Proximity-Induced Bipedal DNA Walker for Accurately Visualizing microRNA in Living Cancer Cell.

DNA walker, a type of dynamic DNA device that is capable of moving progressively along prescribed walking tracks, has emerged as an ideal and powerful tool for biosensing and bioimaging. However, most of the reported three-dimensional (3D) DNA walker were merely designed for the detection of a single target, and they were not capable of achieving universal applicability. Herein, we reported for the first time the development of a proximity-induced 3D bipedal DNA walker for imaging of low abundance biomolecules. As a proof of concept, miRNA-34a, a biomarker of breast cancer, is chosen as the model system to demonstrate this approach. In our design, the 3D bipedal DNA walker can be generated only by the specific recognition of two proximity probes for miRNA-34a. Meanwhile, it stochastically and autonomously traveled on 3D tracks (gold nanoparticles) via catalytic hairpin assembly (CHA), resulting in the amplified fluorescence signal. In comparison with some conventional DNA walkers that were utilized for living cell imaging, the 3D DNA walkers induced by proximity ligation assay can greatly improve and ensure the high selectivity of bioanalysis. By taking advantage of these unique features, the proximity-induced 3D bipedal DNA walker successfully realizes accurate and effective monitoring of target miRNA-34a expression levels in living cells, affording a universal, valuable, and promising platform for low-abundance cancer biomarker detection and accurate identification of cancer.

Machine learning in the prediction of massive transfusion in trauma: a retrospective analysis as a proof-of-concept.

PURPOSE: Early administration and protocolization of massive hemorrhage protocols (MHP) has been associated with decreases in mortality, multiorgan system failure, and number of blood products used. Various prediction tools have been developed for the initiation of MHP, but no single tool has demonstrated strong prediction with early clinical data. We sought to develop a massive transfusion prediction model using machine learning and early clinical data. METHODS: Using the National Trauma Data Bank from 2013 to 2018, we included severely injured trauma patients and extracted clinical features available from the pre-hospital and emergency department. We subsequently balanced our dataset and used the Boruta algorithm to determine feature selection. Massive transfusion was defined as five units at 4 h and ten units at 24 h. Six machine learning models were trained on the balanced dataset and tested on the original. RESULTS: A total of 326,758 patients met our inclusion with 18,871 (5.8%) requiring massive transfusion. Emergency department models demonstrated strong performance characteristics with mean areas under the receiver-operating characteristic curve of 0.83. Extreme gradient boost modeling slightly outperformed and demonstrated adequate predictive performance with pre-hospital data only, as well as 4-h transfusion thresholds. CONCLUSIONS: We demonstrate the use of machine learning in developing an accurate prediction model for massive transfusion in trauma patients using early clinical data. This research demonstrates the potential utility of artificial intelligence as a clinical decision support tool.