A one-pot CRISPR/Cas13a-based contamination-free biosensor for low-cost and rapid nucleic acid diagnostics.

The pandemic due to the outbreak of 2019 coronavirus disease (COVID-19) caused by novel severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has raised significant public health concerns. Rapid, affordable, and accurate diagnostic testing not only paves the way for the effective treatment of diseases, but also plays a crucial role in preventing the spreading of infectious diseases. Herein, a one-pot CRISPR/Cas13a-based visual biosensor was proposed and developed for the rapid and low-cost nucleic acid detection. By combining Cas13a cleavage and Recombinase Polymerase Amplification (RPA) in a one-pot reaction in a disposable tube-in-tube vessel, amplicon contamination could be completely avoided. The RPA reaction is carried out in the inner tube containing two hydrophobic holes at the bottom. After the completion of amplification reaction, the reaction solution enters the outer tube containing pre-stored Cas13a reagent under the action of centrifugation or shaking. Inner and outer tubes are combined to form an independent reaction pot to complete the nucleic acid detection without opening the lid. This newly developed nucleic acid detection method not only meets the need of rapid nucleic acid detection at home without the need for any specialized equipment, but also fulfils the requirement of rapid on-site nucleic acid detection with the aid of small automated instruments. In this study, CRISPR/Cas13a and CRISPR/Cas12a were used to verify the reliability of the developed one-pot nucleic acid detection method. The performance of the system was verified by detecting the DNA virus, i.e., African swine fever virus (ASFV) and the RNA virus, i.e., SARS-Cov-2. The results indicate that the proposed method possesses a limit of detection of 3 copy/µL. The negative and positive test results are consistent with the results of real-time fluorescence quantitative polymerase chain reaction (PCR), but the time required is shorter and the cost is lower. Thus, this study makes this method available in resource-limited areas for the purpose of large-scale screening and in case of epidemic outbreak.

On-line monitoring of pharmaceutical production processes using Hidden Markov Model.

This article presents a new method for on-line monitoring of pharmaceutical production process, especially the powder blending process. The new method consists of two parts: extracting features from the Near Infrared (NIR) spectroscopy signals and recognizing patterns from the features. Features are extracted from spectra by using Partial Least Squares method (PLS). The pattern recognition is done by using Hidden Markov Model (HMM). A series of experiments are conducted to evaluate the effectiveness of this new method. In the experiments, wheat powder and corn powder are blended together at a set concentration. The proposed method can effectively detect the blending uniformity (the success rate is 99.6%). In comparison to the conventional Moving Block of Standard Deviation (MBSD), the proposed method has a number of advantages, including higher reliability, higher robustness and more transparent decision making. It can be used for effective on-line monitoring of pharmaceutical production processes.