This paper presents a multi-particle parallel manipulation optoelectronic tweezers system integrated with computer vision technology, enabling the parallel and precise manipulation of dozens of particles. This system significantly enhances manipulation efficiency while maintaining high precision. By real-time monitoring of particle motion and light patterns, the system can rapidly adjust and optimize its manipulation strategy, thereby improving the stability and reliability of multi-particle synchronization in complex environments. Extensive experimental results demonstrate the system’s outstanding performance. For instance, it can quickly arrange complex patterns and letter sequences, facilitate the coordinated assembly of organoids from particle groups, and efficiently perform the precise separation and arrangement of mixed particles. The core advantage of this system lies in its high parallelism and flexibility, enabling it to handle large-scale synchronous manipulation tasks with exceptional operating accuracy. With continuous technological advancements and the broadening of application scenarios, this system is expected to have a profound impact in fields such as cell sorting, micro-device assembly, and organoid construction, providing robust support for research and technological development in these areas.

The efficacy of targeted cancer drug therapy is significantly compromised by imprecise drug delivery mechanisms. Micro/nano robots (MNRs), characterized by their controllable motion, present a promising solution to this challenge. However, the non-Newtonian nature of blood, with its high viscosity and blood cells’ interference, poses substantial limitations on the upstream efficiency of MNRs. This paper innovatively discusses for the first time the effects of blood viscosity and blood cell interference on the motion of MNRs, investigating their upstream motion capabilities in blood through comprehensive theoretical modeling, simulation, and experimental validation. A dynamic model of MNR motion was developed, and the velocity formula for MNRs in non-Newtonian fluid was derived. Experiments were conducted using different magnetic fields in pure water, high-viscosity simulated blood, and diluted blood. Results indicated that under a gradient magnetic field, the upstream velocities of MNRs in pure water, simulated blood, and diluted blood were 45.0, 14.4, and 11.1 mm/s, respectively. Under a rotating magnetic field, the velocities of vortex swarms were 825, 240, and 145 µm/s, respectively. Increased fluid viscosity reduced MNR velocity by 70%, while blood cells caused an additional 10% reduction. This research establishes a theoretical and experimental framework for the upstream motion of MNRs against blood flow, enhancing their potential in targeted drug delivery and broader biomedical applications.

Magnetically controlled micro-nano robots hold revolutionary significance in the clinical targeted treatment of brain tumors. Imaging and tracking miniature robots can provide feedback for precise magnetic field control. The cooperation among micro-nano robots, magnetic field control system, and imaging system is a significant challenge for transitioning micro-nano robots from laboratory research to clinical applications. This study explores the control and spatial localization of magnetic nanorobot swarms in a highly realistic, human-sized vascular phantom which is manufactured using the raw CT scan images. The cerebral arterial vessels are the key focus area with four main inlets and twenty-six branch outlets. The simulation results show that, under the influence of a magnetic field, the nanorobots can accumulate at the target tumor site. The Kernelized Correlation Filter (KCF) algorithm was employed to achieve single-plane tracking of nanorobots. Furthermore, based on a biplanar imaging system, three-dimensional spatial trajectory tracking of nanorobots was realized. This study provides a reference for in vivo spatial localization and imaging of magnetic nanorobot swarms (MNRS) transported through vascular system.

Protein acetylation is one of the extensively studied post-translational modifications (PTMs) due to its significant roles across a myriad of biological processes. Although many computational tools for acetylation site identification have been developed, there is a lack of benchmark dataset and bespoke predictors for non-histone acetylation site prediction. To address these problems, we have contributed to both dataset creation and predictor benchmark in this study. First, we construct a non-histone acetylation site benchmark dataset, namely NHAC, which includes 11 subsets according to the sequence length ranging from 11 to 61 amino acids. There are totally 886 positive samples and 4707 negative samples for each sequence length. Secondly, we propose TransPTM, a transformer-based neural network model for non-histone acetylation site predication. During the data representation phase, per-residue contextualized embeddings are extracted using ProtT5 (an existing pre-trained protein language model). This is followed by the implementation of a graph neural network framework, which consists of three TransformerConv layers for feature extraction and a multilayer perceptron module for classification. The benchmark results reflect that TransPTM has the competitive performance for non-histone acetylation site prediction over three state-of-the-art tools. It improves our comprehension on the PTM mechanism and provides a theoretical basis for developing drug targets for diseases. Moreover, the created PTM datasets fills the gap in non-histone acetylation site datasets and is beneficial to the related communities. The related source code and data utilized by TransPTM are accessible at https://www.github.com/TransPTM/TransPTM.

