Mechanical properties play an important role in regulating cellular activities and are critical for unlocking the mysteries of life. Atomic force microscopy (AFM) enables researchers to measure mechanical properties of single living cells under physiological conditions. Here, AFM was used to investigate the topography and mechanical properties of red blood cells (RBCs) and three types of aggressive cancer cells (Burkitt’s lymphoma Raji, cutaneous lymphoma Hut, and chronic myeloid leukemia K562). The surface topography of the RBCs and the three cancer cells was mapped with a conventional AFM probe, while mechanical properties were investigated with a micro-sphere glued onto a tip-less cantilever. The diameters of RBCs are significantly smaller than those of the cancer cells, and mechanical measurements indicated that Young’s modulus of RBCs is smaller than those of the cancer cells. Aggressive cancer cells have a lower Young’s modulus than that of indolent cancer cells, which may improve our understanding of metastasis.

Nanorobotics, which has long been a fantasy in the realm of science fiction, is now a reality due to the considerable developments in diverse fields including chemistry, materials, physics, information and nanotechnology in the past decades. Not only different prototypes of nanorobots whose sizes are nanoscale are invented for various biomedical applications, but also robotic nanomanipulators which are able to handle nano-objects obtain substantial achievements for applications in biomedicine. The outstanding achievements in nanorobotics have significantly expanded the field of medical robotics and yielded novel insights into the underlying mechanisms guiding life activities, remarkably showing an emerging and promising way for advancing the diagnosis & treatment level in the coming era of personalized precision medicine. In this review, the recent advances in nanorobotics (nanorobots, nanorobotic manipulations) for biomedical applications are summarized from several facets (including molecular machines, nanomotors, DNA nanorobotics, and robotic nanomanipulators), and the future perspectives are also presented.

The main problem of atomic force microscopy (AFM)-based nanomanipulation is the lack of real-time visual feedback. Although this problem has been partially solved by virtual reality technology, the faulty display caused by random drift and modeling errors in the virtual reality interface are still limiting the efficiency of the AFM-based nanomanipulation. Random drift aroused from an uncontrolled manipulation environment generates a position error between the manipulation coordinate and the true environment. Modeling errors due to the uncertainties of the nanoenvironment often result in displaying a wrong position of the object. Since there is no feedback to check the validity of the display, the faulty display cannot be detected in real time and leads to a failed manipulation. In this paper, a real-time fault detection and correction (RFDC) method is proposed to solve these problems by using the AFM tip as an end effector as well as a force sensor during manipulation. Based on the interaction force measured from the AFM tip, the validity of the visual feedback is monitored in real time by the developed Kalman filter. Once the faulty display is detected, it can be corrected online through a quick local scan without interrupting manipulation. In this way, the visual feedback keeps consistent with the true environment changes during manipulation, which makes it possible for several operations to finish without an image scan in between. The theoretical study and the implementation of the RFDC method are elaborated. Experiments of manipulating nanomaterials including nanoparticles and nanorods have been carried out to demonstrate its effectiveness and efficiency.

Assist-as-needed (AAN) assistance can promote active voluntary participation in rehabilitation and motor function recovery of post-stroke patients. However, different patients have personalized damaged regions and recovery states, causing difficulties to obtain adaptive and customized assistance in robot-assisted rehabilitation. This paper presents a mirror Adaptive Assist-As-Needed (AAAN) scheme, including two modules of Multi-Sensors Fused Estimation (MSFE) and Online Incremental Mirror Adaptation (OIMA) , to encourage the subjects to actively participate in rehabilitation. Specifically, the first MSFE module can obtain the needed assistance based on the functional capability of the post-stroke patients via the data fusion of biological and motional signals using Kalman Filter. The second OIMA module fine-tunes the control torques estimated by MSFE to adapt the muscle fatigue and stiffness varieties of the affected limb based on the motion and physiological reference of the mirror healthy limb. The results demonstrate that the AAAN strategy can realize the transparent mode for healthy subjects and promote post-stroke patients to rehabilitate the affected limb with active participation using EMG signals 90.5% similar to those of the mirror healthy limb. The proposed method can be expected to greatly enhance power assistance and rehabilitation outcome of post-stroke patients using exoskeletons by provoking active participation. Note to Practitioners —For robotic rehabilitation, it is crucial to provide suitable assistances that can maximize the participation of post-stroke patients, which can promote the recovery outcome of therapies. The main purpose of this work is to achieve the adaptive assist-as-needed control strategy for upper limb rehabilitation tasks in two steps. Firstly, the elbow joint torques of a post-stroke patient are estimated by data fusion of motion and electromyography (EMG) signals using Kalman Filter, which can make up for the shortcomings of the individual signals, such as poor reliability and low sensitivity. Secondly, the joint motion and EMG signals of the mirror healthy limb are used as the reference to calculate the adaptive needed assistance to rehabilitate the affected limb. The preliminary experiments with healthy and post-stroke subjects demonstrate that this approach can obtain stable motion with the natural physiological states of subjects and enhance active voluntary participation in rehabilitation. In the future study, it will be investigated how to accelerate the adaptation of new patients based on the knowledge of the learned individuals using machine learning methods, such as lifelong learning and incremental learning.