We demonstrate noncontact transport of microscale objects in liquid environments using untethered, magnetic microrobots. The flow and vortices generated by the rotating microrobot are efficient for selective and gentle trapping, stable transport, and targeted delivery of microscale cargo. The motion of the microrobots can be precisely controlled even at very low frequencies using an advanced magnetic control signal. The design and control methodology presented here can be followed to develop microrobots utilizing the generated fluid flows and performing a variety of biomedical manipulation tasks.

We report a cooperative transport strategy that uses engineered microbars and multiple helical microcarriers. Cooperation of microcarriers generates higher propulsive forces while application of forces at multiple locations results in motion control with multiple degrees of freedom.

Micro/nanomotors (MNMs) are intelligent, efficient and promising micro/nanorobots (MNR) that can respond to externalstimuli (e.g., chemical energy, temperature, light, pH, ultrasound, magnetic, biosignals, ions) and perform specific tasks.

The Caco-2 cell model is widely used to evaluate the in vitro human intestinal permeability of drugs due to its morphological and functional similarity to human enterocytes. Although it is safe and relatively economic, it is time-consuming. A rapid and accurate quantitative structure-property relationship (QSPR) model of Caco-2 permeability is helpful to improve the efficiency of oral drug development. The aim of our study is to explore the predictive ability of the QSPR model, to study its permeation mechanism, and to develop a potential permeability prediction model, for Caco-2 cells. In our study, a relatively large data set was collected and the abnormal data were eliminated using the Monte Carlo regression and hybrid quantum particle swarm optimization (HQPSO) algorithm. Then, the remaining 1827 compounds were used to establish QSPR models. To generate multiple chemically diverse training and test sets, we used a combination of principal component analysis (PCA) and self-organizing mapping (SOM) neural networks to split the modeling data set characterized by PaDEL-descriptors. After preliminary selection of descriptors by the mean decrease impurity (MDI) method, the HQPSO algorithm was used to select the key descriptors. Six different methods, namely, multivariate linear regression (MLR), support vector machine regression (SVR), xgboost, radial basis function (RBF) neural networks, dual-SVR and dual-RBF were employed to develop QSPR models. The best dual-RBF model was obtained finally with R² = 0.91, and R_cv5 ² = 0.77, for the training set, and R_T ² = 0.77, for the test set. A series of validation methods were used to assess the robustness and predictive ability of the dual-RBF model under OECD principles. A new application domain (AD) definition method based on the descriptor importance-weighted and distance-based (IWD) method was proposed, and the outliers were analyzed carefully. Combined with the importance of the descriptors used in the dual-RBF model, we concluded that the “H E-state” and hydrogen bonds are important factors affecting the permeability of drugs passing through the Caco-2 cell. Compared with the reported studies, our method exhibits certain advantages in data size, transparency of modeling process and prediction accuracy to some extent, and is a promising tool for virtual screening in the early stage of drug development.