Soft crawling robots have been widely studied and applied because of their excellent environmental adaptability and flexible movement. However, most existing soft crawling robots typically exhibit a single-motion mode and lack diverse capabilities. Inspired by Drosophila larvae, this paper proposes a compact soft crawling robot (weight, 13 g; length, 165 mm; diameter, 35 mm) with multimodal locomotion (forward, turning, rolling, and twisting). Each robot module uses 4 sets of high-power-density shape memory alloy actuators, endowing it with 4 degrees of motion freedom. We analyze the mechanical characteristics of the robot modules through experiments and simulation analysis. The plug-and-play modules can be quickly assembled to meet different motion and task requirements. The soft crawling robot can be remotely operated with an external controller, showcasing multimodal motion on various material surfaces. In a narrow maze, the robot demonstrates agile movement and effective maneuvering around obstacles. In addition, leveraging the inherent bistable characteristics of the robot modules, we used the robot modules as anchoring units and installed a microcamera on the robot's head for pipeline detection. The robot completed the inspection in horizontal, vertical, curved, and branched pipelines, adjusted the camera view, and twisted a valve in the pipeline for the first time. Our research highlights the robot's superior locomotion and application capabilities, providing an innovative strategy for the development of lightweight, compact, and multifunctional soft crawling robots.

Stochastic resonance (SR) typically manifests in nonlinear systems, wherein the detection of a weak signal is bolstered by the addition of noise. Since its first discovery in a study of ice ages on Earth, various types of SRs have been observed in biological and physical systems and have been implemented in sensors to benefit from noise. However, a universally designed sensor architecture capable of accommodating different types of SRs has not been proposed, and the widespread applications of SRs in daily environments have not yet been demonstrated. Here, we propose a sensor architecture to simultaneously realize multi-type SRs and demonstrate their wide applications in mechanical, optical, and acoustic sensing domains. In particular, we find the coexistence of excitable SR and bistable SR in a sensor architecture composed of wirelessly coupled inductor–capacitor resonators connected to a nonlinearly saturable amplifier. In both types of SRs, adding noise to the system leads to a characteristic noise-enhanced signal-to-noise ratio (SNR). We further validate our findings through mechanical, optical, and acoustic sensing experiments and obtain noise-enhanced SNR by 9 dB, 3 dB, and 7 dB, respectively, compared to the standard methods devoid of SR integration. Our findings provide a general strategy to design various types of SRs and pave the way for the development of a distinctive class of sensors leveraging environmental noise, with potential applications ranging from biomedical devices to ambient sensing.

The conductive polymer poly-3,4-ethylenedioxythiophene (PEDOT), recognized for its superior electrical conductivity and biocompatibility, has become an attractive material for developing wearable technologies and bioelectronics. Nevertheless, the complexities associated with PEDOT's patterning synthesis on diverse substrates persist despite recent technological progress. In this study, we introduce a novel deep eutectic solvent (DES)-induced vapor phase polymerization technique, facilitating nonrestrictive patterning polymerization of PEDOT across diverse substrates. By controlling the quantity of DES adsorbed per unit area on the substrates, PEDOT can be effectively patternized on cellulose, wood, plastic, glass, and even hydrogels. The resultant patterned PEDOT exhibits numerous benefits, such as an impressive electronic conductivity of 282 S·m⁻¹, a high specific surface area of 5.29 m²·g⁻¹, and an extensive electrochemical stability range from −1.4 to 2.4 V in a phosphate-buffered saline. To underscore the practicality and diverse applications of this DES-induced approach, we present multiple examples emphasizing its integration into self-supporting flexible electrodes, neuroelectrode interfaces, and precision circuit repair methodologies.

The accumulation of senescent cells in kidneys is considered to contribute to age-related diseases and organismal aging. Mitochondria are considered a regulator of cell senescence process. Atrazine as a triazine herbicide poses a threat to renal health by disrupting mitochondrial homeostasis. Melatonin plays a critical role in maintaining mitochondrial homeostasis. The present study aims to explore the mechanism by which melatonin alleviates atrazine-induced renal injury and whether parkin-mediated mitophagy contributes to mitigating cell senescence. The study found that the level of parkin was decreased after atrazine exposure and negatively correlated with senescent markers. Melatonin treatment increased serum melatonin levels and mitigates atrazine-induced renal tubular epithelial cell senescence. Mechanistically, melatonin maintains the integrity of mitochondrial crista structure by increasing the levels of mitochondrial contact site and cristae organizing system, mitochondrial transcription factor A (TFAM), adenosine triphosphatase family AAA domain-containing protein 3A (ATAD3A), and sorting and assembly machinery 50 (Sam50) to prevent mitochondrial DNA release and subsequent activation of cyclic guanosine 5′-monophosphate–adenosine 5′-monophosphate synthase pathway. Furthermore, melatonin activates Sirtuin 3–superoxide dismutase 2 axis to eliminate the accumulation of reactive oxygen species in the kidney. More importantly, the antisenescence role of melatonin is largely determined by the activation of parkin-dependent mitophagy. These results offer novel insights into measures against cell senescence. Parkin-mediated mitophagy is a promising drug target for alleviating renal tubular epithelial cell senescence.

