingly, seven of these genes were originally activated by HBV, which suggested that these seven genes might be involved in the HBV-host cell interaction. Finally, we have compared the effects of siRNA and lamivudine on HBV and host cells, which revealed that siRNA is more effective at inhibiting HBV expression at the mRNA and protein level in vitro, and the gene expression profile of HepG2.2.15 cells treated by lamivudine is totally different from that seen with siRNA.

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The coronavirus disease 2019 (COVID‐19) pandemic has led to a public health crisis and global panic. This infectious disease is caused by a novel coronavirus named severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2). Digital polymerase chain reaction (dPCR), which is an emerging nucleic acid amplification technology that allows absolute quantification of nucleic acids, plays an important role in the detection of SARS‐CoV‐2. In this review, we introduce the principle and advantages of dPCR, and review the applications of dPCR in the COVID‐19 pandemic, including detection of low copy number viruses, measurement of the viral load, preparation of reference materials, monitoring of virus concentration in the environment, detection of viral mutations, and evaluation of anti‐SARS‐CoV‐2 drugs. We also discuss the challenges of dPCR in clinical practice.

Highlights RT-qPCR and ddPCR were used for SARS-CoV-2 nucleic acids detection. ddPCR shows higher sensitivity and lower limit of detection than RT-qPCR. ddPCR successfully detected the dynamic changes in viral load while RT-qPCR failed to detect it. Low-viral-load samples were not uncommon in clinical SARS-CoV-2 nucleic acids testing.

Background COVID-19 continues to threaten human life worldwide. We explored how human behaviours have been influenced by the COVID-19 pandemic in Hong Kong, and how the transmission of other respiratory diseases (e.g. influenza) has been influenced by human behaviour. Methods We focused on the spread of COVID-19 and influenza infections based on reported COVID-19 cases and influenza surveillance data, and investigated the changes in human behaviour due to COVID-19 based on mass transit railway data and the data from a telephone survey. We did the simulation based on SEIR model to assess the risk reduction of influenza transmission caused by the changes in human behaviour. Results During the COVID-19 pandemic, the number of passengers fell by 52.0% compared with the same period in 2019. Residents spent 32.2% more time at home. Each person on average came into close contact with 17.6 and 7.1 people per day during the normal and pandemic periods, respectively. Students, workers, and older people reduced their daily number of close contacts by 83.0%, 48.1%, and 40.3%, respectively. The close contact rates in residences, workplaces, places of study, restaurants, shopping centres, markets, and public transport decreased by 8.3%, 30.8%, 66.0%, 38.5%, 48.6%, 41.0%, and 36.1%, respectively. Based on the simulation, these changes in human behaviours reduced the effective reproduction number of influenza by 63.1%. Conclusions Human behaviours were significantly influenced by the COVID-19 pandemic in Hong Kong. Close contact control contributed more than 47% to the reduction in infection risk of COVID-19.

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