MOTIVATION: Nanopores are cutting-edge interdisciplinary tools that can analyze biomolecules at the single-molecule level for many applications, e.g. DNA sequencing. Efforts are underway to extend nanopores to proteomics, including the development of machine learning algorithms for protein sequencing and identification. However, single-molecule data are intrinsically noisy and hard to process. Moreover, the development and performance of machine learning for nanopore is jeopardized by data scarcity. Self-supervised learning is an emerging method that may yield advantages in nanopore scenarios. RESULTS: We propose and experimentally validate Nanopore analysis using Self-Supervised Learning (NanoSSL), a generative self-supervised learning framework based on attention mechanisms for the identification of protein signals from nanopores. Leveraging a two-step approach consisting of self-supervised pre-training and supervised fine-tuning, NanoSSL learns useful feature representations from empirical data to facilitate downstream classification tasks. Inspired by the concept of fragmentation in conventional protein sequencing technologies, during pretraining each translocation event is split into multiple non-overlapping fragments of equal size, some of which are randomly masked and reconstructed using a masked autoencoder. Learning the feature representations of the reconstructed nanopore events facilitates molecular identification in fine-tuning. In this study, we retested a publicly available nanopore multiplexed protein sensing dataset for model iteration, and subsequently measured Alzheimer's disease biomarker Aβ1-42 using homemade solid-state nanopores. Empirical results indicated NanoSSL achieved an unprecedented performance across four metrics: accuracy, precision, recall, and F1 score, when classifying two mutated Aβ1-42, E22G and G37R. The self-supervised learning and attention mechanism were verified as the source of performance gains. AVAILABILITY AND IMPLEMENTATION: The main program is available at https://doi.org/10.5281/zenodo.17172822.

Biohybrid microrobots with autonomous movement capabilities have broad application prospects in targeted delivery, attracting researchers to study their movement characteristics. However, its automatic control is still challenging, and exploring real-time detection of its environment for path planning to achieve stable closed-loop control is highly important for its practical application. Here, we applied deep learning for the detection of biohybrid microrobots and their targets and obstacles, followed by real-time path planning and trajectory tracking of biohybrid microrobots for targeted delivery. The proposed detection algorithm introduces attention and multi-scale feature fusion mechanisms in YOLOv7 algorithm (AM-YOLOv7) with the aim of enhancing the precision of detecting small-scale targets when robots, obstacles and targets are displayed globally, and the detection capabilities are verified through simulations and experiments. The proposed planning algorithm introduces a turning penalty function and a path smoothing strategy into A* algorithm (PS-A*) to make the planned path short and smooth, which has been verified through simulation and experiments. The adaptive fuzzy PID method is used to track the robot's trajectory, and experiments and simulations show that the biohybrid microrobot can move according to the preset trajectory better. The final cell scene experimental results show that the biohybrid microrobot using this system can effectively avoid obstacle cells and be delivered to target cells. The system can detect biohybrid microrobots, obstacle cells and target cells, plan short and smooth trajectories, and track them accurately. The proposed method has certain generalizability and broad application prospects in targeted delivery.

Magnetic microrobots can be navigated by an external magnetic field to autonomously move within living organisms with complex and unstructured environments. Potential applications include drug delivery, diagnostics, and therapeutic interventions. Existing techniques commonly impart magnetic properties to the target object, or drive the robot to contact and then manipulate the object, both probably inducing physical damage. This paper considers a non-contact formulation, where the robot spins to generate a repulsive field to push the object without physical contact. Under such a formulation, the main challenge is that the motion model between the input of the magnetic field and the output velocity of the target object is commonly unknown and difficult to analyze. To deal with it, this paper proposes a data-driven-based solution. A neural network is constructed to efficiently estimate the motion model. Then, an approximate model-based optimal control scheme is developed to push the object to track a time-varying trajectory, maintaining the non-contact with distance constraints. Furthermore, a straightforward planner is introduced to assess the adaptability of non-contact manipulation in a cluttered unstructured environment. Experimental results are presented to show the tracking and navigation performance of the proposed scheme.

Magnetic catheter robots have great potential for application in minimally invasive surgical interventions that require submillimeter soft catheters to be inserted into the body. Compared to other medical continuum robots, the kinematic characteristics of the magnetic continuum robot are required to be improved, including operating dexterity. The opposite magnetic continuum robot (OMCR), which has oppositely-magnetized magnets on the robot, is capable of deforming in large and high-order curvatures and shows improved dexterity. However, unstable kinematic behaviors emerge owing to solution multiplicity and affect the actual operation. For example, snapping, which indicates the sudden jump from one state to another, induces danger for medical operations. In addition, the motion sequence of the external mobile magnet affects the final state of OMCR. Therefore, in this study, we first analyze the reason for these emerging behaviors by understanding the solution multiplicity of an OMCR. We then modified the OMCR kinematic model with instability analysis and additional constraints, to predict the critical point for snapping and avoid other unexpected behavior for path planning. Herein, our study aims to analyze the stability of the OMCR with relatively unstable configurations in their actuation space to ensure stable and dexterous motions for medical applications. Note to Practitioners—The motivation of this work is to analyze, predict, and avoid the unexpected behaviors of the medical magnetic catheter robots, and ensure stable and dexterous operation. Submillimeter magnetic catheter robots have received considerable attention in the past ten years owing to their small size and dexterous motion. They have great potential for minimally invasive surgical interventions that require the navigation of “tighter corners” inside the human body. However, there may exist multiple local solutions under an external field, especially when the robot deforms in high-order curvature for dexterous operation. This situation induces unexpected behaviors and danger for medical use, such as a sudden jump from one state to another. Herein, we analyze the solution evolution as the external field varies and predict the unexpected behaviors using a modified kinematic model with instability analysis. Preliminary experiments demonstrated the effectiveness of the modified model, which can predict unexpected behaviors, and evaluate and enhance the stability during path planning. This method can be implemented for the dexterous and stable operation of magnetic catheter robots, which is significant for medical applications.

A manipulation technique based on optothermally generated surface bubbles is proposed in this paper. The manipulation and assembly of microstructures are completed by using bubbles. In addition, the hydrogel microstructures are also used as microrobots driven by the bubble to operate and pattern the microspheres. Considering that many materials and lasers with different wavelength have been used for generating bubbles by optothermal effects, absorptivity and transmissivity are used as indicators of selections. Besides, the size of the bubble can be controlled by the frequency and time of the laser. This technique is supposed to be applied for manipulation of cells, microparticles and microstructures.