An Automated Method for Multi-DOF Cell Rotational Control Contributing to Orientation-based Cell Surgery Applications
An autonomous control framework for cell out-of-plane reorientation has been proposed by utilizing a robot-tweezers cell manipulation system. A serious of experiments that include cell in-plane, out-of-plane rotational control and cellular orientation-based enucleation manipulation have been performed, demonstrating the necessity and significance of cell orientation control involved in many cell surgery studies. The outcome of this study represents an advancement in single cell manipulation, and contributes to many cellular orientation-based cell surgery applications, such as cell nucleus biopsy and transplantation, cloning technology, and PGD.
A Method for Rapid, Reliable, and Low-Volume Measurement of Lithium in Blood for Use in Bipolar Disorder Treatment Management
For over 60 years, lithium has proved most effective in the treatment of Bipolar Disorder. Blood lithium levels must be closely monitored to prevent toxicity, but compliance with the regular blood test requirement falls below standard and is associated with high rates of treatment non-adherence with consequent relapse of illness
The study proposes a novel opto-impedance technique, that combines high specificity and sensitivity attributes of optical and electrical impedance methods into a personal lithium analyser for use by patients. Results demonstrate the accuracy of the proposed technique and its ability to offer a solution for rapid assessment of lithium.
Wireless thermometry for real-time temperature recording on thousand-cell level
It is vitally necessary to measure the cellular temperature to fully understand life sciences. A method allowing to measure the cellular temperature for a normal growing state without doing damage to the cells and disturbing their intercellular communication is needed. Here, a wireless, real-time, high-throughput temperature detection method is developed. The acquisition system applies a high-precision reference resistor and a low real-time measurement current (below or equal to 0.14 mA) to reduce self-heating via the intermittent measurement. Cells of a small volume cell medium are cultured on the surface of the platinum thermal resistor and subsequently measured in the incubator.
Medical Image Synthesis with Deep Convolutional Adversarial Networks
We propose a generative adversarial approach to address the medical image synthesis problem. Specifically, we train a fully convolutional network (FCN) to generate a target image given a source image. To produce more realistic image, we propose to use adversarial learning strategy to better model the FCN. Moreover, an image-gradient-difference based loss function is proposed to avoid generating blurry images. Also, a long-term residual unit is explored to help the training of the network. We further apply auto-context model to implement a context-aware framework. Experimental results show the robustness and accuracy of our method in synthesizing various medical images.
RF Channel Modeling for Implant-to-Implant Communication and Implant to Sub-Cutaneous Implant Communication for Future Leadless Cardiac Pacemakers
The study proposes the radio-frequency channel modeling for in-body to in-body implant communication for next generation multinodal capsule-like leadless cardiac pacemaker technology. The method is based on detailed numerical simulations of complex digital human models. The research also investigates the placement of a subcutaneous implant transceiver and channel behaviour based on ventricular blood volume change to find out the appropriate timing of the signal transmission between the implants. The overall study would facilitate the design of the world’s first complete prototype of the multi-node leadless capsule pacemaker technology in the future.
Is EMG a Viable Alternative to BCI for Detecting Movement Intention in Severe Stroke?
EEG-BCI has been used to detect movement intention in severely affected stroke patients during assisted therapy, but current EEG-BCI systems are not practical for routine use. Here, we investigated the possibility of using EMG from the forearm muscles to detect movement intention using data from 30 severely affected chronic stroke patients with no residual movements. Overall, we found that a simple EMG detector could detect movement intention from EMG in 22/30 patients. This suggests that a large proportion of severely affected stroke patients have detectable residual EMG, which yields a direct and practical way to trigger robot-assisted training than EEG-BCI.