As a key executioner of pyroptosis, Gasdermin D (GSDMD) plays a crucial role in host defense and emerges as an essential therapeutic target in the treatment of inflammatory diseases. So far, the understanding of the mechanisms that regulate the protein level of GSDMD to prevent detrimental effects and maintain homeostasis is currently limited. Here, we unveil that ubiquitin-specific peptidase 18 (USP18) works as a negative regulator of pyroptosis by targeting GSDMD for degradation and preventing excessive innate immune responses. Mechanically, USP18 recruits E3 ubiquitin ligase mind bomb homolog 2 (MIB2) to catalyze ubiquitination on GSDMD at lysine (K) 168, which acts as a recognition signal for the selective autophagic degradation of GSDMD. We further confirm the alleviating effect of USP18 on LPS-triggered inflammation in vivo. Collectively, our study demonstrates the role of USP18 in regulating GSDMD-mediated pyroptosis and reveals a previously unknown mechanism by which GSDMD protein level is rigorously controlled by selective autophagy.

Infection with severe acute respiratory syndrome coronavirus 2 Omicron variants still causes neurological complications in elderly individuals. However, whether and how aging brains are affected by Omicron variants in terms of neuroinvasiveness and neurovirulence are unknown. Here, we utilize resected paracarcinoma brain tissue from elderly individuals to generate primary brain spheroids (BSs) for investigating the replication capability of live wild-type (WT) strain and Omicron (BA.1/BA.2), as well as the mechanisms underlying their neurobiological effects. We find that both WT and Omicron BA.1/BA.2 are able to enter BSs but weakly replicate. There is no difference between Omicron BA.1/BA.2 and WT strains in neurotropism in aging BSs. However, Omicron BA.1/BA.2 exhibits ameliorating neurological damage. Transcriptional profiling indicates that Omicron BA.1/BA.2 induces a lower neuroinflammatory response than WT strain in elderly BSs, suggesting a mechanistic explanation for their attenuated neuropathogenicity. Moreover, we find that both Omicron BA.1/BA.2 and WT strain infections disrupt neural network activity associated with neurodegenerative disorders by causing neuron degeneration and amyloid-β deposition in elderly BSs. These results uncover Omicron-specific mechanisms and cellular immune responses associated with severe acute respiratory syndrome coronavirus 2-induced neurological complications.

Consuming a high-fat diet (HFD) is widely recognized to cause obesity and result in chronic brain inflammation that impairs cognitive function. Repetitive transcranial magnetic stimulation (rTMS) has shown effectiveness in both weight loss and cognitive improvement, although the exact mechanism is still unknown. Our study examined the effects of rTMS on the brain and intestinal microecological dysfunction. rTMS successfully reduced cognitive decline caused by an HFD in behavioral assessments involving the Y maze and novel object recognition. This was accompanied by an increase in the number of new neurons and the transcription level of genes related to synaptic plasticity (spindlin 1, synaptophysin, and postsynaptic protein-95) in the hippocampus. It was reached that rTMS decreased the release of high mobility group box 1, activation of microglia, and inflammation in the brains of HFD rats. rTMS also reduced hypothalamic hypocretin levels and improved peripheral blood lipid metabolism. In addition, rTMS recovered the HFD-induced gut microbiome imbalances, metabolic disorders, and, in particular, reduced levels of the microvirus. Our research emphasized that rTMS enhanced cognitive abilities, resulting in positive impacts on brain inflammation, neurodegeneration, and the microbiota in the gut, indicating the potential connection between the brain and gut, proposing that rTMS could be a new approach to addressing cognitive deficits linked to obesity.

Ultraviolet (UV) light, invisible to the human eye, possesses both benefits and risks. To harness its potential, UV photodetectors (PDs) have been engineered. These devices can convert UV photons into detectable signals, such as electrical impulses or visible light, enabling their application in diverse fields like environmental monitoring, healthcare, and aerospace. Wide bandgap semiconductors, with their high-efficiency UV light absorption and stable opto-electronic properties, stand out as ideal materials for UV PDs. This review comprehensively summarizes recent advancements in both traditional and emerging wide bandgap-based UV PDs, highlighting their roles in UV imaging, communication, and alarming. Moreover, it examines methods employed to enhance UV PD performance, delving into the advantages, challenges, and future research prospects in this area. By doing so, this review aims to spark innovation and guide the future development and application of UV PDs.

Pushing the information states' acquisition efficiency has been a long-held goal to reach the measurement precision limit inside scattering spaces. Recent studies have indicated that maximal information states can be attained through engineered modes; however, partial intrusion is generally required. While non-invasive designs have been substantially explored across diverse physical scenarios, the non-invasive acquisition of information states inside dynamic scattering spaces remains challenging due to the intractable non-unique mapping problem, particularly in the context of multi-target scenarios. Here, we establish the feasibility of non-invasive information states' acquisition experimentally for the first time by introducing a tandem-generated adversarial network framework inside dynamic scattering spaces. To illustrate the framework's efficacy, we demonstrate that efficient information states' acquisition for multi-target scenarios can achieve the Fisher information limit solely through the utilization of the external scattering matrix of the system. Our work provides insightful perspectives for precise measurements inside dynamic complex systems.

Muscle strength (MS) is related to our neural and muscle systems, essential for clinical diagnosis and rehabilitation evaluation. Although emerging wearable technology seems promising for MS assessment, problems still exist, including inaccuracy, spatiotemporal differences, and analyzing methods. In this study, we propose a wearable device consisting of myoelectric and strain sensors, synchronously acquiring surface electromyography and mechanical signals at the same spot during muscle activities, and then employ a deep learning model based on temporal convolutional network (TCN) + Transformer (Tcnformer), achieving accurate grading and prediction of MS. Moreover, by combining with deep clustering, named Tcnformer deep cluster (TDC), we further obtain a 25-level classification for MS assessment, refining the conventional 5 levels. Quantification and validation showcase a patient's postoperative recovery from level 3.2 to level 3.6 in the first few days after surgery. We anticipate that this system will importantly advance precise MS assessment, potentially improving relevant clinical diagnosis and rehabilitation outcomes.