Recent outbreaks and the worldwide spread of COVID-19 have challenged mankind with unprecedented difficulties. The introduction of autonomous disinfection robots appears to be indispensable as consistent sterilization is in desperate demand under limited manpower. In this study, we developed an autonomous navigation robot capable of recognizing objects and locations with a high probability of contamination and capable of providing quantified sterilization effects. In order to quantify the 99.9% sterilization effect of various bacterial strains, as representative contaminants with robots operated under different modules, the operating parameters of the moving speed, distance between the sample and the robot, and the radiation angle were determined. We anticipate that the sterilization effect data we obtained with our disinfection robot, to the best of our knowledge, for the first time, will serve as a type of stepping stone, leading to practical applications at various sites requiring disinfection.

Current imaging techniques suffer from a lack of specificity and resolution, leading to inaccurate tumor imaging and limited applicability of targeted contrast agents, as they require cancer‐specific development. The need for enhanced contrast through improved tumor‐to‐background ratio (TBR) and the toxicity from repeated injections due to fading fluorescent signals further complicate the issue. Additionally, challenges in visualizing the entire 3D tumor with surface‐stained contrast agents highlight the demand for advanced imaging solutions for more precise surgical guidance. A novel approach is proposed utilizing Streptavidin Associated Salmonella (SAS) as a contrast agent for image‐guided surgeries. SAS selectively proliferates in cancerous tissues and secretes streptavidin upon induction, enabling the binding of subsequently injected biotin‐conjugated fluorescent dyes. This approach enhances tumor visualization with a TBR of up to 15.3, far surpassing conventional agents (TBR ∼ 2), while enabling prolonged 3‐day imaging, deep tumor penetration, and precise invasive margin delineation with a single contrast agent injection. Furthermore, biosafety evaluations confirmed efficient bacterial clearance, absence of systemic toxicity, and stable physiological responses, supporting its potential for safe clinical translation. This innovative method offers substantial improvements over existing fluorescent contrast agents and holds promise for both diagnostic and therapeutic applications in cancer surgery.

Motivation: Many organisms’ survival and behavior hinge on their responses to environmental signals. While research on bacteria-directed therapeutic agents has increased, systematic exploration of real-time modulation of bacterial motility remains limited. Current studies often focus on permanent motility changes through genetic alterations, restricting the ability to modulate bacterial motility dynamically on a large scale. To address this gap, we propose a novel real-time control framework for systematically modulating bacterial motility dynamics. Results: We introduce MotGen, a deep learning approach leveraging Generative Adversarial Networks to analyze swimming performance statistics of motile bacteria based on live cell imaging data. By tracking objects and optimizing cell trajectory mapping under environmentally altered conditions, we trained MotGen on a comprehensive statistical dataset derived from real image data. Our experimental results demonstrate MotGen’s ability to capture motility dynamics from real bacterial populations with low mean absolute error in both simulated and real datasets. MotGen allows us to approach optimal swimming conditions for desired motility statistics in real-time. MotGen’s potential extends to practical biomedical applications, including immune response prediction, by providing imputation of bacterial motility patterns based on external environmental conditions. Our short-term, in-situ interventions for controlling motility behavior offer a promising foundation for the development of bacteria-based biomedical applications. Availability and implementation: MotGen is presented as a combination of Matlab image analysis code and a machine learning workflow in Python. Codes are available at https://github.com/bgmseo/MotGen, for cell tracking and implementation of trained models to generate bacterial motility statistics.

Minimally invasive surgery (MIS) using robots minimizes patient injury but is challenging due to reduced bending force in smaller-diameter tools. Lowering the bending resistance of actuating cables is critical, particularly when tool diameters are small and cable tension is limited. Traditional methods rely on costly, low-resistance materials. This study proposes a cost-effective design using multiple thin stainless-steel cables and cable slots instead of single cables and conventional cable holes. To optimize the proposed multi-cable design, simulations and polynomial regression were employed to determine the ideal slot dimensions, focusing on reducing bending resistance. Kinematic analysis and experiments confirmed reduced bending resistance in multi-cable designs. A 3 mm prototype showed 25% higher bending force and 63% increased bending distance under a 1.196 N payload compared to single-cable mechanisms. Circular trajectory experiments demonstrated improved accuracy and reduced velocity variation. A prototype successfully excised a tumor from a BALB/c mouse, verifying sufficient force for surgical tasks. Future work will address assembly challenges and explore cable arrangement effects on bending force and hysteresis.