MotGen: a closed-loop bacterial motility control framework using generative adversarial networks.

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.

Immune Activation Modulation via Magnetically Localized Bacteria Based Micro/Bio Robot (BBMBR).

Understanding tumor's microenvironment is one of the key factors in the cancer therapy. Especially, from the perspective of immunotherapy, immune desert or cold tumor is referred as significantly downregulated T cell in-filtration due to lack of immune surveillance in the tumor microenvironment. There are many studies are dedicated to convert cold tumor to hot tumor for enhancing the efficacy of immunotherapy. In this study, we suggested selective immune activation system through the spatiotemporal control of the bacteria as an immune boosting agent. To this end, we have developed bacteria-based micro/bio robot system (BBMBR) by attaching bacteria with magnetic nanoparticles (MNP) so that the localization can be controlled through the magnetic field. The biomanufacturing results showed that BBMBR includes 6.6 ± 1.54 MNP attached and the presence ratio of bacteria-MNP out of total bacteria population reached 75.2 ± 3.37%. Spatial controllability experiments have shown that rotational and translation localization has been controlled as intended. The function of the immune modulation system through BBMBR was confirmed through experiments that magnetically driven BBMBR localization induced localized immune activation. M1-phenotype differentiation of macrophage cells were quantified CD80 staining, and overall immune response level was evaluated through IL-6 measurements. As the distance from the activation point increased, the population of M1 differentiated macrophages decreased, and when the movement of BBMBR was magnetically restricted, overall immune activation was found to be regulated downward. Proposed BBMBR and immune modulation framework could introduce a powerful new paradigm in cancer treatment by improving the localization controllability of immune-boosting agent and the spatial immune activation strategies.

Standard for the Quantification of a Sterilization Effect Using an Artificial Intelligence Disinfection Robot.

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.

Artificial Neural Network enabling Clinically Meaningful Biological Image Data Generation.

Biological experiments for developing efficient cancer therapeutics require significant resources of time and costs particularly in acquiring biological image data. Thanks to recent advances in AI technologies, there have been active researches in generating realistic images by adapting artificial neural networks. Along the same lines, this paper proposes a learning-based method to generate images inheriting biological characteristics. Through a statistical comparison of tumor penetration metrics between generated images and real images, we have shown that forged micrograph images contain vital characteristics to analyze tumor penetration performance of infecting agents, which opens up the promising possibilities for utilizing proposed methods for generating clinically meaningful image data.

Multicolor fluorescence imaging using a single RGB-IR CMOS sensor for cancer detection with smURFP-labeled probiotics.

A multicolor fluorescence imaging device was recently developed for image-guided surgery. However, conventional systems are typically bulky and function with two cameras. To overcome these issues, we developed an economical home-built fluorescence imaging device based on a single RGB-IR sensor that can acquire both color and fluorescence images simultaneously. The technical feasibility of RGB-IR imaging was verified ex vivo in chicken breast tissue using fluorescein isothiocyanate (FITC), cyanine 5 (Cy5), and indocyanine green (ICG) as fluorescent agents. The minimum sensitivities for FITC, Cy5, and ICG were 0.200 µM, 0.130 µM, and 0.065 µM, respectively. In addition, we validated the fluorescence imaging of this device in vitro during a minimally invasive procedure using smURFP-labeled probiotics, which emit a spectrum similar to that of Cy5. Our preliminary study of the ex vivo tissue suggests that Cy5 and ICG are good candidates for deep tissue imaging. In addition, the tumor-specific amplification process was visualized using cancer cells incubated with probiotics that had been labeled with a fluorescent protein. Our approach indicates the potential for in vivo screening of tumors in rodent tumor models.

Erratum: Multicolor fluorescence imaging using a single RGB-IR CMOS sensor for cancer detection with smURFP-labeled probiotics: publisher's note.

[This corrects the article on p. 2951 in vol. 11, PMID: 32637234.].

Nanoscale Bacteria-Enabled Autonomous Drug Delivery System (NanoBEADS) Enhances Intratumoral Transport of Nanomedicine.

