Enzymatically Synthesized DNA Polymer as Co-carrier for Enhanced RNA Interference

Polymerization of small interfering RNA (siRNA) has been demonstrated as a promising strategy to improve siRNA delivery, which will change the low-charge and rigid properties of single siRNA and enhance its electrostatic interactions with cationic polymers. For such polymerization strategy, a major breakthrough is still needed to fully eliminate chemical processes and further improve the nanocomplex-forming ability of polymerized siRNAs. Herein, the extremely strong interaction between the DNA product of rolling circle amplification (RCA) and linear poly­(ether imide) (PEI) has been disclosed; accordingly, a stable nanocomplex is formed just at its charge neutralization point, which benefits from the high molecular weight of the RCA product (>3 000 000 Da). In addition, as the sequence of the RCA product is determined by the cyclic template, the programmable nature of DNA can simplify the optimization process and maximize the hybridization efficiency between RCA and sticky siRNAs, realizing a superior siRNA polymerization efficiency. Depending on these two effects, the RCA DNA is utilized as a cocarrier material to organize siRNA polymerization and substantially reduce the usage amount of PEI, which greatly improves RNAi efficiency of PEI/RCA-siRNA polyplex both in vitro and in vivo, providing evidence that RCA DNA is a promising material to promote the RNAi-based therapeutics

DataSheet1_AI-boosted CRISPR-Cas13a and total internal reflection fluorescence microscopy system for SARS-CoV-2 detection.docx

Integrating artificial intelligence with SARS-CoV-2 diagnostics can help in the timely execution of pandemic control and monitoring plans. To improve the efficiency of the diagnostic process, this study aims to classify fluorescent images via traditional machine learning and deep learning-based transfer learning. A previous study reported a CRISPR-Cas13a system combined with total internal reflection fluorescence microscopy (TIRFM) to detect the existence and concentrations of SARS-CoV-2 by fluorescent images. However, the lack of professional software and excessive manual labor hinder the practicability of the system. Here, we construct a fluorescent image dataset and develop an AI-boosted CRISPR-Cas13a and total internal reflection fluorescence microscopy system for the rapid diagnosis of SARS-CoV-2. Our study proposes Fluorescent Images Classification Transfer learning based on DenseNet-121 (FICTransDense), an approach that uses TIRF images (before and after sample introduction, respectively) for preprocessing, including outlier exclusion and setting and division preprocessing (i.e., SDP). Classification results indicate that the FICTransDense and Decision Tree algorithms outperform other approaches on the SDP dataset. Most of the algorithms benefit from the proposed SDP technique in terms of Accuracy, Recall, F1 Score, and Precision. The use of AI-boosted CRISPR-Cas13a and TIRFM systems facilitates rapid monitoring and diagnosis of SARS-CoV-2.</p

DataSheet1_AI-boosted CRISPR-Cas13a and total internal reflection fluorescence microscopy system for SARS-CoV-2 detection.docx

Integrating artificial intelligence with SARS-CoV-2 diagnostics can help in the timely execution of pandemic control and monitoring plans. To improve the efficiency of the diagnostic process, this study aims to classify fluorescent images via traditional machine learning and deep learning-based transfer learning. A previous study reported a CRISPR-Cas13a system combined with total internal reflection fluorescence microscopy (TIRFM) to detect the existence and concentrations of SARS-CoV-2 by fluorescent images. However, the lack of professional software and excessive manual labor hinder the practicability of the system. Here, we construct a fluorescent image dataset and develop an AI-boosted CRISPR-Cas13a and total internal reflection fluorescence microscopy system for the rapid diagnosis of SARS-CoV-2. Our study proposes Fluorescent Images Classification Transfer learning based on DenseNet-121 (FICTransDense), an approach that uses TIRF images (before and after sample introduction, respectively) for preprocessing, including outlier exclusion and setting and division preprocessing (i.e., SDP). Classification results indicate that the FICTransDense and Decision Tree algorithms outperform other approaches on the SDP dataset. Most of the algorithms benefit from the proposed SDP technique in terms of Accuracy, Recall, F1 Score, and Precision. The use of AI-boosted CRISPR-Cas13a and TIRFM systems facilitates rapid monitoring and diagnosis of SARS-CoV-2.</p

Detection of Frog Virus 3 by Integrating RPA-CRISPR/Cas12a-SPM with Deep Learning

A fast, easy-to-implement, highly sensitive, and point-of-care (POC) detection system for frog virus 3 (FV3) is proposed. Combining recombinase polymerase amplification (RPA) and CRISPR/Cas12a, a limit of detection (LoD) of 100 aM (60.2 copies/μL) is achieved by optimizing RPA primers and CRISPR RNAs (crRNAs). For POC detection, smartphone microscopy is implemented, and an LoD of 10 aM is achieved in 40 min. The proposed system detects four positive animal-derived samples with a quantitation cycle (Cq) value of quantitative PCR (qPCR) in the range of 13 to 32. In addition, deep learning models are deployed for binary classification (positive or negative samples) and multiclass classification (different concentrations of FV3 and negative samples), achieving 100 and 98.75% accuracy, respectively. Without temperature regulation and expensive equipment, the proposed RPA-CRISPR/Cas12a combined with smartphone readouts and artificial-intelligence-assisted classification showcases the great potential for FV3 detection, specifically POC detection of DNA virus