Amplification-Free Detection of Viruses in Minutes using Single-Particle Imaging and Machine Learning
Biophysical Journal Elsevier 120:3 (2021) 195a
Single-Molecule Fret Analysis of Key Protein Conformational Changes During Promoter Escape by RNA Polymerase
Biophysical Journal Elsevier 120:3 (2021) 109a
High-Throughput Super-Resolution Microscopy of Viral Particles Reveals Insights into their Morphology and Organisation
(2021)
Quantification of purified endogenous miRNAs with high sensitivity and specificity
Nature Communications Springer Nature 11:1 (2020) 6033
Abstract:
MicroRNAs (miRNAs) are short (19–24 nt) non-coding RNAs that suppress the expression of protein coding genes at the post-transcriptional level. Differential expression profiles of miRNAs across a range of diseases have emerged as powerful biomarkers, making a reliable yet rapid profiling technique for miRNAs potentially essential in clinics. Here, we report an amplification-free multi-color single-molecule imaging technique that can profile purified endogenous miRNAs with high sensitivity, specificity, and reliability. Compared to previously reported techniques, our technique can discriminate single base mismatches and single-nucleotide 3′-tailing with low false positive rates regardless of their positions on miRNA. By preloading probes in Thermus thermophilus Argonaute (TtAgo), miRNAs detection speed is accelerated by more than 20 times. Finally, by utilizing the well-conserved linearity between single-molecule spot numbers and the target miRNA concentrations, the absolute average copy numbers of endogenous miRNA species in a single cell can be estimated. Thus our technique, Ago-FISH (Argonaute-based Fluorescence In Situ Hybridization), provides a reliable way to accurately profile various endogenous miRNAs on a single miRNA sensing chip.Reinfection with SARS-CoV-2: discrete SIR (Susceptible, Infected, Recovered) modeling using empirical infection data
JMIR Public Health and Surveillance JMIR Publications 6:4 (2020) e21168
Abstract:
BACKGROUND:The novel coronavirus SARS-CoV-2, which causes the COVID-19 disease, has resulted in a global pandemic. Since its emergence in December 2019, the virus has infected millions of people, caused the deaths of hundreds of thousands, and resulted in incalculable social and economic damage. Understanding the infectivity and transmission dynamics of the virus is essential to determine how best to reduce mortality while ensuring minimal social restrictions on the lives of the general population. Anecdotal evidence is available, but detailed studies have not yet revealed whether infection with the virus results in immunity.
OBJECTIVE:
The objective of this study was to use mathematical modeling to investigate the reinfection frequency of COVID-19.
METHODS:
We have used the SIR (Susceptible, Infected, Recovered) framework and random processing based on empirical SARS-CoV-2 infection and fatality data from different regions to calculate the number of reinfections that would be expected to occur if no immunity to the disease occurred.
RESULTS:
Our model predicts that cases of reinfection should have been observed by now if primary SARS-CoV-2 infection did not protect individuals from subsequent exposure in the short term; however, no such cases have been documented.
CONCLUSIONS:
This work concludes that infection with SARS-CoV-2 provides short-term immunity to reinfection and therefore offers useful insight for serological testing strategies, lockdown easing, and vaccine development.