Zhejiang University of Technology

MicroRNAs (miRNAs) play crucial roles in various biological processes, and their dysregulation is associated with numerous diseases. Accurate quantification of mature miRNAs is essential for their use as biomarkers. However, distinguishing between mature miRNAs and pre-miRNAs remains challenging. This study introduces a novel method called Blocker Displacement SMOS-qPCR (BL-SMOS-qPCR) to enhance the differentiation between miRNAs and pre-miRNAs. The method employs a blocker sequence complementary to the 3' end of pre-miRNA cDNA, effectively competing with the Linker sequence and reducing non-specific amplification. We optimized various parameters, including blocker modification, length, concentration, and reaction temperature. Results showed that MGB-modified blockers at optimal concentrations significantly improved discrimination between miRNAs and pre-miRNAs, reducing pre-miRNA signals by approximately 3.2-fold. The BL-SMOS-qPCR maintained similar dynamic ranges (6˟10⁸ to 6˟10¹ copies per reaction) and R² values compared to the original SMOS-qPCR method across multiple miRNA targets. Furthermore, the new method successfully distinguished between normal controls and esophageal cancer patients in serum samples, demonstrating its effectiveness in clinical applications. This study provides a novel approach for precise miRNA quantification, addressing the challenges of differentiating between mature miRNAs and their precursors in complex biological samples.

High-temperature Daqu Baijiu is known for its superior flavor and value. However, the adulteration of high-grade bottled low-grade Baijiu has become a significant concern. To enhance detection portability, a multi-channel visual sensor array was developed using metalloporphyrin pseudo-peroxidase activity to authenticate the same flavor type Baijiu. The competitive coordination between pyrazine nitrogen atoms and metal ions in porphyrins, along with π-π stacking between aromatic and porphyrin rings, inhibits the pseudo-peroxidase activity of metal porphyrins, leading to 3,3',5,5'-tetramethylbenzidine (TMB) color changes and amplified spectral signals. Combining the colorimetric array sensor with the random forest (RF) algorithm, compared with a single sensor, it can significantly improve the classification accuracy of Baijiu. Among them, sensor points such as TPP_Mg, TPP_Zn, TPP_Cu, and TPP have made outstanding contributions to the identification of Baijiu. In addition, a DD-SIMCA model for identifying adulteration was constructed, and the recognition accuracy rate reached more than 99%. Another, an original smartphone app based on machine learning cloud algorithm was developed for external method verification, and its recognition accuracy rate reached more than 96.83%. Finally, through random forest regression (RFR) analysis combined with the color response of compounds, it was found that Maillard reaction products such as pyrazines, aldehydes and ketones made significant contributions to the identification and classification of Baijiu. The deviation between the actual value and the predicted value of the compound content predicted by this method is less than 0.2473% ± 1.0785%. This study offers a foundation for developing "instrument-free" rapid visual detection methods for high-temperature Daqu Baijiu.