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As an ideal method to manipulate biological particles, the dielectrophoresis (DEP) technique has been widely used in clinical diagnosis, disease treatment, drug development, immunoassays, cell sorting, etc. This review summarizes the research in the field of bioparticle manipulation based on DEP techniques. Firstly, the basic principle of DEP and its classical theories are introduced in brief; Secondly, a detailed introduction on the DEP technique used for bioparticle manipulation is presented, in which the applications are classified into five fields: capturing bioparticles to specific regions, focusing bioparticles in the sample, characterizing biomolecular interaction and detecting microorganism, pairing cells for electrofusion and separating different kinds of bioparticles; Thirdly, the effect of DEP on bioparticle viability is analyzed; Finally, the DEP techniques are summarized and future trends in bioparticle manipulation are suggested.

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DdCAD-1 is a novel Ca(2+)-dependent cell adhesion molecule that lacks a hydrophobic signal peptide and a transmembrane domain. DdCAD-1 is expressed by the social amoeba Dictyostelium discoideum at the onset of development. It is synthesized as a soluble protein and then transported to the plasma membrane by contractile vacuoles. Here we describe the novel features of the solution structures of Ca(2+)-free and Ca(2+)-bound monomeric DdCAD-1. DdCAD-1 contains two beta-sandwich domains, belonging to the betagamma-crystallin and immunoglobulin fold classes, respectively. Whereas the N-terminal domain has a major role in homophilic binding, the C-terminal domain tethers the protein to the cell membrane. From structural and mutational analyses, we propose a model for the Ca(2+)-bound DdCAD-1 dimer as a basis for understanding DdCAD-1-mediated cell-cell adhesion at the molecular level. Our results provide new insights into Ca(2+)-dependent mechanisms for cell-cell adhesion.

Wearable sweat sensors can analyze the abundant composition of solutes and metabolites in sweat to reflect the health state of the wearers in real time. The realization of active motion control for sweat droplets is significant for a multifunctional sweat monitoring device with several analysis chambers. Here, a wearable droplet-based human sweat monitoring platform (WSMP), by combining an electrowetting on dielectrics (EWOD) device and a triboelectric nanogenerator (TENG), is demonstrated. It allows to collect and transport sweat droplets in different chambers by dielectric wetting effect and eventually merge and react with a pH indicator. The mechanical and electrical model of WSMP is introduced to describe the relationship between the open-circuit voltage of the TENG and the voltage applied on the EWOD device. The highvoltage electrical field generated by the TENG can change the wettability of solid-liquid interfaces and realize the controlling of droplet motion. The contact angle of electrolyte droplets changes over 30% with the triboelectric voltage of 5 kV. The driving, merging, and color reaction can be realized by actively controlling the motion of droplets. Finally, a wearable WSMP worn on the shank successfully demonstrates the preliminary detection of the pH level of human sweat.

Biological cell injection is a laborious work which requires lengthy training and suffers from a low success rate. Even a tiny excessive manipulation force can destroy the membrane or tissue of the biological cell, and lead to failure of the biomanipulation task. This makes the control of the injection force an important factor in cell injection process. In this paper, a vision-based impedance force control algorithm is proposed based on the modeling of a laboratory test-bed injection system. Visual feedback is used to estimate the injection force, based on which an impedance force control algorithm is developed. Motion planning of the injection pipette is also proposed to complete the whole injection process. Finally, preliminary experimental results are given to show the effectiveness of the proposed approach.

Over the past decade, the rapid development of biotechnologies such as gene injection, in-vitro fertilization, intracytoplasmic sperm injection (ICSI) and drug development have led to great demand for highly automated, high precision equipment for microinjection. Recently a new cell injection technology using piezo-driven pipettes with a very small mercury column was proposed and successfully applied in ICSI to a variety of mammal species. Although this technique significantly improves the survival rates of the ICSI process, shortcomings due to the toxicity of mercury and damage to the cell membrane due to large lateral tip oscillations of the injector pipette may limit its application. In this paper, a new cell injection system for automatic batch injection of suspended cells is developed. A new design of the piezo-driven cell injector is proposed for automated suspended cell injection. This new piezo-driven cell injector design relocates the piezo oscillation actuator to the injector pipette which eliminates the vibration effect on other parts of the micromanipulator. A small piezo stack is sufficient to perform the cell injection process. Harmful lateral tip oscillations of the injector pipette are reduced substantially without the use of a mercury column. Furthermore, ultrasonic vibration micro-dissection (UVM) theory is utilized to analyze the piezo-driven cell injection process, and the source of the lateral oscillations of the injector pipette is investigated. From preliminary experiments of cell injection of a large number of zebrafish embryos (n = 200), the injector pipette can easily pierce through the cell membrane at a low injection speed and almost no deformation of the cell wall, and with a high success rate(96%) and survival rate(80.7%) This new injection approach shows good potential for precision injection with less damage to the injected cells.

Approaches to reliably predict the developmental potential of embryos and select suitable embryos for blastocyst culture are needed. The development of time-lapse monitoring (TLM) and artificial intelligence (AI) may help solve this problem. Here, we report deep learning models that can accurately predict blastocyst formation and usable blastocysts using TLM videos of the embryo’s first three days. The DenseNet201 network, focal loss, long short-term memory (LSTM) network and gradient boosting classifier were mainly employed, and video preparation algorithms, spatial stream and temporal stream models were developed into ensemble prediction models called STEM and STEM+. STEM exhibited 78.2% accuracy and 0.82 AUC in predicting blastocyst formation, and STEM+ achieved 71.9% accuracy and 0.79 AUC in predicting usable blastocysts. We believe the models are beneficial for blastocyst formation prediction and embryo selection in clinical practice, and our modeling methods will provide valuable information for analyzing medical videos with continuous appearance variation.