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Engineered bacteria are increasingly recognized as sustained and intelligent sources for sensing and therapeutics due to their unique capabilities such as in situmultiplication, tissue targeting, and genetic editability. However, the clinical applications of these living agents are hindered by the ineffective immunoisolation, residency, and removal against the complex and dynamic in vivoenvironment. Existing approaches focus on surface decoration and encapsulation of engineered bacteria, or “microencapsulation,” but there are limits to what can be achieved with modifications of bacteria themselves. An emerging strategy combines millimeter- to centimeter-scale engineered devices and systems with bacteria, or “macroencapsulation,” offering unique advantages such as extending the in vivolifetime and engraftment of bacteria, enhancing immunoisolation, and enabling real-time signal readouts via wireless electronic technologies. In this review, the design rationales for macroencapsulated bacteria toward in vivoapplications are discussed, and examples in bacterial devices for transdermal and oral applications are highlighted. Since the gastrointestinal tract represents a major site for engineered bacteria, we also summarize and compare various strategies for synthetic engraftment of orally administered encapsulated bacteria.

Cervical cancer is a leading cause of cancer-related mortality worldwide, killing over 300,000 women annually. Lack of resources to perform screening and treatment of cervical cancer in low- and middle-income countries (LMICs) as well as the stigma associated with screening has led to high mortality rates in these regions. Specifically, the speculum – a duck bill-shaped device used to open the vaginal canal for visualization of the cervix by a clinician – is not always readily available, does not always provide a clear line of sight to the cervix, and can cause patient discomfort due to the pressure it exerts on the vaginal canal. To overcome these barriers, we developed a low-cost screening device for use in LMIC settings that enables better visualization of the cervix and is more comfortable than the speculum. We found our our device provided better visualization of the cervix at low cervical angles and exerted less pressure on the vaginal walls compared to a speculum. Future iterations of our device will focus on selection and sterilization of an endoscope, ergonomic improvements, integration with tools commonly used during cervical exams, and use of artificial intelligence to aid in cancer diagnosis.

Soft robotic grippers possess high structural compliance and adaptability, allowing them to grasp objects with unknown and irregular shapes and sizes. To enable more dexterous manipulation, soft sensors that are similar in mechanical properties to common elastomer materials are desired to be integrated into soft grippers. In this paper, we develop ionic hydrogel-based strain and tactile sensors and integrate these sensors into a three-finger soft gripper for learning-based object recognition and force-controlled grasping. Such hydrogel-based sensors have excellent conductivity, high stretchability and toughness, good ambient stability, and unique antifreezing property; they can be readily attached to a soft gripper at desired locations for strain and tactile sensing. By using a deep-learning model, the sensory soft gripper is demonstrated to be capable of grasping and recognizing objects at both room and freezing temperatures, and achieving close to 100% recognition accuracy for ten typical objects. Moreover, the capacitive tactile feedback of the gripper is utilized to develop a closed-loop force controller and realize force-controlled grasping of fragile or highly deformable objects. A new slip detection and compensation strategy is also proposed and validated for the sensory gripper for adjusting the grasping force in real time upon detecting slippage. Note to Practitioners—The multimodal sensation of a soft robotic gripper could enrich its grasping functionalities and improve its manipulation performance. This research integrates novel antifreezing ionic hydrogel-based strain and tactile sensors into a three-finger soft robotic gripper for learning-based object recognition and force-controlled grasping. Constructed from a highly stretchable, ambient-stable, and antifreezing ionic hydrogel, the strain and tactile sensors can be readily integrated at the desired locations on the soft gripper, and can reliably operate at both ambient and freezing temperatures with excellent mechanical and electrical properties. Based on the feedback of the strain and tactile sensors, a deep learning model is employed to enable high-accuracy object recognition while grasping, which can be useful for manipulation in vision-free environments. Closed-loop force control and slip compensation strategies are also demonstrated for reliably grasping fragile/deformable objects and handling slip events during the manipulation of heavy objects. The sensory soft gripper and the associated object recognition and force control methods could find practical applications in a variety of robotic manipulation tasks.