Yanchi Luo

Clinical pathogen diagnostics detect targets by qPCR (but with low sensitivity) or blood culturing (but time-consuming). Here we leverage a dual-stem-loop DNA amplifier to enhance non-specific collateral enzymatic cleavage of an oligonucleotide linker between a fluophore and its quencher by CRISPR-CasΦ, achieving ultrasensitive target detection. Specifically, the target pathogens are lysed to release DNA, which binds its complementary gRNA in CRISPR-CasΦ to activate the collateral DNA-cleavage capability of CasΦ, enabling CasΦ to cleave the stem-loops in the amplifier. The cleavage product binds its complementary gRNA in another CRISPR-CasΦ to activate more CasΦ. The activated CasΦ collaterally cleaves the linker, releasing the fluophore to recover its fluorescent signal. The cycle of stem-loop-cleavage/CasΦ-activation/fluorescence-recovery amplifies the detection signal. Our target amplification-free collateral-cleavage-enhancing CRISPR-CasΦ method (TCC), with a detection limit of 0.11 copies/μL, demonstrates enhanced sensitivity compared to qPCR. It can detect pathogenic bacteria as low as 1.2 CFU/mL in serum within 40 min.

Ultrasonic pulse velocity (UPV) testing is widely employed as a nondestructive technique in rock engineering to determine the essential physical properties of rocks. This study introduces UPVnet, an innovative deep learning-based neural network specifically developed to enhance the accuracy and reliability of first-arrival picking in UPV testing. By advancing multidimensional feature extraction methods and optimizing the network architecture, UPVnet achieves significantly higher picking accuracy and enhanced generalization capability compared to traditional methods. Additionally, the study validates the feasibility of UPV testing on small-sized rock samples, demonstrating that P-wave and S-wave velocities measured on 20-mm-thick samples exhibit good consistency with those from standard plug samples, despite challenges such as size effects and velocity dispersion.