Magnetic microrobots exhibit enormous potential in targeted drug delivery owing to the remote wireless manipulation and minimum invasion for medical treatment. High degree of freedom offers the magnetic propelled robots extraordinary application prospect since they can be controlled precisely when different magnetic fields sources working cooperatively. However, the biocompatibility of microrobots have attracted sustained and general concern. Therefore, it is highly necessary to develop a promising carrier with high biocompatibility and investigate the mechanism of drug loading-release triggered by special microenvironment in the targeted region. In this paper, we proposed a magnetically controlled cell robots (MCRs) based on macrophages propelled by a rotating magnetic field. The innovative MCRs exhibit good biocompatibility and low toxicity by optimizing the concentration of polylysine-coated Fe nanoparticles (PLL@FeNPs) to 40 µg/mL. These MCRs loaded with murine interleukin-12 (IL-12), murine chemokine (C-C motif) ligand 5 (CCL-5), and murine C-X-C motif chemokine ligand 10 (CXCL-10) which can stimulate T cell differentiation and recruitment of monocytes, respectively. The macrophages showed an obvious M1-polarization tendency of macrophages to phagocytose intracellular pathogens and resist the growth of tumor cells. Under the control of a magnetic propelling system composed of 3 pairs of Helmholtz coil, the cell robot can be propelled wirelessly and moved along a predefined path with high accuracy. Moreover, the MCRs could approach to cancer cells and stop at places of interest in vitro. In conclusion, we have accomplished the preliminary construction of a targeted drug delivery system which displays great immune-enhancing potential for targeted drug delivery.

Macrophage immunotherapy is a promising clinical approach to treat cancer. However, low targeting efficiency severely limits the immunotherapeutic effect of macrophages. Here, we report a unique macrophage robot that can target and kill cancer cells using a combination of external acoustic and magnetic fields. First, the inactive macrophages (Mø) are magnetized by endocytosis of the$\gamma$-Fe2O3nanoparticles (FeNPs). Then, the magnetized M⊘can be moved towards the capillary wall under the influence of an acoustic radiation force generated from a lead zirconate titanate piezoelectric (PZT) transducer. Finally, the magnetized cells rotate forward under the action of alternating magnetic fields (AMF). During the process of magnetizing macrophages, FeNPs activate the anti-tumor immune activity of macrophages (M1) to induce cancer cell death. Overall, the present study highlights a novel cell robot that can target and kill cancer cells. Considering that the nanoparticles, macrophages, magnetic fields, and ultrasound technology have all been FDA approved for clinical settings, our targeted delivery system has tremendous clinical translational potential.

The rapid development of the blockchain leads to a blowout of on-chain transactions, contracts, and currencies, which will further accelerate the increase of data. The existing blockchain systems typically support exact transaction queries, which, however, cannot satisfy the QoS requirements with intelligent adjustment in the blockchain systems. To this end, this paper takes the first step to define and address the practically important problem of transaction cardinality estimation for QoS-adjustable intelligent blockchain systems. We first establish a mathematical relationship between the bit string and transaction cardinality. Thus, we can leverage the number of leading 1s of the obtained bit string to estimate the transaction cardinality. We then improve the block header and body with a corresponding search algorithm to access bit strings in blocks. We also propose an estimation protocol with intelligent adjustable QoS to support accuracy-guaranteed and efficiency-optimized estimation. Finally, we design an authentication scheme and guarantee the reliability of our protocol through rigorous theoretical derivation. When achieving the transaction cardinality estimation in blockchain, two technical challenges need to be addressed. (i) To ensure efficient, verifiable, and overhead-saving bit string accessing mechanism in blockchain, we propose the Merkle Cardinality Tree (MCT) and target block filtering mechanism based on Bloom Filter (BF) in off-chain and improve on-chain block header by joining the abstract of MCT and BF. (ii) To improve estimation efficiency while guaranteeing accuracy requirements in hybrid blockchain scheme, we propose a Dynamic One-round Sampling-based cardinality Estimation (DOSE) protocol and integrate BF-DOSE to intelligently accelerate estimation. We build MCT in Ethereum and store the MCT Root in the block header for estimation authentication. Extensive experiments reveal that our BF-DOSE protocol can well satisfy various accuracy and efficiency requirements of QoS-adjustable intelligent blockchain systems, and is one to two orders of magnitude faster compared with benchmark schemes.

Micro-nano-robots are considered to be a promising platform for drug delivery in biological organisms, but there are still urgent technical problems in biocompatibility and degradability of 3D-printed-based micro-robots that need to be solved. Therefore, in this paper, we design a magnetized bio-hybrid robot, which uses mouse macrophages as carriers, and allowed it to swallow Fe2O3particles with a diameter of 10 nm. The robot takes advantage of macrophage’s natural biocompatibility and targeting characteristics to reach and function in complex environments such as: eye, knee, tumor, etc., and finally being able to be actively metabolized by the organism. More importantly, the cell robot can move precisely along a preplanned path under the control of a three-dimensional magnetic control system built in this study, and be delivered accurately to the vicinity of cancer cells in vitro environment. In future work, cellular robots could be allowed to carry anti-cancer drugs and release them in a targeted manner at the lesion. These microrobots have shown great potential for tumor reginal targeted drug delivery.