Comparative analysis of Escherichia coli Nissle 1917 ghosts quality: a study of two chemical methods.

The gram-negative bacterium Escherichia coli Nissle 1917 (EcN) has long been recognized for its therapeutic potential in treating various intestinal diseases. Bacterial ghosts (BGs) are empty shells of non-living bacterial cells that demonstrate enormous potential for medicinal applications. Genetic and chemical techniques can create these BGs. In the current study, we produced Escherichia coli Nissle 1917 ghosts (EcNGs) for the first time using benzoic acid (BA) and sodium hydroxide (SH). BA is a feeble acidic chemical that enhances gram-negative bacteria's external membrane permeability, reduces energy production, and decreases internal pH. SH has shown success in producing BGs from some gram-negative and gram-positive organisms. This research aims to produce EcNGs using the minimum inhibitory concentration (MIC) of SH and BA, specifically 3.125 mg/mL. We assessed the bacterial quality of the BGs produced using quantitative PCR (qPCR) and Bradford protein assays. Field emission scanning electron microscopy (FE-SEM) showed the three-dimensional structure of EcNGs. The study confirmed the presence of tunnel-like pores on the outer surface, indicating the preservation of cell membrane integrity. Importantly, this investigation introduces BA as a novel chemical inducer of EcNGs, suggesting its potential alongside SH for efficient EcNG formation.

Intelligent Navigation of a Magnetic Microrobot with Model-Free Deep Reinforcement Learning in a Real-World Environment.

Microrobotics has opened new horizons for various applications, especially in medicine. However, it also witnessed challenges in achieving maximum optimal performance. One key challenge is the intelligent, autonomous, and precise navigation control of microrobots in fluid environments. The intelligence and autonomy in microrobot control, without the need for prior knowledge of the entire system, can offer significant opportunities in scenarios where their models are unavailable. In this study, two control systems based on model-free deep reinforcement learning were implemented to control the movement of a disk-shaped magnetic microrobot in a real-world environment. The training and results of an off-policy SAC algorithm and an on-policy TRPO algorithm revealed that the microrobot successfully learned the optimal path to reach random target positions. During training, the TRPO exhibited a higher sample efficiency and greater stability. The TRPO and SAC showed 100% and 97.5% success rates in reaching the targets in the evaluation phase, respectively. These findings offer basic insights into achieving intelligent and autonomous navigation control for microrobots to advance their capabilities for various applications.

Nanofibers in Respiratory Masks: An Alternative to Prevent Pathogen Transmission.

Recent global outbreak of COVID-19 has raised serious awareness about our abilities to protect ourselves from hazardous pathogens and volatile organic compounds. Evidence suggests that personal protection equipment such as respiratory masks can radically decrease rates of transmission and infections due to contagious pathogens. To increase filtration efficiency without compromising breathability, application of nanofibers in production of respiratory masks have been proposed. The emergence of nanofibers in the industry has since introduced a next generation of respiratory masks that promises improved filtration efficiency and breathability via nanometric pores and thin fiber thickness. In addition, the surface of nanofibers can be functionalized and enhanced to capture specific particles. In addition to conventional techniques such as melt-blown, respiratory masks by nanofibers have provided an opportunity to prevent pathogen transmission. As the surge in global demand for respiratory masks increases, herein, we reviewed recent advancement of nanofibers as an alternative technique to be used in respiratory mask production.