Cancer drug delivery remains a formidable challenge due to systemic toxicity and inadequate extravascular transport of nanotherapeutics to cells distal from blood vessels. It is hypothesized that, in absence of an external driving force, the Salmonella enterica serovar Typhimurium could be exploited for autonomous targeted delivery of nanotherapeutics to currently unreachable sites. To test the hypothesis, a nanoscale bacteria-enabled autonomous drug delivery system (NanoBEADS) is developed in which the functional capabilities of the tumor-targeting S. Typhimurium VNP20009 are interfaced with poly(lactic-co-glycolic acid) nanoparticles. The impact of nanoparticle conjugation is evaluated on NanoBEADS' invasion of cancer cells and intratumoral transport in 3D tumor spheroids in vitro, and biodistribution in a mammary tumor model in vivo. It is found that intercellular (between cells) self-replication and translocation are the dominant mechanisms of bacteria intratumoral penetration and that nanoparticle conjugation does not impede bacteria's intratumoral transport performance. Through the development of new transport metrics, it is demonstrated that NanoBEADS enhance nanoparticle retention and distribution in solid tumors by up to a remarkable 100-fold without requiring any externally applied driving force or control input. Such autonomous biohybrid systems could unlock a powerful new paradigm in cancer treatment by improving the therapeutic index of chemotherapeutic drugs and minimizing systemic side effects.

Design And Experiment Of A Passive Sit-To-Stand And Walking (STSW) Assistance Device For The Elderly.

Most elderly people complain about the discomfort of movements such as a sit-to-stand (STS) motion through losing their muscular strength of lower extremity over aging. This paper presents a novel passive sit-to-stand and walking (STSW) assistance device to aid in physical support for indoor daily life of the elderly. The STSW assistance device is actuated by a pneumatic cylinder and a gas spring. The standing motion is driven by extension of actuators and the sitting motion is driven naturally with gravitational force of user's weight that reloads actuators. The effect of physical support by using this device was evaluated by experiment with measuring electromyograph signals of subjects during STS motion. As a result, the reduction rate of average maximal voluntary isometric contraction with assistance by the STSW device is about 51.2%. This means that users with the assistive device can stand up by using only about half of muscular activation in the case of natural standing up.

Optimizing the restored chemotactic behavior of anticancer agent Salmonella enterica serovar Typhimurium VNP20009.

Bacteria, including strains of Salmonella, have been researched and applied as therapeutic cancer agents for centuries. Salmonella are particularly of interest due to their facultative anaerobic nature, facilitating colonization of differentially oxygenated tumor regions. Additionally, Salmonella can be manipulated with relative ease, resulting in the ability to attenuate the pathogen or engineer vectors for drug delivery. It was recently discovered that the anti-cancer Salmonella enterica serovar Typhimurium strain VNP20009 is lacking in chemotactic ability, due to a non-synonymous single nucleotide polymorphism in cheY. Replacing the mutated copy of cheY with the wild-type sequence restored chemotaxis to 70% of the parental strain. We aimed to investigate further if chemotaxis of VNP20009 can be optimized. By restoring the gene msbB in VNP20009 cheY+, which confers attenuation by lipid A modification, we observed a 9% increase in swimming speed, 13% increase in swim plate performance, 19% increase in microfluidic device partitioning towards the attractant at the optimum concentration gradient, and mitigation of a non-motile cell subpopulation. We conclude that chemotaxis can be enhanced further but at the cost of changing one defining characteristic of VNP20009. A less compromised strain might be needed to employ for investigating bacterial chemotaxis in tumor interactions.

Bacterial chemotaxis-enabled autonomous sorting of nanoparticles of comparable sizes.

High throughput sorting of micro/nanoparticles of similar sizes is of significant interest in many biological and chemical applications. In this work, we report a simple and cost-effective sorting technique for separation of similarly-sized particles of dissimilar surface properties within a diffusion-based microfluidic platform using chemotaxis in Escherichia coli bacteria. Differences in surface chemistry of two groups of similarly-sized nanoparticles in a mixture were exploited to selectively assemble one particle group onto motile E. coli, through either specific or non-specific adhesion, and separate them from the remaining particle group via chemotaxis of the attached bacteria. To enable optimal operation of the sorting platform, the chemotaxis behavior of E. coli bacteria in response to casamino acids, the chemoeffector of choice was first characterized. The chemical concentration gradient range within which the bacteria exhibit a positive chemotactic response was found to be within 0.25 × 10(-7)-1.0 × 10(-3) g ml(-1) mm(-1). We demonstrate that at the optimum concentration gradient of 5.0 × 10(-4) g ml(-1) mm(-1), a sorting efficiency of up to 81% at a throughput of 2.4 × 10(5) particles per min can be achieved. Sensitivity of the sorting efficiency to the adhesion mechanism and particle size in the range of 320-1040 nm was investigated.